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Mathematical Physics·Captain: Lucas

Einstein's Static Universe: the Λ\LambdaΛ Balance and Its InstabilityTextbook

Motivation

The cosmological constant Λ\LambdaΛ entered physics in 1917, when Einstein added a term Λgμν\Lambda g_{\mu\nu}Λgμν​ to his field equations in order to allow a spatially homogeneous universe that neither expands nor contracts (Einstein, Kosmologische Betrachtungen zur allgemeinen Relativitätstheorie, 1917). The resulting Einstein static universe is the unique balance in which the attraction of pressureless matter, the repulsion carried by Λ\LambdaΛ, and a positive spatial curvature cancel exactly. Two facts about that balance shaped the next century of cosmology:

  1. it is possible only for one specific relation between Λ\LambdaΛ, the matter density and the curvature, so the static model is fine-tuned by construction;
  2. it is unstable: an arbitrarily small increase of the scale factor grows, and an arbitrarily small decrease collapses.

After Hubble's 1929 observations Einstein dropped the term; it returned in 1998, when the supernova measurements of the Supernova Cosmology Project and the High-zzz Supernova Search Team required Λ>0\Lambda>0Λ>0. Interpreting Λ\LambdaΛ as a vacuum energy density ρvac=Λ/(8πG)\rho_{\text{vac}}=\Lambda/(8\pi G)ρvac​=Λ/(8πG) with equation of state w=−1w=-1w=−1 then produced the cosmological constant problem: the measured value, about 10−52 m−210^{-52}\,\mathrm{m}^{-2}10−52m−2, is roughly 10−12210^{-122}10−122 in Planck units, tens of orders of magnitude below the scale that effective quantum field theory suggests (Wikipedia, Cosmological constant problem).

This mission formalizes the classical ordinary-differential-equation content of that story: the static solution, its uniqueness, its instability, the vacuum-energy reading of Λ\LambdaΛ, the density-parameter bookkeeping, and the numerical size of the discrepancy.

Setting

A homogeneous isotropic universe is described by a positive scale factor a:I→Ra:I\to\mathbb{R}a:I→R on a set of times I⊆RI\subseteq\mathbb{R}I⊆R. Matter is pressureless dust, whose density dilutes with volume,

ρ(a)=ρ0(a0a)3,\rho(a)=\rho_0\left(\frac{a_0}{a}\right)^{3},ρ(a)=ρ0​(aa0​​)3,

where ρ0>0\rho_0>0ρ0​>0 and a0>0a_0>0a0​>0 are reference values. Units are geometrized, c=1c=1c=1, so Λ\LambdaΛ has dimension (length)−2^{-2}−2; G>0G>0G>0 is Newton's constant and k∈Rk\in\mathbb{R}k∈R the curvature constant.

The dynamics are the two Friedmann equations. The first Friedmann equation is the constraint

a˙(t)2=8πG3 ρ(a(t)) a(t)2−k+Λ3a(t)2,t∈I,\dot a(t)^2=\frac{8\pi G}{3}\,\rho\big(a(t)\big)\,a(t)^2-k+\frac{\Lambda}{3}a(t)^2 ,\qquad t\in I,a˙(t)2=38πG​ρ(a(t))a(t)2−k+3Λ​a(t)2,t∈I,

and the acceleration equation for dust is

a¨(t)=F(a(t)),F(x):=−4πG3 ρ(x) x+Λ3x=−4πG3ρ0a03x2+Λ3x.\ddot a(t)=F\big(a(t)\big),\qquad F(x):=-\frac{4\pi G}{3}\,\rho(x)\,x+\frac{\Lambda}{3}x=-\frac{4\pi G}{3}\frac{\rho_0a_0^3}{x^{2}}+\frac{\Lambda}{3}x .a¨(t)=F(a(t)),F(x):=−34πG​ρ(x)x+3Λ​x=−34πG​x2ρ0​a03​​+3Λ​x.

The Hubble parameter is H=a˙/aH=\dot a/aH=a˙/a, the critical density is ρc=3H2/(8πG)\rho_c=3H^2/(8\pi G)ρc​=3H2/(8πG), and the density parameters are

Ωm=ρρc,ΩΛ=Λ3H2,Ωk=−ka2H2.\Omega_m=\frac{\rho}{\rho_c},\qquad \Omega_\Lambda=\frac{\Lambda}{3H^2},\qquad \Omega_k=-\frac{k}{a^2H^2}.Ωm​=ρc​ρ​,ΩΛ​=3H2Λ​,Ωk​=−a2H2k​.

Formalization targets

Goal — the static universe exists for exactly one balance, and it is unstable

For G,ρ0,a0,aE>0G,\rho_0,a_0,a_E>0G,ρ0​,a0​,aE​>0, the constant function a≡aEa\equiv a_Ea≡aE​ satisfies both Friedmann equations if and only if

Λ=4πG ρ(aE)andk=ΛaE2,\Lambda=4\pi G\,\rho(a_E)\qquad\text{and}\qquad k=\Lambda a_E^{2},Λ=4πGρ(aE​)andk=ΛaE2​,

in which case Λ>0\Lambda>0Λ>0, F(aE)=0F(a_E)=0F(aE​)=0, F′(aE)=Λ>0F'(a_E)=\Lambda>0F′(aE​)=Λ>0, and the equilibrium is unstable in the following exact sense: every solution of a¨=F(a)\ddot a=F(a)a¨=F(a) on [0,∞)[0,\infty)[0,∞) with a(0)>aEa(0)>a_Ea(0)>aE​ and a˙(0)≥0\dot a(0)\ge 0a˙(0)≥0 is strictly increasing and satisfies a(t)→∞a(t)\to\inftya(t)→∞; and every positive solution on [0,T][0,T][0,T] with a(0)<aEa(0)<a_Ea(0)<aE​ and a˙(0)≤0\dot a(0)\le 0a˙(0)≤0 is strictly decreasing.

Supporting targets

Λ\LambdaΛ is exactly a perfect fluid of constant density ρvac=Λ/(8πG)\rho_{\text{vac}}=\Lambda/(8\pi G)ρvac​=Λ/(8πG) and pressure pvac=−ρvacp_{\text{vac}}=-\rho_{\text{vac}}pvac​=−ρvac​; the density parameters satisfy Ωm+Ωk+ΩΛ=1\Omega_m+\Omega_k+\Omega_\Lambda=1Ωm​+Ωk​+ΩΛ​=1; the acceleration equation is a consequence of the first Friedmann equation along a solution with a˙≠0\dot a\ne0a˙=0; and the observed Λ\LambdaΛ is smaller than 10−12010^{-120}10−120 in Planck units.

Significance

The static solution is the historical origin of Λ\LambdaΛ, and its instability is the reason the static model was never a viable cosmology even before the expansion was observed: the balance point is a local maximum of the effective potential, not a minimum, so the equilibrium cannot be maintained. The same function FFF and the same linearization δ¨=Λ δ\ddot\delta=\Lambda\,\deltaδ¨=Λδ govern the late-time behaviour of the standard Λ\LambdaΛCDM model, where the growing mode becomes the de Sitter expansion eΛ/3 te^{\sqrt{\Lambda/3}\,t}eΛ/3​t.

What the mission produces on top of the textbook statements: a machine-checked ODE treatment of a dust-plus-Λ\LambdaΛ cosmology that does not stop at the linearized equation. The classical physics argument computes F′(aE)=ΛF'(a_E)=\LambdaF′(aE​)=Λ and calls the equilibrium unstable; the formal statements here ask for the nonlinear conclusion — monotone escape to infinity on one side and strict collapse on the other — which is what "unstable" has to mean for the exact equations. Mathlib contains neither the Friedmann equations nor a general instability criterion for second-order autonomous scalar ODEs, so both have to be built here.

Difficulty

The linearized step is routine calculus. The nonlinear instability is not: from a¨=F(a)\ddot a=F(a)a¨=F(a) with F(aE)=0F(a_E)=0F(aE​)=0 and FFF strictly increasing on (0,∞)(0,\infty)(0,∞), one must run a bootstrapping argument in which the sign of a¨\ddot aa¨ controls a˙\dot aa˙, which in turn controls whether aaa stays in the region where the sign of a¨\ddot aa¨ was known. The obvious approach — solve the ODE explicitly — is not available: the dust-plus-Λ\LambdaΛ equation with the static value of kkk has no elementary closed-form solution through the static point, and the first Friedmann equation, which is the conserved energy of the second, degenerates exactly at a=aEa=a_Ea=aE​, where a˙=0\dot a=0a˙=0. Divergence a(t)→∞a(t)\to\inftya(t)→∞ additionally requires ruling out that a˙\dot aa˙ stalls, i.e. a quantitative lower bound on a˙\dot aa˙ once the solution has left a neighbourhood of aEa_EaE​.

Formalization scope

Everything is stated for real-valued functions a:R→Ra:\mathbb{R}\to\mathbb{R}a:R→R of a real time variable; derivatives are the usual derivative of a real function, and each equation is asserted pointwise on an explicit set of times (R\mathbb{R}R, [0,∞)[0,\infty)[0,∞), [0,T][0,T][0,T], or a general open set), never globally by fiat. Dust density, the Friedmann equations, the acceleration function FFF, the Hubble parameter, the critical density and the three density parameters are all fixed in one definition file shared by every statement, so all items commit to the same conventions: c=1c=1c=1, curvature entering as −k-k−k in the equation for a˙2\dot a^2a˙2, and ρ0,a0\rho_0,a_0ρ0​,a0​ as fixed reference constants rather than the value of the density at a particular time.

Two trivializing readings are ruled out explicitly. First, the contracting-side statement is posed on a bounded interval [0,T][0,T][0,T] with positivity of aaa assumed there, because a collapsing solution reaches a=0a=0a=0 in finite time; posing it on [0,∞)[0,\infty)[0,∞) would make its hypotheses unsatisfiable and the statement vacuous. Second, the escape statement asks for a(t)→∞a(t)\to\inftya(t)→∞, not merely a˙≥0\dot a\ge0a˙≥0, so it cannot be satisfied by a solution that converges to a finite limit.

Useful contributions beyond the listed items: a reusable instability criterion for x¨=F(x)\ddot x=F(x)x¨=F(x) at a zero of FFF with F′>0F'>0F′>0, and the corresponding stability criterion, both of which are independent of cosmology.

Selected references

  • Wikipedia, Cosmological constant. https://en.wikipedia.org/wiki/Cosmological_constant
  • Wikipedia, Cosmological constant problem. https://en.wikipedia.org/wiki/Cosmological_constant_problem
  • A. Einstein, Kosmologische Betrachtungen zur allgemeinen Relativitätstheorie, Sitzungsberichte der Preussischen Akademie der Wissenschaften, 1917.
  • S. Weinberg, The cosmological constant problem, Reviews of Modern Physics 61 (1989) 1. https://doi.org/10.1103/RevModPhys.61.1
  • J. Martin, Everything you always wanted to know about the cosmological constant problem (but were afraid to ask), Comptes Rendus Physique 13 (2012) 566. https://doi.org/10.1016/j.crhy.2012.04.008
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Mathematical Physics·Captain: Lucas

Coupling Constant: One-Loop Running, Asymptotic Freedom and the Landau PoleTextbook

Motivation

A coupling constant is the number that fixes the strength of an interaction: it is the coefficient that multiplies the interaction term of a Lagrangian relative to its kinetic term, and in a perturbative treatment it is the parameter the expansion is organised in. The central structural fact about such a coupling in a quantum field theory is that it is not constant: once quantum corrections are resummed, the coupling depends on the energy scale μ\muμ at which the theory is probed. This scale dependence — the running of the coupling — is what distinguishes quantum electrodynamics, whose coupling grows with energy until perturbation theory breaks down (the Landau pole), from quantum chromodynamics, whose coupling decreases logarithmically at high energy (asymptotic freedom, Gross–Politzer–Wilczek, Nobel Prize in Physics 2004).

This mission formalizes the elementary mathematics behind those statements: the one-loop renormalization-group equation, its exact solution, and the two qualitatively different behaviours it produces depending on the sign of the one-loop coefficient. The source is the Wikipedia article Coupling constant (sections Beta functions, QED and the Landau pole, QCD and asymptotic freedom), which states these facts in prose and in the single displayed formula for the running QCD coupling.

Setting

Write ggg for a single real coupling and t=log⁡μt = \log \mut=logμ for the logarithm of the energy scale. The beta function is defined by

dgdt  =  μdgdμ  =  β(g),\frac{dg}{dt} \;=\; \mu \frac{dg}{d\mu} \;=\; \beta(g),dtdg​=μdμdg​=β(g),

and at one loop β\betaβ is a cubic monomial,

β(g)  =  b g3,\beta(g) \;=\; b\,g^{3},β(g)=bg3,

with a scheme- and theory-dependent real coefficient bbb. Saying that ggg runs at one loop on a set of scales SSS means that ggg is differentiable at every t∈St \in St∈S with g′(t)=b g(t)3g'(t) = b\,g(t)^{3}g′(t)=bg(t)3.

For QCD the article records the one-loop form of the strong coupling directly in terms of the process energy QQQ and the QCD scale Λ\LambdaΛ:

α(Q)  =  1β0 log⁡ ⁣(Q2/Λ2),β0>0, Λ>0.\alpha(Q) \;=\; \frac{1}{\beta_0 \,\log\!\big(Q^{2}/\Lambda^{2}\big)},\qquad \beta_0>0,\ \Lambda>0 .α(Q)=β0​log(Q2/Λ2)1​,β0​>0, Λ>0.

Target

The goal theorem is the dichotomy governed by the sign of bbb, for an initial condition g(0)=g0>0g(0) = g_0 > 0g(0)=g0​>0 at the reference scale t=0t = 0t=0:

  1. Asymptotic freedom (b<0b < 0b<0). Every solution on [0,∞)[0,\infty)[0,∞) satisfies
lim⁡t→∞g(t)=0,lim⁡t→∞2∣b∣ t g(t)2=1,\lim_{t\to\infty} g(t) = 0, \qquad \lim_{t\to\infty} 2|b|\,t\,g(t)^{2} = 1,t→∞lim​g(t)=0,t→∞lim​2∣b∣tg(t)2=1,

i.e. g(t)2∼1/(2∣b∣log⁡μ)g(t)^{2} \sim 1/(2|b|\log\mu)g(t)2∼1/(2∣b∣logμ): the coupling dies off logarithmically in the energy.

  1. Landau pole (b>0b > 0b>0). No solution exists on the whole closed interval
[ 0, 12 b g02 ];\Big[\,0,\ \frac{1}{2\,b\,g_0^{2}}\,\Big];[0, 2bg02​1​];

the flow cannot be continued up to the finite scale t∗=1/(2bg02)t_\ast = 1/(2bg_0^{2})t∗​=1/(2bg02​), which is the Landau pole of the one-loop approximation.

Both follow from the exact one-loop solution, which is itself a milestone:

g(t)2 (1−2b g02 t)  =  g02.g(t)^{2}\,\big(1 - 2b\,g_0^{2}\,t\big) \;=\; g_0^{2}.g(t)2(1−2bg02​t)=g02​.

Significance

The dichotomy above is the mathematical content of the two headline claims made about running couplings: that a positive one-loop coefficient drives the coupling to infinity at a finite energy, and that a negative one makes it vanish logarithmically at high energy. The milestones also isolate the statement that a vanishing beta function means the coupling does not run at all (scale invariance), and record the elementary analytic properties of the explicit QCD formula α(Q)=1/(β0log⁡(Q2/Λ2))\alpha(Q) = 1/(\beta_0\log(Q^2/\Lambda^2))α(Q)=1/(β0​log(Q2/Λ2)): it is strictly decreasing above Λ\LambdaΛ, tends to 000 as Q→∞Q\to\inftyQ→∞, and satisfies the one-loop flow equation dα/dQ=−(2/Q) β0 α2d\alpha/dQ = -(2/Q)\,\beta_0\,\alpha^{2}dα/dQ=−(2/Q)β0​α2.

Nothing here is open mathematics: these are exercises in the theory of a separable scalar ODE. What the mission produces is a machine-checked, reusable Lean development of the one-loop renormalization-group flow — a small piece of formalized physics infrastructure that later missions on renormalization or dimensional transmutation can import.

Difficulty

The only real subtlety is that the exact solution is obtained by differentiating t↦g(t)−2t \mapsto g(t)^{-2}t↦g(t)−2, which requires knowing in advance that the solution never vanishes. Positivity is not assumed: it is a separate milestone, and must be derived from the initial condition together with the flow equation (if ggg reached 000 at a first time t1t_1t1​, the identity g(t)−2=g0−2−2btg(t)^{-2} = g_0^{-2} - 2btg(t)−2=g0−2​−2bt on [0,t1)[0,t_1)[0,t1​) forces g(t1)2g(t_1)^2g(t1​)2 to have a strictly positive limit). The Landau-pole statement is a non-existence claim and has no analytic content beyond this, but it does depend on the positivity step.

Formalization scope

Scales are parametrised logarithmically: the independent variable is t=log⁡μ∈Rt = \log\mu \in \mathbb{R}t=logμ∈R, the coupling is a function g:R→Rg : \mathbb{R} \to \mathbb{R}g:R→R, and the flow hypothesis is imposed pointwise as a two-sided derivative at each point of the given set (at an endpoint of a closed interval this is slightly stronger than a one-sided derivative, which only strengthens the hypotheses of the positive results and weakens the non-existence claim — the Landau pole statement is therefore the conservative reading). No uniqueness or existence theory for ODEs is assumed; the statements quantify over arbitrary functions satisfying the flow equation. The QCD formula is taken with real arguments and no constraint tying β0\beta_0β0​ or Λ\LambdaΛ to a particular number of flavours; the numerical values quoted in the source (αs(MZ2)=0.1179±0.0010\alpha_s(M_Z^2) = 0.1179 \pm 0.0010αs​(MZ2​)=0.1179±0.0010, Λ≈332\Lambda \approx 332Λ≈332 MeV) are measurements and are deliberately not formalized.

Degenerate cases are visible in the statements rather than hidden: b=0b = 0b=0 appears as its own milestone (constant coupling), and the value of α(Q)\alpha(Q)α(Q) for Q≤ΛQ \le \LambdaQ≤Λ, where the logarithm is non-positive or zero, is never asserted.

Selected references

  • Coupling constant, Wikipedia, revision 1354339557, https://en.wikipedia.org/w/index.php?title=Coupling_constant&oldid=1354339557
  • A. Deur, S. J. Brodsky, G. F. de Téramond, "The QCD running coupling", Progress in Particle and Nuclear Physics 90 (2016) 1–74, https://arxiv.org/abs/1604.08082
  • Particle Data Group (P. A. Zyla et al.), "Review of Particle Physics — Quantum Chromodynamics", PTEP 2020 (8), https://doi.org/10.1093/ptep/ptaa104
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Mathematical Physics·Captain: Lucas

Celestial Mechanics I: Binary Star SystemsTextbook

Motivation

Roughly half of the stars in our galaxy belong to binary star systems: pairs of stars close enough to each other that their mutual gravitational attraction dominates every other influence, and far enough from every other star that the rest of the galaxy can be ignored. Such a pair is the cleanest physical realization of the Newtonian two-body problem, and it is the reason the two-body problem is not merely a textbook exercise: the masses of stars other than the Sun are known almost entirely because binaries can be observed, and the observation is converted into a mass by exactly the chain of reasoning formalized in this mission.

This mission formalizes Section 4.16, "Binary star systems", of Richard Fitzpatrick's Celestial Mechanics lecture notes (University of Texas at Austin), Eqs. (4.108)–(4.119). It is the first mission of a planned series on the same source; the whole series shares the Lean namespace CelestialMechanics.

Setting

Two point masses m1,m2>0m_1, m_2 > 0m1​,m2​>0 move in R3\mathbb{R}^3R3 under Newtonian gravity, with position vectors r1(t)\mathbf{r}_1(t)r1​(t) and r2(t)\mathbf{r}_2(t)r2​(t) defined for all times t∈Rt \in \mathbb{R}t∈R. Write r=r2−r1\mathbf{r} = \mathbf{r}_2 - \mathbf{r}_1r=r2​−r1​ for the relative separation and r=∥r∥r = \lVert \mathbf{r} \rVertr=∥r∥ for the distance between the stars. The gravitational force the first star exerts on the second is Eq. (4.108),

f=−G m1m2r3 r,\mathbf{f} = -\frac{G\,m_1 m_2}{r^{3}}\,\mathbf{r},f=−r3Gm1​m2​​r,

and Newton's second law for each star reads

m1 r¨1=G m1m2r3 r,m2 r¨2=−G m1m2r3 r.m_1\,\ddot{\mathbf{r}}_1 = \frac{G\,m_1m_2}{r^{3}}\,\mathbf{r}, \qquad m_2\,\ddot{\mathbf{r}}_2 = -\frac{G\,m_1m_2}{r^{3}}\,\mathbf{r}.m1​r¨1​=r3Gm1​m2​​r,m2​r¨2​=−r3Gm1​m2​​r.

A pair of twice continuously differentiable, never-colliding trajectories satisfying these two equations is what this mission calls a Newtonian gravitational two-body system.

Three derived objects organize the material. The center of mass is R=(m1r1+m2r2)/(m1+m2)\mathbf{R} = (m_1\mathbf{r}_1 + m_2\mathbf{r}_2)/(m_1+m_2)R=(m1​r1​+m2​r2​)/(m1​+m2​). The relative Kepler problem with gravitational parameter GMGMGM, M=m1+m2M = m_1+m_2M=m1​+m2​, is the equation

r¨=−GMr3 r(4.110)\ddot{\mathbf{r}} = -\frac{G M}{r^{3}}\,\mathbf{r} \tag{4.110}r¨=−r3GM​r(4.110)

for a nowhere-vanishing curve r\mathbf{r}r. A Keplerian conic orbit with major radius aaa, eccentricity eee and angular momentum per unit reduced mass hhh is a planar curve given in polar coordinates (ρ,θ)(\rho, \theta)(ρ,θ) by

r=(ρcos⁡θ, ρsin⁡θ, 0),ρ=a (1−e2)1+ecos⁡θ,θ˙=hρ2,\mathbf{r} = (\rho\cos\theta,\ \rho\sin\theta,\ 0), \qquad \rho = \frac{a\,(1-e^{2})}{1+e\cos\theta}, \qquad \dot\theta = \frac{h}{\rho^{2}},r=(ρcosθ, ρsinθ, 0),ρ=1+ecosθa(1−e2)​,θ˙=ρ2h​,

which are Eqs. (4.112)–(4.114); the elements are tied together by Eq. (4.115), a=h2/((1−e2)GM)a = h^{2}/\bigl((1-e^{2})GM\bigr)a=h2/((1−e2)GM).

Target

The goal theorem is an existence-and-description statement: for every G>0G>0G>0, every pair of masses m1,m2>0m_1,m_2>0m1​,m2​>0 and every choice of orbital elements a>0a>0a>0, 0≤e<10 \le e < 10≤e<1, there exist trajectories r1,r2\mathbf{r}_1, \mathbf{r}_2r1​,r2​, polar functions ρ,θ\rho, \thetaρ,θ and a time T>0T>0T>0 such that

(i) r1,r2 solve the two-body equations above;(ii) m1r1+m2r2=0;\text{(i) } \mathbf{r}_1, \mathbf{r}_2 \text{ solve the two-body equations above}; \qquad \text{(ii) } m_1\mathbf{r}_1 + m_2\mathbf{r}_2 = \mathbf{0};(i) r1​,r2​ solve the two-body equations above;(ii) m1​r1​+m2​r2​=0; (iii) r2−r1 is the Keplerian conic with elements (a,e,h),h=(1−e2) G (m1+m2) a;\text{(iii) } \mathbf{r}_2 - \mathbf{r}_1 \text{ is the Keplerian conic with elements } (a, e, h), \quad h = \sqrt{(1-e^{2})\,G\,(m_1+m_2)\,a};(iii) r2​−r1​ is the Keplerian conic with elements (a,e,h),h=(1−e2)G(m1​+m2​)a​; (iv) r1=−m2m1+m2 r,r2=m1m1+m2 r;(v) T=4π2a3G(m1+m2) and both orbits are T-periodic.\text{(iv) } \mathbf{r}_1 = -\frac{m_2}{m_1+m_2}\,\mathbf{r}, \quad \mathbf{r}_2 = \frac{m_1}{m_1+m_2}\,\mathbf{r}; \qquad \text{(v) } T = \sqrt{\frac{4\pi^{2}a^{3}}{G(m_1+m_2)}} \text{ and both orbits are } T\text{-periodic}.(iv) r1​=−m1​+m2​m2​​r,r2​=m1​+m2​m1​​r;(v) T=G(m1​+m2​)4π2a3​​ and both orbits are T-periodic.

The milestones are the individual steps of Section 4.16, ordered as in the source: the reduction of the two-body system to the one-body problem (4.110)–(4.111); the uniform motion of the center of mass, which makes the center-of-mass frame inertial; the positions of the two stars in that frame (4.118)–(4.119); the verification that the conic (4.112)–(4.115) solves (4.110); the period of revolution (4.116)–(4.117); and the inversion of these relations that yields the two stellar masses from observation.

Significance

The result itself. The theorem is the complete qualitative and quantitative picture of a binary orbit: each star traverses an ellipse about the common center of mass, the two stars are diametrically opposite one another at every instant, the distances from the center of mass are in the fixed ratio m2:m1m_2 : m_1m2​:m1​, and the period depends only on the major radius and the total mass. The last two facts are what astronomers use: the measured pair (a,T)(a, T)(a,T) gives the mass sum m1+m2=4π2a3/(GT2)m_1 + m_2 = 4\pi^{2}a^{3}/(GT^{2})m1​+m2​=4π2a3/(GT2), the observed ratio of the two apparent orbits gives m1/m2m_1/m_2m1​/m2​, and sum plus ratio give the individual masses. The same reduction also supplies the finite-mass correction to one-body solar-system orbit theory, in which GM⊙G M_{\odot}GM⊙​ is replaced by G(M⊙+m)G(M_{\odot} + m)G(M⊙​+m).

Formalizing it. The mathematics has been settled since Newton, so the contribution here is a machine-checked development rather than a new result. Mathlib currently has extensive analysis infrastructure — iterated derivatives, ContDiff, Euclidean spaces — but no development of the Kepler problem: neither the conic solution of the inverse-square equation of motion nor Kepler's laws. The reusable output of this mission is therefore a small, self-contained Kepler library: the two-body reduction, the conic solution, and the period law, stated for curves in R3\mathbb{R}^3R3 in a form that later missions in the series (perturbations, orbital elements, the restricted three-body problem) can import unchanged.

Difficulty

Two milestones carry essentially all of the analytic work.

The first is showing that the conic really solves (4.110). The obvious route — differentiate ρ(t)\rho(t)ρ(t) twice and simplify — runs into the fact that ρ\rhoρ is given as a function of θ(t)\theta(t)θ(t) and θ\thetaθ is given only implicitly, through the angular-momentum law θ˙=h/ρ2\dot\theta = h/\rho^{2}θ˙=h/ρ2. Even the regularity in the conclusion has to be earned: the hypotheses supply only differentiability of θ\thetaθ, and twice-continuous differentiability of the curve must be bootstrapped from the two polar equations. Getting the Cartesian second derivative into the form −(GM/ρ3)r-(GM/\rho^{3})\mathbf{r}−(GM/ρ3)r requires the relation h2=(1−e2)GMah^{2} = (1-e^{2})GMah2=(1−e2)GMa at exactly the right place, and this is where a sign or a factor typically goes wrong.

The second is the goal theorem, which is an existence statement and therefore needs the angular equation θ˙=h/ρ(θ)2\dot\theta = h/\rho(\theta)^{2}θ˙=h/ρ(θ)2 to be solved globally in time, on all of R\mathbb{R}R and not merely on a small interval. The right-hand side is smooth and, for 0≤e<10 \le e < 10≤e<1, bounded above and below by positive constants, so the solution exists for all time and advances by 2π2\pi2π in a fixed time TTT; but "exists for all time" is a real obligation in Lean, and a naive appeal to a local existence theorem does not discharge it.

Formalization scope

Space is EuclideanSpace ℝ (Fin 3), so the norm is the Euclidean one and the three Cartesian components are indexed 0,1,20, 1, 20,1,2; the orbital plane is fixed to be the xxx-yyy plane, exactly as in Eq. (4.112). Trajectories are total functions R→R3\mathbb{R} \to \mathbb{R}^3R→R3: all statements are global in time, and no restriction to an interval or to positive times is made. Derivatives are iteratedDeriv, and smoothness is ContDiff ℝ 2. Masses and GGG are positive reals wherever the physics needs it, and this is stated explicitly rather than left implicit. Eccentricity is restricted to 0≤e<10 \le e < 10≤e<1 throughout, so only bound (elliptical and circular) orbits are in scope; the parabolic and hyperbolic cases of Sections 4.13–4.15 are left to later missions.

Two trivializing formalizations are ruled out by construction. The definitions never allow the separation to vanish, so no statement is satisfied by a collision; and in the goal theorem the angular momentum hhh and the period TTT are not existentially quantified but pinned to their Keplerian values in terms of (G,m1+m2,a,e)(G, m_1+m_2, a, e)(G,m1​+m2​,a,e), so the orbit cannot be rescaled to make the conditions easy to meet.

Contributions of independent interest are welcome: conservation of angular momentum and of energy for (4.110), the equal-areas law, the eccentricity (Laplace–Runge–Lenz) vector, and the Kepler equation relating mean and eccentric anomaly all sit naturally on top of this development.

Selected references

  • Richard Fitzpatrick, Celestial Mechanics, University of Texas at Austin lecture notes, Chapter 4 "Keplerian orbits", Section 4.16 "Binary star systems", https://farside.ph.utexas.edu/teaching/celestial/Celestial/node38.html. The source of this mission; Eqs. (4.108)–(4.119).
  • Isaac Newton, Philosophiæ Naturalis Principia Mathematica, 1687. Book I contains the original solution of the inverse-square two-body problem.
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An Introduction to Chaotic Dynamical Systems II: Sarkovskii's TheoremTextbook

Motivation

In 1964 A. N. Sarkovskii proved a theorem about continuous maps of the real line that is remarkable both for how little it assumes — continuity, nothing more — and for how much it concludes: the set of periods of the periodic orbits of such a map is completely constrained by a single linear ordering of the positive integers. Its best-known corollary, rediscovered by Li and Yorke in 1975 under the slogan period three implies chaos, says that a continuous map of R\mathbb{R}R with an orbit of period three has orbits of every period.

Devaney presents the theorem in §1.10 of An Introduction to Chaotic Dynamical Systems (2nd edition, Westview Press, 2003), calling it the chapter's first major theorem, and gives the elementary proof of Block, Guckenheimer, Misiurewicz and Young based on interval covering relations. This mission is the second in a series formalizing the book; it is independent of the first, sharing only the book-wide namespace.

Timeline: Sarkovskii (1964) proved the full ordering theorem, in Ukrainian, and it went largely unnoticed in the West; Li and Yorke (1975) independently proved the period-three case and gave the field the word "chaos"; Štefan (1977) and Block–Guckenheimer–Misiurewicz–Young (1980) gave the short interval-covering proofs, the latter being the one Devaney reproduces.

Setting

Let f:R→Rf : \mathbb{R} \to \mathbb{R}f:R→R be continuous. A point xxx has prime period n≥1n \ge 1n≥1 if fn(x)=xf^{n}(x) = xfn(x)=x and fm(x)≠xf^{m}(x) \ne xfm(x)=x for every 0<m<n0 < m < n0<m<n.

The Sarkovskii ordering of the positive integers is

3 ▹ 5 ▹ 7 ▹ ⋯ ▹ 2⋅3 ▹ 2⋅5 ▹ ⋯ ▹ 22⋅3 ▹ 22⋅5 ▹ ⋯ ▹ 23 ▹ 22 ▹ 2 ▹ 1:3 \,\triangleright\, 5 \,\triangleright\, 7 \,\triangleright\, \cdots \,\triangleright\, 2\cdot 3 \,\triangleright\, 2\cdot 5 \,\triangleright\, \cdots \,\triangleright\, 2^2\cdot 3 \,\triangleright\, 2^2 \cdot 5 \,\triangleright\, \cdots \,\triangleright\, 2^3 \,\triangleright\, 2^2 \,\triangleright\, 2 \,\triangleright\, 1 :3▹5▹7▹⋯▹2⋅3▹2⋅5▹⋯▹22⋅3▹22⋅5▹⋯▹23▹22▹2▹1:

first the odd numbers greater than one in increasing order, then 222 times the odds, then 222^222 times the odds, and so on; the powers of two come last, in decreasing order. Writing k=2apk = 2^{a}pk=2ap and ℓ=2bq\ell = 2^{b}qℓ=2bq with p,qp, qp,q odd, k▹ℓk \triangleright \ellk▹ℓ holds exactly when either p,q>1p, q > 1p,q>1 and (a,p)(a,p)(a,p) precedes (b,q)(b,q)(b,q) lexicographically, or p>1p > 1p>1 and q=1q = 1q=1, or p=q=1p = q = 1p=q=1 and b<ab < ab<a.

Two elementary facts drive the proof. If III is a closed interval with f(I)⊇If(I) \supseteq If(I)⊇I, then fff has a fixed point in III; and if closed intervals satisfy f(Ai)⊇Ai+1f(A_i) \supseteq A_{i+1}f(Ai​)⊇Ai+1​ for i<ni < ni<n, then some point of A0A_0A0​ has fi(x)∈Aif^{i}(x) \in A_ifi(x)∈Ai​ for all i≤ni \le ni≤n. One writes I→JI \to JI→J, "f(I)f(I)f(I) covers JJJ", for J⊆f(I)J \subseteq f(I)J⊆f(I).

Target

The goal is Devaney's Theorem 10.2: for continuous f:R→Rf : \mathbb{R} \to \mathbb{R}f:R→R,

if f has a point of prime period k and k▹ℓ, then f has a point of prime period ℓ.\text{if } f \text{ has a point of prime period } k \text{ and } k \triangleright \ell, \text{ then } f \text{ has a point of prime period } \ell .if f has a point of prime period k and k▹ℓ, then f has a point of prime period ℓ.

The milestones are the steps of the book's proof: the two covering observations, the period-three special case (Theorem 10.1), the odd case, the power-of-two case and the mixed case p⋅2mp \cdot 2^mp⋅2m into which the general theorem is decomposed, the remark that a period which is not a power of two forces infinitely many periodic points, and the converse direction, witnessed by the piecewise-linear map with a period-five orbit and no period-three orbit.

Significance

Sarkovskii's theorem is the sharpest general statement known about the period structure of one-dimensional dynamics, and it is sharp in both directions: the ordering is realized, so no stronger implication holds. Its first consequence — only powers of two can occur as the set of periods of a map with finitely many periodic points — is what makes the period-doubling cascade the canonical route to chaos, a theme the book returns to in §1.17.

The theorem is emphatically one-dimensional: it fails on the circle, where a rotation by 120∘120^\circ120∘ has every point of period three and no other period.

Formalizing it contributes a reusable Lean treatment of interval covering relations and of the Sarkovskii ordering itself; we are not aware of these in Mathlib at the pinned revision, and the covering machinery is exactly what §1.13 and §1.16 of the book reuse.

Difficulty

The period-three case is a short argument once the covering observations are available, and it is a reasonable first milestone. The general theorem is not: the odd case requires choosing the right interval I1=[xi,xi+1]I_1 = [x_i, x_{i+1}]I1​=[xi​,xi+1​] on the orbit, building the increasing family of unions OℓO_\ellOℓ​ of covered intervals, and showing that the shortest return loop has length exactly n−1n-1n−1 — a combinatorial argument on the cyclic order of the orbit that is easy to draw and tedious to formalize. Attempts to shortcut the ordering with a naive induction on nnn fail: the statement for nnn genuinely depends on the geometric arrangement of the orbit points.

Formalization scope

  1. Periodicity is prime period throughout: fn(x)=xf^n(x) = xfn(x)=x together with minimality of nnn. The statements would be false or trivial with "period" read as "fixed by fnf^nfn".
  2. The Sarkovskii relation is defined arithmetically, in terms of the 222-adic valuation and the odd part of an integer, rather than as a listed order; it is a strict relation, so k▹kk \triangleright kk▹k is false and the goal theorem says nothing about ℓ=k\ell = kℓ=k (which holds by hypothesis anyway).
  3. 000 is outside the ordering: the relation is false whenever either argument is 000.
  4. Intervals in the covering lemmas are closed intervals [a,b][a,b][a,b] with a≤ba \le ba≤b, given by their endpoints; "covers" means containment of the interval in the image, J⊆f(I)J \subseteq f(I)J⊆f(I).
  5. The converse milestone asserts the existence of a continuous map with a period-five point and no period-three point; the book's witness is piecewise linear on [1,5][1,5][1,5], but the statement does not prescribe it.

Selected references

  • Robert L. Devaney, An Introduction to Chaotic Dynamical Systems, 2nd edition, Westview Press, 2003 (ISBN 0-8133-4085-3) — §1.10, pp. 60–68. The mission's primary source.
  • T. Y. Li and J. A. Yorke, Period three implies chaos, American Mathematical Monthly 82 (1975), 985–992, DOI: 10.1080/00029890.1975.11994008.
  • L. Block, J. Guckenheimer, M. Misiurewicz, L. S. Young, Periodic points and topological entropy of one-dimensional maps, in Global Theory of Dynamical Systems, Lecture Notes in Mathematics 819, Springer, 1980, 18–34, DOI: 10.1007/BFb0086977 — the proof Devaney follows.

Audit note (provenance of the read-backs)

The read-backs attached to every draft item in this proposal are not independent. They were written by the same agent that drafted the Lean statements, not by a separate auditor working blind from the code alone. They are included because they are still useful as a line-by-line rendering of each statement, but they are not independent testimony: any misreading baked into a formalization is likely repeated in its read-back, and agreement between the two should not be taken as confirmation of faithfulness. Each read-back repeats this warning in its own first paragraph. Reviewers who want independent testimony should commission fresh, blind read-backs.

Every definition and statement in this proposal was compiled locally against this mission's environment (Lean 4.33.1, Mathlib 0df444a360eaa60ab8c11dca51a86af692955474): all files elaborate with no errors, the only warnings being the expected sorry placeholders in the theorem bodies.

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An Introduction to Chaotic Dynamical Systems I: Chaos in the Quadratic FamilyTextbook

Motivation

The word chaos entered mathematics with a precise meaning, and Robert L. Devaney's An Introduction to Chaotic Dynamical Systems (2nd edition, Westview Press, 2003) is the text that fixed the meaning now used in most of the literature: a map is chaotic when it is unpredictable (sensitive dependence on initial conditions), indecomposable (topological transitivity), and nevertheless regular (dense periodic points). The book develops this definition on the simplest possible object — the real quadratic family Fμ(x)=μx(1−x)F_\mu(x) = \mu x(1-x)Fμ​(x)=μx(1−x) on the unit interval — and shows that for large μ\muμ the map is chaotic on an invariant Cantor set, by exhibiting an exact symbolic model for it.

This mission is the first of a planned series formalizing the book. It covers §1.5–§1.8: the invariant set of the quadratic family, symbolic dynamics on the sequence space Σ2\Sigma_2Σ2​, topological conjugacy, and Devaney's definition of chaos. Everything later in the book — Sarkovskii's theorem, the horseshoe, hyperbolic toral automorphisms, Julia sets — is written in the vocabulary fixed here, so a faithful Lean version of this chapter fixes the vocabulary of the whole series.

Setting

Write I=[0,1]I = [0,1]I=[0,1] and let Fμ(x)=μx(1−x)F_\mu(x) = \mu x(1-x)Fμ​(x)=μx(1−x) for a real parameter μ\muμ. Iterates are written FμnF_\mu^nFμn​, with Fμ0F_\mu^0Fμ0​ the identity.

For μ>4\mu > 4μ>4 the maximum value μ/4\mu/4μ/4 of FμF_\muFμ​ exceeds 111, so some points of III leave III after one iteration. Let

A0={x∈I:Fμ(x)>1},An={x∈I:Fμ n(x)∈A0},A_0 = \{x \in I : F_\mu(x) > 1\}, \qquad A_n = \{x \in I : F_\mu^{\,n}(x) \in A_0\},A0​={x∈I:Fμ​(x)>1},An​={x∈I:Fμn​(x)∈A0​},

so that AnA_nAn​ is the set of points escaping from III at the (n+1)(n+1)(n+1)-st iteration. The set of points that never escape is

Λ=I∖⋃n≥0An={x:Fμ n(x)∈I for all n≥0}.\Lambda = I \setminus \bigcup_{n \ge 0} A_n = \{x : F_\mu^{\,n}(x) \in I \text{ for all } n \ge 0\}.Λ=I∖n≥0⋃​An​={x:Fμn​(x)∈I for all n≥0}.

The complement I∖A0I \setminus A_0I∖A0​ consists of two closed intervals, I0I_0I0​ to the left of the midpoint 1/21/21/2 and I1I_1I1​ to its right.

On the symbolic side, Σ2\Sigma_2Σ2​ is the set of one-sided infinite sequences s=(s0s1s2… )s = (s_0 s_1 s_2 \dots)s=(s0​s1​s2​…) with si∈{0,1}s_i \in \{0,1\}si​∈{0,1}, metrized by

d[s,t]=∑i=0∞∣si−ti∣2i,d[s,t] = \sum_{i=0}^{\infty} \frac{|s_i - t_i|}{2^i},d[s,t]=i=0∑∞​2i∣si​−ti​∣​,

and σ:Σ2→Σ2\sigma : \Sigma_2 \to \Sigma_2σ:Σ2​→Σ2​ is the shift map σ(s0s1s2… )=(s1s2s3… )\sigma(s_0 s_1 s_2 \dots) = (s_1 s_2 s_3 \dots)σ(s0​s1​s2​…)=(s1​s2​s3​…). The itinerary of x∈Λx \in \Lambdax∈Λ is the sequence S(x)=(s0s1s2… )S(x) = (s_0 s_1 s_2 \dots)S(x)=(s0​s1​s2​…) with sj=0s_j = 0sj​=0 when Fμ j(x)∈I0F_\mu^{\,j}(x) \in I_0Fμj​(x)∈I0​ and sj=1s_j = 1sj​=1 when Fμ j(x)∈I1F_\mu^{\,j}(x) \in I_1Fμj​(x)∈I1​.

Following Devaney, f:J→Jf : J \to Jf:J→J is topologically transitive if for every pair of open sets U,VU, VU,V meeting JJJ there is k>0k > 0k>0 with fk(U∩J)∩V≠∅f^k(U \cap J) \cap V \neq \emptysetfk(U∩J)∩V=∅; it has sensitive dependence on initial conditions if there is δ>0\delta > 0δ>0 such that every point of JJJ has points of JJJ arbitrarily near it whose orbit eventually separates from its own by more than δ\deltaδ; and it is chaotic on JJJ when it has sensitive dependence, is topologically transitive, and has a dense set of periodic points in JJJ.

Target

The goal is Devaney's Example 8.8: for μ>2+5\mu > 2 + \sqrt 5μ>2+5​,

Fμ is chaotic on Λ.F_\mu \text{ is chaotic on } \Lambda .Fμ​ is chaotic on Λ.

The milestones are the results the book uses to get there, in the book's own order: the escape of orbits outside III (Proposition 5.2), the tame regime 1<μ<31 < \mu < 31<μ<3 (Proposition 5.3), the Cantor structure of Λ\LambdaΛ (Theorem 5.6), the metric and dynamics of the shift (Propositions 6.3, 6.5, 6.6), the itinerary conjugacy (Theorems 7.2, 7.3), its dynamical consequences (Theorem 7.5), sensitive dependence (Example 8.3), and the chaos of F4F_4F4​ on all of III (Example 8.9).

Significance

The theorem is the prototype for every later "chaos via symbolic dynamics" argument: the horseshoe, hyperbolic toral automorphisms, and the quadratic Julia sets are all proved chaotic by producing a conjugacy with a shift. The conjugacy also gives quantitative information that is otherwise inaccessible — for example, that FμF_\muFμ​ has exactly 2n2^n2n points fixed by Fμ nF_\mu^{\,n}Fμn​, which no direct computation with the degree-2n2^n2n polynomial delivers.

Formalizing it produces reusable Lean infrastructure that Mathlib currently lacks: Devaney's three chaos conditions, the sequence space Σ2\Sigma_2Σ2​ with its metric and shift, topological conjugacy of maps on subsets, and the notion of a Cantor subset of the interval. These are the foundation the rest of the book's series will import.

Difficulty

The obvious route to the goal — analyze FμF_\muFμ​ on Λ\LambdaΛ directly — fails, because Λ\LambdaΛ has no explicit description: it is a nested intersection of 2n+12^{n+1}2n+1 intervals whose endpoints are not available in closed form. The whole argument therefore goes through the itinerary map, and its two hard steps are: (i) surjectivity of the itinerary map, which needs the nested-interval construction Is0…sn=Is0∩Fμ−1(Is1)∩⋯∩Fμ−n(Isn)I_{s_0 \dots s_n} = I_{s_0} \cap F_\mu^{-1}(I_{s_1}) \cap \dots \cap F_\mu^{-n}(I_{s_n})Is0​…sn​​=Is0​​∩Fμ−1​(Is1​​)∩⋯∩Fμ−n​(Isn​​) together with the fact that these intervals are nonempty and nested; and (ii) injectivity, which needs the hyperbolicity estimate ∣Fμ′∣>λ>1|F_\mu'| > \lambda > 1∣Fμ′​∣>λ>1 on I0∪I1I_0 \cup I_1I0​∪I1​, valid exactly because μ>2+5\mu > 2 + \sqrt 5μ>2+5​, and the mean value theorem. The hypothesis μ>2+5\mu > 2 + \sqrt 5μ>2+5​ is not cosmetic: Devaney notes the results hold for μ>4\mu > 4μ>4, but only with a more delicate argument.

Formalization scope

The Lean development fixes the following conventions.

  1. Λ\LambdaΛ is defined as {x:∀n, Fμ n(x)∈[0,1]}\{x : \forall n,\ F_\mu^{\,n}(x) \in [0,1]\}{x:∀n, Fμn​(x)∈[0,1]} — the points whose whole forward orbit stays in III — rather than as a complement of the sets AnA_nAn​; the two descriptions agree, and the definitional form makes invariance immediate. The sets A0,An,I0,I1A_0, A_n, I_0, I_1A0​,An​,I0​,I1​ are nonetheless defined, since the book's arguments refer to them.
  2. The itinerary is defined as a total function of a real argument, taking entry 000 at step nnn when Fμ n(x)≤1/2F_\mu^{\,n}(x) \le 1/2Fμn​(x)≤1/2 and 111 otherwise. On Λ\LambdaΛ this agrees with Devaney's I0/I1I_0/I_1I0​/I1​ test, since the midpoint 1/21/21/2 lies in the gap A0A_0A0​ when μ>4\mu > 4μ>4.
  3. Σ2\Sigma_2Σ2​ carries Devaney's metric ddd literally, as a summable series, not merely a topology; the metric space instance is part of the definitional layer, so Proposition 6.2 is not a separate milestone.
  4. Sensitive dependence, transitivity, chaos and periodicity are stated for a map f:X→Xf : X \to Xf:X→X of a metric space together with an invariant subset JJJ, using open sets of the ambient space intersected with JJJ; this avoids subtype bookkeeping while keeping the relative formulation of the book.
  5. Cardinality claims ("Per⁡n\operatorname{Per}_nPern​ has 2n2^n2n elements") are stated with Set.ncard and are restricted to n>0n > 0n>0; for n=0n = 0n=0 every point is fixed by F0F^0F0 and the claim would be false.
  6. Nothing here is vacuous: the hypothesis μ>2+5\mu > 2+\sqrt 5μ>2+5​ is satisfiable, Λ\LambdaΛ is nonempty (it contains 000), and the chaos predicate is a conjunction of three nontrivial conditions rather than a definitional abbreviation.

Contributions of any kind are welcome: full proofs, reductions splitting a milestone into lemmas, and reusable lemmas about Σ2\Sigma_2Σ2​ or about conjugacy that later missions in the series can import.

Selected references

  • Robert L. Devaney, An Introduction to Chaotic Dynamical Systems, 2nd edition, Westview Press, 2003 (ISBN 0-8133-4085-3) — §1.5 (pp. 31–38), §1.6 (pp. 39–43), §1.7 (pp. 44–47), §1.8 (pp. 49–52). The mission's primary and authoritative source.
  • J. Banks, J. Brooks, G. Cairns, G. Davis, P. Stacey, On Devaney's definition of chaos, American Mathematical Monthly 99 (1992), 332–334, DOI: 10.1080/00029890.1992.11995856 — proves that transitivity plus dense periodic points already imply sensitive dependence.

Audit note (provenance of the read-backs)

The read-backs attached to every draft item in this proposal are not independent. They were written by the same agent that drafted the Lean statements, not by a separate auditor working blind from the code alone. They are included because they are still useful as a line-by-line rendering of each statement, but they are not independent testimony: any misreading baked into a formalization is likely repeated in its read-back, and agreement between the two should not be taken as confirmation of faithfulness. Each read-back repeats this warning in its own first paragraph. Reviewers who want independent testimony should commission fresh, blind read-backs.

Every definition and statement in this proposal was compiled locally against this mission's environment (Lean 4.33.1, Mathlib 0df444a360eaa60ab8c11dca51a86af692955474): all files elaborate with no errors, the only warnings being the expected sorry placeholders in the theorem bodies.

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Number Theory·Captain: Lucas

Kawahira: The Riemann Hypothesis and Holomorphic Index in Complex DynamicsResearch Paper

Motivation

The Riemann hypothesis asserts that every non-trivial zero of the Riemann zeta function ζ\zetaζ lies on the line Re⁡s=1/2\operatorname{Re} s = 1/2Res=1/2; the simplicity hypothesis asserts in addition that every such zero is a simple zero of ζ\zetaζ. Both are statements about the location and the order of a discrete set of points in the complex plane, and almost every reformulation of them stays inside analytic number theory.

Kawahira (2016) gives a reformulation of a different kind. He attaches to ζ\zetaζ an explicit meromorphic self-map of the Riemann sphere and shows that the Riemann hypothesis together with the simplicity hypothesis is equivalent to a statement about the local dynamics of that map: it has no attracting fixed point. The translation is elementary once the right object is in place — the holomorphic index (residue fixed point index) of a fixed point — and it turns a question about zeros into a question about stability. This mission formalizes that translation, together with the supporting propositions on indices and multipliers that make it work.

Setting

For a non-constant meromorphic g:C→C^g : \mathbb{C} \to \widehat{\mathbb{C}}g:C→C, define the nu function

νg(z)  =  z−g(z)z g′(z).\nu_g(z) \;=\; z - \frac{g(z)}{z\,g'(z)}.νg​(z)=z−zg′(z)g(z)​.

If α≠0\alpha \neq 0α=0 is a zero of ggg of order m≥1m \ge 1m≥1, then α\alphaα is a fixed point of νg\nu_gνg​ with multiplier

λ  =  νg′(α)  =  1−1mα,\lambda \;=\; \nu_g'(\alpha) \;=\; 1 - \frac{1}{m\alpha},λ=νg′​(α)=1−mα1​,

and if α\alphaα is a pole of order mmm the multiplier is 1+1mα1 + \frac{1}{m\alpha}1+mα1​. A fixed point α\alphaα of a holomorphic map fff is attracting if ∣f′(α)∣<1|f'(\alpha)| < 1∣f′(α)∣<1, indifferent if ∣f′(α)∣=1|f'(\alpha)| = 1∣f′(α)∣=1, and repelling if ∣f′(α)∣>1|f'(\alpha)| > 1∣f′(α)∣>1.

The holomorphic index of fff at a fixed point α\alphaα is

ι(f,α)  =  12πi∮Cdzz−f(z),\iota(f,\alpha) \;=\; \frac{1}{2\pi i}\oint_{C} \frac{dz}{z - f(z)},ι(f,α)=2πi1​∮C​z−f(z)dz​,

the integral being over a small positively oriented circle around α\alphaα. When the multiplier λ\lambdaλ is not 111 one has ι=11−λ\iota = \frac{1}{1-\lambda}ι=1−λ1​, and the Möbius map λ↦11−λ\lambda \mapsto \frac{1}{1-\lambda}λ↦1−λ1​ carries the unit disk onto the half-plane Re⁡ι>1/2\operatorname{Re}\iota > 1/2Reι>1/2. So a fixed point is attracting, indifferent or repelling exactly according to whether Re⁡ι\operatorname{Re}\iotaReι is >1/2> 1/2>1/2, =1/2= 1/2=1/2 or <1/2< 1/2<1/2: the critical line reappears, in the index plane.

The point of the construction is that νg\nu_gνg​ is engineered so that the index of νg\nu_gνg​ at a simple zero α\alphaα of ggg is α\alphaα itself (and mαm\alphamα at a zero of order mmm). Writing νζ=νg\nu_\zeta = \nu_gνζ​=νg​ for g=ζg = \zetag=ζ: a non-trivial zero α\alphaα of order mmm has index mαm\alphamα, so Re⁡ι=mRe⁡α\operatorname{Re}\iota = m\operatorname{Re}\alphaReι=mReα, and asking that this equal 1/21/21/2 is asking for m=1m = 1m=1 and Re⁡α=1/2\operatorname{Re}\alpha = 1/2Reα=1/2.

Formalization targets

Goal — Theorem 1 of the paper, conditions (a), (b), (c)

(RH∧simplicity)  ⟺  (every non-trivial zero is an indifferent fixed point of νζ)  ⟺  (νζ has no attracting fixed point).\Big(\text{RH} \wedge \text{simplicity}\Big) \iff \Big(\text{every non-trivial zero is an indifferent fixed point of } \nu_\zeta\Big) \iff \Big(\nu_\zeta \text{ has no attracting fixed point}\Big).(RH∧simplicity)⟺(every non-trivial zero is an indifferent fixed point of νζ​)⟺(νζ​ has no attracting fixed point).

Supporting targets

The milestones are the paper's Propositions 3, 4, 5, 7, 8, 9, its Theorem 11 (the variant for the Riemann xi function ξ\xiξ), and Proposition 13 of the appendix (the Newton map Ng(z)=z−g(z)/g′(z)N_g(z) = z - g(z)/g'(z)Ng​(z)=z−g(z)/g′(z), for which every zero of ggg becomes an attracting fixed point — the contrast that explains why νg\nu_gνg​, and not NgN_gNg​, sees the critical line).

Significance

The equivalence converts the simultaneous truth of the Riemann and simplicity hypotheses into the non-existence of an attracting fixed point of one explicitly given meromorphic function. Nothing in the translation is conjectural: the content is the index computation, the symmetry α↦1−α\alpha \mapsto 1 - \alphaα↦1−α of the non-trivial zeros supplied by the functional equation, and the classification of fixed points by the real part of the index. What a formalization adds is a machine-checked statement of the dictionary, and a reusable Lean development of the holomorphic index, which Mathlib does not currently contain — the index, its relation to the multiplier, and its behaviour at zeros and poles are general facts of one-variable complex dynamics, independent of this application.

Status, precisely: the Riemann hypothesis is open, and this mission does not ask anyone to settle it. Every target here is a theorem with a published proof; the work is to formalize those proofs. The goal theorem is an equivalence between two open statements, so it is provable without deciding either side.

Difficulty

The obvious route to the goal — compute νζ′\nu_\zeta'νζ′​ at a zero, apply the classification, done — fails in one direction. From "no attracting fixed point" one gets Re⁡(mα)≤1/2\operatorname{Re}(m\alpha) \le 1/2Re(mα)≤1/2 for each non-trivial zero α\alphaα of order mmm, which alone excludes neither a multiple zero nor a zero to the left of the critical line. The functional equation must be used to pair α\alphaα with 1−α1-\alpha1−α, whose index is m(1−α)m(1-\alpha)m(1−α); only the two inequalities together force m=1m = 1m=1 and Re⁡α=1/2\operatorname{Re}\alpha = 1/2Reα=1/2. A complete Lean proof therefore needs, besides the local computation: that the non-trivial zeros lie in the open strip 0<Re⁡s<10 < \operatorname{Re} s < 10<Res<1, that α\alphaα and 1−α1-\alpha1−α are zeros of the same order, and that the trivial zeros and the pole at s=1s = 1s=1 give repelling fixed points.

The index milestone (Proposition 3) is a residue computation on a small circle, and the hypotheses have to be arranged so that z−f(z)z - f(z)z−f(z) has exactly one zero inside; the other genuinely analytic milestone is the order-mmm computation of νg′\nu_g'νg′​, where g′g'g′ vanishes at the fixed point when m≥2m \ge 2m≥2 and the singularity is removable rather than absent.

Formalization scope

The development is over C\mathbb{C}C with Mathlib's riemannZeta. Conventions the Lean statements commit to:

  1. Non-trivial zero means: a zero of ζ\zetaζ that is not one of −2,−4,−6,…-2, -4, -6, \dots−2,−4,−6,…. Nothing about the critical strip is built into the definition; that the non-trivial zeros lie in 0<Re⁡s<10 < \operatorname{Re} s < 10<Res<1 is part of the work.
  2. Simplicity of a zero α\alphaα is expressed as ζ′(α)≠0\zeta'(\alpha) \neq 0ζ′(α)=0.
  3. νg\nu_gνg​ is a total function C→C\mathbb{C} \to \mathbb{C}C→C, using Lean's convention that division by zero returns zero. At a zero of ggg this total function agrees with the genuine holomorphic extension of νg\nu_gνg​, so multipliers there are the true ones. At a point where ggg is non-zero and g′g'g′ vanishes, and at a pole of ggg, the total function takes an artefactual value; the statements about νζ\nu_\zetaνζ​ therefore carry the explicit guard ζ(α)=0∨ζ′(α)≠0\zeta(\alpha) = 0 \vee \zeta'(\alpha) \neq 0ζ(α)=0∨ζ′(α)=0 together with α≠0,1\alpha \neq 0, 1α=0,1. The excluded points are exactly the pole of ζ\zetaζ (a repelling fixed point, by Proposition 7 of the paper) and the poles of νζ\nu_\zetaνζ​, so the guarded statements are equivalent to the paper's, but they are guarded, and a reader should check that they consider the guards faithful.
  4. The xi function is taken in Kawahira's normalization ξ(z)=12z(1−z)π−z/2Γ(z/2)ζ(z)\xi(z) = \frac{1}{2}z(1-z)\pi^{-z/2}\Gamma(z/2)\zeta(z)ξ(z)=21​z(1−z)π−z/2Γ(z/2)ζ(z), written in Lean through Mathlib's entire function Λ0\Lambda_0Λ0​ so that the Lean ξ\xiξ is entire and has the correct values at z=0,1z = 0, 1z=0,1 rather than removable-singularity artefacts; the definition file carries a proved lemma identifying it with 12z(1−z)Λ(z)\frac{1}{2}z(1-z)\Lambda(z)21​z(1−z)Λ(z) off {0,1}\{0,1\}{0,1}.
  5. Conditions (d) and (e) of the paper's Theorems 1 and 10 — the purely topological reformulations in terms of a topological disk DDD with νζ(D)⊂D\nu_\zeta(D) \subset Dνζ​(D)⊂D, and their homeomorphic deformations — are not part of this mission. They rest on the topological characterization of attracting fixed points (the paper's Proposition 2), whose proof uses the Riemann mapping theorem and the Schwarz–Pick lemma; the Riemann mapping theorem is not available in Mathlib, and the intended strength of the inclusion νζ(D)⊂D\nu_\zeta(D) \subset Dνζ​(D)⊂D (compact containment) needs to be fixed before the statement can be formalized faithfully. Theorem 14 of the appendix, which is of the same topological kind, is likewise out of scope. A contribution supplying Proposition 2 in a defensible form would be welcome, as a separate mission.

Nothing here is vacuous: the goal is an equivalence of two statements each of which is satisfiable in form, and the guards exclude only points at which the Lean encoding of νζ\nu_\zetaνζ​ is known not to model the meromorphic map.

Reusable beyond this mission: the holomorphic index, the multiplier classification, the general nu-function and Newton-map computations at a zero of order mmm — all stated for an arbitrary function analytic at the point, not for ζ\zetaζ.

Selected references

  • T. Kawahira, The Riemann Hypothesis and Holomorphic Index in Complex Dynamics, Experimental Mathematics (2016). https://doi.org/10.1080/10586458.2016.1217443
  • J. Milnor, Dynamics in One Complex Variable, 3rd ed., Annals of Mathematics Studies 160, Princeton University Press, 2006. (Holomorphic index: Lemma 12.2; topological characterization of fixed points: Section 8.)
  • E. C. Titchmarsh, The Theory of the Riemann Zeta Function, 2nd ed., Oxford University Press, 1986. (Functional equation; trivial zeros; the xi function.)
  • D. Schleicher, Newton's Method as a Dynamical System: Efficient Root Finding of Polynomials and the Riemann ζ\zetaζ Function, Fields Inst. Commun. 53 (2008), 213–224.
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Group TheoryTopology·Captain: dbenbenn

Brin–Squier: the group of piecewise-linear homeomorphisms of the line with finitely many breakpoints has no free subgroup of rank greater than oneResearch Paper

Motivation

Von Neumann introduced amenability in 1929 in response to the Banach–Tarski paradox: a group is amenable when it carries a finitely additive, translation-invariant probability measure on its subsets, and no amenable group contains a free subgroup of rank 222 — which is exactly what the paradox needs. The converse is the von Neumann conjecture, and it is false: Ol'shanskii in 1980 and Adyan in 1982 produced finitely generated counterexamples. A finitely presented counterexample was harder, and one candidate stood out — Richard Thompson's group FFF, finitely presented, with nobody able to decide whether it was amenable.

Brin and Squier attacked it and in 1985 got what they called "half a success": they proved that FFF, and more generally the group PLF(R)\mathrm{PLF}(\mathbb{R})PLF(R) of piecewise-linear homeomorphisms of the line with finitely many breakpoints, contains no free subgroup of rank greater than 111. Whether FFF is amenable they could not determine, and it is still open today; claimed proofs have appeared in both directions and none has been accepted. Finitely presented counterexamples were eventually found by other routes (Ol'shanskii–Sapir 2002; Lodha–Moore 2016), so FFF is no longer needed as a candidate. This mission formalizes the half that was settled.

Setting

Let Homeo+(R)\mathrm{Homeo}_+(\mathbb{R})Homeo+​(R) be the group of orientation-preserving homeomorphisms of the line: the strictly increasing bijections R→R\mathbb{R}\to\mathbb{R}R→R under composition. The support of fff is the set of points it moves, supp⁡f={ t:f(t)≠t }\operatorname{supp} f = \{\,t : f(t)\neq t\,\}suppf={t:f(t)=t}, an open subset of R\mathbb{R}R.

A continuous fff is piecewise linear when there is a discrete set BBB of breakpoints with fff differentiable off BBB and f′f'f′ constant on each component of R∖B\mathbb{R}\setminus BR∖B; for finite BBB this is the same as fff being affine on a neighbourhood of every point outside BBB. Nothing is required at the points of BBB, so the two affine pieces meeting at a breakpoint may disagree — that is what makes such an fff more than an affine map. Write PL(R)\mathrm{PL}(\mathbb{R})PL(R) for the piecewise-linear elements of Homeo+(R)\mathrm{Homeo}_+(\mathbb{R})Homeo+​(R) and

PLF(R)={ f∈PL(R):f has a finite breakpoint set }\mathrm{PLF}(\mathbb{R}) = \{\, f \in \mathrm{PL}(\mathbb{R}) : f \text{ has a finite breakpoint set} \,\}PLF(R)={f∈PL(R):f has a finite breakpoint set}

for the subgroup this mission is about. The distinction matters: the goal below holds in PLF(R)\mathrm{PLF}(\mathbb{R})PLF(R) and fails in PL(R)\mathrm{PL}(\mathbb{R})PL(R), where Brin and Squier build free subgroups of rank 222 by lifting them from the circle. Write PLF′(R)\mathrm{PLF}'(\mathbb{R})PLF′(R) for the commutator subgroup, which Brin and Squier identify as the elements whose slope at each end is 111 — an element has slope aaa at an end when it agrees with a single affine map of slope aaa on a ray out to that end. Thompson's group FFF — the piecewise-linear homeomorphisms of [0,1][0,1][0,1] with dyadic breakpoints and power-of-two slopes — is realized inside PLF(R)\mathrm{PLF}(\mathbb{R})PLF(R).

Formalization targets

Goal — no two elements generate freely

for f,g∈PLF(R),F2→PLF(R), a↦f, b↦gis never injective.\text{for } f,g \in \mathrm{PLF}(\mathbb{R}), \quad F_2 \to \mathrm{PLF}(\mathbb{R}),\ a \mapsto f,\ b \mapsto g \quad\text{is never injective.}for f,g∈PLF(R),F2​→PLF(R), a↦f, b↦gis never injective.

Since a free group of rank greater than 111 contains one of rank 222, this is Brin and Squier's Theorem (3.1).

The dichotomy it rests on

G≤PLF′(R)  ⟹  G abelian, or G contains a free abelian subgroup of rank 2.G \le \mathrm{PLF}'(\mathbb{R}) \implies G \text{ abelian, or } G \text{ contains a free abelian subgroup of rank } 2.G≤PLF′(R)⟹G abelian, or G contains a free abelian subgroup of rank 2.

Their Theorem (3.2), with the conclusion weakened to what the goal consumes. What they prove is infinite rank, which needs the general form of their Lemma (1.2); that general Lemma and the full-strength (3.2) are milestones of their own here. The goal itself only ever uses the rank-two form.

The twenty-four milestones

Thirteen are numbered results of theirs: the support observations (1.1a), (1.1b); Lemma (1.2) in its rank-two case and in its general form; Lemmas (3.4) and (3.5), already formalized and published, entering as references; the commutator facts (2.14a), (2.14b), (2.14c); Theorem (3.2) both as the rank-two dichotomy the goal consumes and at full strength; and Corollary (3.3), likewise in both strengths. Five are piecewise-linear infrastructure the source treats as routine: closure of PLF(R)\mathrm{PLF}(\mathbb{R})PLF(R) under composition and under inverse, the same two for slope 111 at each end, and finiteness of the number of components of a support. Five more are steps the source asserts without proof — that the line carries no non-fixed periodic points, that a map fixing a set's complement preserves its components, that the iterated images of a pushed-forward interval are pairwise disjoint, that the closure of a commutator's moved set stays inside the union of the two supports (p. 495), and that the derived subgroup of a free group of rank two is non-abelian (p. 494). The last is not from the source at all — that an abelian subgroup of a free group is cyclic, which is what lets the goal finish through Nielsen–Schreier.

Significance

The theorem closes the standard route to proving a group non-amenable. To show a group amenable the classical routes are elementary amenability and subexponential growth, and FFF is neither elementary amenable (Cannon–Floyd–Parry, Theorem 4.10) nor of subexponential growth, having exponential growth (their Corollary 4.7). To show a group non-amenable the standard route is to exhibit a free subgroup of rank 222 — the route this theorem closes. FFF sits in the gap, which is why its status has survived sustained attention.

The result reaches past FFF. Monod's groups of piecewise projective homeomorphisms are counterexamples to von Neumann's conjecture, and Monod's theorem that they contain no non-abelian free subgroup is, in Monod's words, "a sequacious generalization of the corresponding theorem of Brin–Squier about piecewise affine transformations"; of its own proof that paper says it will "largely follow [Brin–Squier, § 3]".

What formalizing it adds. Mathlib has no piecewise-linear maps and no amenability predicate for groups. This mission builds the piecewise-linear layer: a workable PLF(R)\mathrm{PLF}(\mathbb{R})PLF(R), its closure properties, and the structure of supports.

Difficulty

The obstruction is bookkeeping across two finiteness facts of different kinds. Finiteness of the breakpoint set is what gives an element slopes at ±∞\pm\infty±∞ at all, and what makes two elements affine on each side of a common fixed point. Compactness is the other — throughout, [f,g]=fgf−1g−1[f,g] = fgf^{-1}g^{-1}[f,g]=fgf−1g−1 — and it splits: that the closure of supp⁡[f,g]\operatorname{supp}[f,g]supp[f,g] is compact needs only slope 111 at each end, with no piecewise linearity at all, which is why (2.14b) is formalized under a weaker hypothesis than the source's; that the closure stays inside supp⁡f∪supp⁡g\operatorname{supp} f \cup \operatorname{supp} gsuppf∪suppg is what reaches back to finiteness. Keeping straight which fact does which job is most of the work.

The first idea a newcomer has about (3.2) is the wrong one. Its proof produces a family of commuting elements, and it is not disjointness of supports that makes them commute: only their intersections with one chosen component are disjoint, and commutation is deduced instead from a minimality argument. A proof routed through disjoint supports will not close.

Formalization scope

What the Lean fixes. Elements are order isomorphisms of R\mathbb{R}R — strictly increasing bijections, automatically homeomorphisms — rather than a homeomorphism type. Composition follows Mathlib's convention (f⋅g)(x)=f(g(x))(f\cdot g)(x) = f(g(x))(f⋅g)(x)=f(g(x)), the opposite of the source's right action, so the conjugation identity reads supp⁡(fgf−1)=f(supp⁡g)\operatorname{supp}(fgf^{-1}) = f(\operatorname{supp} g)supp(fgf−1)=f(suppg) here; getting this backwards states a different theorem that still compiles. A support is the bare moved set, with no closure taken. Piecewise linearity is a finite breakpoint set together with local affineness off it — the set need not be minimal and may be empty. A copy of Z2\mathbb{Z}^2Z2 is an injectivity statement about (m,n)↦umvn(m,n)\mapsto u^m v^n(m,n)↦umvn, not a subgroup isomorphism, and the goal is about a single pair f,gf,gf,g rather than a subgroup. The dichotomy hypothesises slope 111 at both ends directly, not membership in a derived subgroup — that these coincide is the source's result, and both inclusions are formalized here: (2.14a) gives one, and the identification asserted on p. 493 gives the other. Beyond a workable PLF(R)\mathrm{PLF}(\mathbb{R})PLF(R), the development needs Nielsen–Schreier, already in Mathlib as subgroupIsFreeOfIsFree: it is what lets an abelian subgroup of a free group be cyclic, and so lets the goal finish without the source's metabelian ending. That ending is formalized too, as Corollary (3.3) together with the p. 494 remark that the derived subgroup of a free group of rank two is non-abelian; the goal simply does not route through it.

One trivializing reading is ruled out. Slope 111 at both ends is not a compact-support condition — every translation satisfies it — so (3.2) is not secretly a statement about compactly supported maps.

Nothing is built for FFF specifically, and amenability is not touched. That is the one piece deliberately omitted, and contributions are welcome on it: modelling FFF and embedding it in PLF(R)\mathrm{PLF}(\mathbb{R})PLF(R). The piecewise-linear layer is reusable beyond this theorem — Thompson's groups TTT and VVV, and piecewise-linear topology generally, need exactly it.

Selected references

  • M. G. Brin and C. C. Squier, Groups of piecewise linear homeomorphisms of the real line, Invent. math. 79 (1985), 485–498, doi:10.1007/BF01388519. Theorem (3.1) is the goal.
  • J. W. Cannon, W. J. Floyd and W. R. Parry, Introductory notes on Richard Thompson's groups, L'Enseignement Mathématique 42 (1996), 215–256. Theorem 4.10 and Corollary 4.7.
  • N. Monod, Groups of piecewise projective homeomorphisms, Proc. Natl. Acad. Sci. USA 110 (2013), 4524–4527, arXiv:1209.5229.
  • A. Yu. Ol'shanskii and M. V. Sapir, Non-amenable finitely presented torsion-by-cyclic groups, Publ. Math. IHÉS 96 (2002), 43–169, doi:10.1007/s10240-002-0006-7.
  • Y. Lodha and J. T. Moore, A nonamenable finitely presented group of piecewise projective homeomorphisms, Groups Geom. Dyn. 10 (2016), 177–200, doi:10.4171/ggd/347.
  • V. Guba, Amenability problem for Thompson's group FFF: state of the art, J. Groups Complex. Cryptol. 15 (2023), arXiv:2305.07113.
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AnalysisOptimization·Captain: mikedeng1

Examples of Pathological Dynamics of the Subgradient Method for Lipschitz Path-Differentiable Functions: A Bounded Subgradient Sequence That Does Not Converge for a Whitney-Stratifiable KŁ FunctionResearch Paper

Motivation

The small-step subgradient method is the basic algorithm for minimizing a nonsmooth, nonconvex, locally Lipschitz function f:Rn→Rf:\mathbb R^n\to\mathbb Rf:Rn→R. It is also the scheme behind stochastic gradient training of neural networks with nonsmooth activations. From a starting point x0x_0x0​ it iterates

xi+1=xi−εivi,vi∈∂cf(xi),x_{i+1}=x_i-\varepsilon_i v_i,\qquad v_i\in\partial^c f(x_i),xi+1​=xi​−εi​vi​,vi​∈∂cf(xi​),

with step sizes εi→0\varepsilon_i\to0εi​→0 and ∑iεi=+∞\sum_i\varepsilon_i=+\infty∑i​εi​=+∞. The basic question is whether a bounded run converges. For smooth fff, and for continuous-time subgradient flows, convergence follows from a Kurdyka–Łojasiewicz (KŁ) inequality together with tame geometry such as Whitney stratifiability (Bolte, Daniilidis, Lewis, Shiota 2007; Absil, Mahony, Andrews 2005). Whether the same hypotheses suffice for the discrete nonsmooth method was open.

R. Ríos-Zertuche (arXiv:2007.11699, published in Math. Oper. Res. 2022) is a companion to the work of Bolte, Pauwels and Ríos-Zertuche on the long-term dynamics of the subgradient method for path-differentiable functions (arXiv:2006.00098). That work proves positive results, such as slowdown near limit points and convergence of the values under the weak Sard condition. The examples paper shows that these results cannot be improved to convergence of the iterates. Its first example (Section 2) lives on R2\mathbb R^2R2 and is the subject of this mission.

Setting

For a locally Lipschitz f:Rn→Rf:\mathbb R^n\to\mathbb Rf:Rn→R, the Clarke subdifferential ∂cf(x)\partial^c f(x)∂cf(x) is the convex envelope of all limits lim⁡∇f(yk)\lim\nabla f(y_k)lim∇f(yk​) along sequences yk→xy_k\to xyk​→x of points where fff is differentiable. The critical set is crit⁡f={x:0∈∂cf(x)}\operatorname{crit}f=\{x:0\in\partial^c f(x)\}critf={x:0∈∂cf(x)}.

A subgradient sequence (Definition 1) is a sequence obeying the recursion above with positive steps εi→0\varepsilon_i\to0εi​→0, ∑εi=+∞\sum\varepsilon_i=+\infty∑εi​=+∞. The function fff is path-differentiable (Definition 2) if for every Lipschitz curve γ\gammaγ and almost every ttt, (f∘γ)′(t)=v⋅γ′(t)(f\circ\gamma)'(t)=v\cdot\gamma'(t)(f∘γ)′(t)=v⋅γ′(t) for all v∈∂cf(γ(t))v\in\partial^c f(\gamma(t))v∈∂cf(γ(t)). It satisfies the weak Sard condition (Definition 3) if it is constant on each connected component of crit⁡f\operatorname{crit}fcritf. It is Whitney C∞C^\inftyC∞-stratifiable (Definition 5) if its graph in Rn+1\mathbb R^{n+1}Rn+1 is a locally finite union of disjoint connected C∞C^\inftyC∞ submanifolds. These pieces must satisfy the frontier condition and Whitney's condition A: limits of tangent spaces of a stratum contain the tangent spaces of the strata in its boundary.

The example works on R2\mathbb R^2R2. For i≥2i\ge2i≥2 let

ϑi=∑k=2i1klog⁡k,xi=[1+(−1)ii](cos⁡ϑi,sin⁡ϑi),\vartheta_i=\sum_{k=2}^{i}\frac1{k\log k},\qquad x_i=\Bigl[1+\frac{(-1)^i}{i}\Bigr](\cos\vartheta_i,\sin\vartheta_i),ϑi​=k=2∑i​klogk1​,xi​=[1+i(−1)i​](cosϑi​,sinϑi​),

εi=∥xi+1−xi∥\varepsilon_i=\|x_{i+1}-x_i\|εi​=∥xi+1​−xi​∥ and vi=−(xi+1−xi)/εiv_i=-(x_{i+1}-x_i)/\varepsilon_ivi​=−(xi+1​−xi​)/εi​. Let φ(x)=∣1−∥x∥∣\varphi(x)=|1-\|x\||φ(x)=∣1−∥x∥∣ be the distance to the unit circle S1S^1S1. Let ψ\psiψ be a smooth radial bump with values in [0,1][0,1][0,1], equal to 111 on B1(0)B_1(0)B1​(0), vanishing for ∥x∥≥2\|x\|\ge2∥x∥≥2 and non-increasing along rays. Set ψi(x)=ψ(2i(x−xi))\psi_i(x)=\psi(2^i(x-x_i))ψi​(x)=ψ(2i(x−xi​)) and Vi(x)=(x−xi)⋅vi+1/iV_i(x)=(x-x_i)\cdot v_i+1/iVi​(x)=(x−xi​)⋅vi​+1/i. For i0≥2i_0\ge2i0​≥2,

f(x)=(1−∑i≥i0ψi(x))φ(x)+∑i≥i0ψi(x)Vi(x).(4)f(x)=\Bigl(1-\sum_{i\ge i_0}\psi_i(x)\Bigr)\varphi(x)+\sum_{i\ge i_0}\psi_i(x)V_i(x).\tag{4}f(x)=(1−i≥i0​∑​ψi​(x))φ(x)+i≥i0​∑​ψi​(x)Vi​(x).(4)

Formalization targets

Goal

There are f:R2→Rf:\mathbb R^2\to\mathbb Rf:R2→R, steps (εi)(\varepsilon_i)(εi​) and a sequence (xi)(x_i)(xi​) such that (xi)(x_i)(xi​) is a bounded, non-convergent subgradient sequence of fff, and fff has all of the following properties:

  • it is Lipschitz and path-differentiable;
  • it satisfies the weak Sard condition;
  • it is Whitney C∞C^\inftyC∞-stratifiable;
  • it is C∞C^\inftyC∞ on a ball around every xix_ixi​;
  • it satisfies
∥∇f(x)∥≥12for all x∉crit⁡f.\|\nabla f(x)\|\ge\tfrac12\qquad\text{for all }x\notin\operatorname{crit}f.∥∇f(x)∥≥21​for all x∈/critf.

This is the paper's negative answer to its question Q9, combined with its remarks in Q1 and Q12. The witness is left existential.

Milestones

The milestones are the properties of the explicit witness (4):

  • the step-size estimates (εi→0\varepsilon_i\to0εi​→0, ∑εi=∞\sum\varepsilon_i=\infty∑εi​=∞, ∑εi2<∞\sum\varepsilon_i^2<\infty∑εi2​<∞);
  • Lemma 7: viv_ivi​ is close to ±xi/∥xi∥\pm x_i/\|x_i\|±xi​/∥xi​∥, and radial projection is Lipschitz near xix_ixi​;
  • Proposition 6 (i)–(vii): smoothness off S1∪{0}S^1\cup\{0\}S1∪{0}; ∇f=vi\nabla f=v_i∇f=vi​ on B1/2i(xi)B_{1/2^i}(x_i)B1/2i​(xi​), so that (xi)(x_i)(xi​) is a subgradient sequence; ∂cf(p)={ap:a∈[−1,1]}\partial^c f(p)=\{ap:a\in[-1,1]\}∂cf(p)={ap:a∈[−1,1]} on S1S^1S1; crit⁡f=S1∪{0}\operatorname{crit}f=S^1\cup\{0\}critf=S1∪{0}; Lipschitz path-differentiability; Whitney stratifiability; and ∥∇f∥>1/2\|\nabla f\|>1/2∥∇f∥>1/2 off the critical set;
  • the weak Sard condition;
  • acc⁡{xi}=S1\operatorname{acc}\{x_i\}=S^1acc{xi​}=S1, with boundedness and non-convergence.

Significance

The example is a counterexample: the standard toolbox of tame geometry plus a KŁ inequality does not give convergence of the discrete small-step subgradient method, even with a uniform gradient lower bound away from the critical set and the weak Sard condition. In continuous time the same hypotheses give convergence. The gap therefore comes from discretization. Any convergence proof for nonsmooth subgradient methods needs further structure, such as definability in an o-minimal structure or a stronger KŁ inequality near the critical set. Q12 adds that requiring the iterates to stay in the full-measure set of differentiability points does not help.

The result is proved in the paper; to our knowledge none of it is formalized. Formalizing it gives the first machine-checked Clarke-subdifferential, path-differentiability and Whitney-stratification counterexample. It also gives reusable estimates for an explicit sequence spiralling onto a circle.

Difficulty

Most of the steps are explicit estimates, but three of them are delicate.

  • The Clarke subdifferential on S1S^1S1. It must be computed although infinitely many bumps accumulate there. Near each xix_ixi​ the gradient of fff contains the term ∇ψi (Vi−φ)\nabla\psi_i\,(V_i-\varphi)∇ψi​(Vi​−φ), where ∇ψi\nabla\psi_i∇ψi​ is of size 2i2^i2i. Showing that the limiting gradients are still only ±p\pm p±p requires the second-order agreement of ViV_iVi​ and φ\varphiφ on B1/2i−1(xi)B_{1/2^{i-1}}(x_i)B1/2i−1​(xi​), and that rests on the estimate of Lemma 7.
  • Path differentiability. It does not follow from local Lipschitz continuity. A Lipschitz curve may cross the circle at uncountably many times, so the chain rule has to be checked for every element of ∂cf(γ(t))\partial^c f(\gamma(t))∂cf(γ(t)) at almost every such time.
  • Whitney's condition A. It must hold where the two-dimensional strata of the graph meet the circle stratum. That requires control of the tangent planes of the graph along sequences entering the bump regions.

Formalization scope

Everything lives in EuclideanSpace ℝ (Fin n). The definitions follow these conventions:

  • ∂cf(x)\partial^c f(x)∂cf(x) is the convex hull of the published ShorNonsmooth.AlmostDiff.almostGradients, which uses cluster points of the gradients rather than limits; the two sets are the same.
  • Path differentiability quantifies over every Lipschitz curve and every element of ∂cf(γ(t))\partial^c f(\gamma(t))∂cf(γ(t)), and also asserts that γ\gammaγ is differentiable at ttt, which holds almost everywhere by Rademacher's theorem.
  • Strata are connected, pairwise disjoint and locally finite at the points of the graph. Each is a CkC^kCk coordinate-slice submanifold (the published ProjLikeRetr.Retractor.IsSubmanifold) for every kkk. Tangent spaces are spans of tangent cones, and Grassmannian convergence is convergence of the subspace gap to 000.
  • The bump is an arbitrary BumpProfile with exactly the properties listed on p. 6, and the series in (4) are tsums. The indices i<2i<2i<2, where 1/0=01/0=01/0=0 and log⁡1=0\log 1=0log1=0 in Lean, are never used.

Four statements are adjusted, and each adjustment is disclosed in its item:

  • Proposition 6 (i) is stated on R2∖(S1∪{0})\mathbb R^2\setminus(S^1\cup\{0\})R2∖(S1∪{0}), as in the paper's proof, because fff has a cone point at 000.
  • The last link "<2/i<2/i<2/i" of the step-size chain is false and is dropped.
  • Proposition 6 (ii), (iv), (vii) and the weak Sard claim hold for i0i_0i0​ beyond a threshold depending on the bump. For small i0i_0i0​ the bump supports overlap.
  • The KŁ clauses also assert differentiability, since Lean's gradient is 000 at non-differentiable points.

The goal cannot be met trivially. Boundedness of the sequence and the conditions εi→0\varepsilon_i\to0εi​→0, ∑εi=∞\sum\varepsilon_i=\infty∑εi​=∞ are part of it, and they rule out an affine fff. Every regularity property is a separate conjunct. Contributions that develop the geometry of Whitney stratifications and path differentiability in Lean are reusable well beyond this example.

Selected references

  • R. Ríos-Zertuche, Examples of pathological dynamics of the subgradient method for Lipschitz path-differentiable functions, arXiv:2007.11699v1, 2020; Math. Oper. Res., 2022. https://arxiv.org/abs/2007.11699
  • J. Bolte, E. Pauwels, R. Ríos-Zertuche, Long term dynamics of the subgradient method for Lipschitz path differentiable functions, arXiv:2006.00098, 2020. https://arxiv.org/abs/2006.00098
  • J. Bolte, A. Daniilidis, A. Lewis, M. Shiota, Clarke subgradients of stratifiable functions, SIAM J. Optim. 18 (2007). https://doi.org/10.1137/060670080
  • D. Davis, D. Drusvyatskiy, S. Kakade, J. D. Lee, Stochastic subgradient method converges on tame functions, Found. Comput. Math. 20 (2020). https://arxiv.org/abs/1804.07795
  • P.-A. Absil, R. Mahony, B. Andrews, Convergence of the iterates of descent methods for analytic cost functions, SIAM J. Optim. 16 (2005). https://doi.org/10.1137/040605266
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Control TheoryOperations Research·Captain: mikedeng1

Optimal Control of an Epidemic through Social Distancing: Under Limited Capacity Effects Every Optimal Policy Is Single-Peaked and Infections Are Log-Concave While Measures EscalateResearch Paper

Motivation

During the Covid-19 epidemic, governments had to decide not only whether to impose social distancing but how to schedule it over time: when to escalate, when to relax, and whether a relaxation might later have to be reversed. Epidemiology supplies the dynamics (the SIR model of Kermack and McKendrick, 1927), and optimal control supplies the language for trading off deaths against the economic cost of distancing. Most of the prior theoretical literature on optimal epidemic control assumed a cost of distancing that is linear in the reduction of the transmission rate, and focused on numerical solutions.

Kruse and Strack (Cowles Foundation Discussion Paper 2229R, 2020; later published as Optimal Dynamic Control of an Epidemic, Operations Research, 2024, doi:10.1287/opre.2022.0161) characterize the shape of every optimal policy for a general convex cost of distancing and a convex death-rate function. Their main structural result is that, when hospital-capacity effects are moderate, optimal policies are single-peaked: measures are escalated and then relaxed, never relaxed and re-escalated.

Setting

The population is split into a susceptible share s(t)s(t)s(t), an infected share i(t)i(t)i(t) and the rest. A planner chooses a measurable transmission rate β(t)∈B=[b‾,bˉ]⊂(0,∞)\beta(t)\in B=[\underline b,\bar b]\subset(0,\infty)β(t)∈B=[b​,bˉ]⊂(0,∞), with b‾<bˉ\underline b<\bar bb​<bˉ. With recovery rate γ>0\gamma>0γ>0 the shares follow

s′(t)=−β(t)i(t)s(t),i′(t)=β(t)i(t)s(t)−γi(t),s'(t)=-\beta(t)i(t)s(t),\qquad i'(t)=\beta(t)i(t)s(t)-\gamma i(t),s′(t)=−β(t)i(t)s(t),i′(t)=β(t)i(t)s(t)−γi(t),

from s(0)=s0s(0)=s_0s(0)=s0​, i(0)=i0i(0)=i_0i(0)=i0​, with s0,i0∈(0,1)s_0,i_0\in(0,1)s0​,i0​∈(0,1) and s0+i0≤1s_0+i_0\le1s0​+i0​≤1. A vaccine arrives at T>0T>0T>0. The planner minimizes

J(β)=∫0T(v(i(t))+c(β(t))) dt+vˉ(i(T)),vˉ(x)=∫0xv(z)zγ dz,J(\beta)=\int_0^T\big(v(i(t))+c(\beta(t))\big)\,dt+\bar v(i(T)),\qquad \bar v(x)=\int_0^x\frac{v(z)}{z\gamma}\,dz,J(β)=∫0T​(v(i(t))+c(β(t)))dt+vˉ(i(T)),vˉ(x)=∫0x​zγv(z)​dz,

where the death-rate cost vvv is convex, strictly increasing and C1C^1C1 with v(0)=0v(0)=0v(0)=0. The cost of distancing ccc is convex, non-increasing and continuous on BBB, with c(bˉ)=0<c(b)c(\bar b)=0<c(b)c(bˉ)=0<c(b) for b<bˉb<\bar bb<bˉ. A control is optimal if it minimizes JJJ over all admissible controls.

Write M(y)=min⁡b∈B[yb+c(b)]M(y)=\min_{b\in B}[yb+c(b)]M(y)=minb∈B​[yb+c(b)]. Proposition 1 of the paper provides a positive costate η∗\eta^*η∗ along every optimal path, and the gain from reducing the transmission rate is g∗(t)=η∗(t)i∗(t)s∗(t)g^*(t)=\eta^*(t)i^*(t)s^*(t)g∗(t)=η∗(t)i∗(t)s∗(t); the optimal control minimizes g∗(t)b+c(b)g^*(t)b+c(b)g∗(t)b+c(b) over BBB. Assumption 1 (limited capacity effects) asks that vvv be C2C^2C2 with

v′′(i)γ<274 b‾bˉ−b‾ ∣c′(bˉ−)∣(i∈[0,1]),\frac{v''(i)}{\gamma}<\frac{27}{4}\,\frac{\underline b}{\bar b-\underline b}\,\big|c'(\bar b-)\big|\qquad (i\in[0,1]),γv′′(i)​<427​bˉ−b​b​​​c′(bˉ−)​(i∈[0,1]),

where c′(bˉ−)c'(\bar b-)c′(bˉ−) is the left derivative of ccc at bˉ\bar bbˉ.

Formalization targets

Goal: Proposition 7

Under Assumption 1, for every optimal control β∗\beta^*β∗ there is t∗∈[0,T]t^*\in[0,T]t∗∈[0,T] such that

β∗ is non-increasing on [0,t∗] and non-decreasing on [t∗,T],log⁡i∗ is strictly concave on [0,t∗].\beta^*\ \text{is non-increasing on }[0,t^*]\text{ and non-decreasing on }[t^*,T],\qquad \log i^*\ \text{is strictly concave on }[0,t^*].β∗ is non-increasing on [0,t∗] and non-decreasing on [t∗,T],logi∗ is strictly concave on [0,t∗].

Thus i∗i^*i∗ has at most one local maximum on [0,t∗][0,t^*][0,t∗]. The monotonicity claim refers to the version of β∗\beta^*β∗ singled out by the paper's convention (see Formalization scope).

Milestones

In the paper's order of proof:

  • existence of an optimal policy and the Pontryagin multipliers (Lemma 9);
  • the behaviour of the control where the costate is non-positive (Lemma 10);
  • the reduced costate equation (Lemma 11) and positivity of the costate (Lemma 12, Proposition 1);
  • the choice of an everywhere-optimal version of the control (Lemma 13);
  • monotonicity and concavity of MMM;
  • the gain equation, bounds and monotone comparative statics (Proposition 2 (i)–(iii));
  • the inequality (33) s2i≤4/27s^2i\le 4/27s2i≤4/27, the second-order inequality (37) for the gain;
  • strict quasi-concavity of the gain.

Significance

Proposition 7 rules out a whole class of policies (relax, then tighten again) for every convex cost of distancing, not only for the linear costs earlier work treated. The same argument also gives Proposition 8 of the paper, a three-phase bang-bang structure under linear cost. It also explains the factor 27/427/427/4: it is the reciprocal of max⁡s∈[0,1]s2(1−s)\max_{s\in[0,1]}s^2(1-s)maxs∈[0,1]​s2(1−s).

The mission produces a machine-checked development of a deterministic optimal-control problem with measurable controls: existence of an optimal control, a Pontryagin principle with a state-dependent terminal cost, and a qualitative analysis of the adjoint system. The paper's result is proved on paper; none of it is formalized. The SIR model, its integral-form solutions and the costate machinery are reusable for other epidemic-control papers.

Difficulty

Existence and the necessary conditions rest on the existence theory and the Pontryagin principle for measurable controls, which Mathlib does not have. The general Pontryagin principles already on the platform do not apply, because they require a globally Lipschitz vector field or a cost that is smooth in the control.

The step specific to the paper is the reduction of the two multipliers to one costate, followed by the analysis of the gain g∗g^*g∗. The gain is C1C^1C1, but its derivative involves M(g∗(t))M(g^*(t))M(g∗(t)) and β∗(t)\beta^*(t)β∗(t), so (g∗)′′(g^*)''(g∗)′′ exists only almost everywhere. It must be bounded through a case split on the size of g∗(t)g^*(t)g∗(t) relative to −c′(bˉ−)-c'(\bar b-)−c′(bˉ−). Showing that g∗g^*g∗ is single-peaked requires turning this almost-everywhere bound into a strict sign change of (g∗)′(g^*)'(g∗)′ at each critical point. A naive appeal to "g∗′′<0g^{*\prime\prime}<0g∗′′<0 at critical points" fails because (g∗)′′(g^*)''(g∗)′′ need not exist there.

Formalization scope

All objects live in EpiControl.SinglePeak; parameters are a structure Params with a predicate Standing for the assumptions of §2. Conventions:

  • Dynamics in integral form. Solutions are continuous on [0,T][0,T][0,T] and satisfy the integral equations; adjoint equations are integral equations from the terminal value. This is how absolute continuity is carried.
  • Admissibility on [0,T][0,T][0,T]. Controls are measurable with values in BBB on [0,T][0,T][0,T], the only part entering JJJ.
  • Added hypotheses, all disclosed. b‾<bˉ\underline b<\bar bb​<bˉ; c>0c>0c>0 on [b‾,bˉ)[\underline b,\bar b)[b​,bˉ) (the page prints the sign slip 0>c(b)0>c(b)0>c(b)); global C1C^1C1, resp. C2C^2C2, smoothness of vvv as an extension of the page's smoothness on [0,1][0,1][0,1].
  • MMM is an infimum over the image of the compact interval BBB, hence a minimum. c′(bˉ−)c'(\bar b-)c′(bˉ−) is a real parameter with its defining one-sided limit.
  • The costate is a predicate (IsCostate) taken as a hypothesis in every statement about η∗\eta^*η∗ or g∗g^*g∗, never an arbitrary function and never a Classical.choose.
  • Null sets. An optimal control is defined only up to a null set, so monotonicity of an arbitrary version is false. The paper fixes the version by requiring the argmin condition at every time (justified by Lemma 13). The goal therefore asserts the existence of an admissible β^=β∗\hat\beta=\beta^*β^​=β∗ almost everywhere on [0,T][0,T][0,T] with the stated monotonicity; milestones that need the convention take it as an explicit hypothesis. Stating monotonicity of β∗\beta^*β∗ itself, or dropping optimality, would make the goal false or vacuous; neither is done.

Infrastructure that a complete development needs: Carathéodory solutions of the SIR system, existence of optimal controls (a Filippov-type argument), a Pontryagin principle for measurable controls with terminal cost, and Lipschitz/almost-everywhere differentiability arguments. Contributions of these general results as separate theorems are welcome.

Selected references

  • T. Kruse and P. Strack, Optimal Control of an Epidemic through Social Distancing, Cowles Foundation Discussion Paper No. 2229R, 2020. https://ssrn.com/abstract=3581295
  • T. Kruse and P. Strack, Optimal Dynamic Control of an Epidemic, Operations Research, 2024. https://doi.org/10.1287/opre.2022.0161
  • W. O. Kermack and A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. Lond. A 115, 1927. https://doi.org/10.1098/rspa.1927.0118
  • F. Clarke, Functional Analysis, Calculus of Variations and Optimal Control, Springer GTM 264, 2013. https://doi.org/10.1007/978-1-4471-4820-3
17 thms1 active userReviewed
Group TheoryRepresentation Theory·Captain: Lucas

Spectral Gap Conjecture (Wikipedia): Random Elements of SU(2)Open Problem

Motivation

Finitely many rotations of the sphere generate a group whose orbits are typically dense. A basic quantitative question is how fast those orbits fill the sphere: after words of length ℓ\ellℓ in the generators, how uniformly are the points spread? The answer is controlled by the spectral gap of an averaging operator built from the generators. The question goes back to the Banach–Ruziewicz problem (uniqueness of rotation-invariant finitely additive measures on spheres), was studied by Lubotzky, Phillips and Sarnak in their work on distributing points on S2S^2S2 (LPS 1987), and was formulated as a conjecture about random generators by Gamburd, Jakobson and Sarnak. It also matters in quantum computing: a spectral gap for a gate set gives efficient approximation of every single-qubit gate by short gate sequences (Sawicki–Karnas 2017).

Timeline.

  • 1987 — Lubotzky, Phillips and Sarnak construct explicit rotations with optimal spectral gap using automorphic forms and pose the general question (LPS).
  • 2006 — Fisher proves a zero–one law: for n≥3n \ge 3n≥3 generators, either almost every nnn-tuple has a spectral gap or almost every nnn-tuple has none (Fisher, IMRN 2006, arXiv:math/0601050).
  • 2008 — Bourgain and Gamburd prove the spectral gap for elements of SU(2)SU(2)SU(2) with algebraic entries generating a free group (Invent. Math. 171).
  • 2012 — Bourgain and Gamburd extend this to dense subgroups of SU(d)SU(d)SU(d), d≥2d \ge 2d≥2, generated by finitely many elements with algebraic entries (JEMS 14, arXiv:1108.6264).

The tuples with algebraic entries form a Haar-null set, so the conjecture for random tuples remains open.

Setting

Let SU(d)SU(d)SU(d) be the group of complex d×dd\times dd×d matrices UUU with U∗U=UU∗=IU^*U = UU^* = IU∗U=UU∗=I and det⁡U=1\det U = 1detU=1, a compact group with its normalized Haar measure μd\mu_dμd​ (the unique left-invariant Borel probability measure). For g1,…,gn∈SU(d)g_1,\dots,g_n \in SU(d)g1​,…,gn​∈SU(d) the Hecke operator acts on functions f:SU(d)→Cf : SU(d) \to \mathbb Cf:SU(d)→C by

(zgf)(x)=∑j=1n(f(gjx)+f(gj−1x)).(z_g f)(x) = \sum_{j=1}^{n} \bigl( f(g_j x) + f(g_j^{-1} x) \bigr).(zg​f)(x)=j=1∑n​(f(gj​x)+f(gj−1​x)).

On L2(SU(d),μd)L^2(SU(d),\mu_d)L2(SU(d),μd​) it is bounded and self-adjoint, constants are eigenfunctions with eigenvalue 2n2n2n, and it preserves the space L02L^2_0L02​ of mean-zero functions. Writing λ1(zg)\lambda_1(z_g)λ1​(zg​) for the supremum of the spectrum of zgz_gzg​ on L02L^2_0L02​, one always has λ1≤2n\lambda_1 \le 2nλ1​≤2n, and zgz_gzg​ (or the tuple ggg) has a spectral gap when

λ1(zg)<2n.\lambda_1(z_g) < 2n .λ1​(zg​)<2n.

For SU(2)SU(2)SU(2), each UUU acts on the sphere S2S^2S2 by rotations, and zgz_gzg​ is the operator ϕU1+ϕU1−1+⋯+ϕUn+ϕUn−1\phi_{U_1} + \phi_{U_1}^{-1} + \dots + \phi_{U_n} + \phi_{U_n}^{-1}ϕU1​​+ϕU1​−1​+⋯+ϕUn​​+ϕUn​−1​ of the source.

Formalization targets

Goal: the spectral gap conjecture

For every n>2n > 2n>2, if U1,…,UnU_1,\dots,U_nU1​,…,Un​ are chosen independently and uniformly (Haar) at random in SU(2)SU(2)SU(2), then with probability 111

λ1(zU1,…,Un)<2n.\lambda_1\bigl(z_{U_1,\dots,U_n}\bigr) < 2n .λ1​(zU1​,…,Un​​)<2n.

Milestones from the literature

  1. Fisher's zero–one law (2006, Theorem 1.2): for n≥3n \ge 3n≥3, either almost every nnn-tuple has a spectral gap, or almost every nnn-tuple has none.
  2. Bourgain–Gamburd in SU(2)SU(2)SU(2) (2008): tuples with algebraic entries (up to simultaneous conjugation) that freely generate a free group of rank n≥2n\ge 2n≥2 have a spectral gap.
  3. Bourgain–Gamburd in SU(d)SU(d)SU(d) (2012, Theorem 1): for d≥2d \ge 2d≥2, tuples with algebraic entries generating a dense subgroup of SU(d)SU(d)SU(d) have a spectral gap.

By Fisher's law, the goal follows once a spectral gap is shown on a set of positive Haar measure.

Significance

A spectral gap for ggg means that random walks driven by g1±1,…,gn±1g_1^{\pm1},\dots,g_n^{\pm1}g1±1​,…,gn±1​ equidistribute in SU(2)SU(2)SU(2) (and orbits equidistribute on S2S^2S2) at an exponential rate, and that the action of the generated group on S2S^2S2 is strongly ergodic. In quantum computation it says that a universal single-qubit gate set is automatically efficiently universal. The conjecture asserts that this holds for a typical gate set, not just for the arithmetically special ones covered by Bourgain–Gamburd.

On the formal side, the milestones are proved theorems (Fisher; Bourgain–Gamburd) that have, to the knowledge of this proposal, no machine-checked proofs. Formalizing any of them requires substantial new libraries: Haar measure and harmonic analysis on SU(d)SU(d)SU(d), ergodicity of Aut(Fn)\mathrm{Aut}(F_n)Aut(Fn​) on Hom(Fn,SU(2))\mathrm{Hom}(F_n, SU(2))Hom(Fn​,SU(2)) (Goldman), and the arithmetic-combinatorial machinery of Bourgain–Gamburd. The goal itself is an open problem.

Difficulty

Spectral gap is not detected by density alone: every tuple generating a dense subgroup gives an ergodic action, but ergodicity only shows λ1\lambda_1λ1​ is not an eigenvalue, not that it is bounded away from 2n2n2n. Known proofs need either arithmetic structure (automorphic forms, or the algebraic entries used by Bourgain–Gamburd to rule out short relations that come too close to the identity) or rigidity (property (T)). A generic tuple has neither.

Formalization scope

  • SU(d)SU(d)SU(d) is Matrix.specialUnitaryGroup (Fin d) ℂ with the subspace topology and its Borel σ-algebra; it is shown to be a compact topological group, and haarSU d is the Haar measure normalized to total mass 111.
  • The Hecke operator acts on all functions f:SU(d)→Cf : SU(d) \to \mathbb Cf:SU(d)→C by the formula above.
  • "Spectral gap" is encoded through the quadratic form: there is ε>0\varepsilon > 0ε>0 such that Re⁡⟨zgf,f⟩≤(2n−ε) ∥f∥22\operatorname{Re}\langle z_g f, f\rangle \le (2n-\varepsilon)\,\|f\|_2^2Re⟨zg​f,f⟩≤(2n−ε)∥f∥22​ for every f∈L2f \in L^2f∈L2 with ∫f dμ=0\int f\,d\mu = 0∫fdμ=0. For a bounded self-adjoint operator this is exactly λ1<2n\lambda_1 < 2nλ1​<2n (one-sided gap, as in Fisher and Bourgain–Gamburd).
  • "Chosen uniformly at random" is the product Haar measure on SU(2)nSU(2)^nSU(2)n; "with probability 111" is an almost-everywhere statement.
  • The goal is stated on L2(SU(2))L^2(SU(2))L2(SU(2)), as in the source. The operator on L2(S2)L^2(S^2)L2(S2) is the restriction to a subrepresentation, so a gap on L02(SU(2))L^2_0(SU(2))L02​(SU(2)) implies a gap on L02(S2)L^2_0(S^2)L02​(S2). Fisher (2006, §1) remarks that the two versions of the conjecture are equivalent.
  • Trivializations are ruled out: for g1=⋯=gn=Ig_1=\dots=g_n=Ig1​=⋯=gn​=I the quadratic form equals 2n∥f∥22n\|f\|^22n∥f∥2, so no gap holds, and for n=0n=0n=0 the gap fails as well.

Useful reusable contributions include: basic harmonic analysis on compact groups (translation invariance in L2L^2L2, self-adjointness of zgz_gzg​), the Peter–Weyl decomposition of L2(SU(2))L^2(SU(2))L2(SU(2)), conjugation invariance of the gap, and monotonicity of the gap in the generating set.

Selected references

  • A. Lubotzky, R. Phillips, P. Sarnak, Hecke operators and distributing points on S2S^2S2. II, Comm. Pure Appl. Math. 40 (1987). https://doi.org/10.1002/cpa.3160400402
  • D. Fisher, Out(Fn)\mathrm{Out}(F_n)Out(Fn​) and the spectral gap conjecture, IMRN 2006. https://doi.org/10.1155/IMRN/2006/26028 (arXiv:math/0601050)
  • J. Bourgain, A. Gamburd, On the spectral gap for finitely-generated subgroups of SU(2)SU(2)SU(2), Invent. Math. 171 (2008). https://doi.org/10.1007/s00222-007-0072-z
  • J. Bourgain, A. Gamburd, A spectral gap theorem in SU(d)SU(d)SU(d), J. Eur. Math. Soc. 14 (2012). https://doi.org/10.4171/jems/337 (arXiv:1108.6264)
  • A. Sawicki, K. Karnas, Criteria for universality of quantum gates, Phys. Rev. A 95 (2017). https://doi.org/10.1103/PhysRevA.95.062303
  • Wikipedia, Spectral gap conjecture. https://en.wikipedia.org/wiki/Spectral_gap_conjecture
5 thms1 active userReviewed
ProbabilityStochastic Systems·Captain: mikedeng1

Propagation of Chaos for Stochastic Spatially Structured Neuronal Networks with Delay driven by Jump Diffusions 2: The Neuronal Network Converges in L² to Its McKean–Vlasov LimitResearch Paper

Motivation

Large networks of neurons are often modelled as systems of coupled stochastic differential equations: each neuron carries a low-dimensional state (a membrane potential and recovery variables, as in the FitzHugh–Nagumo model), receives synaptic input from the other neurons with a transmission delay, and is perturbed by noise. Because the number of neurons is large, one studies the mean-field limit: as the population grows, each neuron should evolve independently of every other fixed neuron, driven by its own noise and by an averaged population input. This asymptotic independence is called propagation of chaos, and the limiting single-neuron equation is a McKean–Vlasov equation.

Mehri, Scheutzow, Stannat and Zangeneh (arXiv:1805.01654v3, Ann. Appl. Probab. 2020, DOI 10.1214/19-AAP1499) prove propagation of chaos for a model combining features that earlier work treated separately: monotone (one-sided Lipschitz) local dynamics, general path-dependent delays, Lévy-type jump noise, spatial structure with several subpopulations, and a random environment (disorder). Earlier mean-field results for neuronal networks were obtained under global Lipschitz assumptions (Quiñinao and Touboul 2015; Touboul 2014, two papers), for locally Lipschitz coefficients (Baladron, Fasoli, Faugeras and Touboul 2012, with the clarification note of Bossy, Faugeras and Talay 2015) and for one-sided Lipschitz coefficients (Luçon and Stannat 2014; Touboul 2012); with jump noise, only under global Lipschitz or gradient-type local dynamics (Andreis, Dai Pra and Fischer 2018). This mission formalizes the paper's second main result, Theorem 1.8. The first mission of the series formalizes the well-posedness of the McKean–Vlasov equation (Theorem 1.5).

Setting

Neurons sit at positions rrr in a bounded set Γ⊂Rk\Gamma\subset\mathbb R^kΓ⊂Rk, partitioned into PPP measurable subpopulation regions Γα\Gamma_\alphaΓα​. The NNN-th network consists of a finite set AN⊂Γ\mathcal A_N\subset\GammaAN​⊂Γ of NNN positions, and neurons of subpopulation α\alphaα carry the weight 1/SAN,α1/\mathcal S_{\mathcal A_N,\alpha}1/SAN​,α​. The state Xtr,AN∈RdX^{r,\mathcal A_N}_t\in\mathbb R^dXtr,AN​​∈Rd of the neuron at rrr solves the network equation (1)

dXtr,AN=f dt+g dWtr+∫Uh N~r(dt,dξ)+∑α=1P1SAN,α∑r~∈AN∩Γα[θ dt+β dBtr,α+∫Uη N~r,α(dt,dξ)],dX^{r,\mathcal A_N}_t=f\,dt+g\,dW^r_t+\int_U h\,\tilde N^r(dt,d\xi)+\sum_{\alpha=1}^P\frac{1}{\mathcal S_{\mathcal A_N,\alpha}}\sum_{\tilde r\in\mathcal A_N\cap\Gamma_\alpha}\Big[\theta\,dt+\beta\,dB^{r,\alpha}_t+\int_U\eta\,\tilde N^{r,\alpha}(dt,d\xi)\Big],dXtr,AN​​=fdt+gdWtr​+∫U​hN~r(dt,dξ)+α=1∑P​SAN​,α​1​r~∈AN​∩Γα​∑​[θdt+βdBtr,α​+∫U​ηN~r,α(dt,dξ)],

where f,g,hf,g,hf,g,h are evaluated at (t,r,Xt−r,AN,ω′)(t,r,X^{r,\mathcal A_N}_{t-},\omega')(t,r,Xt−r,AN​​,ω′) and θ,β,η\theta,\beta,\etaθ,β,η at (t,r,r~,Xt−r,AN,X(t−τ)−:t−r~,AN,ω′)(t,r,\tilde r,X^{r,\mathcal A_N}_{t-},X^{\tilde r,\mathcal A_N}_{(t-\tau)^-:t^-},\omega')(t,r,r~,Xt−r,AN​​,X(t−τ)−:t−r~,AN​​,ω′), the last argument being the path of the presynaptic neuron over the delay window [t−τ,t][t-\tau,t][t−τ,t]. The noises WrW^rWr, Br,αB^{r,\alpha}Br,α are independent Brownian motions, N~r\tilde N^rN~r, N~r,α\tilde N^{r,\alpha}N~r,α independent compensated Poisson random measures with intensity dt⊗νdt\otimes\nudt⊗ν, and ω′\omega'ω′ is a random disorder with expectation E\mathcal EE. Each neuron starts from a path zrz^rzr on [−τ,0][-\tau,0][−τ,0].

Given a finite measure R\mathcal RR on Γ\GammaΓ with R(Γα)=1\mathcal R(\Gamma_\alpha)=1R(Γα​)=1, the McKean–Vlasov equation (6) describes a continuum of neurons XrX^rXr, r∈Γr\in\Gammar∈Γ, whose interaction is ∑α∫ΓαE~[θ(t,r,r′,Xt−r,X~(t−τ)−:t−r′,ω′)]R(dr′)\sum_\alpha\int_{\Gamma_\alpha}\tilde{\mathbb E}[\theta(t,r,r',X^r_{t-},\tilde X^{r'}_{(t-\tau)^-:t^-},\omega')]\mathcal R(dr')∑α​∫Γα​​E~[θ(t,r,r′,Xt−r​,X~(t−τ)−:t−r′​,ω′)]R(dr′), with X~\tilde XX~ an independent copy of XXX (and likewise for β\betaβ, η\etaη). The mean-field equation (5) for a neuron rrr of the network has the same form, with XrX^rXr replaced by a process Xˉr\bar X^rXˉr that starts from the same zrz^rzr and is driven by the same noises Wr,Br,α,N~r,N~r,αW^r, B^{r,\alpha}, \tilde N^r,\tilde N^{r,\alpha}Wr,Br,α,N~r,N~r,α as neuron rrr of the network, while the interaction is computed from the law of the solution of (6).

Formalization targets

Goal: Theorem 1.8

Under Hypothesis 1.6 and the chaotic initial condition (the zrz^rzr are independent, with zrz^rzr distributed as z^α\hat z^\alphaz^α for r∈Γαr\in\Gamma_\alphar∈Γα​),

lim⁡N→∞Esup⁡t∈[−τ,T], r∈ANE∣Xtr,AN−Xˉtr∣2=0.\lim_{N\to\infty}\mathcal E\sup_{t\in[-\tau,T],\,r\in\mathcal A_N}\mathbb E\big|X^{r,\mathcal A_N}_t-\bar X^r_t\big|^2=0 .N→∞lim​Et∈[−τ,T],r∈AN​sup​E​Xtr,AN​​−Xˉtr​​2=0.

The theorem asserts convergence without a rate, which is the stable form of the result.

Milestones

  1. Proposition 1.2: the network equation (1) is well posed.
  2. Lemma 1.7: positions in one cell Γαm,ε\Gamma^{m,\varepsilon}_\alphaΓαm,ε​ of the partition of Hypothesis 1.6 have solutions of (6) within C2(t,ω′)εC_2(t,\omega')\varepsilonC2​(t,ω′)ε in mean square.
  3. Display (18): the first moment estimate for Xr,AN−XˉrX^{r,\mathcal A_N}-\bar X^rXr,AN​−Xˉr, in terms of the fluctuation terms IαθI^\theta_\alphaIαθ​, IαβI^\beta_\alphaIαβ​, IαηI^\eta_\alphaIαη​.
  4. The bound (p. 20) of each III-term by 6(⋯ )C2(t,ω′)6(\cdots)C_2(t,\omega')6(⋯)C2​(t,ω′).
  5. The Gronwall bound (p. 21) with constant 363636, at a fixed disorder.
  6. The bound (p. 21) after integration over the disorder, with a constant C(T)C(T)C(T) independent of ε\varepsilonε and NNN.

The proof also uses Lemma 1.4 (the moment bound C1C_1C1​ for solutions of (6)), which is a milestone of the first mission of this series and is not restated here.

Significance

Theorem 1.8 justifies replacing a large, spatially structured network of delayed, jump-driven neurons by a single McKean–Vlasov equation indexed by position. The limit is the object on which neural field models and their bifurcation analysis are built; the theorem identifies the hypotheses on the spatial distribution of neurons (condition (10): the neuron counts per cell, normalized by the weights, converge to R\mathcal RR) under which that replacement is valid in a random environment.

The result is proved in the paper; no machine-checked proof exists. Formalizing it requires a coherent Lean layer for strong solutions of delay SDEs with jumps, mean-field interaction terms, Poisson random measures with product marks, and independence of countable families of drivers. The intermediate bounds of §3 are explicit and quantitative, and each is a self-contained target.

Difficulty

The interaction of the network is an empirical average over finitely many neurons, while the limit uses an expectation over a continuum of positions. The difference splits into three errors of different nature: a fluctuation term, whose control requires that the limit processes Xˉr~\bar X^{\tilde r}Xˉr~ of distinct neurons be independent (which holds only because each is driven by its own noise and starts from an independent initial condition); a spatial discretization error within each cell, which requires a quantitative regularity of the solution of (6) in the position variable; and a counting error between the neuron counts and the measure R\mathcal RR. A naive comparison of the network with (6) directly, without the coupled processes Xˉr\bar X^rXˉr of (5), gives no pathwise relation and does not work. The coefficients are only one-sided Lipschitz in the local variable, so moment bounds must come from monotonicity rather than from global Lipschitz estimates, and the disorder enters the constants through exponentials whose integrability is exactly assumption (H6).

Formalization scope

Positions are EuclideanSpace ℝ (Fin k), states EthierKurtz.SDEState d, and subpopulations Fin P (0-based). Neurons carry labels in ℕ with an injective position map, so AN\mathcal A_NAN​ is a finite set of labels and ⋃NAN\bigcup_N\mathcal A_N⋃N​AN​ is countable. Brownian integrals are the published EthierKurtz.HasBrownianItoIntegral per coordinate, compensated jump integrals the published JacodTodorov10.LLN.HasCompInt, and all Poisson measures of the network form one (𝓕_t)-Poisson random measure on a product mark space. Integrands are cut off after the horizon TTT. All expectations, suprema and the disorder expectation E\mathcal EE are taken in [0,∞][0,\infty][0,∞], so no statement can be satisfied by a junk value of a real integral or supremum; (H6) states the boundedness of its supremum explicitly. The copy X^\hat XX^ enters only through its law: the solution of (6) lives on its own probability space and the mean-field terms are expectations over it.

Conventions and corrections, all disclosed in the items: the theorem is stated for every family of solutions of (1), (5) and (6) (hypotheses), not for "the" solutions; (H1′) is printed with x−yx-yx−y and is read as x−x~x-\tilde xx−x~; IαηI^\eta_\alphaIαη​ carries the expectation of (17), which the display on p. 20 omits; the second Gronwall inequality on p. 21 (constant 144144144) drops a factor of C1C_1C1​ and is not posed; the coefficients' path argument carries the product σ-algebra; the filtration satisfies the usual conditions (Appendix A); the solution class of (6) is a bound for every time. The solution notions are not trivializing: every stochastic integral is tied to the given noise by a published predicate, the mean-field terms are computed from the solution of (6) itself, the limit processes share the network's noise and initial conditions, and the finite constant of the integrated bound is quantified before ε\varepsilonε and NNN.

Reusable beyond this mission: the strong-solution predicate for jump SDEs with delay, the noise predicates for countable families of drivers, and the network and mean-field interaction terms. Contributions toward a stochastic calculus for these predicates (Itô's formula for ∣X∣2|X|^2∣X∣2 with jumps, Gronwall arguments with left limits) are welcome.

Selected references

  • S. Mehri, M. Scheutzow, W. Stannat, B. Z. Zangeneh, Propagation of chaos for stochastic spatially structured neuronal networks with delay driven by jump diffusions, Ann. Appl. Probab. 30 (2020); arXiv:1805.01654v3. https://arxiv.org/abs/1805.01654
  • J. Baladron, D. Fasoli, O. Faugeras, J. Touboul, Mean-field description and propagation of chaos in networks of Hodgkin–Huxley and FitzHugh–Nagumo neurons, J. Math. Neurosci. 2:10 (2012). https://doi.org/10.1186/2190-8567-2-10
  • M. Bossy, O. Faugeras, D. Talay, Clarification and complement to "Mean-field description and propagation of chaos in networks of Hodgkin–Huxley and FitzHugh–Nagumo neurons", J. Math. Neurosci. 5:19 (2015). https://doi.org/10.1186/s13408-015-0031-8
  • J. Touboul, Propagation of chaos in neural fields, Ann. Appl. Probab. 24(3):1298–1328 (2014); arXiv:1108.2414. https://arxiv.org/abs/1108.2414
  • J. Touboul, Spatially extended networks with singular multi-scale connectivity patterns, J. Stat. Phys. 156(3):546–573 (2014).
  • C. Quiñinao, J. Touboul, Limits and dynamics of randomly connected neuronal networks, Acta Appl. Math. 136(1):167–192 (2015).
  • E. Luçon, W. Stannat, Mean field limit for disordered diffusions with singular interactions, Ann. Appl. Probab. 24(5):1946–1993 (2014).
  • J. Touboul, Limits and dynamics of stochastic neuronal networks with random heterogeneous delays, J. Stat. Phys. 149(4):569–597 (2012).
  • L. Andreis, P. Dai Pra, M. Fischer, McKean–Vlasov limit for interacting systems with simultaneous jumps, Stoch. Anal. Appl. (2018).
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Control TheoryGraph TheoryLinear algebra·Captain: mikedeng1

Sub/Super-Stochastic Matrix with Applications to Bipartite Tracking Control over Signed Networks 3: ψ < 1/(τd_M), Structural Balance and Leader Reachability Give Asynchronous Bipartite TrackingResearch Paper

Motivation

Networks of agents that interact both cooperatively and competitively are modelled by signed graphs: a positive edge weight means that one agent moves towards another, a negative weight means that it moves towards the other's opposite. Since Altafini's work on consensus over antagonistic networks (IEEE TAC 2013), the standard target on such networks has been bipartite consensus or bipartite tracking: the agents split into two camps that converge to values of equal size and opposite sign. With a leader, the camp containing the leader tracks the leader's state and the other camp tracks its negative.

Most analyses assume that all agents update at the same instants. In practice agents sample and communicate on their own clocks. Shi, Zheng, Shao and Cheng (arXiv:2004.01867v2, SIAM J. Control Optim. 2021) handle this asynchronous setting with a matrix tool: products of sub-stochastic matrices (nonnegative, row sums at most one) whose row-sum deficits propagate along the communication graph. This mission formalizes their first application, bipartite tracking of first-order agents with a static leader (§3).

Setting

There is one leader v0v_0v0​ and nnn followers v1,…,vnv_1,\dots,v_nv1​,…,vn​. Follower weights form a matrix A=[aij]∈Rn×n\mathcal A=[a_{ij}]\in\mathbb R^{n\times n}A=[aij​]∈Rn×n: the edge (vj,vi)(v_j,v_i)(vj​,vi​) exists iff aij≠0a_{ij}\neq 0aij​=0, cooperative if aij>0a_{ij}>0aij​>0, competitive if aij<0a_{ij}<0aij​<0. The number ai0a_{i0}ai0​ is the weight of the leader edge (v0,vi)(v_0,v_i)(v0​,vi​), which exists iff ai0≠0a_{i0}\ne 0ai0​=0. The digraph G~\tilde{\mathcal G}G~​ consists of the followers' digraph, the leader and the leader edges.

  • C1 (structural balance). The agents split as V1∪V2\mathcal V_1\cup\mathcal V_2V1​∪V2​ with v0∈V1v_0\in\mathcal V_1v0​∈V1​, every weight inside a set is ≥0\ge 0≥0 and every weight between the sets is ≤0\le 0≤0.
  • C2 (leader reachability). Every follower is reached from v0v_0v0​ by a directed path in G~\tilde{\mathcal G}G~​.

Time is discrete with step size τ>0\tau>0τ>0. Follower viv_ivi​ communicates only at its own instants s0iτ<s1iτ<⋯s^i_0\tau<s^i_1\tau<\cdotss0i​τ<s1i​τ<⋯ with s0i=0s^i_0=0s0i​=0 and

sk+1i−ski≤h(2.10)s^i_{k+1}-s^i_k\le h\qquad(2.10)sk+1i​−ski​≤h(2.10)

for a constant h∈Z+h\in\mathbb Z_+h∈Z+​. Positions xi(k)∈Rpx_i(k)\in\mathbb R^pxi​(k)∈Rp evolve by x0(k+1)=x0(k)x_0(k+1)=x_0(k)x0​(k+1)=x0​(k) and xi(k+1)=xi(k)+τui(k)x_i(k+1)=x_i(k)+\tau u_i(k)xi​(k+1)=xi​(k)+τui​(k), with the asynchronous protocol

ui(k)=ψ∑vj∈Ni∣aij∣[sgn⁡(aij)xj(k)−xi(k)]+ψ∣ai0∣[sgn⁡(ai0)x0(k)−xi(k)]u_i(k)=\psi\sum_{v_j\in\mathcal N_i}|a_{ij}|\big[\operatorname{sgn}(a_{ij})x_j(k)-x_i(k)\big]+\psi|a_{i0}|\big[\operatorname{sgn}(a_{i0})x_0(k)-x_i(k)\big]ui​(k)=ψvj​∈Ni​∑​∣aij​∣[sgn(aij​)xj​(k)−xi​(k)]+ψ∣ai0​∣[sgn(ai0​)x0​(k)−xi​(k)]

at the instants of viv_ivi​, and ui(k)=0u_i(k)=0ui​(k)=0 otherwise. Here ψ>0\psi>0ψ>0 is a gain. Bipartite tracking (Definition 3.1) means ∥xi(k)−x0(k)∥→0\|x_i(k)-x_0(k)\|\to 0∥xi​(k)−x0​(k)∥→0 for vi∈V1v_i\in\mathcal V_1vi​∈V1​ and ∥xi(k)+x0(k)∥→0\|x_i(k)+x_0(k)\|\to 0∥xi​(k)+x0​(k)∥→0 for vi∈V2v_i\in\mathcal V_2vi​∈V2​. With dM=max⁡i(∑vj∈Ni∣aij∣+∣ai0∣)d_M=\max_i\big(\sum_{v_j\in\mathcal N_i}|a_{ij}|+|a_{i0}|\big)dM​=maxi​(∑vj​∈Ni​​∣aij​∣+∣ai0​∣), the gain condition is

ψ<1τdM.(3.7)\psi<\frac{1}{\tau d_M}.\qquad(3.7)ψ<τdM​1​.(3.7)

In the error coordinates ei=xi−x0e_i=x_i-x_0ei​=xi​−x0​ on V1\mathcal V_1V1​ and ei=−xi−x0e_i=-x_i-x_0ei​=−xi​−x0​ on V2\mathcal V_2V2​, the closed loop is e(k+1)=(M(k)⊗Ip)e(k)e(k+1)=(M(k)\otimes I_p)e(k)e(k+1)=(M(k)⊗Ip​)e(k) with M(k)=In−τψD(k)−τψB(k)+τψ∣A(k)∣M(k)=I_n-\tau\psi\mathcal D(k)-\tau\psi\mathcal B(k)+\tau\psi|\mathcal A(k)|M(k)=In​−τψD(k)−τψB(k)+τψ∣A(k)∣, where only the rows of followers active at time kτk\taukτ differ from the identity.

Formalization targets

Goal: Theorem 3.3, sufficiency

ψ<1τdM,  C1,  C2 ⟹ lim⁡k→∞∥xi(k)−x0(k)∥=0 (vi∈V1),lim⁡k→∞∥xi(k)+x0(k)∥=0 (vi∈V2),\psi<\frac1{\tau d_M},\ \ \mathrm{C1},\ \ \mathrm{C2}\ \Longrightarrow\ \lim_{k\to\infty}\|x_i(k)-x_0(k)\|=0\ (v_i\in\mathcal V_1),\quad \lim_{k\to\infty}\|x_i(k)+x_0(k)\|=0\ (v_i\in\mathcal V_2),ψ<τdM​1​,  C1,  C2 ⟹ k→∞lim​∥xi​(k)−x0​(k)∥=0 (vi​∈V1​),k→∞lim​∥xi​(k)+x0​(k)∥=0 (vi​∈V2​),

for every choice of instants satisfying (2.10) and every initial state.

Milestones

  1. Theorem 2.4 1): a product Fq⋯F1F_q\cdots F_1Fq​⋯F1​ of sub-stochastic matrices is sub-stochastic.
  2. Theorem 2.4 2): with positive diagonals, if every row i2i_2i2​ is fed through [Fs2]i2i1>0[F_{s_2}]_{i_2i_1}>0[Fs2​​]i2​i1​​>0 by a row i1i_1i1​ with Λi1[Fs1]<1\Lambda_{i_1}[F_{s_1}]<1Λi1​​[Fs1​​]<1, s1<s2s_1<s_2s1​<s2​, then ∥∏s=1qFs∥∞<1\|\prod_{s=1}^qF_s\|_\infty<1∥∏s=1q​Fs​∥∞​<1 and ρ(∏s=1qFs)<1\rho(\prod_{s=1}^qF_s)<1ρ(∏s=1q​Fs​)<1.
  3. The matrices M(k)M(k)M(k) (p. 15): under (3.7) they are sub-stochastic with positive diagonals, and Λi[M(k)]<1\Lambda_i[M(k)]<1Λi​[M(k)]<1 exactly when viv_ivi​ hears the leader at kτk\taukτ.
  4. Error system (3.6): under C1, ei(k+1)=∑j[M(k)]ijej(k)e_i(k+1)=\sum_j[M(k)]_{ij}e_j(k)ei​(k+1)=∑j​[M(k)]ij​ej​(k).
  5. Lemma 3.2: under (3.7), C1 and C2, ∥∏s=kk+Ph−1M(s)∥∞<1\big\|\prod_{s=k}^{k+Ph-1}M(s)\big\|_\infty<1​∏s=kk+Ph−1​M(s)​∞​<1 for every kkk, where P=max⁡id(v0,vi)P=\max_i d(v_0,v_i)P=maxi​d(v0​,vi​) is the largest directed distance from the leader.

Significance

The theorem says that asynchrony costs nothing qualitatively: as long as every follower communicates at least once every hhh steps and the gain respects (3.7), the network reaches bipartite tracking under the same topological conditions C1 and C2 as in the synchronous case. No common clock, dwell-time condition or joint connectivity of the switching graphs is required. The same product argument is reused in the rest of the paper for second-order and general linear agents, delays and switching topologies, so the milestones here are the base case of that series.

The result is proved in the paper; it has not been formalized. Formalizing it produces a checked theory of products of sub-stochastic matrices with row-sum deficits (Theorem 2.4), which applies to positive switched systems in general, and a reusable encoding of signed digraphs with a leader, structural balance and asynchronous protocols.

Difficulty

Each single matrix M(k)M(k)M(k) is only marginally stable: its rows of followers that do not hear the leader at time kτk\taukτ sum to exactly one, so no single step contracts, and the spectral radius of an individual M(k)M(k)M(k) may equal one. Contraction appears only in products, and only once a deficit created at a leader edge has had time to travel along the paths of C2, one edge per window of hhh steps; this requires that deficits never disappear again, which is where positive diagonals (from (3.7)) enter. The passage from "every window product has ∥⋅∥∞<1\|\cdot\|_\infty<1∥⋅∥∞​<1" to convergence also needs a uniform bound below one over all windows; the paper's (3.14) uses it without comment. The bound holds because M(k)M(k)M(k) depends on kkk only through the set of active followers, so the window products range over a finite set.

Formalization scope

Followers are Fin n (0-based) and the leader is not an index; aija_{ij}aij​ is a i j and ai0a_{i0}ai0​ is b i. Positions lie in EuclideanSpace ℝ (Fin p); the paper's norm is unspecified and all norms on Rp\mathbb R^pRp give the same limits. Eigenvalues are complex: ρ(F)<1\rho(F)<1ρ(F)<1 means every element of spectrum ℂ of the complexified matrix has modulus <1<1<1. ∥F∥∞\|F\|_\infty∥F∥∞​ is the largest absolute row sum. Products multiply later factors on the left. sgn⁡\operatorname{sgn}sgn is Real.sign. The maxima dMd_MdM​ and PPP are given as values with IsGreatest hypotheses over exactly the printed sets, and (3.7) is kept as printed. C1 is stated for the partition V1={v0}∪V1\mathcal V_1=\{v_0\}\cup V_1V1​={v0​}∪V1​, V2=\mathcal V_2=V2​= the remaining followers; fixing the leader in V1\mathcal V_1V1​ is the paper's "with no loss of generality" convention. Definition 3.1 is stated for that same partition.

Only the "if" half of Theorem 3.3 is a target. Definition 3.1 presupposes the partition of C1, so necessity of C1 has no formal meaning, and the printed necessity argument is an informal case discussion. Theorem 2.4 2) is stated with one (i1,s1,s2)(i_1,s_1,s_2)(i1​,s1​,s2​) per row i2i_2i2​, as its proof and the example on p. 8 use it; this is implied by the literal reading.

Three formalizations would trivialize the goal and are ruled out: a protocol in which every follower updates at every step (the synchronous special case, which drops the asynchronous instants); dropping the sign conditions of C1, or leaving the leader's side free, under which the followers of V2\mathcal V_2V2​ need not approach −x0-x_0−x0​; and a goal stated about the error system (3.6) instead of the agents' positions xix_ixi​.

The development needs finite matrix products, the ∞-norm bound on eigenvalues (Gershgorin), and elementary walk reachability in digraphs. All of these exist in Mathlib in some form. Contributions are welcome on every milestone. The product theorem 2.4 does not depend on the network model and is reusable for positive switched systems.

Selected references

  • L. Shi, W. X. Zheng, J. Shao, Y. Cheng, Sub/super-stochastic matrix with applications to bipartite tracking control over signed networks, arXiv:2004.01867v2, 2020; SIAM J. Control Optim. 59 (2021). https://arxiv.org/abs/2004.01867, https://doi.org/10.1137/19M1239982
  • C. Altafini, Consensus problems on networks with antagonistic interactions, IEEE Trans. Automat. Control 58(4), 2013. https://doi.org/10.1109/TAC.2012.2224251
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ProbabilityStochastic Systems·Captain: mikedeng1

Propagation of Chaos for Stochastic Spatially Structured Neuronal Networks with Delay driven by Jump Diffusions 1: The Spatially Structured McKean–Vlasov Delay Equation with Jumps Is Well-PosedResearch Paper

Motivation

Networks of conductance-based neurons, such as FitzHugh–Nagumo networks, are modelled by systems of stochastic differential equations in which each neuron interacts with the others through synapses, and the signal from one neuron reaches another only after a transmission delay. Biological neurons sit at positions in space, belong to a few distinct subpopulations, are driven by Brownian and Poisson (jump) noise, and vary from one to another; the last feature is modelled by a random disorder parameter. When the number of neurons tends to infinity, the state of a typical neuron is expected to follow a McKean–Vlasov equation: an SDE whose coefficients depend on the law of its own solution. S. Mehri, M. Scheutzow, W. Stannat and B. Z. Zangeneh (arXiv:1805.01654; Ann. Appl. Probab. 30 (2020), DOI 10.1214/19-AAP1499) prove that this limit equation is well-posed, with coefficients that are only monotone (one-sided Lipschitz), a general delay, jump noise, spatial structure and disorder all at once, and then prove propagation of chaos towards it.

Earlier work, as surveyed on p. 3 of the paper, covered parts of this setting: global Lipschitz coefficients (Quiñinao and Touboul 2015, and the paper's references [10, 11]), locally Lipschitz coefficients (Baladron, Fasoli, Faugeras and Touboul 2012, with the clarification of Bossy, Faugeras and Talay 2015 on the localization by hitting times; the same issue affects Touboul 2012), one-sided Lipschitz coefficients with diffusive noise (Luçon and Stannat 2014), and Lévy noise under global Lipschitz assumptions or gradient-type local dynamics (Andreis, Dai Pra and Fischer 2018). The paper constructs the solution by an Euler approximation, which, by the authors' account, had not been applied to McKean–Vlasov equations before.

Setting

Fix dimensions ddd (state), mmm, nnn (noise), kkk (space), a delay τ>0\tau > 0τ>0 and P≥1P \ge 1P≥1 subpopulations, located at pairwise disjoint measurable regions Γ1,…,ΓP⊂Rk\Gamma_1,\dots,\Gamma_P \subset \mathbb R^kΓ1​,…,ΓP​⊂Rk whose union Γ\GammaΓ is bounded. A finite Borel measure R\mathcal RR on Γ\GammaΓ with R(Γα)=1\mathcal R(\Gamma_\alpha) = 1R(Γα​)=1 describes the spatial distribution of neurons. A disorder probability space (Ω′,F′,P′)(\Omega',\mathcal F',\mathbb P')(Ω′,F′,P′) is fixed. On a filtered probability space (Ω,F,(Ft),P)(\Omega,\mathcal F,(\mathcal F_t),\mathbb P)(Ω,F,(Ft​),P) there are an mmm-dimensional Brownian motion WWW, nnn-dimensional Brownian motions B1,…,BPB^1,\dots,B^PB1,…,BP, Poisson random measures N,N1,…,NPN, N^1, \dots, N^PN,N1,…,NP on [0,∞)×U[0,\infty)\times U[0,∞)×U with intensity dt⊗νdt\otimes\nudt⊗ν (ν\nuν σ-finite; N~=N−dt⊗ν\tilde N = N - dt\otimes\nuN~=N−dt⊗ν), all independent, and initial paths z^ζ∈L2(Ω;Caˋdlaˋg([−τ,0];Rd))\hat z^\zeta \in L^2(\Omega;\text{Càdlàg}([-\tau,0];\mathbb R^d))z^ζ∈L2(Ω;Caˋdlaˋg([−τ,0];Rd)).

For a path yyy, write y(t−τ)−:t−y_{(t-\tau)^-:t^-}y(t−τ)−:t−​ for the segment u↦y(t+u)−u\mapsto y_{(t+u)^-}u↦y(t+u)−​, u∈[−τ,0]u\in[-\tau,0]u∈[−τ,0]. The equation is

dXtr=f(t,r,Xt−r,ω′)dt+g(⋅)dWt+∫Uh(⋅,ξ)N~(dt,dξ)+∑α=1P∫ΓαE~[θ(t,r,r′,Xt−r,X~(t−τ)−:t−r′,ω′)]R(dr′)dtdX^r_t = f(t,r,X^r_{t-},\omega')dt + g(\cdot)dW_t + \int_U h(\cdot,\xi)\tilde N(dt,d\xi) + \sum_{\alpha=1}^P\int_{\Gamma_\alpha}\tilde{\mathbb E}\big[\theta(t,r,r',X^r_{t-},\tilde X^{r'}_{(t-\tau)^-:t^-},\omega')\big]\mathcal R(dr')dtdXtr​=f(t,r,Xt−r​,ω′)dt+g(⋅)dWt​+∫U​h(⋅,ξ)N~(dt,dξ)+α=1∑P​∫Γα​​E~[θ(t,r,r′,Xt−r​,X~(t−τ)−:t−r′​,ω′)]R(dr′)dt +∑α=1P∫ΓαE~[β(⋯ )]R(dr′)dBtα+∑α=1P∫Γα∫UE~[η(⋯ ,ξ)]R(dr′)N~α(dt,dξ),Xtr=z^tζ (r∈Γζ, t∈[−τ,0]),(6)+ \sum_{\alpha=1}^P\int_{\Gamma_\alpha}\tilde{\mathbb E}[\beta(\cdots)]\mathcal R(dr')dB^\alpha_t + \sum_{\alpha=1}^P\int_{\Gamma_\alpha}\int_U\tilde{\mathbb E}[\eta(\cdots,\xi)]\mathcal R(dr')\tilde N^\alpha(dt,d\xi),\qquad X^r_t = \hat z^\zeta_t\ (r\in\Gamma_\zeta,\ t\in[-\tau,0]), \tag{6}+α=1∑P​∫Γα​​E~[β(⋯)]R(dr′)dBtα​+α=1∑P​∫Γα​​∫U​E~[η(⋯,ξ)]R(dr′)N~α(dt,dξ),Xtr​=z^tζ​ (r∈Γζ​, t∈[−τ,0]),(6)

where X~\tilde XX~ is an independent copy of the solution XXX and E~\tilde{\mathbb E}E~ its expectation. The local dynamics f,g,hf, g, hf,g,h satisfy the monotonicity and growth conditions (H1)–(H3) of Hypothesis 1.1 with locally integrable random rates Lt(ω′)L_t(\omega')Lt​(ω′), Kt(ω′)K_t(\omega')Kt​(ω′), K~t(R,ω′)\tilde K_t(R,\omega')K~t​(R,ω′); the synaptic coefficients θ,β,η\theta, \beta, \etaθ,β,η are Lipschitz (H4) and of linear growth (H5) in (x,y)(x, y)(x,y), with rates Lˉt(ω′)\bar L_t(\omega')Lˉt​(ω′), Kˉt(ω′)\bar K_t(\omega')Kˉt​(ω′), the path variable measured through a probability measure λ\lambdaλ on [−τ,0][-\tau,0][−τ,0]. In Lean, the model is the definition module NeuroMV.WellPosed.Setting: Space, Coeffs, Hyp1_1, Noise/IsNoise, IsStrongSol6, InClass and the constant C1 of (7).

Formalization targets

Goal: Theorem 1.5 (p. 7)

For every T>0T>0T>0 there is a process Xtr(ω;ω′)X^r_t(\omega;\omega')Xtr​(ω;ω′), measurable in (r,ω′,ω)(r,\omega',\omega)(r,ω′,ω), such that for P′\mathbb P'P′-almost every ω′\omega'ω′, X(ω′)X(\omega')X(ω′) is a strong solution of (6) on [−τ,T][-\tau,T][−τ,T],

X(ω′)∈L∞([−τ,T],dt;L2(Ω×Γ,P⊗R;Rd)),X(\omega') \in L^\infty\big([-\tau,T],dt;L^2(\Omega\times\Gamma,\mathbb P\otimes\mathcal R;\mathbb R^d)\big),X(ω′)∈L∞([−τ,T],dt;L2(Ω×Γ,P⊗R;Rd)),

and every other strong solution YYY in that class satisfies ∫ΓE∣Xtr−Ytr∣2R(dr)=0\int_\Gamma\mathbb E|X^r_t - Y^r_t|^2\mathcal R(dr) = 0∫Γ​E∣Xtr​−Ytr​∣2R(dr)=0 for all t∈[−τ,T]t\in[-\tau,T]t∈[−τ,T].

Milestones

  1. Theorem A.2 (p. 23): well-posedness of the general path-dependent delay SDE (20) with jumps under the monotonicity Hypothesis A.1, joint measurability in (t,ω,ω′)(t,\omega,\omega')(t,ω,ω′), the moment bound (22), and convergence of the Euler approximation (21).
  2. The scheme (11) exists and is measurable (p. 9), and satisfies the uniform bound (12): sup⁡t∈[−τ,T]E∣Xtn,r∣2≤C1(T,ω′)\sup_{t\in[-\tau,T]}\mathbb E|X^{n,r}_t|^2 \le C_1(T,\omega')supt∈[−τ,T]​E∣Xtn,r​∣2≤C1​(T,ω′).
  3. Claims (iv) and (v) (p. 10): the schemes are Cauchy in probability uniformly on [0,T][0,T][0,T], and their limit solves (6).
  4. Lemma 1.4 (p. 6): every solution in the class satisfies sup⁡s∈[−τ,t]E∣Xsr∣2≤C1(t,ω′)\sup_{s\in[-\tau,t]}\mathbb E|X^r_s|^2 \le C_1(t,\omega')sups∈[−τ,t]​E∣Xsr​∣2≤C1​(t,ω′).
  5. Uniqueness (pp. 16–17): two solutions in the class agree in L2(Ω×Γ)L^2(\Omega\times\Gamma)L2(Ω×Γ) at every time.

Significance

Theorem 1.5 makes the infinite-population limit of the network well defined; the second main result of the paper, Theorem 1.8, proves that the network converges to it in L2L^2L2. The theorem applies to coefficients that are only monotone in the state, which covers the cubic nonlinearity of FitzHugh–Nagumo neurons (Example 1.3), and to synaptic weights of conductance type driven by jump noise. Theorem A.2, proved in the appendix, is a well-posedness and Euler-convergence result for monotone stochastic delay equations with jumps and random coefficients, stated by the authors as new in this generality; it is reusable beyond this paper.

The results are proved in the paper; none of them has a machine-checked proof. The mission produces a Lean statement of the limit equation with the published Brownian and compensated Poisson integral relations, and invites formal proofs of the scheme estimates, the Cauchy property, the identification of the limit, the moment bound and uniqueness.

Difficulty

Under monotone coefficients there is no global contraction, so the Picard iteration that proves McKean–Vlasov well-posedness under Lipschitz assumptions does not apply. Localization by stopping times, the standard replacement for SDEs with monotone coefficients, does not localize the mean-field terms, which depend on the law of the solution at all positions; mishandling this step is the gap that Bossy, Faugeras and Talay pointed out in earlier work, and the acknowledgements (p. 30) record that the first version of this paper contained an unjustified application of the moment estimate of Lemma 1.4. A priori bounds must therefore be uniform in the approximation, and the limit of the mean-field stochastic integrals must be identified, with jumps and delay present. A second difficulty is measurability: the mean-field integrals require the solution to be measurable in the position r′r'r′, and the theorem asserts measurability in (r,ω′)(r,\omega')(r,ω′).

Formalization scope

  • Representation. Positions are EuclideanSpace ℝ (Fin k), states EthierKurtz.SDEState d, matrices Matrix (Fin d) (Fin m) ℝ with the Hilbert–Schmidt norm; subpopulations are Fin P, 0-based. Processes are indexed by ℝ on [−τ,T][-\tau,T][−τ,T]; drivers by ℝ≥0. Path segments are functions ℝ → ℝ^d read on [−τ,0][-\tau,0][−τ,0] with the product (cylinder) σ-algebra; the paper's sup-norm Borel σ-algebra is larger, so joint measurability of the coefficients for the cylinder σ-algebra is a mild added requirement.
  • Noise and integrals. Brownian integrals are the published EthierKurtz.HasBrownianItoIntegral, compensated jump integrals the published JacodTodorov10.LLN.HasCompInt, coordinatewise, with integrands cut off after TTT. All Poisson measures are one JacodTodorov10.LLN.IsFPoisson measure on a product mark space, equivalent to independent measures N,NαN, N^\alphaN,Nα. Independence of the initial data from the drivers is encoded by F0\mathcal F_0F0​-measurability.
  • Mean field. The copy X~\tilde XX~ enters only through its law, so E~\tilde{\mathbb E}E~ is an expectation over XXX itself; the mean-field integrands are required to be integrable on Γα×Ω\Gamma_\alpha\times\OmegaΓα​×Ω. Expectations and suprema are in [0,∞][0,\infty][0,∞]; Bochner integrals carry integrability clauses.
  • Hypotheses. Hypothesis 1.1 (H1)–(H5) and the regularity of p. 2; (H6) is omitted from every item, because it involves the network data of (1) only and the proof never uses it. The class L∞L^\inftyL∞–L2L^2L2 is a bound for every ttt rather than a.e. ttt.
  • Every position. The goal requires the integral identity at every r∈Γr\in\Gammar∈Γ, as (6) is posed; §2 proves it for R×P′\mathcal R\times\mathbb P'R×P′-a.e. (r,ω′)(r,\omega')(r,ω′) (p. 16), and on the remaining null set of positions (6) is an ordinary delay SDE solved by Theorem A.2 — a step the paper leaves implicit. Claim (v) keeps the R\mathcal RR-a.e. reading.
  • No trivialization. The stochastic integrals are the published relations tied to WWW, BαB^\alphaBα, NNN, NαN^\alphaNα, not free processes; the mean-field terms are computed from the solution's own segments; the solution class is non-empty for zero coefficients (a sorry-free check accompanies the mission); and the scheme and limit are the paper's, not new processes with their own noise.
  • Not stated. Claims (i)–(iii) of §2 (the remainder estimate and the a priori bounds before the stopping time) are not separate items.
  • Welcome contributions. Itô's formula for jump diffusions in this encoding, Gronwall-type lemmas in [0,∞][0,\infty][0,∞], stability of the published integral relations under convergence in probability, and the monotone delay SDE theory of Appendix A are all reusable beyond this mission.

Selected references

  • S. Mehri, M. Scheutzow, W. Stannat, B. Z. Zangeneh, Propagation of Chaos for Stochastic Spatially Structured Neuronal Networks with Delay driven by Jump Diffusions, arXiv:1805.01654v3, 2019; Ann. Appl. Probab. 30, 2020. https://arxiv.org/abs/1805.01654, https://doi.org/10.1214/19-AAP1499
  • J. Baladron, D. Fasoli, O. Faugeras, J. Touboul, Mean-field description and propagation of chaos in networks of Hodgkin–Huxley and FitzHugh–Nagumo neurons, J. Math. Neurosci. 2:10, 2012.
  • M. Bossy, O. Faugeras, D. Talay, Clarification and complement to "Mean-field description and propagation of chaos in networks of Hodgkin–Huxley and FitzHugh–Nagumo neurons", J. Math. Neurosci. 5:19, 2015.
  • E. Luçon, W. Stannat, Mean field limit for disordered diffusions with singular interactions, Ann. Appl. Probab. 24(5):1946–1993, 2014.
  • C. Quiñinao, J. Touboul, Limits and dynamics of randomly connected neuronal networks, Acta Appl. Math. 136(1):167–192, 2015.
  • J. Touboul, Limits and dynamics of stochastic neuronal networks with random heterogeneous delays, J. Stat. Phys. 149(4):569–597, 2012.
  • L. Andreis, P. Dai Pra, M. Fischer, McKean–Vlasov limit for interacting systems with simultaneous jumps, Stoch. Anal. Appl., 2018.
  • W. Liu, M. Röckner, Stochastic Partial Differential Equations: An Introduction, Universitext, Springer, 2015.

The bibliographic data of the last seven entries are taken from the reference list of the paper (pp. 30–31).

17 thms1 active userReviewed
AnalysisStochastic Systems·Captain: mikedeng1

On Well-Posedness of Forward–Backward SDEs—A Unified Approach 3: With Constant Coefficients, σ₃ ≠ 0, σ₃h ≠ 1 the Dominating ODE Keeps y and (1 − σ₃y)⁻¹ Bounded for Every T Iff One of Four Cases HoldsResearch Paper

Motivation

A forward–backward stochastic differential equation (FBSDE) couples a forward diffusion XXX with a backward equation for a pair (Y,Z)(Y,Z)(Y,Z) whose terminal value depends on XTX_TXT​. Such systems arise from the stochastic maximum principle, from option pricing with large investors, and from stochastic differential games. Whether an FBSDE with Lipschitz coefficients has a unique solution on an arbitrary horizon [0,T][0,T][0,T] is a classical question: small-time solvability is easy, but global solvability can fail even for linear equations.

J. Ma, Z. Wu, D. Zhang and J. Zhang, On well-posedness of forward–backward SDEs—a unified approach (arXiv:1110.4658v2, Ann. Appl. Probab. 25 (2015)), reduce well-posedness to the existence of bounded solutions of two deterministic dominating ordinary differential equations, obtained from the difference quotients of the coefficients. For linear FBSDEs with constant coefficients the two dominating ODEs coincide, and the authors give sharp criteria for global solvability. This mission formalizes the criterion in the case σ3≠0\sigma_3\neq0σ3​=0, where the dominating ODE has a rational right-hand side with a pole.

Earlier global results for coupled FBSDEs rested on the four-step scheme (Ma–Protter–Yong 1994) or on monotonicity conditions (Hu–Peng 1995); §7.4 of the paper compares the dominating-ODE approach with these methods.

Setting

The linear FBSDE (4.1) with constant coefficients is determined by nine reals b1,b2,b3,σ1,σ2,σ3,f1,f2,f3b_1,b_2,b_3,\sigma_1,\sigma_2,\sigma_3,f_1,f_2,f_3b1​,b2​,b3​,σ1​,σ2​,σ3​,f1​,f2​,f3​ and a terminal slope hhh. Its dominating function is (3.9),

F(y)=f1+f2y+y(b1+b2y)+(f3+b3y) y (σ1+σ2y)1−σ3y,F(y)=f_1+f_2y+y(b_1+b_2y)+\frac{(f_3+b_3y)\,y\,(\sigma_1+\sigma_2y)}{1-\sigma_3y},F(y)=f1​+f2​y+y(b1​+b2​y)+1−σ3​y(f3​+b3​y)y(σ1​+σ2​y)​,

defined for y≠1/σ3y\neq1/\sigma_3y=1/σ3​. When σ3≠0\sigma_3\neq0σ3​=0 it can be rewritten as (5.8),

F(y)=α01/σ3−y+α1+α2y+α3y2,α3=b2−b3σ2σ3,F(y)=\frac{\alpha_0}{1/\sigma_3-y}+\alpha_1+\alpha_2y+\alpha_3y^2,\qquad \alpha_3=b_2-\frac{b_3\sigma_2}{\sigma_3},F(y)=1/σ3​−yα0​​+α1​+α2​y+α3​y2,α3​=b2​−σ3​b3​σ2​​,

for constants α0,α1,α2\alpha_0,\alpha_1,\alpha_2α0​,α1​,α2​. The dominating ODE (5.3) is the backward integral equation

yt=h+∫tTF(ys) ds,t∈[0,T].y_t=h+\int_t^TF(y_s)\,ds,\qquad t\in[0,T].yt​=h+∫tT​F(ys​)ds,t∈[0,T].

The paper needs a solution satisfying (5.9): both yyy and (1−σ3y)−1(1-\sigma_3y)^{-1}(1−σ3​y)−1 are bounded on [0,T][0,T][0,T]. Since yT=hy_T=hyT​=h, this forces σ3h≠1\sigma_3h\neq1σ3​h=1 (Remark 5.5).

The comparison tool is Lemma 5.1. For measurable F0,F1,F2F^0,F^1,F^2F0,F1,F2 it gives a unique solution of yt0=h0+∫tTF0(s,ys0) dsy^0_t=h^0+\int_t^TF^0(s,y^0_s)\,dsyt0​=h0+∫tT​F0(s,ys0​)ds lying between a sub-solution y1y^1y1 and a super-solution y2y^2y2 of perturbed equations (5.2).

Formalization targets

Goal: Theorem 5.6 (corrected)

For σ3≠0\sigma_3\neq0σ3​=0 and hσ3≠1h\sigma_3\neq1hσ3​=1, the ODE (5.3) has a solution satisfying (5.9) for every T>0T>0T>0 if and only if one of the following holds:

  1. h<1/σ3h<1/\sigma_3h<1/σ3​, F(h)≤0F(h)\le0F(h)≤0, and FFF has a zero in (−∞,h](-\infty,h](−∞,h] or α3=0\alpha_3=0α3​=0;
  2. h>1/σ3h>1/\sigma_3h>1/σ3​, F(h)≥0F(h)\ge0F(h)≥0, and FFF has a zero in [h,∞)[h,\infty)[h,∞) or α3=0\alpha_3=0α3​=0;
  3. h<1/σ3h<1/\sigma_3h<1/σ3​, F(h)≥0F(h)\ge0F(h)≥0, and FFF has a zero in [h,1/σ3)[h,1/\sigma_3)[h,1/σ3​) or F(y)→0F(y)\to0F(y)→0 as y↑1/σ3y\uparrow1/\sigma_3y↑1/σ3​;
  4. h>1/σ3h>1/\sigma_3h>1/σ3​, F(h)≤0F(h)\le0F(h)≤0, and FFF has a zero in (1/σ3,h](1/\sigma_3,h](1/σ3​,h] or F(y)→0F(y)\to0F(y)→0 as y↓1/σ3y\downarrow1/\sigma_3y↓1/σ3​.

The limit alternatives in cases 3 and 4 are not printed in the paper. Without them the equivalence is false. Take σ3=1\sigma_3=1σ3​=1, f1=1f_1=1f1​=1, f2=−1f_2=-1f2​=−1, all other coefficients 000, and h=0h=0h=0. Then F(y)=1−yF(y)=1-yF(y)=1−y, and yt=1−et−Ty_t=1-e^{t-T}yt​=1−et−T satisfies (5.9) on every horizon, yet none of the printed cases holds. The gap is in the Appendix (p. 48): it asserts that FFF is positive on the closed interval [h,σ3−1][h,\sigma_3^{-1}][h,σ3−1​] when α0=0\alpha_0=0α0​=0, which fails when the polynomial part vanishes at the pole. A companion item records the counterexample.

Milestones

The milestones follow the paper's proof:

  • the decomposition (5.8);
  • the comparison Lemma 5.1;
  • the three sufficiency arguments of p. 23 (case (i) with a zero, case (i) with α3=0\alpha_3=0α3​=0, case (iii));
  • the necessity bounds of the Appendix:
    • in case (i): a zero exists when α3>0\alpha_3>0α3​>0, and F(y)≤−ε(h+1−y)2F(y)\le-\varepsilon(h+1-y)^2F(y)≤−ε(h+1−y)2 when α3<0\alpha_3<0α3​<0 and there is no zero;
    • in case (iii): a zero exists when α0<0\alpha_0<0α0​<0, and F(y)≥ε(σ3−1−y)−1F(y)\ge\varepsilon(\sigma_3^{-1}-y)^{-1}F(y)≥ε(σ3−1​−y)−1 when α0>0\alpha_0>0α0​>0 and there is no zero;
  • the comparison with the explicit solution (σ3−1−y~t)2=(σ3−1−h)2−2ε(T−t)(\sigma_3^{-1}-\tilde y_t)^2=(\sigma_3^{-1}-h)^2-2\varepsilon(T-t)(σ3−1​−y~​t​)2=(σ3−1​−h)2−2ε(T−t);
  • the case α0=0\alpha_0=0α0​=0, with the endpoint positivity made a hypothesis.

Significance

In the paper, Theorem 5.6 is the sharp solvability criterion for linear FBSDEs with constant coefficients and σ3≠0\sigma_3\neq0σ3​=0. The paper describes these sharp conditions as setting the "limits" for the solvability of general FBSDEs, and the nonlinear Theorems 5.9–5.11 and the main well-posedness Theorem 7.4 are modelled on them. Because the criterion is an equivalence, it also says when no regular decoupling field can be built from the dominating ODE, whatever the horizon.

The result is proved in the paper, but the printed statement is not correct as an equivalence (see above). The mission states and asks for a corrected version, a machine-checked counterexample to the printed one, and formal versions of each step of the proof. The deterministic parts (comparison for backward integral equations, finite-time blow-up of scalar autonomous ODEs, pole-reaching for rational vector fields) are basic real analysis with no current Lean library. They are reusable well beyond FBSDEs. No part of this paper has a machine-checked proof.

Difficulty

Each sufficiency case is an a priori bound: a comparison with a constant or explicitly solvable equation keeps the solution in a fixed interval away from the pole. The difficulty is on the necessity side, where three kinds of failure must be excluded:

  • blow-up to ±∞\pm\infty±∞ when α3≠0\alpha_3\neq0α3​=0 has the wrong sign;
  • reaching the pole in finite time when α0>0\alpha_0>0α0​>0;
  • reaching the pole when α0=0\alpha_0=0α0​=0.

The obvious argument for the last case, "FFF is bounded below by a positive constant up to the pole", is exactly what fails when FFF tends to 000 at the pole. There the solution approaches the pole only asymptotically, and the right criterion is the corrected one above. A second subtlety is that FFF is only locally Lipschitz off the pole, so comparison must be carried out on a band in which the solution provably stays.

Formalization scope

Time is real, with 0≤t≤T0\le t\le T0≤t≤T and T>0T>0T>0. The coefficients form a record Coeffs of nine reals, and Coeffs.F is (3.9) verbatim. A solution on [0,T][0,T][0,T] is a function y:R→Ry:\mathbb R\to\mathbb Ry:R→R such that, for every t∈[0,T]t\in[0,T]t∈[0,T], the integrand s↦F(ys)s\mapsto F(y_s)s↦F(ys​) is integrable on [t,T][t,T][t,T] and yt=h+∫tTF(ys) dsy_t=h+\int_t^TF(y_s)\,dsyt​=h+∫tT​F(ys​)ds; values outside [0,T][0,T][0,T] are irrelevant. Condition (5.9) is a uniform bound on ∣y∣|y|∣y∣ together with ∣1−σ3yt∣≥κ|1-\sigma_3y_t|\ge\kappa∣1−σ3​yt​∣≥κ for some κ>0\kappa>0κ>0. "For arbitrary TTT" is a universal quantifier over T>0T>0T>0 outside the existential.

Lean's conventions are total, so several trivializing formalizations are ruled out explicitly:

  • Lean's integral of a non-integrable function is 000, so integrability is part of the solution predicate.
  • Lean's x/0=0x/0=0x/0=0 gives Coeffs.F a junk value at the pole. No statement evaluates FFF there: every zero point, bound and limit in the statements lives strictly on one side of 1/σ31/\sigma_31/σ3​.
  • (5.9) is never encoded as a bound on the Lean inverse (1−σ3y)−1(1-\sigma_3y)^{-1}(1−σ3​y)−1, which is 000 at the pole.
  • σ3≠0\sigma_3\neq0σ3​=0 is assumed wherever α3\alpha_3α3​ or 1/σ31/\sigma_31/σ3​ appears.
  • Lemma 5.1 adds the hypothesis y1≤y2y^1\le y^2y1≤y2, without which it is false, and asks the cic^ici to be integrable.

The constants α0,α1,α2\alpha_0,\alpha_1,\alpha_2α0​,α1​,α2​ of (5.8) enter the necessity milestones only through the decomposition hypothesis, never through a formula.

The development needs a Picard–Lindelöf or comparison argument for backward scalar integral equations with locally Lipschitz right-hand sides, monotonicity of autonomous scalar solutions, and the intermediate value theorem. Contributions of a general comparison lemma for scalar integral equations would serve this mission and both other missions of the series.

Selected references

  • J. Ma, Z. Wu, D. Zhang, J. Zhang, On well-posedness of forward–backward SDEs—a unified approach, Ann. Appl. Probab. 25(4):2168–2214, 2015. arXiv:1110.4658v2, doi:10.1214/14-AAP1046
  • J. Ma, P. Protter, J. Yong, Solving forward–backward stochastic differential equations explicitly—a four step scheme, Probab. Theory Related Fields 98:339–359, 1994. doi:10.1007/BF01192258
  • Y. Hu, S. Peng, Solution of forward–backward stochastic differential equations, Probab. Theory Related Fields 103:273–283, 1995. doi:10.1007/BF01204218
  • J. Ma, J. Yong, Forward–Backward Stochastic Differential Equations and Their Applications, Lecture Notes in Math. 1702, Springer, 1999. doi:10.1007/978-3-540-48831-6
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ProbabilityStochastic Systems·Captain: mikedeng1

Coexistence and Extinction for Stochastic Kolmogorov Systems 2: If a Boundary Ergodic Measure Is a Sink, then E(min_i X_i(t))^δ → 0 for Every Small δ > 0Research Paper

Why stochastic extinction matters

A population can disappear because its average growth rate is negative when it is rare, even if its deterministic dynamics would support coexistence. Interactions among several species make this harder to assess: a species may be rare while the remaining species fluctuate among several possible long-run configurations. Hening and Nguyen study this problem for nonlinear systems driven by correlated environmental Brownian noise. Their analysis treats the noncompact state space [0,∞)n[0,\infty)^n[0,∞)n, where population sizes have no fixed upper bound. The central result of this mission gives a quantitative form of approach to the boundary when one boundary community acts as a sink: the expected positive moment of the smallest population size tends to zero. The source is Hening and Nguyen, Coexistence and extinction for stochastic Kolmogorov systems, arXiv:1704.06984v1, Theorems 1.2 and 5.1.

The population system and its boundary laws

There are n≥1n\ge1n≥1 species. Their abundances form X(t)=(X1(t),…,Xn(t))∈R+n=[0,∞)nX(t)=(X_1(t),\ldots,X_n(t))\in\mathbb R^n_+=[0,\infty)^nX(t)=(X1​(t),…,Xn​(t))∈R+n​=[0,∞)n. Species iii has per capita drift fi(x)f_i(x)fi​(x) and environmental noise intensity gi(x)g_i(x)gi​(x) at state xxx. A matrix Γ\GammaΓ correlates the noise: E=Γ⊤BE=\Gamma^\top BE=Γ⊤B for a vector BBB of independent standard Brownian motions, and Σ=Γ⊤Γ=(σij)\Sigma=\Gamma^\top\Gamma=(\sigma_{ij})Σ=Γ⊤Γ=(σij​). The stochastic Kolmogorov system is

dXi(t)=Xi(t)fi(X(t)) dt+Xi(t)gi(X(t)) dEi(t),i=1,…,n.dX_i(t)=X_i(t)f_i(X(t))\,dt+X_i(t)g_i(X(t))\,dE_i(t),\qquad i=1,\ldots,n.dXi​(t)=Xi​(t)fi​(X(t))dt+Xi​(t)gi​(X(t))dEi​(t),i=1,…,n.

The factor XiX_iXi​ makes the state Xi=0X_i=0Xi​=0 absorbing in that coordinate. Consequently each collection of present species defines an invariant face of the orthant. The interior R+n,∘=(0,∞)n\mathbb R^{n,\circ}_+=(0,\infty)^nR+n,∘​=(0,∞)n consists of states where every species is present. The paper uses ∥x∥=∑i∣xi∣\|x\|=\sum_i|x_i|∥x∥=∑i​∣xi​∣ throughout. Its standing Assumption 1.1 requires local Lipschitz coefficients, nondegenerate noise in each population subspace, and a negative large-state drift condition (1.2) expressed through positive cic_ici​ and γb\gamma_bγb​. These conditions place the same control on the unbounded part of the orthant in every theorem below. Source, §1.1, pp. 3–4.

A probability measure μ\muμ is invariant if the law of X(t)X(t)X(t) remains μ\muμ when X(0)X(0)X(0) has law μ\muμ. It is ergodic if it is an extreme point of the invariant probability measures. Let MMM contain the ergodic invariant measures supported on the boundary ∂R+n\partial\mathbb R^n_+∂R+n​. This set includes the Dirac law δ∗\delta_*δ∗​ at zero. For any invariant μ\muμ, the invasion rate of species iii is

λi(μ)=∫(fi(x)−12σiigi(x)2) μ(dx).\lambda_i(\mu)=\int\left(f_i(x)-\tfrac12\sigma_{ii}g_i(x)^2\right)\,\mu(dx).λi​(μ)=∫(fi​(x)−21​σii​gi​(x)2)μ(dx).

The term σiigi2/2\sigma_{ii}g_i^2/2σii​gi2​/2 records the effect of environmental variation on logarithmic population growth. For μ∈M\mu\in Mμ∈M, let IμI_\muIμ​ be its present species and R+μ\mathbb R^\mu_+R+μ​ its coordinate face. The set MμM_\muMμ​ contains boundary ergodic measures supported on the boundary of that face. Assumption 1.3 requires λi(μ)<0\lambda_i(\mu)<0λi​(μ)<0 for every i∉Iμi\notin I_\mui∈/Iμ​ and, when Iμ≠∅I_\mu\ne\varnothingIμ​=∅, a positive invasion rate among IμI_\muIμ​ for every finite convex combination of measures in MμM_\muMμ​. Thus the missing species decline near μ\muμ, while the community on its face resists collapse to a smaller face. Source, pp. 5–6.

Formalization targets

The goal is Theorem 1.2 in the explicit quantification of Theorem 5.1. Let δ0>0\delta_0>0δ0​>0 satisfy the paper's bound (3.2). For every 0<δ<δ00<\delta<\delta_00<δ<δ0​ and every x∈R+n,∘x\in\mathbb R^{n,\circ}_+x∈R+n,∘​,

lim⁡t→∞Ex(min⁡1≤i≤nXi(t))δ=0.\lim_{t\to\infty}\mathbb E_x\left(\min_{1\le i\le n}X_i(t)\right)^\delta=0.t→∞lim​Ex​(1≤i≤nmin​Xi​(t))δ=0.

This is the moment form of approach to the boundary. It does not specify which species tends to zero on a given path. The companion targets retain the paper's route: strong solutions and face invariance in Lemma 3.1, moment and Feller estimates in Lemma 3.2, zero resident invasion rates in Lemma 5.1, the weighted negative margin (5.1)–(5.2), the averaged drift bound in Lemma 5.2, local contraction in Proposition 5.1, global control in Lemma 5.3, the capped one-step bound (5.11)–(5.12), and transience in the interior. Source, §§3 and 5.

What the result gives

The conclusion detects extinction pressure without asking in advance which coordinate disappears. Since the minimum of the coordinates is small whenever the system is near any boundary face, it applies even when trajectories may approach different faces. The paper uses this result before its stronger classification theorem, which adds assumptions to determine the fate of particular species. Theorem 1.2 is proved in the cited paper; this mission seeks a machine-checked development of that known result and its supporting stochastic estimates. In particular, the formalization makes explicit how invariant measures on smaller faces control a statement about solutions starting in the full interior. Source, Theorems 1.2 and 1.3.

Where the difficulty lies

A negative invasion rate at one boundary law does not by itself control the whole trajectory. The surviving species can fluctuate toward a still smaller face, where a missing species may have a different growth rate. Assumption 1.3 excludes that obstruction by quantifying over boundary ergodic laws of the selected face. Another obstacle is unbounded population size: a local bound near the face cannot alone give convergence of expected moments. The paper combines face-specific estimates with global moment control; encoding both regimes faithfully is the main mathematical burden. The compact-state intuition cited in the paper does not immediately apply to the noncompact orthant. Source, Remark 1.6 and §5.

Formalization scope

Lean represents Rn\mathbb R^nRn by the published EthierKurtz.SDEState n, with coordinates indexed by Fin n from zero to n−1n-1n−1. The SDE uses the published strong Brownian solution interface; a common probability space, Brownian motion, and solution family replace the paper's notation Px\mathbb P_xPx​ and Ex\mathbb E_xEx​. The model reads coefficients at the componentwise positive part off the orthant, where the source never evaluates them. Assumption 1.1(3)'s strict limit superior becomes an equivalent eventual negative ε\varepsilonε bound. Ergodicity is extremality. The index set IμI_\muIμ​ is defined through positive μ\muμ-mass of xi>0x_i>0xi​>0. Finite convex combinations, rather than a closure of their hull, implement the paper's Conv⁡\operatorname{Conv}Conv.

The stated goal requires n≥1n\ge1n≥1 and δ>0\delta>0δ>0; δ=0\delta=0δ=0 would make the displayed expectation equal to one. Theorem 5.1 suppresses the latter restriction, while Theorem 1.2 states it. Lemma 5.2's displayed average divides by TTT, so its TeT_eTe​ is positive, as its proof says, despite the printed Te≥0T_e\ge0Te​≥0. The weights, time threshold, and contraction exponent are chosen before the points and times over which their bounds hold. Boundary ergodic measures are not assumed to be absent; the explicit δ∗∈M\delta_*\in Mδ∗​∈M target records nonemptiness. The definitions of UθU_\thetaUθ​, invariant measures and invasion rates, together with finite-dimensional stochastic integration and moment estimates, are reusable beyond this mission. Contributions that establish these interfaces or prove the listed paper statements are in scope.

The radius MMM of (3.1) is positive. The freely chosen δ0\delta_0δ0​ is below one, as Appendix A uses, and the extinction weights are selected with ∑i∈Iμp^i≤δ0\sum_{i\in I_\mu}\widehat p_i\le\delta_0∑i∈Iμ​​p​i​≤δ0​. Scaling all positive weights and the margin preserves (5.2) and makes the paper's CUC_UCU​ finite. Bounds involving real expectations explicitly include integrability, so Lean's value for a nonintegrable integral cannot satisfy them by default. The transience target applies to each compact interior set with nonempty interior, as the proof on p. 24 does.

Selected references

  • Alexandru Hening and Dang H. Nguyen, Coexistence and extinction for stochastic Kolmogorov systems, Annals of Applied Probability 28(3), 2018. Formalization uses arXiv:1704.06984v1, 23 April 2017, especially pp. 3–7, 13–14, and 20–24.
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ProbabilityStochastic Systems·Captain: mikedeng1

Coexistence and Extinction for Stochastic Kolmogorov Systems 3: Almost Surely the Occupation Measures Settle on One Sink Measure µ, and Every Species Absent from µ Dies Out at Rate λ_i(µ) < 0Research Paper

Motivation

Ecologists model interacting populations — competitors, predators and prey, food chains — by Kolmogorov systems, in which the growth of each species is proportional to its current abundance. Environmental fluctuations turn these into stochastic differential equations, and the basic question becomes which species persist and which go extinct. Hening and Nguyen (arXiv:1704.06984, Ann. Appl. Probab. 28(3), 2018) gave sharp answers for a general class of such systems on the non-compact state space [0,∞)n[0,\infty)^n[0,∞)n, extending the compact-simplex theory of Schreiber, Benaïm and Atchadé (SBA11).

The paper has three main results: persistence when every boundary ergodic measure repels (Theorem 1.1), extinction in mean when one boundary measure attracts (Theorem 1.2), and a classification of which species die out (Theorem 1.3). This mission formalizes the third. When the community does not persist, Theorem 1.3 says that each path settles near exactly one "sink" measure on the boundary, and identifies the species that vanish and their rates of decay.

Timeline: Schreiber, Benaïm and Atchadé (2011) proved persistence criteria for stochastic population models on the simplex {y∈R+n:y1+⋯+yn=1}\{y \in \mathbb R^n_+ : y_1 + \dots + y_n = 1\}{y∈R+n​:y1​+⋯+yn​=1}. Evans, Hening and Schreiber (EHS15, 2015), studying patch-selection strategies in stochastic environments, extended the limit theorem for occupation measures to locally compact state spaces. Hening and Nguyen (2017/2018) proved persistence with exponential convergence and the extinction classification for general Kolmogorov systems.

Setting

Let n≥1n \ge 1n≥1, R+n=[0,∞)n\mathbb R^n_+ = [0,\infty)^nR+n​=[0,∞)n, R+n,∘=(0,∞)n\mathbb R^{n,\circ}_+ = (0,\infty)^nR+n,∘​=(0,∞)n, ∂R+n=R+n∖R+n,∘\partial\mathbb R^n_+ = \mathbb R^n_+\setminus\mathbb R^{n,\circ}_+∂R+n​=R+n​∖R+n,∘​, and ∥x∥=∑i∣xi∣\|x\| = \sum_i |x_i|∥x∥=∑i​∣xi​∣. The abundances X=(X1,…,Xn)X = (X_1,\dots,X_n)X=(X1​,…,Xn​) solve

dXi(t)=Xi(t)fi(X(t)) dt+Xi(t)gi(X(t)) dEi(t),i=1,…,n,dX_i(t) = X_i(t)f_i(X(t))\,dt + X_i(t)g_i(X(t))\,dE_i(t),\qquad i = 1,\dots,n,dXi​(t)=Xi​(t)fi​(X(t))dt+Xi​(t)gi​(X(t))dEi​(t),i=1,…,n,

where E=Γ⊤BE = \Gamma^\top BE=Γ⊤B for an nnn-dimensional standard Brownian motion BBB, and Σ=Γ⊤Γ=(σij)\Sigma = \Gamma^\top\Gamma = (\sigma_{ij})Σ=Γ⊤Γ=(σij​). Assumption 1.1 asks that (gigjσij)(g_ig_j\sigma_{ij})(gi​gj​σij​) be positive definite, that fi,gif_i, g_ifi​,gi​ be locally Lipschitz, and that a dissipativity condition (1.2) with a vector c>0c > 0c>0 and a constant γb>0\gamma_b > 0γb​>0 hold at infinity.

An invariant probability measure μ\muμ is one with ∫P(t,x,A) μ(dx)=μ(A)\int P(t,x,A)\,\mu(dx) = \mu(A)∫P(t,x,A)μ(dx)=μ(A) for all ttt; it is ergodic if it is an extreme point of the invariant probability measures. M\mathcal MM is the set of ergodic invariant probability measures supported on ∂R+n\partial\mathbb R^n_+∂R+n​; it contains the Dirac mass δ∗\delta^*δ∗ at the origin. For μ∈M\mu \in \mathcal Mμ∈M, IμI_\muIμ​ is the set of species charged by μ\muμ and IμcI^c_\muIμc​ its complement. The Lyapunov exponent of species iii at μ\muμ is

λi(μ)=∫(fi(x)−σiigi2(x)2)μ(dx).\lambda_i(\mu) = \int\Big(f_i(x) - \frac{\sigma_{ii}g_i^2(x)}{2}\Big)\mu(dx).λi​(μ)=∫(fi​(x)−2σii​gi2​(x)​)μ(dx).

A measure μ∈M\mu \in \mathcal Mμ∈M is a sink (Assumption 1.3) if λi(μ)<0\lambda_i(\mu) < 0λi​(μ)<0 for all i∈Iμci \in I^c_\mui∈Iμc​ and, if Iμ≠∅I_\mu \ne \emptysetIμ​=∅, every finite convex combination ν\nuν of ergodic measures living on the relative boundary of the face of μ\muμ has max⁡i∈Iμλi(ν)>0\max_{i\in I_\mu}\lambda_i(\nu) > 0maxi∈Iμ​​λi​(ν)>0. M1\mathcal M^1M1 is the set of sinks and M2=M∖M1\mathcal M^2 = \mathcal M\setminus\mathcal M^1M2=M∖M1.

The occupation measures of a path are Π~t=1t∫0t1{X(s)∈⋅}ds\widetilde\Pi_t = \frac1t\int_0^t\mathbf 1_{\{X(s)\in\cdot\}}dsΠt​=t1​∫0t​1{X(s)∈⋅}​ds, and U(ω)\mathcal U(\omega)U(ω) is the set of weak limits of Π~t(ω)\widetilde\Pi_t(\omega)Πt​(ω) as t→∞t\to\inftyt→∞. Assumption 1.4 asks that ∥x∥δ1∑igi2/(1+∑i(∣fi∣+gi2))→0\|x\|^{\delta_1}\sum_ig_i^2/(1+\sum_i(|f_i|+g_i^2)) \to 0∥x∥δ1​∑i​gi2​/(1+∑i​(∣fi​∣+gi2​))→0 for some δ1>0\delta_1 > 0δ1​>0; Assumption 1.5 asks that M2=∅\mathcal M^2 = \emptysetM2=∅ or that every finite convex combination ν\nuν of measures of M2\mathcal M^2M2 has max⁡iλi(ν)>0\max_i\lambda_i(\nu) > 0maxi​λi​(ν)>0.

Formalization targets

Goal: Theorem 1.3

Under Assumptions 1.1, 1.4, 1.5 and M1≠∅\mathcal M^1 \ne \emptysetM1=∅, for every x∈R+n,∘x \in \mathbb R^{n,\circ}_+x∈R+n,∘​,

∑μ∈M1Pxμ=1,Pxμ:=Px{U(ω)={μ}, lim⁡t→∞ln⁡Xi(t)t=λi(μ)<0, i∈Iμc}>0.\sum_{\mu\in\mathcal M^1} P^\mu_x = 1,\qquad P^\mu_x := \mathbb P_x\Big\{\mathcal U(\omega) = \{\mu\},\ \lim_{t\to\infty}\frac{\ln X_i(t)}{t} = \lambda_i(\mu) < 0,\ i\in I^c_\mu\Big\} > 0 .μ∈M1∑​Pxμ​=1,Pxμ​:=Px​{U(ω)={μ}, t→∞lim​tlnXi​(t)​=λi​(μ)<0, i∈Iμc​}>0.

Milestones

In the order the proof uses them: δ∗∈M\delta^* \in \mathcal Mδ∗∈M; Lemma 3.3 (moment bound and a zero-mean identity for invariant measures); Lemma 5.1 (λi(μ)=0\lambda_i(\mu) = 0λi​(μ)=0 for i∈Iμi \in I_\mui∈Iμ​); Lemma 5.4 (bounded weighted time averages and a strong law for the stochastic integral); Lemma 5.5 (time spent in a compact set, and staying away from a face on a finite horizon); Lemma 5.6 (convergence of unbounded time averages); Lemma 5.7 (U(ω)\mathcal U(\omega)U(ω) consists of invariant measures); Lemma 5.8 (a limit set inside the face of a sink is that sink); Lemma 5.9 (sinks attract nearby paths with high probability); and the case M2=∅\mathcal M^2 = \emptysetM2=∅, where M=M1={δ∗}\mathcal M = \mathcal M^1 = \{\delta^*\}M=M1={δ∗}.

Significance

Theorem 1.3 turns local information — the signs of finitely many Lyapunov exponents at boundary ergodic measures — into a global almost-sure description of long-run behaviour: the community converges, in the sense of empirical distributions, to one of the sinks, each sink is reached with positive probability, and the species absent from it decay exponentially at explicit rates. In the applications of §2 of the paper (competitive Lotka–Volterra systems, food chains) this answers which species survive.

The theorem is proved on paper. No part of it is machine-checked. A formalization would produce a checked interface between Itô calculus for multidimensional SDEs, ergodic theory of Feller processes (occupation measures, invariant and ergodic measures), and the stochastic-persistence framework; the statements of Lemmas 5.4–5.7 are reusable for other SDE models with a Lyapunov-type condition.

Difficulty

The obvious argument — the occupation measures converge to some invariant measure, and the Lyapunov exponents at that measure decide the fate of each species — breaks down at two points. First, the state space is not compact and the functions fi−σiigi2/2f_i - \sigma_{ii}g_i^2/2fi​−σii​gi2​/2 are unbounded, so weak convergence of occupation measures does not by itself give convergence of time averages of these functions; a pathwise uniform-integrability bound (Lemmas 5.4 and 5.6) is required, and this is where Assumption 1.4 enters. Second, a limit point of occupation measures is in general a mixture of ergodic measures, possibly including repellers; ruling out every mixture that is not a single sink requires the separate arguments of Lemma 5.8 and of the M2\mathcal M^2M2 step, together with a supermartingale built from a Lyapunov function near each sink (Lemma 5.9).

Formalization scope

The state space is EuclideanSpace ℝ (Fin n) with the published Ethier–Kurtz strong-SDE layer; coordinates are indexed by Fin n, the norm in every assumption is the ℓ1\ell^1ℓ1 norm, and n≥1n \ge 1n≥1 is a hypothesis. The process is a family of strong solutions XxX^xXx, one from each x∈R+nx \in \mathbb R^n_+x∈R+n​, driven by one Brownian motion on one probability space. The coefficients are read at the positive part of the state, which is the state itself on R+n\mathbb R^n_+R+n​. Ergodic means extremal among invariant probability measures, IμI_\muIμ​ is the set of coordinates that are not μ\muμ-a.s. zero, Conv\mathrm{Conv}Conv means finite convex combinations with positive weights (as on p. 5), and weak limits are taken along sequences, tested against bounded continuous functions. The sum in (1.11) is an unconditional sum in [0,∞][0,\infty][0,∞] over the set M1\mathcal M^1M1, with no finiteness assumption.

Deviations from the page, each disclosed in the item: (5.19) is stated for every finite horizon rather than for neTen_eT_ene​Te​, which is undefined under the lemma's hypotheses; Lemma 5.9 takes k≥1k \ge 1k≥1; the convention δ1≤δ0\delta_1 \le \delta_0δ1​≤δ0​ of Assumption 1.4 is an explicit hypothesis where the exponent δ1\delta_1δ1​ appears; integrability claims that the paper treats as automatic are part of the conclusions; and (5.17) is stated for every continuous version of the Itô integral. Lemma 5.6 is stated for each path that stays in the orthant, which the solution does almost surely. The proof's claim "U(ω)⊂Conv(M)∖Conv(M2)\mathcal U(\omega) \subset \mathrm{Conv}(\mathcal M)\setminus\mathrm{Conv}(\mathcal M^2)U(ω)⊂Conv(M)∖Conv(M2)" is left as source context: its inclusion in the finite convex hull of M\mathcal MM requires an additional argument when M\mathcal MM is infinite.

The statements cannot be made vacuous by the definitions: δ∗∈M\delta^* \in \mathcal Mδ∗∈M is itself a milestone, so M\mathcal MM is never empty, and the occupation measures are probability measures for t>0t > 0t>0; every constant (K^\widehat KK, K^1\widehat K_1K1​, β\betaβ, Δ\DeltaΔ) is chosen before the points it must be uniform in.

A complete development needs Itô's formula for ln⁡Xi\ln X_ilnXi​, the strong law for continuous martingales with bounded quadratic-variation growth, Prokhorov's theorem and lower semicontinuity of integrals under weak convergence, and the Feller and strong Markov properties of (1.1). Contributions of any of these, and of the milestones in any order, are welcome.

Selected references

  • A. Hening, D. H. Nguyen, Coexistence and extinction for stochastic Kolmogorov systems, Ann. Appl. Probab. 28(3), 2018, DOI 10.1214/17-AAP1347; arXiv:1704.06984v1. https://arxiv.org/abs/1704.06984
  • S. J. Schreiber, M. Benaïm, K. A. S. Atchadé, Persistence in fluctuating environments, J. Math. Biol. 62(5), 655–683, 2011. MR 2786721, https://mathscinet.ams.org/mathscinet-getitem?mr=2786721
  • S. N. Evans, A. Hening, S. J. Schreiber, Protected polymorphisms and evolutionary stability of patch-selection strategies in stochastic environments, J. Math. Biol. 71(2), 325–359, 2015. MR 3367678, https://mathscinet.ams.org/mathscinet-getitem?mr=3367678
  • R. Khasminskii, Stochastic Stability of Differential Equations, 2nd ed., Springer, 2012. MR 2894052, https://mathscinet.ams.org/mathscinet-getitem?mr=2894052
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AnalysisProbabilityStochastic Systems·Captain: mikedeng1

Averaging Dynamics Driven by Fractional Brownian Motion: Young Solutions of dxⁿ = fₙ(t, xⁿ)dB Converge in Probability in C^α to the Solution of dx = f̄(x)dB When |fₙ − f̄|_{−κ,γ} → 0Research Paper

Why oscillating coefficients matter

An equation driven by fractional Brownian motion can respond to a rapidly varying coefficient even when that coefficient has no useful pointwise limit. Hairer and Li study this issue for fractional Brownian motion with Hurst index above one half. Their Section 3 isolates a deterministic averaging statement: if the time antiderivatives of the coefficients approach those of a limiting coefficient in a suitable negative Hölder norm, the solutions approach the limiting solution in probability. This statement supplies a common target for averaging models whose fast variables have very different origins. The authors use it later with mixing fast processes, and also present a quasi-periodic example. The result and its role in the paper are stated in Hairer and Li, Section 3.

The problem sits at the boundary of two familiar views of differential equations. A pathwise Young equation is defined from individual sufficiently regular paths, whereas the available estimates for the oscillating coefficient and the fractional Brownian driver involve probability and conditional expectation. The mission records the precise point where these descriptions meet. It treats the published article, rather than a preprint, and targets its Theorem 3.13.

Driver, coefficients, and solutions

An mmm-dimensional fractional Brownian motion B=(B1,…,Bm)B=(B^1,\ldots,B^m)B=(B1,…,Bm) with index HHH is a centered Gaussian process with independent coordinates and covariance

E[BsiBtj]=1i=j∣s∣2H+∣t∣2H−∣t−s∣2H2.\mathbb E[B_s^iB_t^j]=\mathbf1_{i=j}\frac{|s|^{2H}+|t|^{2H}-|t-s|^{2H}}2.E[Bsi​Btj​]=1i=j​2∣s∣2H+∣t∣2H−∣t−s∣2H​.

The paper works with a two-sided process and 1/2<H<11/2<H<11/2<H<1. It uses a version whose sample paths are locally β\betaβ-Hölder for every β<H\beta<Hβ<H. The mission names that version condition separately, because the covariance law alone does not select particular sample paths. The relevant past sigma algebra at time sss is generated by increments Bu−BvB_u-B_vBu​−Bv​ with u,v≤su,v\le su,v≤s. These conventions come from pp. 1826 and 1831–1832 of the article.

For a path xxx on [0,T][0,T][0,T], write ∣x∣α|x|_\alpha∣x∣α​ for its homogeneous Hölder seminorm and ∣x∣∞|x|_\infty∣x∣∞​ for its supremum norm. The norm on Cα[0,T]C^\alpha[0,T]Cα[0,T] is ∣x∣∞+∣x∣α|x|_\infty+|x|_\alpha∣x∣∞​+∣x∣α​. A Young integral ∫stfr dbr\int_s^t f_r\,db_r∫st​fr​dbr​ is the limit of left-point sums over partitions of [s,t][s,t][s,t] as the largest cell shrinks to zero. A Young solution from x0x_0x0​ is an α\alphaα-Hölder path satisfying its integral equation at every time, with value x0x_0x0​ at time zero. Here BC2BC^2BC2 means bounded, twice continuously differentiable maps with bounded derivatives through order two; Cζ,2C^{\zeta,2}Cζ,2 means ζ\zetaζ-Hölder in time with values in BC2BC^2BC2. These are the paper's conventions on pp. 1829 and 1835–1838.

The negative Hölder bound ∣f∣−κ,γ≤K|f|_{-\kappa,\gamma}\le K∣f∣−κ,γ​≤K means that the time antiderivative of fff has increments bounded by K∣t−s∣1−κK|t-s|^{1-\kappa}K∣t−s∣1−κ, and that the difference of those increments at spatial points x,yx,yx,y is bounded by K∣t−s∣1−κ∣x−y∣γK|t-s|^{1-\kappa}|x-y|^\gammaK∣t−s∣1−κ∣x−y∣γ. Both bounds apply on the time window in question when ∣t−s∣≤1|t-s|\le1∣t−s∣≤1. This is equation (3.12) in the article. It measures cancellation over time; it does not require f(t,x)f(t,x)f(t,x) itself to be small.

Formalization targets

The goal is the dBdBdB part of Theorem 3.13. Let fnf_nfn​ be Cζ,2C^{\zeta,2}Cζ,2, let the limiting fˉ\bar ffˉ​ be autonomous and in BC2BC^2BC2, and suppose ∣fn−fˉ∣−κ,γ→0|f_n-\bar f|_{-\kappa,\gamma}\to0∣fn​−fˉ​∣−κ,γ​→0. The exponents satisfy 1/2<α<H−κ1/2<\alpha<H-\kappa1/2<α<H−κ, H−κ>1/2H-\kappa>1/2H−κ>1/2, H−κ+γα>1H-\kappa+\gamma\alpha>1H−κ+γα>1, and α<ζ≤1\alpha<\zeta\le1α<ζ≤1. For Young solutions with the same starting point,

dxtn=fn(t,xtn) dBt,dxt=fˉ(xt) dBt,dx_t^n=f_n(t,x_t^n)\,dB_t,\qquad dx_t=\bar f(x_t)\,dB_t,dxtn​=fn​(t,xtn​)dBt​,dxt​=fˉ​(xt​)dBt​,

the target is

∀ε>0,P(∥xn−x∥Cα[0,T]>ε)⟶0.\forall\varepsilon>0,\qquad P\bigl(\|x^n-x\|_{C^\alpha[0,T]}>\varepsilon\bigr)\longrightarrow0.∀ε>0,P(∥xn−x∥Cα[0,T]​>ε)⟶0.

The milestone list follows the statements used around this target: the Hölder composition estimate of Lemma 2.1, the RKHS estimate of Lemma 3.3, the fractional Brownian integral estimate of Lemma 3.4, the stochastic sewing lemma of Lemma 3.5, the Young case of (3.18) from Lemmas 3.10 and 3.12, the Kolmogorov inclusions (3.24), and the deterministic stability estimate of Lemma 2.2. Each is a separate formal theorem. The authors' proofs follow their statements in Sections 2 and 3 and the Appendix; Lemma 3.5 is quoted from Lê, Theorem 2.1 without proof in the article.

What the result provides

The target gives convergence of entire solution paths in a Hölder topology, in probability. In the paper, this lets a model-specific estimate of the negative Hölder distance be used to conclude convergence of the corresponding dynamical system. Theorem A is obtained by adding such an estimate for a stationary mixing fast process; the deterministic statement here isolates the part independent of that model. These uses are described in Sections 1 and 3.

The published paper proves the result. This mission asks for machine-checked proofs of the stated Lean theorems. Its reusable work would include a pathwise Young integral over arbitrary partitions, Hölder norms that retain the value +∞+\infty+∞, a two-sided vector fractional Brownian motion interface, and stochastic estimates for time-dependent integrands. The goal and milestones in this proposal are statements with proof placeholders; the local build checks their types, not their proofs.

Main obstruction

Pointwise control of fn−fˉf_n-\bar ffn​−fˉ​ is unavailable: a rapidly oscillating function can be large at most times and still have small time antiderivatives. Applying a pathwise Young stability estimate directly to fn−fˉf_n-\bar ffn​−fˉ​ therefore leaves a coefficient norm that need not tend to zero. The integral estimate must connect cancellation measured by ∣⋅∣−κ,γ|\cdot|_{-\kappa,\gamma}∣⋅∣−κ,γ​ with LpL^pLp bounds on the driven equation. The dependence on the past of the fractional Brownian motion makes this step more delicate than an estimate for a deterministic integrand. The required estimates and their domains appear in Lemmas 3.4–3.12.

Formalization scope

Time is R\mathbb RR for the two-sided driver; coefficient and solution assertions use [0,T][0,T][0,T] with T>0T>0T>0. State spaces are finite-dimensional real Euclidean spaces. The goal takes H∈(1/2,1)H\in(1/2,1)H∈(1/2,1) from the standing setting of Section 3.1, and it assumes the locally Hölder version of BBB described on p. 1826. The solutions are required almost surely to satisfy the Young equation from the same x0x_0x0​. This requirement fixes the processes to the equations; the convergence claim cannot be obtained by choosing arbitrary paths. Probability of the norm event uses the measure of that set, and the conclusion is convergence in probability in the full CαC^\alphaCα norm.

The Lean definitions use [0,∞][0,\infty][0,∞] for path and process seminorms, so unbounded suprema are not replaced by a default real value. The negative Hölder hypothesis uses real bounds Kn→0K_n\to0Kn​→0 instead of an infimum that could be meaningless on an empty class. The Young integral quantifies over every monotone partition and the mesh limit of its left-point sums. The fBm definition includes Gaussianity, means, and every cross-coordinate covariance; its path version is explicit. Interval integrals in the coefficient bound are applied to time-continuous coefficients in the goal. Lemmas 3.4 and 3.10 use the increment filtration, the case needed for this theorem.

The paper's Lemma 2.2 needs a common initial value for its homogeneous-seminorm estimate; the formal statement includes it and records the printed issue. The paper writes the limiting coefficient with a time argument but uses an autonomous coefficient in (3.23), which the goal adopts. This proposal covers the dBdBdB equation. The final dtdtdt extension in Theorem 3.13 and the conditional-expectation estimate (3.19) remain outside its current items. Contributions that close these gaps, or supply a general conditional Wiener integral matching the article's notation, would extend the development.

Selected references

  • M. Hairer and X.-M. Li, Averaging dynamics driven by fractional Brownian motion, Annals of Probability 48(4), 2020, 1826–1860. DOI.
  • K. Lê, A stochastic sewing lemma and applications, 2018, arXiv:1810.10500, cited as [23] in Hairer and Li. arXiv.
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Control TheoryPartial Differential Equations·Captain: mikedeng1

Local Exponential H² Stabilization of a 2 × 2 Quasilinear Hyperbolic System Using Backstepping 3: The Feedback with Dynamic Extension Locally Exponentially Stabilizes the Quasilinear System in H²Research Paper

Motivation

First-order hyperbolic systems of balance laws zt+Λ(z,x)zx+f(z,x)=0z_t+\Lambda(z,x)z_x+f(z,x)=0zt​+Λ(z,x)zx​+f(z,x)=0 model open-channel flow (the Saint-Venant equations), gas pipelines, traffic and heat exchangers. In most of these applications the state can be acted on only at one end of the domain, so stabilizing an equilibrium is a boundary control problem. For 2×22\times22×2 systems without source term (f=0f=0f=0), static boundary feedback built from a Lyapunov function works (Coron–Bastin–d'Andréa-Novel 2008); with a source term, Lyapunov functions of the diagonal form ∫01zTQ(x)z dx\int_0^1z^{\mathsf T}Q(x)z\,dx∫01​zTQ(x)zdx can fail (Bastin and Coron 2011, recalled in Remark 1 of the paper). Backstepping, introduced for parabolic equations by Krstic and Smyshlyaev, replaces the search for a Lyapunov function by an invertible integral change of variables that maps the linearized plant to a target system whose stability is evident.

Coron, Vazquez, Krstic and Bastin (arXiv:1208.6475v1, 2012; SIAM J. Control Optim. 51(3), 2013) design such a feedback for the linearization of a 2×22\times22×2 quasilinear system and prove that it stabilizes the nonlinear system locally in H2H^2H2. This mission formalizes that main result, Theorem 4.1.

Setting

The state is z(x,t)=(z1,z2)∈R2z(x,t)=(z_1,z_2)\in\mathbb R^2z(x,t)=(z1​,z2​)∈R2 on x∈[0,1]x\in[0,1]x∈[0,1], t≥0t\ge0t≥0, governed by

zt+Λ(z,x)zx+f(z,x)=0,z1(0,t)=G0(z2(0,t)),z2(1,t)=U(t).z_t+\Lambda(z,x)z_x+f(z,x)=0,\qquad z_1(0,t)=G_0(z_2(0,t)),\qquad z_2(1,t)=U(t).zt​+Λ(z,x)zx​+f(z,x)=0,z1​(0,t)=G0​(z2​(0,t)),z2​(1,t)=U(t).

The standing assumptions (§2) are: Λ\LambdaΛ, fff, G0G_0G0​ are C2C^2C2; Λ(0,x)=diag⁡(Λ1(x),Λ2(x))\Lambda(0,x)=\operatorname{diag}(\Lambda_1(x),\Lambda_2(x))Λ(0,x)=diag(Λ1​(x),Λ2​(x)) with Λ1>0>Λ2\Lambda_1>0>\Lambda_2Λ1​>0>Λ2​ on [0,1][0,1][0,1]; f(0,x)=0f(0,x)=0f(0,x)=0; G0(0)=0G_0(0)=0G0​(0)=0. Write fij=∂fi/∂zj(0,x)f_{ij}=\partial f_i/\partial z_j(0,x)fij​=∂fi​/∂zj​(0,x) and q=G0′(0)q=G_0'(0)q=G0′​(0); the paper treats q≠0q\ne0q=0.

The scaling w=Φz=(φ1z1,φ2z2)w=\Phi z=(\varphi_1z_1,\varphi_2z_2)w=Φz=(φ1​z1​,φ2​z2​), φ1=exp⁡∫0xf11/Λ1\varphi_1=\exp\int_0^xf_{11}/\Lambda_1φ1​=exp∫0x​f11​/Λ1​, φ2=exp⁡∫0xf22/Λ2\varphi_2=\exp\int_0^xf_{22}/\Lambda_2φ2​=exp∫0x​f22​/Λ2​, removes the diagonal of the linearization, which becomes wt=Σwx+Cww_t=\Sigma w_x+Cwwt​=Σwx​+Cw with Σ=diag⁡(−ϵ1,ϵ2)\Sigma=\operatorname{diag}(-\epsilon_1,\epsilon_2)Σ=diag(−ϵ1​,ϵ2​), ϵ1=Λ1\epsilon_1=\Lambda_1ϵ1​=Λ1​, ϵ2=−Λ2\epsilon_2=-\Lambda_2ϵ2​=−Λ2​, and CCC antidiagonal with entries c1=−f12φ1/φ2c_1=-f_{12}\varphi_1/\varphi_2c1​=−f12​φ1​/φ2​, c2=−f21φ2/φ1c_2=-f_{21}\varphi_2/\varphi_1c2​=−f21​φ2​/φ1​. The kernels Kvu,KvvK^{vu},K^{vv}Kvu,Kvv solve a pair of first-order hyperbolic equations on the triangle T={0≤ξ≤x≤1}\mathcal T=\{0\le\xi\le x\le1\}T={0≤ξ≤x≤1} with boundary data on its diagonal and on the edge ξ=0\xi=0ξ=0 ((3.32), (3.33), (3.36), (3.37)). The gain is k(x)=(φ1(x)Kvu(1,x),φ2(x)Kvv(1,x))/φ2(1)k(x)=(\varphi_1(x)K^{vu}(1,x),\varphi_2(x)K^{vv}(1,x))/\varphi_2(1)k(x)=(φ1​(x)Kvu(1,x),φ2​(x)Kvv(1,x))/φ2​(1).

The closed loop adds a dynamic extension:

z2(1,t)=∫01k(ξ)Tz(ξ,t) dξ+a(t)+b(t),a˙=−d1a,b˙=−d2b,z_2(1,t)=\int_0^1k(\xi)^{\mathsf T}z(\xi,t)\,d\xi+a(t)+b(t),\qquad \dot a=-d_1a,\quad \dot b=-d_2b,z2​(1,t)=∫01​k(ξ)Tz(ξ,t)dξ+a(t)+b(t),a˙=−d1​a,b˙=−d2​b,

with d1,d2>0d_1,d_2>0d1​,d2​>0, d1≠d2d_1\ne d_2d1​=d2​, and a(0)a(0)a(0), b(0)b(0)b(0) determined by the initial profile z0z_0z0​ through two linear functionals P1(z0)P_1(z_0)P1​(z0​), P2(z0)P_2(z_0)P2​(z0​). The norm is ∥z∥H2=∥z∥L2+∥zx∥L2+∥zxx∥L2\|z\|_{H^2}=\|z\|_{L^2}+\|z_x\|_{L^2}+\|z_{xx}\|_{L^2}∥z∥H2​=∥z∥L2​+∥zx​∥L2​+∥zxx​∥L2​ on [0,1][0,1][0,1].

Formalization targets

Goal: Theorem 4.1

For every 0<λ<2min⁡(d1,d2)0<\lambda<2\min(d_1,d_2)0<λ<2min(d1​,d2​) there are δ,c>0\delta,c>0δ,c>0 such that every classical solution with ∥z0∥H2≤δ\|z_0\|_{H^2}\le\delta∥z0​∥H2​≤δ satisfying the compatibility conditions (4.16), (4.18) obeys

∥z(⋅,t)∥H22+a2(t)+b2(t)≤c e−λt(∥z0∥H22+a2(0)+b2(0))(t≥0).\|z(\cdot,t)\|_{H^2}^2+a^2(t)+b^2(t)\le c\,e^{-\lambda t}\big(\|z_0\|_{H^2}^2+a^2(0)+b^2(0)\big)\qquad(t\ge0).∥z(⋅,t)∥H22​+a2(t)+b2(t)≤ce−λt(∥z0​∥H22​+a2(0)+b2(0))(t≥0).

Milestones, in the order the proof uses them

Write γ=(α,β)=K[Φz]=Φz−∫0xK(x,ξ)Φz(ξ)dξ\gamma=(\alpha,\beta)=\mathcal K[\Phi z]=\Phi z-\int_0^xK(x,\xi)\Phi z(\xi)d\xiγ=(α,β)=K[Φz]=Φz−∫0x​K(x,ξ)Φz(ξ)dξ for the full 2×22\times22×2 kernel KKK, η=γt\eta=\gamma_tη=γt​, θ=γtt\theta=\gamma_{tt}θ=γtt​, and ∥γ∥∞=sup⁡x(∣α∣+∣β∣)\|\gamma\|_\infty=\sup_x(|\alpha|+|\beta|)∥γ∥∞​=supx​(∣α∣+∣β∣).

  1. H2H^2H2 equivalence (p. 12): ∥γ∥H2\|\gamma\|_{H^2}∥γ∥H2​ and ∥z∥H2\|z\|_{H^2}∥z∥H2​ are equivalent.
  2. Proposition 5.1: for V1=∫01γTDγV_1=\int_0^1\gamma^{\mathsf T}D\gammaV1​=∫01​γTDγ and small ∥γ∥∞\|\gamma\|_\infty∥γ∥∞​, V˙1≤−λ1V1−λ2(α2(1,t)+β2(0,t))+C1V13/2+C2∥γx∥∞V1+C3(a2+b2)\dot V_1\le-\lambda_1V_1-\lambda_2(\alpha^2(1,t)+\beta^2(0,t))+C_1V_1^{3/2}+C_2\|\gamma_x\|_\infty V_1+C_3(a^2+b^2)V˙1​≤−λ1​V1​−λ2​(α2(1,t)+β2(0,t))+C1​V13/2​+C2​∥γx​∥∞​V1​+C3​(a2+b2).
  3. Lemma B.6: the sup and L2L^2L2 norms of η\etaη and γx\gamma_xγx​ are comparable.
  4. Proposition 5.3: for V2=∫01ηTR[γ]ηV_2=\int_0^1\eta^{\mathsf T}R[\gamma]\etaV2​=∫01​ηTR[γ]η, V˙2≤−λ3V2−λ4(η12(1,t)+η22(0,t))+K1V2∥η∥∞+K2(a2+b2)\dot V_2\le-\lambda_3V_2-\lambda_4(\eta_1^2(1,t)+\eta_2^2(0,t))+K_1V_2\|\eta\|_\infty+K_2(a^2+b^2)V˙2​≤−λ3​V2​−λ4​(η12​(1,t)+η22​(0,t))+K1​V2​∥η∥∞​+K2​(a2+b2).
  5. Proposition 5.4: for V3=∫01θTR[γ]θV_3=\int_0^1\theta^{\mathsf T}R[\gamma]\thetaV3​=∫01​θTR[γ]θ and small ∥γ∥∞+∥η∥∞\|\gamma\|_\infty+\|\eta\|_\infty∥γ∥∞​+∥η∥∞​, V˙3≤−λ5V3−λ6(θ12(1,t)+θ22(0,t))+K1V3V21/2+K2V2V31/2+K3V33/2+K4∥η∥∞η22(0,t)+K5(a2+b2)\dot V_3\le-\lambda_5V_3-\lambda_6(\theta_1^2(1,t)+\theta_2^2(0,t))+K_1V_3V_2^{1/2}+K_2V_2V_3^{1/2}+K_3V_3^{3/2}+K_4\|\eta\|_\infty\eta_2^2(0,t)+K_5(a^2+b^2)V˙3​≤−λ5​V3​−λ6​(θ12​(1,t)+θ22​(0,t))+K1​V3​V21/2​+K2​V2​V31/2​+K3​V33/2​+K4​∥η∥∞​η22​(0,t)+K5​(a2+b2).
  6. Proposition B.5: the norms of θ\thetaθ and γxx\gamma_{xx}γxx​ are comparable.

Significance

Theorem 4.1 shows that a feedback computed entirely from the linearization, by solving linear kernel equations, is a valid local stabilizer of the quasilinear system in H2H^2H2, the natural space for classical C1C^1C1 solutions of quasilinear hyperbolic systems. The dynamic extension (a,b)(a,b)(a,b) is what makes the result usable: without it, the static feedback imposes artificial compatibility conditions (4.17), (4.19) on the initial data.

The result is proved in the paper (§5 and Appendix B); none of it is machine-checked. A formal proof needs a rigorous treatment of the Volterra transformation and its inverse in H2H^2H2, the three-level Lyapunov argument (V1V_1V1​, V2V_2V2​, V3V_3V3​), and the comparison of these functionals with the H2H^2H2 norm. The formalization also records corrections to the printed formulas: the sign of φ2\varphi_2φ2​ in (4.1), the entries of CCC in (4.7), the initial values (4.26), and the admissible decay rates (see below).

Difficulty

The obvious argument — the linear closed loop is exponentially stable, so the nonlinear one is locally stable — fails for hyperbolic systems: the nonlinear terms ΛNL(w,x)wx\Lambda_{NL}(w,x)w_xΛNL​(w,x)wx​ contain a derivative of the state, so they are not small in L2L^2L2 relative to ∥w∥L2\|w\|_{L^2}∥w∥L2​, and an L2L^2L2 estimate does not close. One must control γ\gammaγ, γt\gamma_tγt​ and γtt\gamma_{tt}γtt​ simultaneously, each with its own Lyapunov functional whose weight R[γ]R[\gamma]R[γ] depends on the state, and exchange time derivatives for space derivatives through the equation, which is possible only while ∥γ∥∞\|\gamma\|_\infty∥γ∥∞​ and ∥γt∥∞\|\gamma_t\|_\infty∥γt​∥∞​ stay small.

Formalization scope

States are Fin 2 → ℝ, matrices Matrix (Fin 2) (Fin 2) ℝ, indices 0-based. A solution is one C2C^2C2 function z:R×R→R2z:\mathbb R\times\mathbb R\to\mathbb R^2z:R×R→R2 (space first) with the PDE imposed on [0,1]×[0,∞)[0,1]\times[0,\infty)[0,1]×[0,∞) and the boundary conditions for t≥0t\ge0t≥0; this is the theorem restricted to classical solutions, since Mathlib has no Sobolev spaces. Global existence is not asserted. Coefficient functions are defined on all of R\mathbb RR with global C2C^2C2 regularity and sign conditions on [0,1][0,1][0,1]. H1H^1H1, H2H^2H2 norms are sums of L2L^2L2 norms, as on p. 11; ∥⋅∥∞\|\cdot\|_\infty∥⋅∥∞​ uses ∣α∣+∣β∣|\alpha|+|\beta|∣α∣+∣β∣.

Added or corrected relative to the page, all disclosed in the items:

  • q≠0q\neq0q=0 (the case the paper proves; q=0q=0q=0 is a remark);
  • φ2=exp⁡(+∫f22/Λ2)\varphi_2=\exp(+\int f_{22}/\Lambda_2)φ2​=exp(+∫f22​/Λ2​), c1=−f12φ1/φ2c_1=-f_{12}\varphi_1/\varphi_2c1​=−f12​φ1​/φ2​, c2=−f21φ2/φ1c_2=-f_{21}\varphi_2/\varphi_1c2​=−f21​φ2​/φ1​, a(0)=(P2−d2P1)/(d1−d2)a(0)=(P_2-d_2P_1)/(d_1-d_2)a(0)=(P2​−d2​P1​)/(d1​−d2​), b(0)=(d1P1−P2)/(d1−d2)b(0)=(d_1P_1-P_2)/(d_1-d_2)b(0)=(d1​P1​−P2​)/(d1​−d2​);
  • 0<λ<2min⁡(d1,d2)0<\lambda<2\min(d_1,d_2)0<λ<2min(d1​,d2​) instead of "every λ>0\lambda>0λ>0", which fails for fixed d1,d2d_1,d_2d1​,d2​ because a(t)=a(0)e−d1ta(t)=a(0)e^{-d_1t}a(t)=a(0)e−d1​t;
  • milestones take a full C2C^2C2 kernel (the page's claim via Theorem A.2, guaranteed for C3C^3C3 data); the goal takes only C1C^1C1 kernels Kvu,KvvK^{vu},K^{vv}Kvu,Kvv;
  • Proposition 5.3's last term is K2(a2+b2)K_2(a^2+b^2)K2​(a2+b2) (printed K2b2K_2b^2K2​b2).
  • Proposition 5.4 is stated for closed-loop solutions of class C3C^3C3, since V˙3\dot V_3V˙3​ involves γttt\gamma_{ttt}γttt​.

Trivializing formalizations are ruled out: δ\deltaδ, ccc and every milestone constant are quantified after the data and before the solution; the kernels are pinned down by all their equations and boundary conditions (unique by Theorem A.1 of the paper); the corrected a(0)a(0)a(0), b(0)b(0)b(0) are the ones every classical solution has, so no small initial profile is excluded; and the H2H^2H2 norm contains ∥zxx∥L2\|z_{xx}\|_{L^2}∥zxx​∥L2​.

Not formalized: Lemma 5.2 (its printed bound (5.38), ∥γ∥∞(1+∥γx∥∞)\|\gamma\|_\infty(1+\|\gamma_x\|_\infty)∥γ∥∞​(1+∥γx​∥∞​), fails for profiles with small ∥γ∥∞\|\gamma\|_\infty∥γ∥∞​ and large ∥γx∥∞\|\gamma_x\|_\infty∥γx​∥∞​, and (5.37) compares a matrix with a scalar), and Lemmas B.1–B.4, B.7, B.8. Contributions of a reusable theory of Volterra integral transformations on [0,1][0,1][0,1] with C2C^2C2 kernels, and of the comparison between sup and L2L^2L2 norms of solutions of hyperbolic systems, are welcome.

Selected references

  • J.-M. Coron, R. Vazquez, M. Krstic, G. Bastin, Local Exponential H² Stabilization of a 2 × 2 Quasilinear Hyperbolic System Using Backstepping, arXiv:1208.6475v1, 2012; SIAM J. Control Optim. 51(3), 2013. https://arxiv.org/abs/1208.6475v1
  • G. Bastin, J.-M. Coron, On boundary feedback stabilization of non-uniform linear 2 × 2 hyperbolic systems over a bounded interval, Systems and Control Letters 60, 2011, pp. 900–906 (reference [1] of arXiv:1208.6475v1). https://arxiv.org/abs/1208.6475v1
  • M. Krstic, A. Smyshlyaev, Boundary Control of PDEs: A Course on Backstepping Designs, SIAM, 2008. https://doi.org/10.1137/1.9780898718607
  • J.-M. Coron, G. Bastin, B. d'Andréa-Novel, Dissipative boundary conditions for one-dimensional nonlinear hyperbolic systems, SIAM J. Control Optim. 47(3), 2008. https://doi.org/10.1137/060650222
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Control TheoryPartial Differential Equations·Captain: mikedeng1

Local Exponential H² Stabilization of a 2 × 2 Quasilinear Hyperbolic System Using Backstepping 2: Backstepping Feedback Makes a 2 × 2 Linear Hyperbolic System Vanish in Finite TimeResearch Paper

Motivation

Systems of two first-order hyperbolic equations with opposite transport speeds model open-channel flow (the Saint-Venant equations), gas pipelines, heat exchangers and traffic. In such systems one boundary is actuated, the other boundary reflects, and the coupling terms move energy between the two families of waves. Boundary feedback design for them is a standard problem of distributed-parameter control.

The backstepping method designs a feedback by means of an invertible Volterra integral transformation that turns the plant into a target system whose behaviour is known in closed form. Coron, Vazquez, Krstic and Bastin (arXiv:1208.6475; SIAM J. Control Optim. 51(3), 2013) carry out this design for a general linear 2×22\times22×2 hyperbolic system with spatially varying speeds and couplings, in §3 of their paper. The resulting linear feedback is the controller they later apply to a quasilinear system. This mission formalizes the linear result.

Timeline:

  • 2008: Krstic and Smyshlyaev, Backstepping boundary control for first-order hyperbolic PDEs and application to systems with actuator and sensor delays (Systems & Control Letters 57), give the scalar transport case.
  • 2012/2013: Coron, Vazquez, Krstic and Bastin treat the 2×22\times22×2 case. The kernel equations of the transformation form a Goursat-type system on a triangle, and they prove it is well-posed in their Appendix A.

Setting

Let ϵ1,ϵ2\epsilon_1,\epsilon_2ϵ1​,ϵ2​ be C1C^1C1 functions on [0,1][0,1][0,1] with ϵ1,ϵ2>0\epsilon_1,\epsilon_2>0ϵ1​,ϵ2​>0, let c1,c2c_1,c_2c1​,c2​ be continuous, and let q∈Rq\in\mathbb Rq∈R. The state w(x,t)=[u v]T∈R2w(x,t)=[u\ v]^T\in\mathbb R^2w(x,t)=[u v]T∈R2 solves

wt=Σ(x)wx+C(x)w,Σ=(−ϵ100ϵ2),C=(0c1c20),x∈[0,1], t≥0,w_t=\Sigma(x)w_x+C(x)w,\qquad \Sigma=\begin{pmatrix}-\epsilon_1&0\\0&\epsilon_2\end{pmatrix},\quad C=\begin{pmatrix}0&c_1\\c_2&0\end{pmatrix},\qquad x\in[0,1],\ t\ge0,wt​=Σ(x)wx​+C(x)w,Σ=(−ϵ1​0​0ϵ2​​),C=(0c2​​c1​0​),x∈[0,1], t≥0,

so uuu travels to the right and vvv to the left. The boundary conditions are u(0,t)=q v(0,t)u(0,t)=q\,v(0,t)u(0,t)=qv(0,t) and v(1,t)=U(t)v(1,t)=U(t)v(1,t)=U(t), where UUU is the control. The norm is ∥w(⋅,t)∥L2=∫01(u2+v2) dξ\|w(\cdot,t)\|_{L^2}=\sqrt{\int_0^1(u^2+v^2)\,d\xi}∥w(⋅,t)∥L2​=∫01​(u2+v2)dξ​.

The kernels Kvu,KvvK^{vu},K^{vv}Kvu,Kvv are functions on the triangle T={(x,ξ):0≤ξ≤x≤1}\mathcal T=\{(x,\xi):0\le\xi\le x\le1\}T={(x,ξ):0≤ξ≤x≤1} that solve

ϵ2(x)Kxvu−ϵ1(ξ)Kξvu=ϵ1′(ξ)Kvu+c2(ξ)Kvv,ϵ2(x)Kxvv+ϵ2(ξ)Kξvv=−ϵ2′(ξ)Kvv+c1(ξ)Kvu,\epsilon_2(x)K^{vu}_x-\epsilon_1(\xi)K^{vu}_\xi=\epsilon_1'(\xi)K^{vu}+c_2(\xi)K^{vv},\qquad \epsilon_2(x)K^{vv}_x+\epsilon_2(\xi)K^{vv}_\xi=-\epsilon_2'(\xi)K^{vv}+c_1(\xi)K^{vu},ϵ2​(x)Kxvu​−ϵ1​(ξ)Kξvu​=ϵ1′​(ξ)Kvu+c2​(ξ)Kvv,ϵ2​(x)Kxvv​+ϵ2​(ξ)Kξvv​=−ϵ2′​(ξ)Kvv+c1​(ξ)Kvu,

with Kvu(x,x)=−c2(x)/(ϵ1(x)+ϵ2(x))K^{vu}(x,x)=-c_2(x)/(\epsilon_1(x)+\epsilon_2(x))Kvu(x,x)=−c2​(x)/(ϵ1​(x)+ϵ2​(x)) and Kvv(x,0)=(qϵ1(0)/ϵ2(0))Kvu(x,0)K^{vv}(x,0)=(q\epsilon_1(0)/\epsilon_2(0))K^{vu}(x,0)Kvv(x,0)=(qϵ1​(0)/ϵ2​(0))Kvu(x,0). The control law (3.48) is

U(t)=∫01Kvu(1,ξ)u(ξ,t) dξ+∫01Kvv(1,ξ)v(ξ,t) dξ.U(t)=\int_0^1K^{vu}(1,\xi)u(\xi,t)\,d\xi+\int_0^1K^{vv}(1,\xi)v(\xi,t)\,d\xi .U(t)=∫01​Kvu(1,ξ)u(ξ,t)dξ+∫01​Kvv(1,ξ)v(ξ,t)dξ.

The target system is γt=Σ(x)γx\gamma_t=\Sigma(x)\gamma_xγt​=Σ(x)γx​ with α(0,t)=qβ(0,t)\alpha(0,t)=q\beta(0,t)α(0,t)=qβ(0,t) and β(1,t)=0\beta(1,t)=0β(1,t)=0, for γ=[α β]T\gamma=[\alpha\ \beta]^Tγ=[α β]T. The direct transformation is γ=w−∫0xK(x,ξ)w(ξ,t) dξ\gamma=w-\int_0^xK(x,\xi)w(\xi,t)\,d\xiγ=w−∫0x​K(x,ξ)w(ξ,t)dξ (3.23), with the full 2×22\times22×2 kernel KKK of (3.30)–(3.37). The inverse transformation is w=γ+∫0xL(x,ξ)γ(ξ,t) dξw=\gamma+\int_0^xL(x,\xi)\gamma(\xi,t)\,d\xiw=γ+∫0x​L(x,ξ)γ(ξ,t)dξ (3.38), with LLL solving (3.40)–(3.47). The finite time is

tF=∫01(1ϵ1(ξ)+1ϵ2(ξ))dξ.t_F=\int_0^1\left(\frac1{\epsilon_1(\xi)}+\frac1{\epsilon_2(\xi)}\right)d\xi .tF​=∫01​(ϵ1​(ξ)1​+ϵ2​(ξ)1​)dξ.

Formalization targets

Goal: Theorem 3.2 (pp. 6–7)

For q≠0q\neq0q=0, every classical solution of the closed loop satisfies

∀λ>0 ∃c>0:∥w(⋅,t)∥L2≤c e−λt∥w(⋅,0)∥L2(t≥0),andw(x,t)=0  (x∈[0,1], t≥tF).\forall\lambda>0\ \exists c>0:\quad \|w(\cdot,t)\|_{L^2}\le c\,e^{-\lambda t}\|w(\cdot,0)\|_{L^2}\quad(t\ge0),\qquad\text{and}\qquad w(x,t)=0\ \ (x\in[0,1],\ t\ge t_F).∀λ>0 ∃c>0:∥w(⋅,t)∥L2​≤ce−λt∥w(⋅,0)∥L2​(t≥0),andw(x,t)=0  (x∈[0,1], t≥tF​).

Here ccc depends on λ\lambdaλ and the data but not on the solution.

Milestones

  1. Proposition 3.1 (p. 3): the same two conclusions for the target system under the section's q≠0q\neq0q=0 assumption.
  2. §3.2 (p. 5): if KKK solves (3.30)–(3.37), the transformation (3.23) maps every closed-loop solution to a target solution.
  3. §3.3 (p. 6): if LLL solves (3.40)–(3.47), the transformation (3.38) maps every solution of γt=Σγx\gamma_t=\Sigma\gamma_xγt​=Σγx​, α(0,t)=qβ(0,t)\alpha(0,t)=q\beta(0,t)α(0,t)=qβ(0,t), to a solution of (3.1) with u(0,t)=qv(0,t)u(0,t)=qv(0,t)u(0,t)=qv(0,t).
  4. §3.5 (p. 8): the conclusion of Theorem 3.2 for q=0q=0q=0, where (3.37) reads Kvv(x,0)=0K^{vv}(x,0)=0Kvv(x,0)=0.

Significance

The theorem shows that a single linear boundary feedback, computed once from the coefficients, drives every state of the system to zero in exactly the time tFt_FtF​. That time is the sum of the travel times of the two characteristic families across [0,1][0,1][0,1]. Exponential stability at every rate follows. The same feedback, rescaled, is the controller of the paper's main result, Theorem 4.1: local exponential H2H^2H2 stabilization of a quasilinear 2×22\times22×2 system, the subject of mission 3 of this series. The well-posedness of the kernel equations, Theorem A.1, is mission 1.

The result is proved in the paper. As far as is known, none of it has been machine-checked. A formal proof needs Volterra transformations of vector-valued functions with differentiation under a variable-limit integral, explicit solutions of transport equations along characteristics, and L2L^2L2 estimates for integral operators with continuous kernels. These tools apply to backstepping designs well beyond this paper.

Difficulty

Proposition 3.1 and the two mapping statements are calculations: an integration by parts and the explicit solution along characteristics. The difficulty of Theorem 3.2 is the step "the transformation is invertible", which the paper's proof takes for granted. The goal hypothesizes only Kvu,KvvK^{vu},K^{vv}Kvu,Kvv, so it is not enough to state the mapping and the inverse separately. The solver has to recover the full state from the target state: either build the remaining kernels Kuu,KuvK^{uu},K^{uv}Kuu,Kuv and the inverse kernel LLL, or argue directly from the closed (Kvu,Kvv)(K^{vu},K^{vv})(Kvu,Kvv) subsystem. Both routes need existence or uniqueness for Goursat-type problems, which the paper proves only in Appendix A. A second, routine obstacle is that the uniform constant ccc must be extracted from the finite-time interval [0,tF][0,t_F][0,tF​] using bounds on the kernels.

Formalization scope

  • States are w : ℝ → ℝ → Fin 2 → ℝ, space first, with Lean index 0 for the paper's first component.
  • Solutions are classical: jointly C1C^1C1 on R2\mathbb R^2R2, with the equations imposed on [0,1]×[0,∞)[0,1]\times[0,\infty)[0,1]×[0,∞). The value w(⋅,0)w(\cdot,0)w(⋅,0) replaces the paper's initial condition w0∈L2w_0\in L^2w0​∈L2. This is the theorem restricted to classical solutions; Mathlib has no Sobolev-space solution theory for these equations.
  • The coefficients and kernels are functions on R\mathbb RR and R2\mathbb R^2R2 with global regularity, and positivity is assumed only on [0,1][0,1][0,1].
  • The goal and Proposition 3.1 assume q≠0q\neq0q=0, the standing assumption of §§3.1–3.4. The separate §3.5 milestone covers q=0q=0q=0.
  • The right boundary condition of the closed loop is the feedback (3.48) evaluated on the same state, not a free input.
  • Only the "if" direction of the §3.2 equivalence is formalized.

Three trivializing readings are excluded. The constant ccc is quantified after the data and before the solution, so it cannot depend on the solution. The finite-time claim is vanishing for every t≥tFt\ge t_Ft≥tF​, with tFt_FtF​ exactly (3.8), not for some unspecified time. The kernels are pinned down by all four of (3.32), (3.33), (3.36), (3.37). With C1C^1C1 coefficients a solution exists (the paper's Theorem A.2), and a sanity file checks that the hypotheses admit a non-zero closed-loop solution.

Welcome contributions: lemmas on differentiating ∫0xK(x,ξ)w(ξ,t) dξ\int_0^xK(x,\xi)w(\xi,t)\,d\xi∫0x​K(x,ξ)w(ξ,t)dξ, transport equations along characteristics, and the invertibility of Volterra operators with continuous kernels.

The paper's proofs are on pp. 3–5 (Proposition 3.1), p. 5 (§3.2), p. 6 (§3.3) and p. 7 (Theorem 3.2).

Selected references

  • J.-M. Coron, R. Vazquez, M. Krstic, G. Bastin, Local Exponential H2H^2H2 Stabilization of a 2×22\times22×2 Quasilinear Hyperbolic System Using Backstepping, arXiv:1208.6475v1, 2012; SIAM J. Control Optim. 51(3), 2013. https://arxiv.org/abs/1208.6475
  • M. Krstic, A. Smyshlyaev, Backstepping boundary control for first-order hyperbolic PDEs and application to systems with actuator and sensor delays, Systems & Control Letters 57(9), 2008. https://doi.org/10.1016/j.sysconle.2008.02.005
  • M. Krstic, A. Smyshlyaev, Boundary Control of PDEs: A Course on Backstepping Designs, SIAM, 2008. https://doi.org/10.1137/1.9780898718607
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Continuous-Time Average-Preserving Opinion Dynamics with Opinion-Dependent Communications 3: The n-Agent Model Approximates the Continuum Model Uniformly on Every Finite HorizonResearch Paper

Motivation

Bounded-confidence models describe a population of agents who repeatedly average their opinions with those of agents whose opinions are close to their own. They are used to study consensus and polarization in opinion formation, and in control as a primitive for decentralized rendezvous of mobile agents. Simulations of these models show a robust phenomenon: opinions converge to clusters, and the distances between clusters are typically close to twice the confidence radius rather than just above it. For finitely many agents this regularity has resisted proof.

V. D. Blondel, J. M. Hendrickx and J. N. Tsitsiklis (SIAM J. Control Optim. 48 (2010) 5214–5240) study a continuous-time version of the model in two forms: with nnn discrete agents, and with a continuum of agents indexed by [0,1][0,1][0,1]. For the continuum they prove that regular initial opinions converge to clusters whose separation obeys a lower bound depending on the cluster weights. To transfer such conclusions to finitely many agents one needs to know that the nnn-agent model is close to the continuum model when nnn is large. Section 4 of the paper proves this on every finite time horizon. This mission formalizes that result.

Setting

Discrete agents. There are n≥1n\ge1n≥1 agents with real opinions ξi(t)\xi_i(t)ξi​(t), t≥0t\ge0t≥0. Agents iii and jjj interact when ∣ξi−ξj∣<1|\xi_i-\xi_j|<1∣ξi​−ξj​∣<1. A solution of (2.1) is a continuous ξ:[0,∞)→Rn\xi:[0,\infty)\to\mathbb R^nξ:[0,∞)→Rn with

ξi(t)=ξi(0)+∫0t∑j: ∣ξi(τ)−ξj(τ)∣<1(ξj(τ)−ξi(τ)) dτ\xi_i(t)=\xi_i(0)+\int_0^t\sum_{j:\,|\xi_i(\tau)-\xi_j(\tau)|<1}\bigl(\xi_j(\tau)-\xi_i(\tau)\bigr)\,d\tauξi​(t)=ξi​(0)+∫0t​j:∣ξi​(τ)−ξj​(τ)∣<1∑​(ξj​(τ)−ξi​(τ))dτ

for all t≥0t\ge0t≥0 and all iii. The right-hand side is discontinuous when the interaction graph changes, so the integral form is used. A solution is proper if it is the unique solution from its initial value, its non-differentiability times have no accumulation point, and two agents that meet stay together.

Continuum agents. Agents are indexed by α∈I=[0,1]\alpha\in I=[0,1]α∈I=[0,1] with Lebesgue measure. YYY is the set of bounded measurable functions I→RI\to\mathbb RI→R. For m,M>0m,M>0m,M>0, XmMX_m^MXmM​ is the set of nondecreasing x~\tilde xx~ with m≤x~(β)−x~(α)β−α≤Mm\le\frac{\tilde x(\beta)-\tilde x(\alpha)}{\beta-\alpha}\le Mm≤β−αx~(β)−x~(α)​≤M for β≠α\beta\ne\alphaβ=α; such x~\tilde xx~ are regular. The interaction operator is

L(x~)(α)=∫I1[∣x~(α)−x~(γ)∣<1] (x~(γ)−x~(α)) dγ,\mathcal L(\tilde x)(\alpha)=\int_I \mathbf 1\bigl[|\tilde x(\alpha)-\tilde x(\gamma)|<1\bigr]\,\bigl(\tilde x(\gamma)-\tilde x(\alpha)\bigr)\,d\gamma ,L(x~)(α)=∫I​1[∣x~(α)−x~(γ)∣<1](x~(γ)−x~(α))dγ,

and a solution of (3.2) from x~0\tilde x_0x~0​ is a measurable x:(α,t)↦xt(α)x:(\alpha,t)\mapsto x_t(\alpha)x:(α,t)↦xt​(α) with every xt∈Yx_t\in Yxt​∈Y and xt(α)=x~0(α)+∫0tL(xτ)(α) dτx_t(\alpha)=\tilde x_0(\alpha)+\int_0^t\mathcal L(x_\tau)(\alpha)\,d\tauxt​(α)=x~0​(α)+∫0t​L(xτ​)(α)dτ for all t≥0t\ge0t≥0 and all α∈I\alpha\in Iα∈I.

The embedding. For ξ∈Rn\xi\in\mathbb R^nξ∈Rn, G(ξ)G(\xi)G(ξ) is the step function with G(ξ)(α)=ξiG(\xi)(\alpha)=\xi_iG(ξ)(α)=ξi​ on [i−1n,in)[\frac{i-1}n,\frac in)[ni−1​,ni​) and G(ξ)(1)=ξnG(\xi)(1)=\xi_nG(ξ)(1)=ξn​. It distributes the nnn agents over nnn blocks of III of length 1/n1/n1/n.

Formalization targets

Goal: Theorem 7 (finite-horizon approximation)

Let x~0∈XmM\tilde x_0\in X_m^Mx~0​∈XmM​, xxx the solution of (3.2) from x~0\tilde x_0x~0​, and for each n≥1n\ge1n≥1 let ξ⟨n⟩\xi^{\langle n\rangle}ξ⟨n⟩ be a proper solution of (2.1) with nondecreasing ξ⟨n⟩(0)\xi^{\langle n\rangle}(0)ξ⟨n⟩(0) and ∥G(ξ⟨n⟩(0))−x~0∥∞→0\|G(\xi^{\langle n\rangle}(0))-\tilde x_0\|_\infty\to0∥G(ξ⟨n⟩(0))−x~0​∥∞​→0. Then for every TTT and ε>0\varepsilon>0ε>0 there is n′n'n′ with

∥G(ξ⟨n⟩(t/n))−xt∥∞≤ε(t∈[0,T], n≥n′).\bigl\|G\bigl(\xi^{\langle n\rangle}(t/n)\bigr)-x_t\bigr\|_\infty\le\varepsilon\qquad(t\in[0,T],\ n\ge n').​G(ξ⟨n⟩(t/n))−xt​​∞​≤ε(t∈[0,T], n≥n′).

The statement fixes no rate in nnn and no dependence of n′n'n′ on TTT; it asserts only uniform convergence on bounded time intervals.

Milestones

  1. Lemma 1: for x~∈Xm\tilde x\in X_mx~∈Xm​ and y~∈Y\tilde y\in Yy~​∈Y, ∥L(x~)−L(y~)∥∞≤(2+8/m)∥x~−y~∥∞\|\mathcal L(\tilde x)-\mathcal L(\tilde y)\|_\infty\le(2+8/m)\|\tilde x-\tilde y\|_\infty∥L(x~)−L(y~​)∥∞​≤(2+8/m)∥x~−y~​∥∞​.
  2. Theorem 4, left inequality of (3.7): a solution from x~0∈XmM\tilde x_0\in X_m^Mx~0​∈XmM​ satisfies xt(β)−xt(α)≥me−t(β−α)x_t(\beta)-x_t(\alpha)\ge m e^{-t}(\beta-\alpha)xt​(β)−xt​(α)≥me−t(β−α) for all t≥0t\ge0t≥0, α<β\alpha<\betaα<β.
  3. Proposition 4: for every ε,T>0\varepsilon,T>0ε,T>0 there is δ>0\delta>0δ>0 such that every solution yyy of (3.2) with ∥y0−x~0∥∞≤δ\|y_0-\tilde x_0\|_\infty\le\delta∥y0​−x~0​∥∞​≤δ satisfies ∥yt−xt∥∞≤ε\|y_t-x_t\|_\infty\le\varepsilon∥yt​−xt​∥∞​≤ε on [0,T][0,T][0,T].
  4. Embedding (Section 4): if ξ\xiξ solves (2.1), then (α,t)↦G(ξ(t/n))(α)(\alpha,t)\mapsto G(\xi(t/n))(\alpha)(α,t)↦G(ξ(t/n))(α) solves (3.2) from G(ξ(0))G(\xi(0))G(ξ(0)).

Significance

The theorem says that the continuum model is the large-population limit of the nnn-agent model over any fixed time window. Combined with the paper's continuum convergence result (Theorem 6), it supports the paper's Conjecture 1 on intercluster distances for finitely many agents, and it is the first step of the paper's Proposition 5, which derives Conjecture 1 for random initial opinions from a stability conjecture for the continuum. It is a mean-field limit for a model whose interaction kernel is discontinuous, where standard Lipschitz mean-field arguments do not apply directly.

All four results and the goal are proved in the paper (with the time-scale correction described below). None of them has a machine-checked proof that this mission is aware of. The formalization contributes a checked continuous-dependence estimate for a discontinuous integral equation, a checked statement that the finite-agent dynamics embed in the continuum dynamics, and a precise record of the time normalization under which the two models agree.

Difficulty

The operator L\mathcal LL is not Lipschitz on YYY: moving one opinion across the confidence threshold changes the interaction set discontinuously. The obvious Gronwall argument for continuous dependence therefore fails for two arbitrary solutions. It works only around a trajectory that stays in some Xm′X_{m'}Xm′​, where few agents sit near the threshold, which is why the lower slope bound must hold at every time and not only at time 000. The perturbed trajectory, coming from nnn agents, is a step function and is never regular, so the estimate must be one-sided in this sense.

A literal reading of the page also fails. The page writes G(ξ⟨n⟩(t))G(\xi^{\langle n\rangle}(t))G(ξ⟨n⟩(t)), but for α\alphaα in block iii the continuum interaction L(G(ξ))(α)\mathcal L(G(\xi))(\alpha)L(G(ξ))(α) equals 1n\frac1nn1​ times the discrete right-hand side of (2.1). With x~0(α)=α/2\tilde x_0(\alpha)=\alpha/2x~0​(α)=α/2 and ξi⟨n⟩(0)=x~0(i/n)\xi^{\langle n\rangle}_i(0)=\tilde x_0(i/n)ξi⟨n⟩​(0)=x~0​(i/n), both models are linear; at t=1t=1t=1 the sup distance between G(ξ⟨n⟩(1))G(\xi^{\langle n\rangle}(1))G(ξ⟨n⟩(1)) and x1x_1x1​ tends to e−1/4e^{-1}/4e−1/4. The discrete time must be rescaled by 1/n1/n1/n.

Formalization scope

Namespace BHTOpinion.Approx. Agents of the discrete model are Fin n (0-based); the continuum index set is Set.Icc (0:ℝ) 1 with Lebesgue measure. Opinion functions are ℝ → ℝ, of which only the values on III matter; trajectories are written x t α, time first, with time in ℝ and explicit t≥0t\ge0t≥0 guards. In 0-based form, G(ξ)(α)=ξmin⁡(⌊nα⌋,n−1)G(\xi)(\alpha)=\xi_{\min(\lfloor n\alpha\rfloor,n-1)}G(ξ)(α)=ξmin(⌊nα⌋,n−1)​ on III; for n=0n=0n=0 the definition returns 000, and every statement assumes n≥1n\ge1n≥1.

Explicit readings of the paper's phrases:

  • Sup norms are written pointwise over III, including α=1\alpha=1α=1: a bound at every α∈I\alpha\in Iα∈I, never a real supremum.
  • "lim⁡n→∞∥G(ξ⟨n⟩(0))−x~0∥∞=0\lim_{n\to\infty}\|G(\xi^{\langle n\rangle}(0))-\tilde x_0\|_\infty=0limn→∞​∥G(ξ⟨n⟩(0))−x~0​∥∞​=0" is: for every δ>0\delta>0δ>0 there is NNN such that the pointwise bound δ\deltaδ holds for all n≥Nn\ge Nn≥N.
  • "There exists n′n'n′" is an explicit ∃ n′\exists\,n'∃n′ with the bound for all n≥n′n\ge n'n≥n′, t∈[0,T]t\in[0,T]t∈[0,T], α∈I\alpha\in Iα∈I.
  • "The solution xxx" of (3.2) is any solution from x~0\tilde x_0x~0​; the theorem holds for each.
  • "Proper initial condition, admitting a unique solution" is kept as printed: uniqueness from ξ⟨n⟩(0)\xi^{\langle n\rangle}(0)ξ⟨n⟩(0), finitely many non-differentiability times on every bounded interval, agents that meet stay together.
  • Solutions of (2.1) and (3.2) require the time integrand to be integrable; Lean's integral of a non-integrable function is 000.
  • The labelled correction: the discrete trajectory enters as ξ⟨n⟩(t/n)\xi^{\langle n\rangle}(t/n)ξ⟨n⟩(t/n) in the embedding claim and in Theorem 7. Equivalently, the page's statement holds for the weighted model (2.4) with weights 1/n1/n1/n; only the rescaled form is stated.
  • Only the left inequality of (3.7) is a milestone. The right inequality Me4t/mMe^{4t/m}Me4t/m is not used by Section 4.

A trivializing formalization is ruled out: the hypotheses are satisfied by x~0(α)=α∈X11\tilde x_0(\alpha)=\alpha\in X_1^1x~0​(α)=α∈X11​, the conclusion is not vacuous because GGG is evaluated at n≥1n\ge1n≥1 only, and the perturbed initial values y0y_0y0​ in Proposition 4 range over all of YYY, not over regular functions.

Infrastructure needed: Gronwall's inequality for a sup-norm difference, measure-theoretic bookkeeping for L\mathcal LL on step functions, and a change of variables τ↦τ/n\tau\mapsto\tau/nτ↦τ/n in interval integrals. The Lipschitz estimate on XmX_mXm​ and the slope bound are reusable for any continuum bounded-confidence model. Contributions to any milestone are welcome; the embedding milestone is independent of the other three.

Selected references

  • V. D. Blondel, J. M. Hendrickx, J. N. Tsitsiklis, Continuous-time average-preserving opinion dynamics with opinion-dependent communications, SIAM J. Control Optim. 48(8), 2010, 5214–5240. https://doi.org/10.1137/090766188
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Continuous-Time Average-Preserving Opinion Dynamics with Opinion-Dependent Communications 2: Regular Initial Opinions Converge to Clusters with |B − A| ≥ 1 + min{W_A, W_B}/max{W_A, W_B}Research Paper

Motivation

Bounded-confidence opinion dynamics model a population in which each agent repeatedly moves its opinion towards the opinions of those agents whose views lie within a fixed distance of its own. Introduced in discrete time by Krause (1997) and studied as the Hegselmann–Krause model, these systems are a standard test case for multi-agent coordination with state-dependent communication: who talks to whom depends on the current state, so the interaction graph changes over time and the linear consensus theory does not apply. Simulations show that opinions settle into clusters that are further apart than the confidence radius, and that the distances between clusters are larger than the radius would force. Explaining those distances is a long-standing question in the area (Lorenz 2007).

Blondel, Hendrickx and Tsitsiklis (SIAM J. Control Optim. 2010) study the continuous-time version, first for finitely many agents and then for a continuum of agents. The continuum model is the limit of a large population and captures the intercluster distances observed in simulations. This mission formalizes the continuum part of the paper, Section 3: well-posedness for regular initial opinions, convergence to clusters, and the intercluster distance bound of Theorem 6.

Setting

Agents are indexed by I=[0,1]I=[0,1]I=[0,1] with Lebesgue measure. An opinion function x~:I→R\tilde x : I\to\mathbb Rx~:I→R gives the opinion x~(α)\tilde x(\alpha)x~(α) of agent α\alphaα. Write YYY for the bounded measurable opinion functions and XXX for the nondecreasing bounded ones. For m,M>0m,M>0m,M>0, XmX_mXm​ is the set of x~∈X\tilde x\in Xx~∈X whose increase rate x~(β)−x~(α)β−α\frac{\tilde x(\beta)-\tilde x(\alpha)}{\beta-\alpha}β−αx~(β)−x~(α)​ is at least mmm for all β≠α\beta\ne\alphaβ=α, and XMX^MXM the set with rate at most MMM. A function is regular if it lies in XmM=Xm∩XMX_m^M=X_m\cap X^MXmM​=Xm​∩XM for some m,M>0m,M>0m,M>0; the uniform profile x~(α)=α\tilde x(\alpha)=\alphax~(α)=α is regular.

Two agents are connected when their opinions differ by less than 111. The interaction operator is

L(x~)(α)=∫01χx~(α,γ)(x~(γ)−x~(α)) dγ,χx~(α,γ)=1[∣x~(α)−x~(γ)∣<1].\mathcal L(\tilde x)(\alpha)=\int_0^1 \chi_{\tilde x}(\alpha,\gamma)\bigl(\tilde x(\gamma)-\tilde x(\alpha)\bigr)\,d\gamma,\qquad \chi_{\tilde x}(\alpha,\gamma)=\mathbf 1\bigl[|\tilde x(\alpha)-\tilde x(\gamma)|<1\bigr].L(x~)(α)=∫01​χx~​(α,γ)(x~(γ)−x~(α))dγ,χx~​(α,γ)=1[∣x~(α)−x~(γ)∣<1].

Given an initial condition x~0\tilde x_0x~0​, a solution of (3.2) is a measurable x:I×[0,∞)→Rx : I\times[0,\infty)\to\mathbb Rx:I×[0,∞)→R, (α,t)↦xt(α)(\alpha,t)\mapsto x_t(\alpha)(α,t)↦xt​(α), with each xt∈Yx_t\in Yxt​∈Y and

xt(α)=x~0(α)+∫0tL(xτ)(α) dτfor every t≥0 and every α∈I.x_t(\alpha)=\tilde x_0(\alpha)+\int_0^t \mathcal L(x_\tau)(\alpha)\,d\tau\qquad\text{for every } t\ge 0 \text{ and every }\alpha\in I .xt​(α)=x~0​(α)+∫0t​L(xτ​)(α)dτfor every t≥0 and every α∈I.

The differential form (3.1) is ddtxt(α)=L(xt)(α)\frac{d}{dt}x_t(\alpha)=\mathcal L(x_t)(\alpha)dtd​xt​(α)=L(xt​)(α).

FFF is the set of nondecreasing s~\tilde ss~ whose distinct values are pairwise more than 111 apart; Fˉ\bar FFˉ the set of nondecreasing s~\tilde ss~ whose values, for almost every pair of agents, are equal or at least 111 apart. A fixed point is an s~∈X\tilde s\in Xs~∈X for which (3.2) started at s~\tilde ss~ has the unique solution xt=s~x_t=\tilde sxt​=s~. A cluster of s~\tilde ss~ is a value AAA held by a set of agents of positive measure WAW_AWA​, its weight.

Formalization targets

Goal: Theorem 6

For a regular x~0\tilde x_0x~0​, a solution xxx of (3.2), and s~=lim⁡t→∞xt\tilde s=\lim_{t\to\infty}x_ts~=limt→∞​xt​ almost everywhere: any two distinct clusters A≠BA\ne BA=B of s~\tilde ss~ satisfy

∣B−A∣ ≥ 1+min⁡{WA,WB}max⁡{WA,WB},|B-A|\ \ge\ 1+\frac{\min\{W_A,W_B\}}{\max\{W_A,W_B\}},∣B−A∣ ≥ 1+max{WA​,WB​}min{WA​,WB​}​,

and s~\tilde ss~ agrees almost everywhere with a member of FFF that is a fixed point.

Milestones

  1. Lemma 1: ∥L(x~)−L(y~)∥∞≤(2+8/m)∥x~−y~∥∞\|\mathcal L(\tilde x)-\mathcal L(\tilde y)\|_\infty\le(2+8/m)\|\tilde x-\tilde y\|_\infty∥L(x~)−L(y~​)∥∞​≤(2+8/m)∥x~−y~​∥∞​ for x~∈Xm\tilde x\in X_mx~∈Xm​, y~∈Y\tilde y\in Yy~​∈Y.
  2. Lemma 2: −(x~(β)−x~(α))≤L(x~)(β)−L(x~)(α)-(\tilde x(\beta)-\tilde x(\alpha))\le\mathcal L(\tilde x)(\beta)-\mathcal L(\tilde x)(\alpha)−(x~(β)−x~(α))≤L(x~)(β)−L(x~)(α), and ≤2m(x~(β)−x~(α))\le\frac 2m(\tilde x(\beta)-\tilde x(\alpha))≤m2​(x~(β)−x~(α)) on XmX_mXm​.
  3. Theorem 4: for x~0∈XmM\tilde x_0\in X_m^Mx~0​∈XmM​, (3.1) and (3.2) have a unique common solution, with xt(β)−xt(α)β−α≥me−t\frac{x_t(\beta)-x_t(\alpha)}{\beta-\alpha}\ge me^{-t}β−αxt​(β)−xt​(α)​≥me−t and xtx_txt​ regular for every ttt.
  4. Lemma 3: for a nondecreasing solution, ∫0cxt\int_0^c x_t∫0c​xt​ converges for every ccc.
  5. Proposition 2: xt(α)x_t(\alpha)xt​(α) converges outside a countable set of agents.
  6. Proposition 3(a)–(c): a.e. limits lie in Fˉ\bar FFˉ; FFF consists of fixed points; nondecreasing fixed points lie in Fˉ\bar FFˉ.
  7. Theorem 5: xt→y~∈Fˉx_t\to\tilde y\in\bar Fxt​→y~​∈Fˉ almost everywhere, and F⊆{fixed points}⊆FˉF\subseteq\{\text{fixed points}\}\subseteq\bar FF⊆{fixed points}⊆Fˉ.

Significance

Theorem 6 turns the qualitative picture "opinions freeze into clusters at least one unit apart", which already follows from Theorem 5, into a quantitative statement depending on the cluster weights: two clusters of equal weight are at least 222 apart, and a light cluster can sit closer to a heavy one. This matches the intercluster distances observed in simulations of large populations and yields Corollary 1 of the paper, a necessary condition for the stability of an equilibrium in the L1L^1L1 sense. Theorem 4 is of independent interest: the right-hand side of (3.1) is discontinuous in the state, a two-valued initial profile admits two different solutions, and well-posedness holds only under the slope bounds.

The results are proved in the paper. Nothing in this mission has a machine-checked proof yet. Formalizing it means building a small theory of integral equations with a state-dependent, discontinuous kernel on L∞L^\inftyL∞-type spaces of opinion profiles: a contraction argument on short intervals, its continuation, monotone convergence of partial integrals, and an a.e. limit argument with Fatou's lemma and Fubini.

Difficulty

The obvious route to Theorem 4, Picard–Lindelöf, fails: L\mathcal LL is not Lipschitz on YYY, because a small perturbation of opinions can switch the connection indicator on a set of positive measure. Lemma 1 restores the Lipschitz property only at functions with a positive increase rate, so existence and uniqueness require a contraction on a carefully chosen set of profiles together with a proof that the dynamics keep the increase rate positive (Lemma 2), and a continuation argument whose step size shrinks as the lower slope decays. For Theorem 6, convergence alone gives only ∣B−A∣≥1|B-A|\ge 1∣B−A∣≥1; the extra min⁡/max⁡\min/\maxmin/max term comes from agents trapped between two clusters, whose existence relies on the continuity in α\alphaα of every xtx_txt​, which the a.e. limit s~\tilde ss~ does not have.

Formalization scope

Opinion functions are ℝ → ℝ, and only their values on Set.Icc 0 1 matter; a trajectory is x : ℝ → ℝ → ℝ with x t α =xt(α)=x_t(\alpha)=xt​(α), time real with t≥0t\ge0t≥0. Lebesgue measure on III is volume.restrict (Set.Icc 0 1), and Fˉ\bar FFˉ uses its product measure. The slope conditions are written multiplied out for α<β\alpha<\betaα<β. Explicit readings of the paper's phrases:

  • "solution of (3.2)": joint measurability on I×[0,∞)I\times[0,\infty)I×[0,∞), xt∈Yx_t\in Yxt​∈Y, integrability of τ↦L(xτ)(α)\tau\mapsto\mathcal L(x_\tau)(\alpha)τ↦L(xτ​)(α) on [0,t][0,t][0,t] (implicit in the paper), and the equation for every t≥0t\ge0t≥0 and every α∈I\alpha\in Iα∈I (footnote 6), not almost every α\alphaα.
  • "the solution": any solution; Theorem 4 makes it unique.
  • "converges": Tendsto … atTop (𝓝 l) to a real limit; "except possibly for a countable set": a countable exceptional set; "a.e.": almost everywhere for Lebesgue measure on III.
  • "y~∈Fˉ\tilde y\in\bar Fy~​∈Fˉ" and "s~∈F\tilde s\in Fs~∈F" for an a.e.-defined limit: some representative equal almost everywhere belongs to the set.
  • "any two clusters": two distinct values of positive weight; weights are measures of level sets.
  • "fixed point": existence and uniqueness of the constant solution.
  • Theorem 4's printed upper bound Me4t/mMe^{4t/m}Me4t/m is replaced by "xtx_txt​ is regular for every ttt", because the paper's proof establishes the printed constant only on the first time interval; the lower bound me−tme^{-t}me−t is kept.

A trivializing formalization is ruled out: clusters are distinct with positive weight, regularity requires a positive lower slope, the solution predicate forbids the junk value of a non-integrable time integral, and x~(α)=α\tilde x(\alpha)=\alphax~(α)=α is checked to be regular, so no hypothesis is vacuous.

The development needs an integral-equation solution theory for a discontinuous kernel, the Banach fixed point theorem (Mathlib ContractingWith), Grönwall-type estimates, countability of discontinuities of monotone functions, Fatou's lemma and Fubini. Lemmas 1 and 2 and the lower bound in Theorem 4 are reused by the companion mission on the discrete-to-continuum approximation (Theorem 7). Contributions are welcome on every milestone, and in particular on the slope estimates, which are self-contained.

Selected references

  • V. D. Blondel, J. M. Hendrickx, J. N. Tsitsiklis, Continuous-time average-preserving opinion dynamics with opinion-dependent communications, SIAM J. Control Optim. 48(8), 2010, 5214–5240. https://doi.org/10.1137/090766188
  • V. D. Blondel, J. M. Hendrickx, J. N. Tsitsiklis, On Krause's multi-agent consensus model with state-dependent connectivity, IEEE Trans. Automat. Control 54(11), 2009, 2586–2597. https://doi.org/10.1109/TAC.2009.2031211
  • J. Lorenz, Continuous opinion dynamics under bounded confidence: A survey, Internat. J. Modern Phys. C 18(12), 2007, 1819–1838. https://doi.org/10.1142/S0129183107011789
  • U. Krause, Soziale Dynamiken mit vielen Interakteuren. Eine Problemskizze, in Modellierung und Simulation von Dynamiken mit vielen interagierenden Akteuren, Bremen, 1997, 37–51.
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Continuous-Time Average-Preserving Opinion Dynamics with Opinion-Dependent Communications 1: Every Proper Solution of the Discrete-Agent Model Converges to Clusters at Least 1 ApartResearch Paper

Motivation

Bounded-confidence opinion dynamics model a population in which each individual revises an opinion, represented by a real number, by looking only at opinions close to its own. The best-known example is the discrete-time model of Krause (1997), also called the Hegselmann–Krause model, in which every agent moves to the average of the opinions within distance 1 of its own. Such models are studied in the social sciences as models of consensus formation and polarization, and in control theory as prototypes of multiagent systems with state-dependent interaction topology: the communication graph is not given in advance but is determined by the state itself, which is what rendezvous algorithms for mobile robots and flocking models share (Lorenz 2007; Olfati-Saber, Fax, Murray 2007).

Blondel, Hendrickx and Tsitsiklis (SIAM J. Control Optim. 48 (2010)) study the continuous-time, symmetric counterpart of Krause's model. Each opinion is continuously attracted by every other opinion differing from it by less than 1, with an intensity proportional to the difference. Simulations show convergence to clusters, groups of agents sharing a common value, with distinct clusters at distance at least 1 and typically close to 2. This mission formalizes the first of the paper's results: convergence to clusters at least 1 apart for finitely many agents.

Timeline.

  • 1997: Krause introduces the discrete-time bounded-confidence model.
  • 2003: Jadbabaie, Lin and Morse prove convergence results for nearest-neighbour coordination with switching topologies (IEEE TAC 48).
  • 2009: Blondel, Hendrickx and Tsitsiklis analyse Krause's discrete-time model and the observed intercluster distance of about 2 (IEEE TAC 54).
  • 2010: the present paper. For the continuous-time model with n agents it proves that every proper solution converges to an equilibrium with clusters at least 1 apart (Theorem 2). It also proves that almost every initial condition is proper (Theorem 1, proof sketched) and gives a continuum-agent analysis with a sharper intercluster bound.

Setting

There are nnn agents, labelled 1,…,n1,\dots,n1,…,n. Agent iii holds a real opinion xi(t)x_i(t)xi​(t) at each time t≥0t\ge 0t≥0, and xix_ixi​ is a continuous function of time. Agent jjj is a neighbour of agent iii at time ttt when ∣xi(t)−xj(t)∣<1|x_i(t)-x_j(t)|<1∣xi​(t)−xj​(t)∣<1; the inequality is strict, and every agent is its own neighbour. The intended dynamics are

x˙i(t)=∑j: ∣xi(t)−xj(t)∣<1(xj(t)−xi(t)).(1.1)\dot x_i(t)=\sum_{j:\,|x_i(t)-x_j(t)|<1}\bigl(x_j(t)-x_i(t)\bigr).\tag{1.1}x˙i​(t)=j:∣xi​(t)−xj​(t)∣<1∑​(xj​(t)−xi​(t)).(1.1)

The right-hand side jumps whenever a pair of opinions crosses distance 1, so (1.1) usually has no differentiable solution. The model is therefore the integral equation

xi(t)=xi(0)+∫0t∑j: ∣xi(τ)−xj(τ)∣<1(xj(τ)−xi(τ)) dτ(t≥0, i=1,…,n).(2.1)x_i(t)=x_i(0)+\int_0^t\sum_{j:\,|x_i(\tau)-x_j(\tau)|<1}\bigl(x_j(\tau)-x_i(\tau)\bigr)\,d\tau\qquad(t\ge0,\ i=1,\dots,n).\tag{2.1}xi​(t)=xi​(0)+∫0t​j:∣xi​(τ)−xj​(τ)∣<1∑​(xj​(τ)−xi​(τ))dτ(t≥0, i=1,…,n).(2.1)

Solutions of (2.1) need not be unique. The two-agent initial condition (−12,12)(-\tfrac12,\tfrac12)(−21​,21​) is a fixed point, but the agents can also start attracting each other immediately. An initial condition x~∈Rn\tilde x\in\mathbb R^nx~∈Rn is proper if (a) (2.1) has exactly one solution xxx with x(0)=x~x(0)=\tilde xx(0)=x~; (b) the set of times at which xxx is not differentiable is at most countable and has no accumulation point; and (c) agents that meet stay together: xi(t)=xj(t)x_i(t)=x_j(t)xi​(t)=xj​(t) implies xi(t′)=xj(t′)x_i(t')=x_j(t')xi​(t′)=xj​(t′) for all t′≥tt'\ge tt′≥t. That solution is then a proper solution.

The set of equilibria is

F={s~∈Rn: for all i,j, s~i=s~j or ∣s~i−s~j∣≥1}.F=\bigl\{\tilde s\in\mathbb R^n:\ \text{for all } i,j,\ \tilde s_i=\tilde s_j\ \text{or}\ |\tilde s_i-\tilde s_j|\ge 1\bigr\}.F={s~∈Rn: for all i,j, s~i​=s~j​ or ∣s~i​−s~j​∣≥1}.

The average opinion is xˉ(t)=1n∑ixi(t)\bar x(t)=\frac1n\sum_i x_i(t)xˉ(t)=n1​∑i​xi​(t), and V(x(t))=∑i(xi(t)−xˉ(t))2V(x(t))=\sum_i\bigl(x_i(t)-\bar x(t)\bigr)^2V(x(t))=∑i​(xi​(t)−xˉ(t))2 is the sum of squared differences from it.

Formalization targets

Goal: Theorem 2

Every proper solution xxx of (2.1) converges to a limit in FFF:

∃ x∗∈F:lim⁡t→∞x(t)=x∗,\exists\,x^*\in F:\qquad \lim_{t\to\infty}x(t)=x^*,∃x∗∈F:t→∞lim​x(t)=x∗,

that is, all limits exist and two agents with different limits end at distance at least 1.

Milestones, in the order the argument uses them

  1. Order preservation (§2.1, p. 5218): if xi(t)≥xj(t)x_i(t)\ge x_j(t)xi​(t)≥xj​(t) for some ttt, then xi(t′)≥xj(t′)x_i(t')\ge x_j(t')xi​(t′)≥xj​(t′) for every t′≥tt'\ge tt′≥t.
  2. Proposition 1 (p. 5218): xˉ\bar xxˉ is constant, V(x(t))V(x(t))V(x(t)) is nonincreasing, and outside a countable set of times dV/dtdV/dtdV/dt is negative when x(t)∉Fx(t)\notin Fx(t)∈/F and zero when x(t)∈Fx(t)\in Fx(t)∈F.
  3. Monotone partial sums (2.3) (p. 5219): for a sorted initial condition and every kkk, outside a countable set of times the derivative of ∑i≤kxi(t)\sum_{i\le k}x_i(t)∑i≤k​xi​(t) equals ∑i≤k∑j>k: ∣xi−xj∣<1(xj−xi)≥0\sum_{i\le k}\sum_{j>k:\,|x_i-x_j|<1}(x_j-x_i)\ge 0∑i≤k​∑j>k:∣xi​−xj​∣<1​(xj​−xi​)≥0, and ∑i≤kxi(t)\sum_{i\le k}x_i(t)∑i≤k​xi​(t) is nondecreasing in ttt.
  4. Convergence of each opinion (p. 5219): every xi(t)x_i(t)xi​(t) has a limit as t→∞t\to\inftyt→∞.

Significance

The result. Theorem 2 establishes the clustering behaviour seen in simulations for every proper solution. The paper's Theorem 1 states that almost every initial condition is proper, so the conclusion covers almost every initial condition. The lower bound 1 on the distance between clusters is the baseline that the paper's continuum analysis improves to 1+min⁡{WA,WB}/max⁡{WA,WB}1+\min\{W_A,W_B\}/\max\{W_A,W_B\}1+min{WA​,WB​}/max{WA​,WB​}. Average preservation and the decrease of VVV (Proposition 1) are the structural facts that the continuum model also has.

Formalization. Theorem 2 is proved in the paper; no machine-checked version exists on Prove2Me or, as far as is known, elsewhere. The mission produces a Lean definition of solutions of a discontinuous integral equation with a state-dependent neighbour graph, and a convergence proof for it. The remaining work is to formalize the known argument, from the integral equation to the existence and location of the limit.

Difficulty

The obvious approach treats (1.1) as an ODE and differentiates along trajectories. That fails because the right-hand side is discontinuous in the state. A solution is only an integral solution, it may fail to be differentiable, and derivative identities such as (2.3) hold only outside an exceptional set of times. Every step from a derivative sign to a monotonicity or limit statement therefore has to go through the integral equation, not the differential one. A second obstacle is that pairs of agents at distance exactly 1 sit on the discontinuity of the interaction rule. This is where uniqueness fails (footnote 3 of the paper) and why conditions (a)–(c) are hypotheses. A decreasing Lyapunov function VVV alone does not give convergence of the trajectory to a single point, so Proposition 1 does not by itself settle Theorem 2.

Formalization scope

Agents are Fin n. Time is ℝ, and only values at t≥0t\ge 0t≥0 enter. A trajectory is x : ℝ → Fin n → ℝ. A solution of (2.1) is continuous on [0,∞)[0,\infty)[0,∞) and satisfies (2.1) for every t≥0t\ge 0t≥0 and every iii, with the neighbour set Finset.univ.filter (fun j => |x τ i - x τ j| < 1). The integrand is also required to be interval-integrable on [0,t][0,t][0,t]; for a continuous trajectory this always holds. Explicit readings of the paper's phrases:

  • "unique solution" in (a) is uniqueness among all solutions of (2.1), with no sortedness or differentiability assumed;
  • condition (b) is "for every TTT, the non-differentiability times in [0,T][0,T][0,T] form a finite set", which is equivalent for subsets of [0,∞)[0,\infty)[0,∞);
  • "converges to a limit" is Tendsto x atTop (𝓝 xstar) in Fin n → ℝ, with real time;
  • "with the exception of a countable set of times" is the existence of a countable set SSS outside which the derivative of V(x(t))V(x(t))V(x(t)) exists and has the stated sign;
  • "constant" and "nonincreasing" are on [0,∞)[0,\infty)[0,∞);
  • Proposition 1 assumes n≥1n\ge1n≥1 so that xˉ\bar xxˉ is meaningful.

The paper's sortedness convention ("we assume … that the components of proper initial conditions are sorted") is a hypothesis only of the partial-sum milestone. Theorem 2 is stated for every proper solution. A formalization that dropped condition (a), restricted solutions to sorted, differentiable or eventually constant trajectories, or replaced (2.1) by the differential equation would prove a different, and in parts trivial, statement. These are excluded.

The work needs interval integrals and the fundamental theorem of calculus for integrands with jumps, monotonicity from almost-everywhere derivative signs, and bounded monotone convergence. The definition of solutions of (2.1) and the equilibrium set is reusable for the stability results of the same paper (Theorem 3), which are not part of this mission. Contributions are welcome at every milestone, as are alternative convergence proofs, such as the paper's reference [9], that avoid symmetry.

Selected references

  • V. D. Blondel, J. M. Hendrickx, J. N. Tsitsiklis, Continuous-time average-preserving opinion dynamics with opinion-dependent communications, SIAM J. Control Optim. 48(8), 2010, 5214–5240. https://doi.org/10.1137/090766188
  • V. D. Blondel, J. M. Hendrickx, J. N. Tsitsiklis, On Krause's multi-agent consensus model with state-dependent connectivity, IEEE Trans. Automat. Control 54(11), 2009, 2586–2597. https://doi.org/10.1109/TAC.2009.2031211
  • A. Jadbabaie, J. Lin, A. S. Morse, Coordination of groups of mobile autonomous agents using nearest neighbor rules, IEEE Trans. Automat. Control 48(6), 2003, 988–1001. https://doi.org/10.1109/TAC.2003.812781
  • J. Lorenz, Continuous opinion dynamics under bounded confidence: a survey, Internat. J. Modern Phys. C 18(12), 2007, 1819–1838. https://doi.org/10.1142/S0129183107011789
  • R. Olfati-Saber, J. A. Fax, R. M. Murray, Consensus and cooperation in networked multi-agent systems, Proc. IEEE 95(1), 2007, 215–233. https://doi.org/10.1109/JPROC.2006.887293
  • U. Krause, Soziale Dynamiken mit vielen Interakteuren. Eine Problemskizze, in Modellierung und Simulation von Dynamiken mit vielen interagierenden Akteuren, Universität Bremen, 1997, 37–51.
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Tikhonov Regularization of a Second Order Dynamical System with Hessian Driven Damping 3: If ∫ ε(t)/t dt = +∞, the Trajectory Converges Strongly in the Ergodic Sense to the Minimum-Norm MinimizerResearch Paper

Motivation

Second order dynamical systems with vanishing damping are continuous-time models of accelerated first-order methods. The system x¨+αtx˙+∇g(x)=0\ddot x + \frac{\alpha}{t}\dot x + \nabla g(x) = 0x¨+tα​x˙+∇g(x)=0 is the continuous limit of Nesterov's accelerated gradient method (Su, Boyd, Candès 2016), and its trajectories converge weakly to a minimizer of a convex ggg when α>3\alpha>3α>3 (Attouch, Chbani, Peypouquet, Redont 2018). Two modifications of this system have been studied separately. A Hessian-driven damping term β∇2g(x)x˙\beta\nabla^2 g(x)\dot xβ∇2g(x)x˙ damps oscillations and keeps the fast rates (Attouch, Peypouquet, Redont 2016). A Tikhonov regularization term ϵ(t)x\epsilon(t)xϵ(t)x with ϵ(t)→0\epsilon(t)\to0ϵ(t)→0 selects one minimizer, the one of minimum norm, and can turn weak convergence into strong convergence (Attouch, Chbani, Riahi 2018).

Boţ, Csetnek and László (arXiv:1911.12845v2, Math. Program. 2021) study the system with both terms. Their results split into two regimes according to how fast ϵ\epsilonϵ decays. This mission covers the slow-decay regime of §4.1, where ∫+∞ϵ(t)/t dt=+∞\int^{+\infty}\epsilon(t)/t\,dt=+\infty∫+∞ϵ(t)/tdt=+∞ and the trajectory approaches the minimum-norm minimizer in a weighted average sense.

Timeline. 2016: Su, Boyd and Candès derive the continuous model of Nesterov's method. 2016: Attouch, Peypouquet and Redont add Hessian-driven damping (α≥3\alpha\ge3α≥3, β>0\beta>0β>0) and prove fast rates and weak convergence. 2018: Attouch, Chbani and Riahi add Tikhonov regularization without Hessian damping and prove, among other results, strong ergodic convergence to the minimum-norm minimizer when ∫ϵ(t)/t dt=+∞\int\epsilon(t)/t\,dt=+\infty∫ϵ(t)/tdt=+∞. 2020: Boţ, Csetnek and László combine both terms and extend that ergodic result (their Theorem 4.2).

Setting

Let H\mathcal HH be a real Hilbert space, t0>0t_0>0t0​>0, α>0\alpha>0α>0, β≥0\beta\ge0β≥0, and u0,v0∈Hu_0,v_0\in\mathcal Hu0​,v0​∈H. The data satisfy the paper's General assumption:

  • g:H→Rg:\mathcal H\to\mathbb Rg:H→R is convex and twice Fréchet differentiable, its gradient ∇g\nabla g∇g is Lipschitz continuous on bounded sets, and argmin⁡g≠∅\operatorname{argmin} g\neq\emptysetargming=∅;
  • ϵ:[t0,+∞)→[0,+∞)\epsilon:[t_0,+\infty)\to[0,+\infty)ϵ:[t0​,+∞)→[0,+∞) is nonincreasing, of class C1C^1C1, and lim⁡t→+∞ϵ(t)=0\lim_{t\to+\infty}\epsilon(t)=0limt→+∞​ϵ(t)=0.

A global C2C^2C2-solution of system (5) is a twice continuously differentiable x:[t0,+∞)→Hx:[t_0,+\infty)\to\mathcal Hx:[t0​,+∞)→H with

x¨(t)+αtx˙(t)+β∇2g(x(t))x˙(t)+∇g(x(t))+ϵ(t)x(t)=0(t≥t0),x(t0)=u0, x˙(t0)=v0.\ddot x(t)+\frac{\alpha}{t}\dot x(t)+\beta\nabla^2 g(x(t))\dot x(t)+\nabla g(x(t))+\epsilon(t)x(t)=0\quad(t\ge t_0),\qquad x(t_0)=u_0,\ \dot x(t_0)=v_0 .x¨(t)+tα​x˙(t)+β∇2g(x(t))x˙(t)+∇g(x(t))+ϵ(t)x(t)=0(t≥t0​),x(t0​)=u0​, x˙(t0​)=v0​.

The set argmin⁡g\operatorname{argmin} gargming is nonempty, closed and convex, so it has a unique element of minimum norm, the minimum-norm minimizer x∗=argmin⁡{∥x∥:x∈argmin⁡g}x^*=\operatorname{argmin}\{\|x\|:x\in\operatorname{argmin} g\}x∗=argmin{∥x∥:x∈argming}. For ϵ>0\epsilon>0ϵ>0 the Tikhonov approximation curve is

xϵ=argmin⁡x∈H(g(x)+ϵ2∥x∥2),x_\epsilon=\operatorname*{argmin}_{x\in\mathcal H}\Big(g(x)+\frac{\epsilon}{2}\|x\|^2\Big),xϵ​=x∈Hargmin​(g(x)+2ϵ​∥x∥2),

the unique minimizer of a strongly convex function. Finally, hx∗(t)=12∥x(t)−x∗∥2h_{x^*}(t)=\tfrac12\|x(t)-x^*\|^2hx∗​(t)=21​∥x(t)−x∗∥2 measures the distance of the trajectory to x∗x^*x∗, with derivative h˙x∗(t)=⟨x˙(t),x(t)−x∗⟩\dot h_{x^*}(t)=\langle\dot x(t),x(t)-x^*\rangleh˙x∗​(t)=⟨x˙(t),x(t)−x∗⟩.

Formalization targets

Goal: Theorem 4.2 (p. 18)

If ∫t0+∞ϵ(t)t dt=+∞\int_{t_0}^{+\infty}\frac{\epsilon(t)}{t}\,dt=+\infty∫t0​+∞​tϵ(t)​dt=+∞ and α>0\alpha>0α>0, then every global C2C^2C2-solution satisfies

lim⁡t→+∞1∫t0tϵ(s)s ds∫t0tϵ(s)s∥x(s)−x∗∥2 ds=0andlim inf⁡t→+∞∥x(t)−x∗∥=0.\lim_{t\to+\infty}\frac{1}{\int_{t_0}^{t}\frac{\epsilon(s)}{s}\,ds}\int_{t_0}^{t}\frac{\epsilon(s)}{s}\|x(s)-x^*\|^2\,ds=0 \qquad\text{and}\qquad \liminf_{t\to+\infty}\|x(t)-x^*\|=0 .t→+∞lim​∫t0​t​sϵ(s)​ds1​∫t0​t​sϵ(s)​∥x(s)−x∗∥2ds=0andt→+∞liminf​∥x(t)−x∗∥=0.

No rate and no constant is asserted, so the goal does not depend on any particular choice of ϵ\epsilonϵ.

Milestones

  1. Lemma 4.1 (p. 16): for α>0\alpha>0α>0, β≥0\beta\ge0β≥0, the velocity is bounded, 1t∥x˙(t)∥2∈L1([t0,+∞))\frac1t\|\dot x(t)\|^2\in L^1([t_0,+\infty))t1​∥x˙(t)∥2∈L1([t0​,+∞)), and sup⁡t≥t01t∣h˙x∗(t)∣<+∞\sup_{t\ge t_0}\frac1t|\dot h_{x^*}(t)|<+\inftysupt≥t0​​t1​∣h˙x∗​(t)∣<+∞ for every x∗∈argmin⁡gx^*\in\operatorname{argmin} gx∗∈argming.
  2. §4, p. 18: ∥xϵ∥≤∥x∗∥\|x_\epsilon\|\le\|x^*\|∥xϵ​∥≤∥x∗∥ for every ϵ>0\epsilon>0ϵ>0.
  3. §4, p. 18: lim⁡ϵ→0+xϵ=x∗\lim_{\epsilon\to0^+}x_\epsilon=x^*limϵ→0+​xϵ​=x∗ (stated in the paper as well known).
  4. (46) (p. 20): under the hypotheses of Theorem 4.2 there is C>0C>0C>0 with
∫t0tϵ(s)s(hx∗(s)−12(∥x∗∥2−∥xϵ(s)∥2))ds≤Cfor every t≥t0.\int_{t_0}^{t}\frac{\epsilon(s)}{s}\Big(h_{x^*}(s)-\frac12\big(\|x^*\|^2-\|x_{\epsilon(s)}\|^2\big)\Big)ds\le C\quad\text{for every }t\ge t_0 .∫t0​t​sϵ(s)​(hx∗​(s)−21​(∥x∗∥2−∥xϵ(s)​∥2))ds≤Cfor every t≥t0​.

Significance

The result shows that slow Tikhonov regularization still selects the minimum-norm minimizer when Hessian damping is added: the weighted time average of ∥x(t)−x∗∥2\|x(t)-x^*\|^2∥x(t)−x∗∥2 tends to zero and the trajectory comes arbitrarily close to x∗x^*x∗ infinitely often. The hypothesis is only α>0\alpha>0α>0, well below the threshold α≥3\alpha\ge3α≥3 that the paper needs for its fast rates, and no growth condition on ϵ\epsilonϵ beyond the divergence of ∫ϵ(t)/t dt\int\epsilon(t)/t\,dt∫ϵ(t)/tdt is imposed. It complements Theorem 4.4 of the same paper (a separate mission), which reaches full strong convergence under fast decay, ∫ϵ(t)/t dt<+∞\int\epsilon(t)/t\,dt<+\infty∫ϵ(t)/tdt<+∞, plus further conditions.

On the formal side, the theorem and its milestones are proved in the paper and in the cited literature, but none of them has a machine-checked proof. The work splits into an energy estimate for a nonautonomous second order ODE in a Hilbert space (Lemma 4.1), two facts about the Tikhonov curve that underlie the whole theory of Tikhonov regularization of convex problems (milestones 2 and 3), an integrated differential inequality (46), and a l'Hospital-type averaging step. The Tikhonov-curve facts are reusable for any formal treatment of viscosity selection and minimum-norm solutions.

Difficulty

The obvious attempt, a Lyapunov function that decreases along the trajectory and controls ∥x(t)−x∗∥\|x(t)-x^*\|∥x(t)−x∗∥, fails because x∗x^*x∗ is not a stationary point of the perturbed system: ∇g(x∗)+ϵ(t)x∗=ϵ(t)x∗≠0\nabla g(x^*)+\epsilon(t)x^*=\epsilon(t)x^*\neq0∇g(x∗)+ϵ(t)x∗=ϵ(t)x∗=0 in general. The distance to x∗x^*x∗ therefore need not decrease, and pointwise convergence is not available under the slow-decay hypothesis alone. The comparison point has to move along the Tikhonov curve xϵ(t)x_{\epsilon(t)}xϵ(t)​, whose convergence to x∗x^*x∗ comes without a rate. The available bound controls only a weighted integral of hx∗h_{x^*}hx∗​ corrected by ∥x∗∥2−∥xϵ(s)∥2\|x^*\|^2-\|x_{\epsilon(s)}\|^2∥x∗∥2−∥xϵ(s)​∥2, so the passage from (46) to the goal needs both xϵ(t)→x∗x_{\epsilon(t)}\to x^*xϵ(t)​→x∗ and the divergence of the weight. In the formal setting the Tikhonov-curve limit is itself a nontrivial weak-compactness argument in a Hilbert space, which the paper does not spell out.

Formalization scope

  • H\mathcal HH is a real Hilbert space (InnerProductSpace ℝ H, CompleteSpace H). ∇g\nabla g∇g is gradient g, and ∇2g(x)v\nabla^2 g(x)v∇2g(x)v is the Fréchet derivative of gradient g at xxx applied to vvv. "Twice Fréchet differentiable" means ggg and ∇g\nabla g∇g are differentiable; Lipschitz continuity on bounded sets is stated on every closed ball around the origin.
  • Trajectories are maps R→H\mathbb R\to\mathcal HR→H with explicit velocity and acceleration maps; derivatives are taken within [t0,+∞)[t_0,+\infty)[t0​,+∞) (one-sided at t0t_0t0​), the acceleration is continuous there, and values before t0t_0t0​ are irrelevant. The same holds for ϵ\epsilonϵ and its derivative.
  • Every theorem is stated for every global C2C^2C2-solution of (5). Existence and uniqueness of the solution is Theorem 2.1 of the paper, a milestone of the first mission of this series, so the hypothesis is not vacuous. The paper's standing α≥3\alpha\ge3α≥3 is replaced by each statement's own hypothesis α>0\alpha>0α>0.
  • The minimum-norm minimizer and the Tikhonov points are predicates on a candidate point; where the curve ϵ↦xϵ\epsilon\mapsto x_\epsilonϵ↦xϵ​ is needed, a selection that minimizes g+ϵ2∥⋅∥2g+\frac\epsilon2\|\cdot\|^2g+2ϵ​∥⋅∥2 for every ϵ>0\epsilon>0ϵ>0 is a hypothesis.
  • ∫t0+∞ϵ(t)/t dt=+∞\int_{t_0}^{+\infty}\epsilon(t)/t\,dt=+\infty∫t0​+∞​ϵ(t)/tdt=+∞ is stated as divergence of T↦∫t0Tϵ(t)/t dtT\mapsto\int_{t_0}^T\epsilon(t)/t\,dtT↦∫t0​T​ϵ(t)/tdt, never as an equation for a Bochner integral, which would take the value 000 on a non-integrable function. Likewise, lim inf⁡∥x(t)−x∗∥=0\liminf\|x(t)-x^*\|=0liminf∥x(t)−x∗∥=0 is stated as "for every δ>0\delta>0δ>0, ∥x(t)−x∗∥<δ\|x(t)-x^*\|<\delta∥x(t)−x∗∥<δ for arbitrarily large ttt", not through Filter.liminf, whose value on an unbounded function is a default. sup⁡<+∞\sup<+\inftysup<+∞ is boundedness from above of the image of [t0,+∞)[t_0,+\infty)[t0​,+∞), and L1L^1L1 membership is integrability on [t0,+∞)[t_0,+\infty)[t0​,+∞).
  • Definitions needed: argmin, the minimum-norm minimizer, Tikhonov points, the General assumption, the solution predicate for (5), and hx∗h_{x^*}hx∗​, h˙x∗\dot h_{x^*}h˙x∗​. These duplicate objects of the other two missions of the series, which are drafted independently.
  • Contributions welcome: proofs of the Tikhonov-curve facts in Mathlib generality, the energy estimate of Lemma 4.1, a continuous l'Hospital/Cesàro lemma for weighted averages, and the goal itself.

Selected references

  • R.I. Boţ, E.R. Csetnek, S.C. László, Tikhonov regularization of a second order dynamical system with Hessian driven damping, Mathematical Programming, 2021, https://doi.org/10.1007/s10107-020-01528-8 (cited from arXiv:1911.12845v2). https://arxiv.org/abs/1911.12845
  • H. Attouch, Z. Chbani, H. Riahi, Combining fast inertial dynamics for convex optimization with Tikhonov regularization, Journal of Mathematical Analysis and Applications 457(2), 1065–1094, 2018. https://arxiv.org/abs/1602.01973
  • H. Attouch, J. Peypouquet, P. Redont, Fast convex optimization via inertial dynamics with Hessian driven damping, Journal of Differential Equations 261(10), 5734–5783, 2016. https://arxiv.org/abs/1601.07113
  • H. Attouch, Z. Chbani, J. Peypouquet, P. Redont, Fast convergence of inertial dynamics and algorithms with asymptotic vanishing viscosity, Mathematical Programming 168, 123–175, 2018. https://arxiv.org/abs/1507.04782
  • W. Su, S. Boyd, E.J. Candès, A differential equation for modeling Nesterov's accelerated gradient method: theory and insights, Journal of Machine Learning Research 17(153), 1–43, 2016. https://arxiv.org/abs/1503.01243
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Machine LearningOptimizationProbability·Captain: mikedeng1

Convergence and Dynamical Behavior of the ADAM Algorithm for Nonconvex Stochastic Optimization 4: Constant-Step Adam Iterates Converge in Probability to the ODE Solution as γ → 0Research Paper

Motivation

Adam (Kingma and Ba, 2015, arXiv:1412.6980) is one of the most widely used optimizers for training neural networks. It is a stochastic gradient method that keeps exponential moving averages of the stochastic gradients and of their coordinatewise squares, rescales both by bias-correction factors, and divides the first by the square root of the second. Its original convergence analysis was shown to be flawed (Reddi, Kale and Kumar, 2018, arXiv:1904.09237), and in the regime in which practitioners actually run it, with a constant step size, the iterates do not converge.

Barakat and Bianchi (arXiv:1810.02263, SIAM J. Math. Data Sci. 3(1), 2021) describe what constant-step Adam does instead: as the step size γ\gammaγ tends to zero, with the averaging parameters tending to one at rates proportional to γ\gammaγ, the interpolated iterates follow a deterministic non-autonomous ordinary differential equation. This mission formalizes that statement, Theorem 4.3 of the paper, together with the lemmas of its proof (§8.1).

Setting

Let Z=Rd×Rd×Rd\mathcal Z = \mathbb R^d\times\mathbb R^d\times\mathbb R^dZ=Rd×Rd×Rd with points z=(x,m,v)z=(x,m,v)z=(x,m,v), and Z+\mathcal Z_+Z+​ its subset with v≥0v\ge0v≥0 coordinatewise. A random sample ξ\xiξ with law μ\muμ on a measurable space Ξ\XiΞ enters through an integrand f(x,ξ)f(x,\xi)f(x,ξ) with gradient ∇f(x,ξ)\nabla f(x,\xi)∇f(x,ξ) in xxx. The objective is F(x)=Ef(x,ξ)F(x)=\mathbb E f(x,\xi)F(x)=Ef(x,ξ) and the second-moment map is S(x)=E∇f(x,ξ)⊙2S(x)=\mathbb E\nabla f(x,\xi)^{\odot2}S(x)=E∇f(x,ξ)⊙2.

Given a step size γ>0\gamma>0γ>0, parameters α=αˉ(γ)\alpha=\bar\alpha(\gamma)α=αˉ(γ), β=βˉ(γ)\beta=\bar\beta(\gamma)β=βˉ​(γ) in [0,1)[0,1)[0,1) and ε>0\varepsilon>0ε>0, the Adam map Tγ,α,β(n,z,ξ)T_{\gamma,\alpha,\beta}(n,z,\xi)Tγ,α,β​(n,z,ξ) sends (x,m,v)(x,m,v)(x,m,v) to

(x−γ(1−αn)−1(αm+(1−α)∇f(x,ξ))ε+(1−βn)−1/2(βv+(1−β)∇f(x,ξ)⊙2)1/2, αm+(1−α)∇f(x,ξ), βv+(1−β)∇f(x,ξ)⊙2),\Big(x-\frac{\gamma(1-\alpha^n)^{-1}(\alpha m+(1-\alpha)\nabla f(x,\xi))}{\varepsilon+(1-\beta^n)^{-1/2}(\beta v+(1-\beta)\nabla f(x,\xi)^{\odot2})^{1/2}},\ \alpha m+(1-\alpha)\nabla f(x,\xi),\ \beta v+(1-\beta)\nabla f(x,\xi)^{\odot2}\Big),(x−ε+(1−βn)−1/2(βv+(1−β)∇f(x,ξ)⊙2)1/2γ(1−αn)−1(αm+(1−α)∇f(x,ξ))​, αm+(1−α)∇f(x,ξ), βv+(1−β)∇f(x,ξ)⊙2),

coordinatewise. The constant-step iterates are z0γ=(x0,0,0)z^\gamma_0=(x_0,0,0)z0γ​=(x0​,0,0) and znγ=Tγ,αˉ(γ),βˉ(γ)(n,zn−1γ,ξn)z^\gamma_n=T_{\gamma,\bar\alpha(\gamma),\bar\beta(\gamma)}(n,z^\gamma_{n-1},\xi_n)znγ​=Tγ,αˉ(γ),βˉ​(γ)​(n,zn−1γ​,ξn​), driven by iid samples ξ1,ξ2,…\xi_1,\xi_2,\dotsξ1​,ξ2​,… with law μ\muμ. The interpolated process zγ\mathsf z^\gammazγ is the piecewise linear function with zγ(nγ)=znγ\mathsf z^\gamma(n\gamma)=z^\gamma_nzγ(nγ)=znγ​.

The parameters satisfy (1−αˉ(γ))/γ→a>0(1-\bar\alpha(\gamma))/\gamma\to a>0(1−αˉ(γ))/γ→a>0 and (1−βˉ(γ))/γ→b>0(1-\bar\beta(\gamma))/\gamma\to b>0(1−βˉ​(γ))/γ→b>0 as γ↓0\gamma\downarrow0γ↓0, with b≤4ab\le4ab≤4a. The limiting Adam ODE is z˙(t)=h(t,z(t))\dot z(t)=h(t,z(t))z˙(t)=h(t,z(t)), z(0)=(x0,0,0)z(0)=(x_0,0,0)z(0)=(x0​,0,0), where for t>0t>0t>0

h(t,z)=(−(1−e−at)−1mε+(1−e−bt)−1v, a(∇F(x)−m), b(S(x)−v)).h(t,z)=\Big(-\frac{(1-e^{-at})^{-1}m}{\varepsilon+\sqrt{(1-e^{-bt})^{-1}v}},\ a(\nabla F(x)-m),\ b(S(x)-v)\Big).h(t,z)=(−ε+(1−e−bt)−1v​(1−e−at)−1m​, a(∇F(x)−m), b(S(x)−v)).

The vector field is singular at t=0t=0t=0. A global solution is a continuous map z:[0,∞)→Z+z:[0,\infty)\to\mathcal Z_+z:[0,∞)→Z+​, continuously differentiable on (0,∞)(0,\infty)(0,∞), that satisfies the equation for every t>0t>0t>0.

Formalization targets

Goal: Theorem 4.3

Under the paper's assumptions (Assumptions 2.2 to 2.5, 4.1, and 4.2 ii) with p=2p=2p=2),

∀T>0, ∀δ>0,lim⁡γ↓0P(sup⁡t∈[0,T]∥zγ(t)−z(t)∥>δ)=0,\forall T>0,\ \forall\delta>0,\qquad \lim_{\gamma\downarrow0}\mathbb P\Big(\sup_{t\in[0,T]}\|\mathsf z^\gamma(t)-z(t)\|>\delta\Big)=0,∀T>0, ∀δ>0,γ↓0lim​P(t∈[0,T]sup​∥zγ(t)−z(t)∥>δ)=0,

where zzz is the global solution of the Adam ODE from (x0,0,0)(x_0,0,0)(x0​,0,0).

Milestones, in the order of the proof

  • Lemma 8.1. A uniform (1+s)(1+s)(1+s)-moment bound for the normalised increment Hγ(n+1,z,ξ)H_\gamma(n+1,z,\xi)Hγ​(n+1,z,ξ) over states whose debiased version eγ(n,z)e_\gamma(n,z)eγ​(n,z) lies in a ball.
  • Lemma 8.2. Uniform integrability of the increments of the truncated iterates zγ,Rz^{\gamma,R}zγ,R, which are stopped when the debiased iterate leaves the ball of radius RRR.
  • Lemma 8.3. Tightness of the truncated interpolated processes zγ,R\mathsf z^{\gamma,R}zγ,R in C([0,∞),Z)C([0,\infty),\mathcal Z)C([0,∞),Z), and vanishing in probability of the accumulated martingale noise.
  • Proposition 7.6. The debiased trajectories eˉ(t+η,z(t))\bar e(t+\eta,z(t))eˉ(t+η,z(t)) of the ODE stay in one compact set, and F(x(t))≤F(x0)F(x(t))\le F(x_0)F(x(t))≤F(x0​).
  • Lemma 8.4. The mean field hγ(φn,zn)h_\gamma(\varphi_n,z_n)hγ​(φn​,zn​) converges to h(t,z)h(t,z)h(t,z) when γφn→t\gamma\varphi_n\to tγφn​→t.
  • Proposition 7.12. The ODE has exactly one global solution.
  • Eq. (8.4). For R≥R0+1R\ge R_0+1R≥R0​+1, with R0=sup⁡t>0∥eˉ(t,z(t))∥R_0=\sup_{t>0}\|\bar e(t,z(t))\|R0​=supt>0​∥eˉ(t,z(t))∥, the truncated process converges in probability to zzz, uniformly on [0,T][0,T][0,T].

Significance

The theorem justifies the continuous-time model of Adam that the rest of the paper studies. The convergence of the ODE to critical points of FFF (Theorem 3.2) and its rates (Theorem 3.4) say something about the algorithm only through this approximation. It also isolates the role of the bias correction: the initial condition (x0,0,0)(x_0,0,0)(x0​,0,0) and the singular factors (1−e−at)−1(1-e^{-at})^{-1}(1−e−at)−1 are exactly the trace of the debiasing step. The result is also the first step towards the long-run statement, Theorem 4.5, which bounds the time the iterates spend away from the critical points as γ→0\gamma\to0γ→0.

The result is proved on paper. To the knowledge of the mission, none of it is machine-checked: there is no formal statement of Adam's dynamics on Prove2Me. A complete development would give a checked instance of the ODE method for a non-autonomous, non-Lipschitz limit field and a non-homogeneous Markov chain. The surrounding stochastic-approximation library (truncation by stopping, uniform integrability, tightness of interpolated processes, identification of limits by uniqueness) applies to other adaptive methods, such as RMSProp and AdaGrad variants, whose limits have the same structure.

Difficulty

The standard argument for constant-step stochastic approximation assumes a time-homogeneous mean field that is locally Lipschitz. Neither holds here. The mean field hγ(n,z)h_\gamma(n,z)hγ​(n,z) depends on the iteration index through the bias-correction factors (1−αˉ(γ)n)−1(1-\bar\alpha(\gamma)^n)^{-1}(1−αˉ(γ)n)−1. Its limit h(t,z)h(t,z)h(t,z) blows up as t↓0t\downarrow0t↓0, and it is not Lipschitz in vvv near v=0v=0v=0. A direct Gronwall comparison between the iterates and the ODE therefore fails at the origin of time, which is exactly where the iterates start.

The iterates are also not known to be bounded. Moment bounds are only available while the debiased iterate stays in a ball. This forces the truncation BRB_RBR​ and an argument that the truncation is asymptotically never triggered. That argument needs uniform-in-time bounds on the ODE solution (Proposition 7.6) and uniqueness of the solution (Proposition 7.12), and both rest on a Lyapunov function specific to the case b≤4ab\le4ab≤4a.

Formalization scope

  • Spaces and norm. Z\mathcal ZZ is E d × E d × E d with E d = EuclideanSpace ℝ (Fin d). The paper's ∥z∥\|z\|∥z∥ is the Euclidean norm of R3d\mathbb R^{3d}R3d, written zNorm, not Mathlib's max norm on products.
  • Randomness. The probability space is explicit, with a probability measure P\mathbb PP. Independence of (ξn+1)n(\xi_{n+1})_n(ξn+1​)n​ is stated with iIndepFun. Every ξn\xi_nξn​ with n≥1n\ge1n≥1 has law μ\muμ, and ξ0\xi_0ξ0​ is unused.
  • Gradient. ∇f\nabla f∇f is a given function, measurable in ξ\xiξ, that equals the gradient of f(⋅,ξ)f(\cdot,\xi)f(⋅,ξ) for μ\muμ-almost every ξ\xiξ.
  • Expectations. Moments, the supremum in Lemma 8.1 and the uniform-integrability expectations are lower Lebesgue integrals in [0,∞][0,\infty][0,∞]. FFF, SSS and hγh_\gammahγ​ are Bochner integrals, which the hypotheses make integrable.
  • Solutions of the ODE. Solutions are maps R→Z\mathbb R\to\mathcal ZR→Z constrained only on [0,∞)[0,\infty)[0,∞), and uniqueness means agreement on [0,∞)[0,\infty)[0,∞). The deterministic Propositions 7.6 and 7.12 are stated for any FFF, SSS satisfying the paper's §7 hypotheses.
  • Limit and supremum. The limit is γ↓0\gamma\downarrow0γ↓0 (𝓝[>] 0). The event sup⁡t∈[0,T]∥⋅∥>δ\sup_{t\in[0,T]}\|\cdot\|>\deltasupt∈[0,T]​∥⋅∥>δ is written as ∃t∈[0,T], ∥⋅∥>δ\exists t\in[0,T],\ \|\cdot\|>\delta∃t∈[0,T], ∥⋅∥>δ, which is the same event.
  • Tightness. Tightness uses the paper's definition with the compact-open topology on C([0,∞),Z)C([0,\infty),\mathcal Z)C([0,∞),Z).
  • Corrections. Two corrections to the page are recorded in the items. Lemma 8.1 restricts its n=0n=0n=0 case to m=0m=0m=0, because as printed that case is false. Lemma 8.4 states the implicit regime γn→0\gamma_n\to0γn​→0.

A trivializing formalization is ruled out. TTT and δ\deltaδ are universally quantified before the limit and do not depend on γ\gammaγ. The limit is in γ\gammaγ, not in nnn at fixed γ\gammaγ; constant-step Adam does not converge in nnn. The solution zzz is tied to the FFF, SSS, aaa, bbb of the stochastic model. All hypotheses are satisfiable, for example by f(x,ξ)=12∥x∥2+ξ⟨1,x⟩f(x,\xi)=\tfrac12\|x\|^2+\xi\langle\mathbf 1,x\ranglef(x,ξ)=21​∥x∥2+ξ⟨1,x⟩ with ξ=±1\xi=\pm1ξ=±1 equiprobable.

A complete development needs:

  • the deterministic ODE theory of the companion missions (Lyapunov function, boundedness, uniqueness);
  • moment and uniform-integrability bounds;
  • a tightness criterion in C([0,∞),Z)C([0,\infty),\mathcal Z)C([0,∞),Z) (Arzelà–Ascoli);
  • Prokhorov's and Skorokhod's theorems, or a direct substitute;
  • dominated convergence for hγh_\gammahγ​.

Contributions to any of these, including reusable statements about interpolated processes, are welcome.

Selected references

  • A. Barakat and P. Bianchi, Convergence and Dynamical Behavior of the ADAM Algorithm for Nonconvex Stochastic Optimization, SIAM J. Math. Data Sci. 3(1), 2021; arXiv:1810.02263v4. https://arxiv.org/abs/1810.02263
  • D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, ICLR, 2015. https://arxiv.org/abs/1412.6980
  • S. J. Reddi, S. Kale and S. Kumar, On the Convergence of Adam and Beyond, ICLR, 2018. https://arxiv.org/abs/1904.09237
  • M. Benaïm, Dynamics of stochastic approximation algorithms, Séminaire de Probabilités XXXIII, LNM 1709, Springer, 1999. https://doi.org/10.1007/BFb0096509
  • P. Bianchi, W. Hachem and A. Salim, Constant step stochastic approximations involving differential inclusions: stability, long-run convergence and applications, Stochastics 91, 2019, pp. 288–320 (reference [6] of the paper; its Lemma 6.2 gives Lemma 8.3). https://arxiv.org/abs/1802.07981
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Machine LearningOptimization·Captain: mikedeng1

Convergence and Dynamical Behavior of the ADAM Algorithm for Nonconvex Stochastic Optimization 1: The Continuous-Time Adam ODE Has a Unique Bounded Global Solution from (x₀, 0, 0)Research Paper

Motivation

Adam (Kingma and Ba, 2015, arXiv:1412.6980) is the default optimizer for training deep neural networks. At each step it keeps exponential moving averages mnm_nmn​ and vnv_nvn​ of the stochastic gradients and of their coordinatewise squares, corrects both for their initialization at zero (the bias-correction or debiasing step m^n=mn/(1−αn)\hat m_n = m_n/(1-\alpha^n)m^n​=mn​/(1−αn), v^n=vn/(1−βn)\hat v_n = v_n/(1-\beta^n)v^n​=vn​/(1−βn)), and moves the iterate by −γ m^n/(ε+v^n)-\gamma\,\hat m_n/(\varepsilon + \sqrt{\hat v_n})−γm^n​/(ε+v^n​​). Despite its use, its convergence theory for nonconvex objectives was thin; the original convergence argument was later shown to be flawed (Reddi, Kale and Kumar, 2018, arXiv:1904.09237).

Barakat and Bianchi (arXiv:1810.02263v4, SIAM J. Math. Data Sci. 3(1), 2021, doi:10.1137/19M1263443) study Adam through the ODE method: when the stepsize γ\gammaγ tends to zero and the averaging constants satisfy (1−α)/γ→a(1-\alpha)/\gamma \to a(1−α)/γ→a, (1−β)/γ→b(1-\beta)/\gamma \to b(1−β)/γ→b, the interpolated iterates follow a deterministic non-autonomous differential equation, the continuous-time Adam ODE. Everything else in the paper (convergence of the trajectory, rates under a Łojasiewicz condition, tracking by the constant-step iterates, almost-sure convergence of a decreasing-step variant) presupposes that this ODE is well posed from Adam's initialization. This mission formalizes that well-posedness result, Theorem 3.1 of the paper.

Setting

Let d≥1d \ge 1d≥1 and Z=Rd×Rd×Rd\mathcal Z = \mathbb R^d \times \mathbb R^d \times \mathbb R^dZ=Rd×Rd×Rd, with points z=(x,m,v)z = (x, m, v)z=(x,m,v); set Z+=Rd×Rd×[0,+∞)d\mathcal Z_+ = \mathbb R^d \times \mathbb R^d \times [0,+\infty)^dZ+​=Rd×Rd×[0,+∞)d, Z+∗=Rd×Rd×(0,+∞)d\mathcal Z_+^* = \mathbb R^d \times \mathbb R^d \times (0,+\infty)^dZ+∗​=Rd×Rd×(0,+∞)d and Z0={(x,0,0)}\mathcal Z_0 = \{(x,0,0)\}Z0​={(x,0,0)}. The data are reals a,b,ε>0a, b, \varepsilon > 0a,b,ε>0, a function F:Rd→RF : \mathbb R^d \to \mathbb RF:Rd→R (the objective) and a map S:Rd→RdS : \mathbb R^d \to \mathbb R^dS:Rd→Rd (in the paper, F(x)=Ef(x,ξ)F(x) = \mathbb E f(x,\xi)F(x)=Ef(x,ξ) and S(x)=E ∇f(x,ξ)⊙2S(x) = \mathbb E\,\nabla f(x,\xi)^{\odot 2}S(x)=E∇f(x,ξ)⊙2, the mean squared gradient). All vector operations are coordinatewise. The Adam vector field is, for t>0t > 0t>0,

h(t,z)=(−(1−e−at)−1mε+(1−e−bt)−1v, a(∇F(x)−m), b(S(x)−v)),h(t,z) = \left( -\frac{(1-e^{-at})^{-1} m}{\varepsilon + \sqrt{(1-e^{-bt})^{-1} v}},\ a(\nabla F(x) - m),\ b(S(x) - v) \right),h(t,z)=(−ε+(1−e−bt)−1v​(1−e−at)−1m​, a(∇F(x)−m), b(S(x)−v)),

and (ODE) is z˙(t)=h(t,z(t))\dot z(t) = h(t, z(t))z˙(t)=h(t,z(t)). The factors (1−e−at)−1(1-e^{-at})^{-1}(1−e−at)−1 and (1−e−bt)−1(1-e^{-bt})^{-1}(1−e−bt)−1 are the continuous-time debiasing. A global solution with initial condition (x0,0,0)(x_0,0,0)(x0​,0,0) is a continuous map z:[0,+∞)→Z+z : [0,+\infty) \to \mathcal Z_+z:[0,+∞)→Z+​, continuously differentiable on (0,+∞)(0,+\infty)(0,+∞), satisfying (ODE) for every t>0t > 0t>0, with z(0)=(x0,0,0)z(0) = (x_0,0,0)z(0)=(x0​,0,0).

The proof works with the regularized equations (ODEη)(\mathrm{ODE}_\eta)(ODEη​): z˙(t)=h(t+η,z(t))\dot z(t) = h(t+\eta, z(t))z˙(t)=h(t+η,z(t)) for η∈[0,+∞)\eta \in [0,+\infty)η∈[0,+∞), and z˙=h∞(z)\dot z = h_\infty(z)z˙=h∞​(z) for η=+∞\eta = +\inftyη=+∞, where h∞(x,m,v)=(−m/(ε+v),a(∇F(x)−m),b(S(x)−v))h_\infty(x,m,v) = (-m/(\varepsilon+\sqrt v), a(\nabla F(x)-m), b(S(x)-v))h∞​(x,m,v)=(−m/(ε+v​),a(∇F(x)−m),b(S(x)−v)). The fields are extended to Z\mathcal ZZ by replacing vvv with ∣v∣|v|∣v∣, and ZTη(z0)Z^\eta_T(z_0)ZTη​(z0​) denotes the set of solutions on [0,T)[0,T)[0,T), T∈(0,+∞]T \in (0,+\infty]T∈(0,+∞]. The Lyapunov function is V(t,z)=F(x)+12∑imi2/U(t,vi)V(t,z) = F(x) + \tfrac12\sum_i m_i^2/U(t,v_i)V(t,z)=F(x)+21​∑i​mi2​/U(t,vi​) with U(t,vi)=a(1−e−at)(ε+vi/(1−e−bt))U(t,v_i) = a(1-e^{-at})(\varepsilon + \sqrt{v_i/(1-e^{-bt})})U(t,vi​)=a(1−e−at)(ε+vi​/(1−e−bt)​), and its limit is V∞V_\inftyV∞​. The debiasing map is eˉ(t,z)=(x,m/(1−e−at),v/(1−e−bt))\bar e(t,z) = (x, m/(1-e^{-at}), v/(1-e^{-bt}))eˉ(t,z)=(x,m/(1−e−at),v/(1−e−bt)).

The hypotheses are those of §7 of the paper: FFF is C1C^1C1 with locally Lipschitz gradient (Assumption 7.1) and coercive (Assumption 2.3); SSS is locally Lipschitz (Assumption 7.2) with S(x)>0S(x) > 0S(x)>0 coordinatewise (Assumption 2.4); and 0<b≤4a0 < b \le 4a0<b≤4a (Assumption 2.5).

Formalization targets

Goal: Theorem 3.1

For every x0∈Rdx_0 \in \mathbb R^dx0​∈Rd there is a global solution zzz of (ODE) with initial condition (x0,0,0)(x_0,0,0)(x0​,0,0) such that

z([0,+∞)) is bounded,z([0,+\infty)) \text{ is bounded},z([0,+∞)) is bounded,

and any two global solutions with initial condition (x0,0,0)(x_0,0,0)(x0​,0,0) coincide on [0,+∞)[0,+\infty)[0,+∞).

Milestones (§7.1–7.2, in the order of the proof)

  1. Lemma 7.3: solutions from (x0,0,0)(x_0,0,0)(x0​,0,0) are C1C^1C1 on [0,T)[0,T)[0,T), with x˙(0)=−∇F(x0)/(ε+S(x0))\dot x(0) = -\nabla F(x_0)/(\varepsilon+\sqrt{S(x_0)})x˙(0)=−∇F(x0​)/(ε+S(x0​)​), m˙(0)=a∇F(x0)\dot m(0) = a\nabla F(x_0)m˙(0)=a∇F(x0​), v˙(0)=bS(x0)\dot v(0) = bS(x_0)v˙(0)=bS(x0​).
  2. Lemma 7.4: solutions of every (ODEη)(\mathrm{ODE}_\eta)(ODEη​) from Z+\mathcal Z_+Z+​ lie in Z+∗\mathcal Z_+^*Z+∗​ for t∈(0,T)t \in (0,T)t∈(0,T).
  3. Lemma 7.5: ⟨∇V∞,h∞⟩≤−ε∥am/U∞(v)∥2\langle \nabla V_\infty, h_\infty\rangle \le -\varepsilon\|am/U_\infty(v)\|^2⟨∇V∞​,h∞​⟩≤−ε∥am/U∞​(v)∥2 and ⟨∇V(t,z),(1,h(t,z))⟩≤−ε2∥am/U(t,v)∥2\langle \nabla V(t,z), (1, h(t,z))\rangle \le -\tfrac\varepsilon2 \|am/U(t,v)\|^2⟨∇V(t,z),(1,h(t,z))⟩≤−2ε​∥am/U(t,v)∥2 on Z+∗\mathcal Z_+^*Z+∗​.
  4. Proposition 7.6: one compact set contains eˉ(t+η,z(t))\bar e(t+\eta, z(t))eˉ(t+η,z(t)) for all η∈[0,∞)\eta \in [0,\infty)η∈[0,∞), all TTT and all solutions from (x0,0,0)(x_0,0,0)(x0​,0,0); and F(x(t))≤F(x0)F(x(t)) \le F(x_0)F(x(t))≤F(x0​).
  5. Lemma 7.8 ii): vi(t)≥cmin⁡(1,t)v_i(t) \ge c\min(1,t)vi​(t)≥cmin(1,t), uniformly in η∈[0,∞)\eta \in [0,\infty)η∈[0,∞).
  6. Corollary 7.9: global solutions of (ODE∞)(\mathrm{ODE}_\infty)(ODE∞​) and of (ODEη)(\mathrm{ODE}_\eta)(ODEη​), η>0\eta > 0η>0, exist.
  7. Lemma 7.10: the regularized solutions (zη)η>0(z_\eta)_{\eta>0}(zη​)η>0​ are equicontinuous.
  8. Proposition 7.11: existence for (ODE).
  9. Proposition 7.12: uniqueness for (ODE).

Significance

Theorem 3.1 makes the continuous-time Adam trajectory a well-defined object. The paper's convergence theorem for this trajectory (Theorem 3.2), its rate under a Łojasiewicz condition (Theorem 3.4) and the tracking result for the constant-step iterates (Theorem 4.3) are all stated about "the" solution, and the semiflow used for the asymptotic pseudotrajectory argument rests on the same uniqueness. The bound of Proposition 7.6 also gives the cost decrease F(x(t))≤F(x0)F(x(t)) \le F(x_0)F(x(t))≤F(x0​), which the authors read as evidence that the bias correction makes early Adam steps safe.

The result is proved in the paper, and as far as is known nothing here has been formalized; no statement about this ODE exists on the platform. The mission formalizes the statement and the numbered lemmas of its proof. A complete development would also be a worked example of existence for an ODE whose field is neither continuous in time at t=0t = 0t=0 nor Lipschitz in space, by regularization, compactness and passage to the limit.

Difficulty

Off-the-shelf theory does not apply. Cauchy–Lipschitz fails because h(t,⋅)h(t,\cdot)h(t,⋅) contains v\sqrt vv​, which is not Lipschitz at v=0v = 0v=0, and the initial condition has v=0v = 0v=0. Cauchy–Peano fails because h(⋅,z)h(\cdot,z)h(⋅,z) blows up as t↓0t \downarrow 0t↓0: the factor (1−e−at)−1(1-e^{-at})^{-1}(1−e−at)−1 behaves like 1/(at)1/(at)1/(at). The obvious remedy, shifting time by η>0\eta > 0η>0, gives solutions zηz_\etazη​, but passing to the limit η↓0\eta \downarrow 0η↓0 needs bounds uniform in η\etaη, both on the trajectory and on how far vvv stays from zero. Uniqueness then has to be proved by a Grönwall argument whose coefficient is not integrable at 000, so the initial behaviour of solutions must be controlled to second order. Solutions from initial conditions with m0≠0m_0 \ne 0m0​=0, v0=0v_0 = 0v0​=0 need not exist at all (p. 5), so the special initialization (x0,0,0)(x_0,0,0)(x0​,0,0) is essential, not cosmetic.

Formalization scope

Vectors are EuclideanSpace ℝ (Fin d) and states are triples; Lean's norm on the product is the maximum of the three Euclidean norms, which changes no boundedness, compactness or continuity statement. FFF and SSS are abstract functions satisfying Assumptions 7.1, 7.2, 2.3 and 2.4, as in §7 of the paper. Under Assumption 2.2 the expectation-defined FFF and SSS of (2.2) satisfy these, so the goal implies the theorem as printed. Coercivity is Tendsto F (cocompact _) atTop. Solutions are maps R→Z\mathbb R \to \mathcal ZR→Z constrained only on [0,T)[0,T)[0,T): uniqueness is agreement on [0,+∞)[0,+\infty)[0,+∞), never ∃! over all functions. The field is evaluated only at t>0t > 0t>0, where Lean's (1 - exp 0)⁻¹ = 0 cannot interfere. η\etaη ranges over WithTop ℝ≥0 (⊤\top⊤ is +∞+\infty+∞ and selects h∞h_\inftyh∞​), and TTT over WithTop ℝ. The solution sets ZTηZ^\eta_TZTη​ are Z\mathcal ZZ-valued with the ∣v∣|v|∣v∣ extension, as on p. 12, so positivity of vvv is a theorem (Lemma 7.4), not an assumption.

A notion of solution without the differential equation, with it only away from t=0t = 0t=0 on a set like t≥1t \ge 1t≥1, or with a derivative not tied to hhh would make existence trivial. The definitions here require z˙(t)=h(t,z(t))\dot z(t) = h(t,z(t))z˙(t)=h(t,z(t)) at every t>0t > 0t>0, continuity at t=0t = 0t=0 and the exact initial value.

Mathlib provides Picard–Lindelöf, Grönwall-type bounds and Arzelà–Ascoli, but no Cauchy–Peano theorem; a reusable Peano existence theorem with maximal-interval extension would be a valuable contribution beyond this mission. Proofs of any milestone are welcome, as are alternative proofs of the goal.

Selected references

  • A. Barakat, P. Bianchi, Convergence and Dynamical Behavior of the ADAM Algorithm for Nonconvex Stochastic Optimization, arXiv:1810.02263v4, 2020; SIAM J. Math. Data Sci. 3(1), 2021. https://arxiv.org/abs/1810.02263, https://doi.org/10.1137/19M1263443
  • D. P. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, ICLR, 2015. https://arxiv.org/abs/1412.6980
  • S. J. Reddi, S. Kale, S. Kumar, On the Convergence of Adam and Beyond, ICLR, 2018. https://arxiv.org/abs/1904.09237
  • M. Benaïm, Dynamics of stochastic approximation algorithms, Séminaire de Probabilités XXXIII, LNM 1709, Springer, 1999. https://doi.org/10.1007/BFb0096509
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