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Mathematical Physics·Captain: Lucas

Naculich 2020: Regge colour basis of the four-gluon amplitude and the all-loop coefficient B_ℓℓResearch Paper

Motivation

At high energies (s≫−ts\gg -ts≫−t, the Regge limit) scattering amplitudes of gauge theories simplify and organise into towers of logarithms log⁡∣s/t∣\log|s/t|log∣s/t∣. For the four-gluon amplitude of N=4\mathcal N=4N=4 super-Yang–Mills theory with gauge group SU(N)SU(N)SU(N), the colour structure is what makes the nonplanar (subleading in 1/N1/N1/N) amplitude hard to control. Naculich (2020) builds a basis of colour factors CikC_{ik}Cik​ adapted to the Regge limit at every loop order, writes the ℓ\ellℓ-loop amplitude in that basis, and uses one of its coefficients, Bℓℓ(ℓ)B^{(\ell)}_{\ell\ell}Bℓℓ(ℓ)​, to motivate a conjectured all-loop relation with the Regge limit of the N=8\mathcal N=8N=8 supergravity four-graviton amplitude. The physics conjecture itself is not a mathematical statement; what is exact and checkable is the colour algebra underlying it — finite-dimensional linear algebra over polynomials in NNN.

Setting

Trace basis. Four-gluon colour factors are expanded in six single and double traces c[1],…,c[6]c[1],\dots,c[6]c[1],…,c[6] of fundamental generators (eq. (2.1)). A colour vector is a column v=(v1,…,v6)v=(v_1,\dots,v_6)v=(v1​,…,v6​) of polynomials in NNN, with complex coefficients.

Colour operators. The ttt-channel Casimir Tt2\mathbf T_t^2Tt2​ and the crossing-odd combination Ts−u2=12(Ts2−Tu2)\mathbf T_{s-u}^2=\tfrac12(\mathbf T_s^2-\mathbf T_u^2)Ts−u2​=21​(Ts2​−Tu2​) act on colour vectors as the explicit 6×66\times66×6 matrices of eq. (4.15) (entries in Z[12][N]\mathbb Z[\tfrac12][N]Z[21​][N]). The tree-level ttt-channel colour factor is C00=c[1]−c[3]C_{00}=c[1]-c[3]C00​=c[1]−c[3]. Crossing (exchange of legs 2 and 3) is the permutation PPP: c[1]↔c[3]c[1]\leftrightarrow c[3]c[1]↔c[3], c[4]↔c[6]c[4]\leftrightarrow c[6]c[4]↔c[6].

Regge colour factors. With [A,B]=AB−BA[A,B]=AB-BA[A,B]=AB−BA,

Cii=(Ts−u2)iC00,Ci1=[Tt2,[Tt2,…,[Tt2,Ts−u2]⋯ ]]C00,Ci,i−1=[Ts−u2,…,[Ts−u2,[Tt2,Ts−u2]]⋯ ]C00,C_{ii}=(\mathbf T_{s-u}^2)^iC_{00},\qquad C_{i1}=[\mathbf T_t^2,[\mathbf T_t^2,\dots,[\mathbf T_t^2,\mathbf T_{s-u}^2]\cdots]]C_{00},\qquad C_{i,i-1}=[\mathbf T_{s-u}^2,\dots,[\mathbf T_{s-u}^2,[\mathbf T_t^2,\mathbf T_{s-u}^2]]\cdots]C_{00},Cii​=(Ts−u2​)iC00​,Ci1​=[Tt2​,[Tt2​,…,[Tt2​,Ts−u2​]⋯]]C00​,Ci,i−1​=[Ts−u2​,…,[Ts−u2​,[Tt2​,Ts−u2​]]⋯]C00​,

each with exactly iii operators. The admissible pairs are (0,0)(0,0)(0,0), (1,1)(1,1)(1,1), (2,1),(2,2)(2,1),(2,2)(2,1),(2,2) and (i,1),(i,i−1),(i,i)(i,1),(i,i-1),(i,i)(i,1),(i,i−1),(i,i) for i≥3i\ge3i≥3.

Extended trace basis. At ℓ\ellℓ loops, tλ+6k(ℓ)=Nℓ−2kc[λ]t^{(\ell)}_{\lambda+6k}=N^{\ell-2k}c[\lambda]tλ+6k(ℓ)​=Nℓ−2kc[λ] for λ=1,2,3\lambda=1,2,3λ=1,2,3 and Nℓ−2k−1c[λ]N^{\ell-2k-1}c[\lambda]Nℓ−2k−1c[λ] for λ=4,5,6\lambda=4,5,6λ=4,5,6, giving 3ℓ+33\ell+33ℓ+3 elements (eq. (2.3)). The colour-ordered amplitudes Aλ(ℓ)A^{(\ell)}_\lambdaAλ(ℓ)​ of an amplitude are its coordinates in this basis (eq. (2.4)).

Regge-basis amplitude. Eq. (4.24):

A(ℓ)=A1(0)a~ℓ∑i=0ℓ∑kBik(ℓ) Nℓ−iCik.\mathcal A^{(\ell)}=A_1^{(0)}\tilde a^\ell\sum_{i=0}^{\ell}\sum_{k}B^{(\ell)}_{ik}\,N^{\ell-i}C_{ik}.A(ℓ)=A1(0)​a~ℓi=0∑ℓ​k∑​Bik(ℓ)​Nℓ−iCik​.

Null vectors. Four vectors per loop order ℓ≥2\ell\ge2ℓ≥2 (eqs. (2.10), (2.12), extended by prepending zeros) encode the SU(N)SU(N)SU(N) group-theory relations among the Aλ(ℓ)A^{(\ell)}_\lambdaAλ(ℓ)​.

Formalization targets

Goal: the all-loop coefficient Bℓℓ(ℓ)B^{(\ell)}_{\ell\ell}Bℓℓ(ℓ)​ (eqs. (4.26)–(4.27))

For every even ℓ\ellℓ,

A1(0)a~ℓ Bℓℓ(ℓ)=12⋅3ℓ/2(A3ℓ+1(ℓ)−A3ℓ+3(ℓ)),A_1^{(0)}\tilde a^\ell\,B^{(\ell)}_{\ell\ell}=\frac{1}{2\cdot3^{\ell/2}}\Big(A^{(\ell)}_{3\ell+1}-A^{(\ell)}_{3\ell+3}\Big),A1(0)​a~ℓBℓℓ(ℓ)​=2⋅3ℓ/21​(A3ℓ+1(ℓ)​−A3ℓ+3(ℓ)​),

and for ℓ=1\ell=1ℓ=1, A1(0)a~ B11(1)=−(A1(1)+A3(1))A_1^{(0)}\tilde a\,B^{(1)}_{11}=-(A^{(1)}_1+A^{(1)}_3)A1(0)​a~B11(1)​=−(A1(1)​+A3(1)​). The paper obtains the even-ℓ\ellℓ formula by guessing from explicit results through ℓ=9\ell=9ℓ=9; a proof for all ℓ\ellℓ is not given there.

Milestones

  1. Tt2C00=NC00\mathbf T_t^2C_{00}=NC_{00}Tt2​C00​=NC00​ (eqs. (4.2), (4.7)).
  2. Crossing parity: PTt2P=Tt2P\mathbf T_t^2P=\mathbf T_t^2PTt2​P=Tt2​, PTs−u2P=−Ts−u2P\mathbf T_{s-u}^2P=-\mathbf T_{s-u}^2PTs−u2​P=−Ts−u2​, PC00=−C00PC_{00}=-C_{00}PC00​=−C00​ (eqs. (3.7), (4.7)).
  3. Signature: PCik=(−1)k+1CikPC_{ik}=(-1)^{k+1}C_{ik}PCik​=(−1)k+1Cik​ for every admissible (i,k)(i,k)(i,k) (Sec. 4.4).
  4. Nℓ−iCikN^{\ell-i}C_{ik}Nℓ−iCik​ expands in t1(ℓ),…,t3ℓ+3(ℓ)t^{(\ell)}_1,\dots,t^{(\ell)}_{3\ell+3}t1(ℓ)​,…,t3ℓ+3(ℓ)​ (eqs. (2.3), (4.24)).
  5. Explicit components through three loops (eqs. (4.16), (4.20)).
  6. Explicit coefficients Bik(2)B^{(2)}_{ik}Bik(2)​, Bik(3)B^{(3)}_{ik}Bik(3)​ (eqs. (4.18), (4.22)).
  7. The Regge colour factors satisfy the four null-vector constraints and have no t2(ℓ)t^{(\ell)}_2t2(ℓ)​ component, for all ℓ≥2\ell\ge2ℓ≥2.
  8. The Regge colour factors are linearly independent, and for ℓ≥2\ell\ge2ℓ≥2 span all constrained Regge-limit amplitudes (the "basis" claim of Sec. 4).

Significance

The result. Eq. (4.26) is the input to the paper's analysis of the infrared behaviour of Bℓℓ(ℓ)B^{(\ell)}_{\ell\ell}Bℓℓ(ℓ)​ (at most 1/ϵℓ1/\epsilon^\ell1/ϵℓ, manifest for even ℓ\ellℓ because only most-subleading-colour amplitudes appear), and hence to the conjectured all-orders relation between Bℓℓ(ℓ)B^{(\ell)}_{\ell\ell}Bℓℓ(ℓ)​ and the Regge limit of N=8\mathcal N=8N=8 supergravity (Sec. 6). An all-ℓ\ellℓ proof replaces an extrapolation from nine loop orders by a theorem.

Formalizing it. The low-loop tables (4.16), (4.20), (4.18), (4.22) are finite computations; the goal and milestones 3, 4, 7, 8 are statements for every loop order and need genuine arguments (induction on ℓ\ellℓ, structure of the operators on the extended basis).

Difficulty

The coefficient Bℓℓ(ℓ)B^{(\ell)}_{\ell\ell}Bℓℓ(ℓ)​ is defined through the whole (3ℓ−2)(3\ell-2)(3ℓ−2)-element basis: a formula for it must vanish on every other Nℓ−iCikN^{\ell-i}C_{ik}Nℓ−iCik​, including the nested commutators Ci1C_{i1}Ci1​ and Ci,i−1C_{i,i-1}Ci,i−1​ whose components grow in complexity with iii. Checking the formula loop order by loop order does not give a proof; one needs to control how Tt2\mathbf T_t^2Tt2​ and Ts−u2\mathbf T_{s-u}^2Ts−u2​ act on the two most-subleading slots of the extended basis uniformly in ℓ\ellℓ.

Formalization scope

  • Colour vectors are Fin 6 → ℂ[X], with NNN the polynomial variable X; operators are Matrix (Fin 6) (Fin 6) ℂ[X] acting by Matrix.mulVec. Coefficients BikB_{ik}Bik​ and the prefactor κ=A1(0)a~ℓ\kappa=A_1^{(0)}\tilde a^\ellκ=A1(0)​a~ℓ are arbitrary complex numbers; BBB is a function ℕ × ℕ → ℂ of which only the admissible pairs matter.
  • Extended-basis indices λ\lambdaλ are 1-based natural numbers, 1≤λ≤3ℓ+31\le\lambda\le3\ell+31≤λ≤3ℓ+3; the coordinate function returns 000 outside this range. The colour-ordered amplitudes are defined as the coordinates of κ∑BikNℓ−iCik\kappa\sum B_{ik}N^{\ell-i}C_{ik}κ∑Bik​Nℓ−iCik​, so the goal says: whatever the coefficients, the stated combination of coordinates returns κBℓℓ\kappa B_{\ell\ell}κBℓℓ​. This is equivalent to the paper's reading for amplitudes lying in the span of the Regge basis (milestone 8).
  • The odd-ℓ\ellℓ formula (4.28) for ℓ>1\ell>1ℓ>1 is not included. It agrees with the model at ℓ=3\ell=3ℓ=3 (it is the B33B_{33}B33​ line of eq. (4.22)), but an exploratory exact-rational computation (not machine-checked) found that at ℓ=5\ell=5ℓ=5 no linear combination of the six amplitudes A3ℓ−8,A3ℓ−6,A3ℓ−5,A3ℓ−3,A3ℓ−2,A3ℓA_{3\ell-8},A_{3\ell-6},A_{3\ell-5},A_{3\ell-3},A_{3\ell-2},A_{3\ell}A3ℓ−8​,A3ℓ−6​,A3ℓ−5​,A3ℓ−3​,A3ℓ−2​,A3ℓ​ reproduces B55(5)B^{(5)}_{55}B55(5)​ in this model. Including it would risk a false milestone.
  • Local checks of the statements used Lean v4.28.0 with Mathlib; only standard Mathlib API (polynomials, matrices, LinearIndependent, finite sums) is used.
  • Welcome contributions: lemmas describing Tt2\mathbf T_t^2Tt2​ and Ts−u2\mathbf T_{s-u}^2Ts−u2​ as operators on extended-basis coordinates (shift of NNN-degree), and general tools for iterated commutators.

Selected references

  • S. G. Naculich, All-loop-orders relation between Regge limits of N=4\mathcal N=4N=4 SYM and N=8\mathcal N=8N=8 supergravity four-point amplitudes, arXiv:2012.00030 (2020), https://arxiv.org/abs/2012.00030.
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Mathematical Physics·Captain: Lucas

Gubser-Klebanov-Polyakov 1998: dimensions of operators dual to massive string statesResearch Paper

Motivation

In 1997 Maldacena conjectured that N=4\mathcal N=4N=4 super-Yang–Mills theory with gauge group U(N)U(N)U(N) is equivalent to type IIB string theory on AdS5×S5\mathrm{AdS}_5\times S^5AdS5​×S5 (hep-th/9711200). The paper of Gubser, Klebanov and Polyakov (hep-th/9802109), together with Witten's paper of the same year (hep-th/9802150), turned the conjecture into a computational rule: the generating functional of connected gauge theory correlators equals the minimum of the supergravity action subject to boundary conditions at the edge of anti-de Sitter space. The concrete outputs of the paper are two-point functions and operator dimensions, read off from the small-momentum, non-analytic behaviour of solutions of a radial ordinary differential equation.

The mathematical core is classical analysis of modified Bessel functions: which solution is regular in the throat, how KνK_\nuKν​ splits into I±νI_{\pm\nu}I±ν​, and what the leading non-analytic term of a logarithmic derivative is as the momentum tends to zero. The physical headline, Eq. (46), is that operators dual to massive string states at level nnn acquire dimensions growing like 2(n gYM2N)1/22(n\,g_{YM}\sqrt{2N})^{1/2}2(ngYM​2N​)1/2 at large 't Hooft coupling.

Setting

Let R>0R>0R>0 be the AdS radius and z>0z>0z>0 the radial coordinate, with the boundary at z=Rz=Rz=R and the horizon at z=∞z=\inftyz=∞. For a bulk field ϕ(x,z)=eik⋅xf~k(z)\phi(x,z)=e^{ik\cdot x}\tilde f_k(z)ϕ(x,z)=eik⋅xf~​k​(z) with Euclidean momentum k>0k>0k>0, the radial equation is

(Dk,μf)(z)  =  z3 ∂z ⁣(z−3∂zf)−k2f−μz2 f  =  0,(\mathcal D_{k,\mu}f)(z)\;=\;z^3\,\partial_z\!\left(z^{-3}\partial_z f\right)-k^2 f-\frac{\mu}{z^2}\,f\;=\;0,(Dk,μ​f)(z)=z3∂z​(z−3∂z​f)−k2f−z2μ​f=0,

with μ=0\mu=0μ=0 for the massless s-wave (Eq. (20)), μ=l(l+4)\mu=l(l+4)μ=l(l+4) for the lll-th partial wave (Eq. (34)), and μ=(mR)2\mu=(mR)^2μ=(mR)2 for a scalar string state of mass mmm (Eq. (40)).

The modified Bessel functions are defined for x>0x>0x>0 and real ν\nuν by

Kν(x)=∫0∞e−xcosh⁡tcosh⁡(νt) dt,Iν(x)=∑j≥0(x/2)2j+νj! Γ(j+ν+1).K_\nu(x)=\int_0^\infty e^{-x\cosh t}\cosh(\nu t)\,dt,\qquad I_\nu(x)=\sum_{j\ge0}\frac{(x/2)^{2j+\nu}}{j!\,\Gamma(j+\nu+1)}.Kν​(x)=∫0∞​e−xcoshtcosh(νt)dt,Iν​(x)=j≥0∑​j!Γ(j+ν+1)(x/2)2j+ν​.

The normalised radial profile is f~k(z)=z2Kν(kz)/(R2Kν(kR))\tilde f_k(z)=z^2K_\nu(kz)/(R^2K_\nu(kR))f~​k​(z)=z2Kν​(kz)/(R2Kν​(kR)), the flux factor is F(k)=[z−3∂zlog⁡f~k(z)]z=R\mathcal F(k)=\big[z^{-3}\partial_z\log\tilde f_k(z)\big]_{z=R}F(k)=[z−3∂z​logf~​k​(z)]z=R​, and the two-point function is ⟨O(k)O(q)⟩=(2π)4δ4(k+q) N216π2F(k)\langle O(k)O(q)\rangle=(2\pi)^4\delta^4(k+q)\,\tfrac{N^2}{16\pi^2}\mathcal F(k)⟨O(k)O(q)⟩=(2π)4δ4(k+q)16π2N2​F(k) (Eq. (26)). For a level-nnn string state m2=4n/α′m^2=4n/\alpha'm2=4n/α′, R4=2NgYM2α′2R^4=2Ng_{YM}^2\alpha'^2R4=2NgYM2​α′2, the Bessel order is ν=4+(mR)2\nu=\sqrt{4+(mR)^2}ν=4+(mR)2​ (Eq. (41)) and the dimension of the dual operator is Δ=2+ν\Delta=2+\nuΔ=2+ν (Eq. (45)).

Formalization targets

Goal (Eqs. (41)–(46))

For n≥1n\ge1n≥1, N,gYM,α′,m,R>0N,g_{YM},\alpha',m,R>0N,gYM​,α′,m,R>0 with m2=4n/α′m^2=4n/\alpha'm2=4n/α′, R4=2NgYM2α′2R^4=2Ng_{YM}^2\alpha'^2R4=2NgYM2​α′2 and ν=4+(mR)2∉Z\nu=\sqrt{4+(mR)^2}\notin\mathbb Zν=4+(mR)2​∈/Z: there is a function AAA real-analytic at 000 with

N216π2F(k)=A(k2)−N28π2Γ(1−ν)Γ(ν)(kR2)2νR−4+o(k2ν)(k→0+),\frac{N^2}{16\pi^2}\mathcal F(k)=A(k^2)-\frac{N^2}{8\pi^2}\frac{\Gamma(1-\nu)}{\Gamma(\nu)}\Big(\frac{kR}{2}\Big)^{2\nu}R^{-4}+o(k^{2\nu})\quad(k\to0^+),16π2N2​F(k)=A(k2)−8π2N2​Γ(ν)Γ(1−ν)​(2kR​)2νR−4+o(k2ν)(k→0+),

Δ=2+ν=2+4+4n gYM2N\Delta=2+\nu=2+\sqrt{4+4n\,g_{YM}\sqrt{2N}}Δ=2+ν=2+4+4ngYM​2N​​, and Δ/(2(n g2N)1/2)→1\Delta\big/\big(2(n\,g\sqrt{2N})^{1/2}\big)\to1Δ/(2(ng2N​)1/2)→1 as g→∞g\to\inftyg→∞.

Milestones

Solutions of the radial equation (Eqs. (22), (35), (39)–(41)), regularity selecting KνK_\nuKν​ (text after (23)), the connection formula Kν=π2sin⁡πν(I−ν−Iν)K_\nu=\frac{\pi}{2\sin\pi\nu}(I_{-\nu}-I_\nu)Kν​=2sinπνπ​(I−ν​−Iν​) (Eq. (42)), the flux factor for integer order (Eqs. (36)–(37)) and non-integer order (Eqs. (43)–(44)), the relation (mR)2=4ngYM2N(mR)^2=4ng_{YM}\sqrt{2N}(mR)2=4ngYM​2N​ (Eq. (41)) and the asymptotics (45)–(46).

Significance

The computation is the prototype of the AdS/CFT dictionary between bulk masses and boundary dimensions, Δ(Δ−4)=(mR)2\Delta(\Delta-4)=(mR)^2Δ(Δ−4)=(mR)2 in five bulk dimensions. The massless case reproduces the non-renormalisation of dimensions of operators dual to supergravity fields; the massive case is the first prediction that unprotected operators acquire dimensions growing without bound at strong coupling.

The mathematical statements are classical, but their formalisation needs modified Bessel functions of real order, their connection formula, and asymptotic expansions near 000 with logarithmic terms — material not currently present in Mathlib as far as the drafter checked. None of the statements below has a machine-checked proof yet.

Corrections to the source. Numerical exploration by the drafter (not machine-checked) indicates that the sign of the logarithmic coefficient printed in Eqs. (36)–(37) is reversed (the standard expansion has (−1)n+1log⁡(x/2) In(x)(-1)^{n+1}\log(x/2)\,I_n(x)(−1)n+1log(x/2)In​(x) in Kn(x)K_n(x)Kn​(x)), and that Eq. (27) is inconsistent with Eq. (37) at l=0l=0l=0: the correct leading term for l=0l=0l=0 is −14k4log⁡(kR)=−18k4log⁡(k2R2)-\tfrac14k^4\log(kR)=-\tfrac18k^4\log(k^2R^2)−41​k4log(kR)=−81​k4log(k2R2). The partial-wave milestone is stated with the corrected coefficient. Eq. (44) (non-integer order), which carries the paper's main prediction, agrees with the standard expansions.

Difficulty

The flux factor is a logarithmic derivative of KνK_\nuKν​ evaluated at the boundary. The leading term of KνK_\nuKν​ near 000 is a pure power, which contributes only an analytic part; the physical information sits in a subleading term of relative size k2νk^{2\nu}k2ν (or k2l+4log⁡kk^{2l+4}\log kk2l+4logk for integer order). Extracting it requires the full series structure of KνK_\nuKν​ near 000, not only its leading asymptotics, together with control of the remainder uniformly enough to pass through a logarithm and a derivative.

Formalization scope

All functions are real-valued; KνK_\nuKν​ is defined by its integral representation and IνI_\nuIν​ by its power series, both used only for x>0x>0x>0. The convention 1/Γ(pole)=01/\Gamma(\text{pole})=01/Γ(pole)=0 is realised by Lean's 1/0=01/0=01/0=0. "An analytic function of k2k^2k2 plus a term plus o(⋅)o(\cdot)o(⋅)" is encoded as: there exists AAA with AnalyticAt ℝ A 0 such that the difference is little-ooo of the given power as k→0+k\to0^+k→0+. Because 2ν2\nu2ν is not an even integer when ν∉Z\nu\notin\mathbb Zν∈/Z (and because of the logarithm in the integer case), the coefficient of the leading non-analytic term is uniquely determined, so the statements are not vacuous. The physics steps (the identification of generating functionals, Eq. (12), and the Fourier transform to position space) are not formalised: the two-point function is defined through the flux factor, Eq. (26). Welcome contributions: general facts about KνK_\nuKν​ and IνI_\nuIν​ (differential equation, positivity, asymptotics, series near 000), reusable beyond this mission.

Selected references

  • S.S. Gubser, I.R. Klebanov, A.M. Polyakov, Gauge theory correlators from non-critical string theory, Phys. Lett. B 428 (1998) 105–114. https://arxiv.org/abs/hep-th/9802109
  • J. Maldacena, The large N limit of superconformal field theories and supergravity, Adv. Theor. Math. Phys. 2 (1998) 231. https://arxiv.org/abs/hep-th/9711200
  • E. Witten, Anti de Sitter space and holography, Adv. Theor. Math. Phys. 2 (1998) 253. https://arxiv.org/abs/hep-th/9802150
  • NIST Digital Library of Mathematical Functions, Chapter 10 (Bessel functions). https://dlmf.nist.gov/10
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AnalysisMathematical Physics·Captain: Lucas

Witten 1998: Anti de Sitter Space and Holography — the massive scalar boundary-value problemResearch Paper

Motivation

In February 1998 Witten's paper Anti de Sitter Space and Holography made precise Maldacena's proposal that large-NNN conformal field theories in ddd dimensions are described by gravity on (d+1)(d+1)(d+1)-dimensional anti de Sitter space AdSd+1\mathrm{AdS}_{d+1}AdSd+1​. The dictionary it proposes identifies boundary values of bulk fields with sources of boundary operators, and reads correlation functions off the dependence of the bulk action on those boundary values. Its most quoted quantitative consequence is the mass–dimension relation: a scalar of mass mmm on AdSd+1\mathrm{AdS}_{d+1}AdSd+1​ corresponds to a boundary operator of conformal dimension

Δ=12(d+d2+4m2),Δ(Δ−d)=m2.\Delta=\tfrac12\big(d+\sqrt{d^2+4m^2}\big),\qquad \Delta(\Delta-d)=m^2 .Δ=21​(d+d2+4m2​),Δ(Δ−d)=m2.

Behind this physics statement sits a piece of classical analysis on hyperbolic space: the Dirichlet-type problem for the operator Δg−m2\Delta_g-m^2Δg​−m2 with prescribed boundary asymptotics, solved by an explicit Poisson-type integral (Section 2 of the paper, eqs. (2.15)–(2.44)). This mission formalizes that analytic core.

Timeline. Maldacena 1997 (hep-th/9711200) conjectured the correspondence; Gubser–Klebanov–Polyakov (hep-th/9802109) and Witten (hep-th/9802150, both February 1998) proposed the precise dictionary; the Euclidean black-hole thermodynamics used in Section 3 goes back to Hawking–Page 1983.

Setting

Euclidean AdSd+1\mathrm{AdS}_{d+1}AdSd+1​ is modelled as the upper half space {(x0,x):x0>0, x∈Rd}\{(x_0,x): x_0>0,\ x\in\mathbb R^d\}{(x0​,x):x0​>0, x∈Rd} with the hyperbolic metric

ds2=1x02(dx02+∑i=1ddxi2)(eq. (2.15)).ds^2=\frac{1}{x_0^2}\Big(dx_0^2+\sum_{i=1}^d dx_i^2\Big)\qquad\text{(eq. (2.15))}.ds2=x02​1​(dx02​+i=1∑d​dxi2​)(eq. (2.15)).

Its Laplace–Beltrami operator is

Δgu=x0d+1∂0(x01−d∂0u)+x02∑j=1d∂j2u=x02(∂02u+∑j∂j2u)−(d−1)x0 ∂0u.\Delta_g u=x_0^{d+1}\partial_0\big(x_0^{1-d}\partial_0 u\big)+x_0^2\sum_{j=1}^d\partial_j^2u = x_0^2\Big(\partial_0^2u+\sum_j\partial_j^2u\Big)-(d-1)x_0\,\partial_0u .Δg​u=x0d+1​∂0​(x01−d​∂0​u)+x02​j=1∑d​∂j2​u=x02​(∂02​u+j∑​∂j2​u)−(d−1)x0​∂0​u.

A massive scalar of squared mass m2∈Rm^2\in\mathbb Rm2∈R solves Δgu=m2u\Delta_g u=m^2uΔg​u=m2u (eqs. (2.34), (2.37)). The conformal dimension is Δ=12(d+d2+4m2)\Delta=\tfrac12(d+\sqrt{d^2+4m^2})Δ=21​(d+d2+4m2​) (eq. (2.44)), the bulk-to-boundary kernel is

KΔ(x0,x)=x0Δ(x02+∣x∣2)Δ(eqs. (2.19), (2.38)),K_\Delta(x_0,x)=\frac{x_0^{\Delta}}{(x_0^2+|x|^2)^{\Delta}}\qquad\text{(eqs. (2.19), (2.38))},KΔ​(x0​,x)=(x02​+∣x∣2)Δx0Δ​​(eqs. (2.19), (2.38)),

and the Poisson-type integral of a boundary datum φ0\varphi_0φ0​ is ∫RdKΔ(x0,x−x′) φ0(x′) dx′\int_{\mathbb R^d}K_\Delta(x_0,x-x')\,\varphi_0(x')\,dx'∫Rd​KΔ​(x0​,x−x′)φ0​(x′)dx′ (eqs. (2.20), (2.41)).

Formalization targets

Goal (eq. (2.41) with (2.43)–(2.44))

For m2>−d2/4m^2>-d^2/4m2>−d2/4 there is a constant c>0c>0c>0 such that for every bounded continuous φ0\varphi_0φ0​ on Rd\mathbb R^dRd,

φ(x0,x)=c∫Rdx0Δ(x02+∣x−x′∣2)Δ φ0(x′) dx′\varphi(x_0,x)=c\int_{\mathbb R^d}\frac{x_0^{\Delta}}{(x_0^2+|x-x'|^2)^{\Delta}}\,\varphi_0(x')\,dx'φ(x0​,x)=c∫Rd​(x02​+∣x−x′∣2)Δx0Δ​​φ0​(x′)dx′

is a C2C^2C2 solution of Δgφ=m2φ\Delta_g\varphi=m^2\varphiΔg​φ=m2φ on the upper half space and x0Δ−d φ(x0,x)→φ0(x)x_0^{\Delta-d}\,\varphi(x_0,x)\to\varphi_0(x)x0Δ−d​φ(x0​,x)→φ0​(x) as x0→0+x_0\to0^+x0​→0+, i.e. φ∼x0d−Δφ0\varphi\sim x_0^{d-\Delta}\varphi_0φ∼x0d−Δ​φ0​ at the boundary (eq. (2.36) with f=x0f=x_0f=x0​).

Milestones

  1. x0Δx_0^{\Delta}x0Δ​ solves the massive equation when Δ(Δ−d)=m2\Delta(\Delta-d)=m^2Δ(Δ−d)=m2 (eqs. (2.16)–(2.17), (2.37)).
  2. The inverted kernel KΔK_\DeltaKΔ​ solves it too (eqs. (2.18)–(2.19), (2.38)).
  3. Scaling: ∫x02Δ−d(x02+∣x∣2)−Δdx\int x_0^{2\Delta-d}(x_0^2+|x|^2)^{-\Delta}dx∫x02Δ−d​(x02​+∣x∣2)−Δdx is independent of x0x_0x0​ (eq. (2.40)).
  4. Approximate identity: these kernels tend to a multiple of δ(x)\delta(x)δ(x) (eq. (2.39)).
  5. The massless case Δ=d\Delta=dΔ=d with φ→φ0\varphi\to\varphi_0φ→φ0​ (eq. (2.20)).
  6. Δ=12(d+d2+4m2)\Delta=\tfrac12(d+\sqrt{d^2+4m^2})Δ=21​(d+d2+4m2​) is the larger root of Δ(Δ−d)=m2\Delta(\Delta-d)=m^2Δ(Δ−d)=m2 (eqs. (2.43)–(2.44)).
  7. The gauge-field profile x0d−2x_0^{d-2}x0d−2​ solves the reduced Maxwell equation (eqs. (2.24)–(2.25)).
  8. Hawking–Page, d=3d=3d=3: β(r+)=12πr+/(1+3r+2)\beta(r_+)=12\pi r_+/(1+3r_+^2)β(r+​)=12πr+​/(1+3r+2​) has a maximum and I(r+)=πr+2(1−r+2)/(1+3r+2)I(r_+)=\pi r_+^2(1-r_+^2)/(1+3r_+^2)I(r+​)=πr+2​(1−r+2​)/(1+3r+2​) is negative for large r+r_+r+​ (eqs. (3.15)–(3.16)).

Significance

The goal is the analytic statement underlying the mass–dimension relation Δ(Δ−d)=m2\Delta(\Delta-d)=m^2Δ(Δ−d)=m2 of the AdS/CFT dictionary: it shows that the boundary datum φ0\varphi_0φ0​ of a massive field is naturally a density of weight d−Δd-\Deltad−Δ, which is how the paper identifies the dimension of the dual operator. The same kernel is the starting point for the two-point function (2.42). The result is classical mathematics (scattering theory on hyperbolic space), proved informally in the paper; this mission produces a machine-checked version together with reusable infrastructure for the hyperbolic Laplacian in half-space coordinates.

Difficulty

The pointwise identities (milestones 1, 2, 6, 7, 8) are calculus. The substance is analytic: differentiating the Poisson-type integral twice under the integral sign with only boundedness of φ0\varphi_0φ0​, and proving the approximate-identity limit with the right normalisation; the scaling integral requires Δ>d/2\Delta>d/2Δ>d/2, and the endpoint m2=−d2/4m^2=-d^2/4m2=−d2/4 (where Δ=d/2\Delta=d/2Δ=d/2 and the kernel is not integrable) must be excluded.

Formalization scope

Points are pairs (x0,x)∈R×EuclideanSpace R (Fin d)(x_0,x)\in\mathbb R\times\mathrm{EuclideanSpace}\ \mathbb R\ (\mathrm{Fin}\ d)(x0​,x)∈R×EuclideanSpace R (Fin d); partial derivatives are one-variable derivs, so the solution predicate also demands C2C^2C2 regularity on {x0>0}\{x_0>0\}{x0​>0}, which rules out junk values of deriv. Real powers are Real.rpow; integrals are Bochner integrals for Lebesgue measure. Boundary data are bounded continuous functions. All declarations live in the namespace WittenAdSHolography, with the shared definitions in one definition file.

Selected references

  • E. Witten, Anti de Sitter Space and Holography, Adv. Theor. Math. Phys. 2 (1998) 253–291. https://arxiv.org/abs/hep-th/9802150
  • J. Maldacena, The Large N Limit of Superconformal Field Theories and Supergravity, Adv. Theor. Math. Phys. 2 (1998) 231–252. https://arxiv.org/abs/hep-th/9711200
  • S. S. Gubser, I. R. Klebanov, A. M. Polyakov, Gauge Theory Correlators from Non-Critical String Theory, Phys. Lett. B 428 (1998) 105–114. https://arxiv.org/abs/hep-th/9802109
  • S. W. Hawking, D. N. Page, Thermodynamics of Black Holes in Anti-de Sitter Space, Commun. Math. Phys. 87 (1983) 577–588. https://doi.org/10.1007/BF01208266
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Coupling Constant II: The Beta Function in the Energy Scale and the Running DichotomyTextbook

Motivation

A coupling constant is the number that fixes the strength of an interaction: in the modern formulation it is the parameter weighting the interaction part of a Lagrangian against its kinetic part. In a quantum field theory such a parameter is not constant. Probing a process at a higher energy resolves more of the virtual activity that dresses the interaction, and the value one measures depends on the energy scale μ\muμ at which the measurement is made. This scale dependence is called the running of the coupling, and it is the organising phenomenon behind two of the best-known qualitative facts about the Standard Model: the electromagnetic coupling grows with energy (α≈1/137\alpha \approx 1/137α≈1/137 at low energy, α≈1/127\alpha \approx 1/127α≈1/127 at the ZZZ mass, about 90 GeV) and apparently diverges at a finite energy — the Landau pole — while the strong coupling decreases logarithmically at high energy, the asymptotic freedom found by Gross, Politzer and Wilczek and recognised by the 2004 Nobel Prize.

The source treats the running through the beta function, defined there by

β(g)  =  μ ∂g∂μ  =  ∂g∂ln⁡μ,\beta(g) \;=\; \mu\,\frac{\partial g}{\partial \mu} \;=\; \frac{\partial g}{\partial \ln \mu},β(g)=μ∂μ∂g​=∂lnμ∂g​,

and through the one-loop strong coupling

αs(k2)  ≈  1β0ln⁡(k2/Λ2).\alpha_s(k^2) \;\approx\; \frac{1}{\beta_0 \ln(k^2/\Lambda^2)} .αs​(k2)≈β0​ln(k2/Λ2)1​.

Everything in this mission is the mathematics of those two formulas: an autonomous scalar ordinary differential equation in the logarithm of the energy, and the behaviour of its solutions according to the sign of the one-loop coefficient.

Setting

Write μ>0\mu > 0μ>0 for the energy scale of a process and ggg, or α\alphaα, for a real-valued coupling as a function of that scale. The beta function in the energy scale is the logarithmic derivative used by the source,

β(g)(μ)  =  μ⋅dgdμ(μ),\beta(g)(\mu) \;=\; \mu \cdot \frac{d g}{d \mu}(\mu),β(g)(μ)=μ⋅dμdg​(μ),

a function of μ\muμ built from ggg; a theory whose beta function vanishes identically is scale-invariant, since its coupling then takes the same value at every scale.

A coupling is said to be one-loop running with coefficient bbb on a set SSS of scales when it satisfies, at every μ∈S\mu \in Sμ∈S,

μ dαdμ(μ)  =  b α(μ)2,\mu \, \frac{d\alpha}{d\mu}(\mu) \;=\; b \, \alpha(\mu)^2 ,μdμdα​(μ)=bα(μ)2,

equivalently dα/dμ=b α2/μd\alpha/d\mu = b\,\alpha^2/\mudα/dμ=bα2/μ. Here b<0b < 0b<0 is the asymptotically free sign (QCD) and b>0b > 0b>0 the sign the source attributes to QED. The equation is the one-loop truncation written in μ\muμ rather than in t=ln⁡μt = \ln \mut=lnμ: both forms appear in the source, and the first milestone is precisely their agreement.

The one-loop strong coupling with one-loop coefficient β0>0\beta_0 > 0β0​>0 and QCD scale Λ>0\Lambda > 0Λ>0 is the explicit function

α(Q)  =  1β0ln⁡(Q2/Λ2),Q>Λ,\alpha(Q) \;=\; \frac{1}{\beta_0 \ln(Q^2/\Lambda^2)}, \qquad Q > \Lambda ,α(Q)=β0​ln(Q2/Λ2)1​,Q>Λ,

the formula the source quotes for αs(k2)\alpha_s(k^2)αs​(k2). It is defined by an existing platform definition, which this mission reuses rather than restates.

Formalization targets

Goal — the running dichotomy in the energy scale

For every reference scale μ0>0\mu_0 > 0μ0​>0 and every positive reference value α0\alpha_0α0​ of the coupling at that scale:

b<0:α(μ)  =  α01−b α0ln⁡(μ/μ0)  (μ≥μ0),α(μ)→μ→∞0;b<0:\quad \alpha(\mu) \;=\; \frac{\alpha_0}{1 - b\,\alpha_0 \ln(\mu/\mu_0)} \ \ (\mu \ge \mu_0), \qquad \alpha(\mu) \xrightarrow[\mu \to \infty]{} 0 ;b<0:α(μ)=1−bα0​ln(μ/μ0​)α0​​  (μ≥μ0​),α(μ)μ→∞​0; b>0:no coupling obeys μ dαdμ=b α2 on all of [μ0, μ0e1/(bα0)] with α(μ0)=α0.b>0:\quad \text{no coupling obeys } \mu\,\frac{d\alpha}{d\mu} = b\,\alpha^2 \text{ on all of } \Big[\mu_0,\ \mu_0 e^{1/(b\alpha_0)}\Big] \text{ with } \alpha(\mu_0)=\alpha_0 .b>0:no coupling obeys μdμdα​=bα2 on all of [μ0​, μ0​e1/(bα0​)] with α(μ0​)=α0​.

The negative-coefficient half is asymptotic freedom together with the uniqueness of the solution; the positive-coefficient half is the Landau pole, stated as the non-existence of any solution up to the pole scale rather than as a divergence of one chosen formula. The pole scale μ0e1/(bα0)\mu_0 e^{1/(b\alpha_0)}μ0​e1/(bα0​) is the one the one-loop equation itself predicts.

Supporting levels

The milestones below range from the definitional identity μ ∂μg=∂ln⁡μg\mu\,\partial_\mu g = \partial_{\ln \mu} gμ∂μ​g=∂lnμ​g, through the three qualitative readings of the sign of β\betaβ that the source states in words (vanishing beta function means scale invariance; positive beta function means the coupling grows with energy; negative means it falls), to the identification of the quoted QCD formula as an exact solution of the one-loop equation and the determination of Λ\LambdaΛ from a single measured value.

Significance

The dichotomy is the mathematical content of two physical statements that the source presents side by side. Asymptotic freedom is what makes perturbative QCD predictive at high energy; the Landau pole is the reason the source warns that the perturbative beta function cannot be trusted at strong coupling, since the one-loop equation has no solution reaching the pole scale at all. Formalizing the uniqueness half also pins down what "the" running coupling means: the explicit formula is not merely one solution of the renormalization-group equation but the only one through a given data point.

An earlier public mission on the same source ("Coupling Constant: One-Loop Running, Asymptotic Freedom and the Landau Pole") formalized the one-loop flow in the logarithmic variable t=ln⁡μt = \ln\mut=lnμ with a cubic beta function β(g)=b g3\beta(g) = b\,g^3β(g)=bg3, and its statements are proved. This mission is stated in the other variable the source uses — the energy scale μ\muμ itself, with the quadratic one-loop equation for α\alphaα — and adds the parts of the source that the earlier mission does not cover: the defining identity between the two forms of the beta function, the sign-to-monotonicity statements for an arbitrary (not necessarily one-loop) coupling, the explicit pole scale, and the determination of the QCD scale Λ\LambdaΛ from a measurement. Where the earlier mission already settles a statement in the quoted formula's variables, this proposal references the published theorem instead of restating it.

Difficulty

The equation is elementary, and the difficulty is entirely in what the statements quantify over. The negative-coefficient half asserts that every function satisfying the equation on [μ0,∞)[\mu_0,\infty)[μ0​,∞) is the explicit one, so it needs a uniqueness argument for a nonlinear scalar ordinary differential equation rather than a verification that a formula works; the substitution u=1/αu = 1/\alphau=1/α that makes the equation linear is legitimate only while α\alphaα is known not to vanish, which is part of what has to be proved. The positive-coefficient half is a non-existence statement on a closed interval reaching the pole, so no local solution can be extended to it; the naive attempt — differentiate the explicit formula and observe that it blows up — proves nothing about the other candidate solutions.

Formalization scope

Couplings are total functions R→R\mathbb{R} \to \mathbb{R}R→R, and every statement restricts to the scales it is about: the positive half-line for the beta-function statements, (Λ,∞)(\Lambda,\infty)(Λ,∞) for the explicit QCD formula, [μ0,∞)[\mu_0,\infty)[μ0​,∞) or a closed interval for the one-loop flow. Outside those ranges nothing is claimed — in particular at Q≤ΛQ \le \LambdaQ≤Λ, where the logarithm in the quoted formula vanishes or is negative and the reciprocal returns a junk value. Differentiability is a hypothesis where it is needed, written as a two-sided derivative at each scale of the set, so that a statement cannot be satisfied by a one-sided artefact at an endpoint. The one-loop property is phrased as an exact differential equation, never as an asymptotic approximation, and the Landau-pole milestone is a non-existence claim rather than a divergence claim, so it cannot be discharged by exhibiting a formula with a singularity.

A complete development needs the mean value theorem, Gronwall- or uniqueness-type arguments for scalar ordinary differential equations, and the standard logarithm and exponential API; the uniqueness lemma for u˙=bu2\dot u = b u^2u˙=bu2 and the reciprocal substitution are reusable beyond this mission. Contributions of a general scalar-ODE uniqueness lemma, usable by both this mission and the earlier one, are especially welcome.

Selected references

  • Wikipedia, Coupling constant, 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
  • ATLAS Collaboration, A precise determination of the strong-coupling constant from the recoil of Z bosons with the ATLAS experiment at s=8\sqrt{s} = 8s​=8 TeV, 2023. https://arxiv.org/abs/2309.12986
  • P. A. Zyla et al. (Particle Data Group), Review of Particle Physics - Chapter 9, Quantum Chromodynamics, PTEP 2020 (8). https://doi.org/10.1093/ptep/ptaa104
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Polyakov Action: Symmetries and Classical Equivalence with Nambu–GotoTextbook

Motivation

The Polyakov action is the standard action for the worldsheet of a bosonic string. It was introduced by Deser and Zumino and independently by Brink, Di Vecchia and Howe in 1976, and is associated with Polyakov after he used it to quantize the string in 1981. Its value over the Nambu–Goto (area) action is that it is quadratic in the embedding fields, which makes quantization tractable, while being classically equivalent to the area action. This mission formalizes the classical facts collected in the Wikipedia article Polyakov action.

Setting

A string sweeps out a two-dimensional worldsheet with coordinates σ=(τ,σ)\sigma=(\tau,\sigma)σ=(τ,σ). Its embedding in a DDD-dimensional target is Xμ(σ)X^\mu(\sigma)Xμ(σ); the target carries a metric gμν(X)g_{\mu\nu}(X)gμν​(X), and the worldsheet carries an independent (auxiliary) metric hab(σ)h_{ab}(\sigma)hab​(σ) with inverse habh^{ab}hab and determinant hhh. With the induced metric Gab=gμν(X)∂aXμ∂bXνG_{ab}=g_{\mu\nu}(X)\partial_aX^\mu\partial_bX^\nuGab​=gμν​(X)∂a​Xμ∂b​Xν and string tension TTT, the Polyakov and Nambu–Goto actions are

SP=T2∫d2σ −h habGab,SNG=T∫d2σ −det⁡Gab.S_P=\frac T2\int d^2\sigma\,\sqrt{-h}\,h^{ab}G_{ab},\qquad S_{NG}=T\int d^2\sigma\,\sqrt{-\det G_{ab}} .SP​=2T​∫d2σ−h​habGab​,SNG​=T∫d2σ−detGab​​.

The stress–energy tensor is Tab=T(Gab−12habhcdGcd)T_{ab}=T\big(G_{ab}-\tfrac12h_{ab}h^{cd}G_{cd}\big)Tab​=T(Gab​−21​hab​hcdGcd​). Signature convention: timelike +++, spacelike −-−.

Formalization targets

Goal: on-shell equivalence with Nambu–Goto

If the auxiliary metric satisfies its equation of motion Tab=0T_{ab}=0Tab​=0 (with hcdGcd≥0h^{cd}G_{cd}\ge0hcdGcd​≥0), then

SP=SNG.S_P=S_{NG}.SP​=SNG​.

Milestones

  1. Poincaré invariance of the target: translations X→X+bX\to X+bX→X+b (exact) and infinitesimal Lorentz transformations X→X+ε ωXX\to X+\varepsilon\,\omega XX→X+εωX (to O(ε2)O(\varepsilon^2)O(ε2)), for a constant target metric.
  2. Worldsheet diffeomorphism invariance and Weyl invariance h→Λhh\to\Lambda hh→Λh.
  3. Tracelessness of the stress–energy tensor, Taa=0T^a{}_a=0Taa​=0.
  4. The variations δ−h=−12−hhabδhab\delta\sqrt{-h}=-\frac12\sqrt{-h}h_{ab}\delta h^{ab}δ−h​=−21​−h​hab​δhab and δS/δhab=T2−h (Gab−12habhcdGcd)\delta S/\delta h^{ab}=\frac T2\sqrt{-h}\,(G_{ab}-\frac12h_{ab}h^{cd}G_{cd})δS/δhab=2T​−h​(Gab​−21​hab​hcdGcd​).
  5. On shell, det⁡G=14h (hcdGcd)2\det G=\frac14h\,(h^{cd}G_{cd})^2detG=41​h(hcdGcd​)2.
  6. Conformal gauge: S=T2∫(X˙2−X′2)S=\frac T2\int(\dot X^2-X'^2)S=2T​∫(X˙2−X′2), the constraints X˙⋅X′=0\dot X\cdot X'=0X˙⋅X′=0, X˙2+X′2=0\dot X^2+X'^2=0X˙2+X′2=0, the wave equation □Xμ=0\Box X^\mu=0□Xμ=0, and its light-cone solution X=X+(τ+σ)+X−(τ−σ)X=X_+(\tau+\sigma)+X_-(\tau-\sigma)X=X+​(τ+σ)+X−​(τ−σ).

Significance

These are the classical foundations of bosonic string theory: the symmetries (Poincaré, diffeomorphism, Weyl) are what make the conformal gauge reachable, and the equivalence with Nambu–Goto justifies using the quadratic action. All statements are textbook results; the work here is to formalize them with explicit hypotheses (differentiability, integrability, positivity of hcdGcdh^{cd}G_{cd}hcdGcd​) that the physics presentation leaves implicit.

Difficulty

The algebraic statements are 2×22\times22×2 matrix identities. The diffeomorphism invariance needs the change-of-variables formula; the infinitesimal Lorentz statement needs care with integrability; the wave-equation milestone needs the fundamental lemma of the calculus of variations; and the light-cone solution is d'Alembert's theorem.

Formalization scope

Worldsheet =R2=\mathbb R^2=R2 (coordinates Fin 2 → ℝ, index 0=τ0=\tau0=τ), target =RD=\mathbb R^D=RD. Actions are Bochner integrals over a region U⊆R2U\subseteq\mathbb R^2U⊆R2. The inverse metric is Mathlib's matrix inverse. Quantization, Virasoro operators, the Liouville action and string boundary conditions are out of scope.

Selected references

  • Wikipedia, Polyakov action, revision 1292212311.
  • S. Deser, B. Zumino, A complete action for the spinning string, Phys. Lett. B 65 (1976) 369–373.
  • L. Brink, P. Di Vecchia, P. Howe, A locally supersymmetric and reparametrization invariant action for the spinning string, Phys. Lett. B 65 (1976) 471–474.
  • A. M. Polyakov, Quantum geometry of bosonic strings, Phys. Lett. B 103 (1981) 207–210.
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Drezet 2023: GHZ Bell-Nonlocality in the Everett MultiverseResearch Paper

Motivation

Whether the many-worlds (Everett) interpretation of quantum mechanics is local in the sense of Bell is a recurring question in the foundations of physics. A number of authors have argued that Everett's theory escapes Bell-type nonlocality theorems because it drops the assumption that each measurement has a single outcome. A. Drezet's paper arXiv:2306.07794 (2023) argues against this claim by revisiting the Greenberger–Horne–Zeilinger (GHZ) nonlocality theorem, which, unlike Bell's inequality, uses only probability-one (perfect) correlations. Its central mathematical step, stated in Section II, is that no locally causal beable model can reproduce the quantum predictions for the GHZ state. The paper's argument is that this step makes no assumption about completeness or hidden variables, and so applies to Everett's theory as well.

Timeline:

  • 1964–1976: J. S. Bell introduces Bell inequalities and, in "The theory of local beables" (1976), the notion of local causality phrased with beables λ\lambdaλ (collected in Speakable and Unspeakable in Quantum Mechanics, 2004).
  • 1989: Greenberger, Horne and Zeilinger give a nonlocality proof for three spin-½ particles that uses no inequality ("Going beyond Bell's theorem").
  • 2015: L. Vaidman discusses the GHZ argument in the many-worlds setting ("Bell inequality and Many-Worlds interpretation").
  • 2023: Drezet's paper, the source of this mission.

Setting

Three spin-½ particles are sent to three distant observers, Alice, Bob and Charlie. Each one measures the spin of their particle along a direction n^j∈{x^,y^}\hat n_j \in \{\hat x, \hat y\}n^j​∈{x^,y^​} and records an outcome α,β,γ∈{+1,−1}\alpha, \beta, \gamma \in \{+1, -1\}α,β,γ∈{+1,−1}. The particles are prepared in the GHZ state

∣ψ⟩=12(∣+z⟩1∣+z⟩2∣+z⟩3−∣−z⟩1∣−z⟩2∣−z⟩3),|\psi\rangle = \tfrac{1}{\sqrt 2}\big(|{+z}\rangle_1|{+z}\rangle_2|{+z}\rangle_3 - |{-z}\rangle_1|{-z}\rangle_2|{-z}\rangle_3\big),∣ψ⟩=2​1​(∣+z⟩1​∣+z⟩2​∣+z⟩3​−∣−z⟩1​∣−z⟩2​∣−z⟩3​),

and quantum mechanics predicts the joint outcome probabilities through the Born rule, P(α,β,γ∣n^1,n^2,n^3,ψ)=∣⟨αn^1,βn^2,γn^3∣ψ⟩∣2P(\alpha,\beta,\gamma\mid\hat n_1,\hat n_2,\hat n_3,\psi) = |\langle \alpha_{\hat n_1},\beta_{\hat n_2},\gamma_{\hat n_3}|\psi\rangle|^2P(α,β,γ∣n^1​,n^2​,n^3​,ψ)=∣⟨αn^1​​,βn^2​​,γn^3​​∣ψ⟩∣2. Here ∣an^⟩|a_{\hat n}\rangle∣an^​⟩ denotes the eigenvector of σn^\sigma_{\hat n}σn^​ with eigenvalue aaa. In Lean this is bornProb n₁ n₂ n₃ α β γ.

A locally causal (Bell-local) model consists of

  • a space Λ\LambdaΛ of beables with a probability distribution ρ(λ)\rho(\lambda)ρ(λ) that does not depend on the settings (Eq. (18), statistical independence), and
  • local response probabilities Pj(α∣λ,n^j)P_j(\alpha\mid\lambda,\hat n_j)Pj​(α∣λ,n^j​), where party jjj's outcome depends only on λ\lambdaλ and its own setting (Eq. (17)).

Such a model predicts

P(α,β,γ∣n^1,n^2,n^3)=∫ΛP1(α∣λ,n^1) P2(β∣λ,n^2) P3(γ∣λ,n^3) dρ(λ).P(\alpha,\beta,\gamma\mid\hat n_1,\hat n_2,\hat n_3) = \int_\Lambda P_1(\alpha\mid\lambda,\hat n_1)\,P_2(\beta\mid\lambda,\hat n_2)\,P_3(\gamma\mid\lambda,\hat n_3)\,d\rho(\lambda).P(α,β,γ∣n^1​,n^2​,n^3​)=∫Λ​P1​(α∣λ,n^1​)P2​(β∣λ,n^2​)P3​(γ∣λ,n^3​)dρ(λ).

In Lean the model is a LocalCausalModel Λ and its prediction is M.predict. The beables are arbitrary: λ\lambdaλ may be a hidden variable, or the quantum state itself as in the Beltrametti–Bugajski representation, Eq. (15).

Formalization targets

Goal (Section II, Eqs. (10)–(27))

No locally causal model reproduces the quantum predictions on the four GHZ settings (x^,x^,x^)(\hat x,\hat x,\hat x)(x^,x^,x^), (x^,y^,y^)(\hat x,\hat y,\hat y)(x^,y^​,y^​), (y^,x^,y^)(\hat y,\hat x,\hat y)(y^​,x^,y^​), (y^,y^,x^)(\hat y,\hat y,\hat x)(y^​,y^​,x^):

¬(∀ GHZ settings n^, ∀α,β,γ: Pmodel(α,β,γ∣n^)=PBorn(α,β,γ∣n^,ψ)).\neg\Big(\forall\,\text{GHZ settings } \hat n,\ \forall \alpha,\beta,\gamma:\ P^{\mathrm{model}}(\alpha,\beta,\gamma\mid\hat n) = P^{\mathrm{Born}}(\alpha,\beta,\gamma\mid\hat n,\psi)\Big).¬(∀GHZ settings n^, ∀α,β,γ: Pmodel(α,β,γ∣n^)=PBorn(α,β,γ∣n^,ψ)).

Milestones

  1. Eqs. (2)–(5). σx(1)σx(2)σx(3)∣ψ⟩=−∣ψ⟩\sigma_x^{(1)}\sigma_x^{(2)}\sigma_x^{(3)}|\psi\rangle = -|\psi\rangleσx(1)​σx(2)​σx(3)​∣ψ⟩=−∣ψ⟩, and the three σxσyσy\sigma_x\sigma_y\sigma_yσx​σy​σy​-type operators fix ∣ψ⟩|\psi\rangle∣ψ⟩.
  2. Born probabilities (Section III, Eqs. (30)–(32)). For (x^,x^,x^)(\hat x,\hat x,\hat x)(x^,x^,x^) each outcome triple has probability 1/41/41/4 if αβγ=−1\alpha\beta\gamma=-1αβγ=−1 and 000 otherwise. For the other three GHZ settings the same holds with αβγ=+1\alpha\beta\gamma=+1αβγ=+1.
  3. Eqs. (10)–(13). No assignment Aj,Bj∈{±1}A_j, B_j \in\{\pm1\}Aj​,Bj​∈{±1} satisfies all four GHZ constraints.
  4. Eqs. (19)–(27). If a product of three probability distributions on {±1}\{\pm1\}{±1} vanishes off the set {αβγ=s}\{\alpha\beta\gamma = s\}{αβγ=s}, then each factor is a point mass, and the three atoms multiply to sss.
  5. Section II, summary (ii). A locally causal model that reproduces the GHZ predictions is, for ρ\rhoρ-almost every λ\lambdaλ, deterministic, with values satisfying (10)–(13).
  6. Section III (B). Local causality implies non-signalling.
  7. Eq. (29). Quantum non-signalling for the GHZ state, with Alice's marginal P1(±1∣x^,ψ)=12P_1(\pm1\mid\hat x,\psi)=\tfrac12P1​(±1∣x^,ψ)=21​.
  8. Appendix 3, Eqs. (39)–(45). Keeping Eq. (17) but letting the beable density depend on the settings (a "superdeterministic" model) does reproduce the GHZ predictions.

Significance

The goal theorem is the mathematical core of the paper's conclusion that any beable account of the GHZ correlations, including one where λ\lambdaλ is just the quantum state, violates Bell's local causality. Milestones 6–8 formalize the paper's separation of notions of locality: non-signalling holds both in quantum mechanics and in every locally causal model, so it cannot tell them apart; and dropping statistical independence (Eq. (18)) is enough to reproduce the correlations. The GHZ theorem itself has been known since 1989. As far as the drafter is aware it has no machine-checked formalization on this platform, and this mission provides one, stated for general measurable beable spaces.

Difficulty

The combinatorial core (milestone 3) is a parity argument. The substance lies in the steps that connect it to probability: showing that perfect correlations force determinism almost surely when ρ\rhoρ is an arbitrary probability measure (null sets, measurability, integrals of nonnegative functions), and computing the Born probabilities exactly in Lean from explicit matrices and eigenvectors.

Formalization scope

  • Outcomes are elements of ℤˣ (exactly {+1,−1}\{+1,-1\}{+1,−1}). Settings form a two-element type Setting with values x and y. Qubit index 0 is ∣+z⟩|{+z}\rangle∣+z⟩ and index 1 is ∣−z⟩|{-z}\rangle∣−z⟩. Three-qubit operators are Kronecker products.
  • A LocalCausalModel Λ has a probability measure ρ on a measurable space Λ and measurable response functions P j λ n α with values in [0,1][0,1][0,1] that sum to one over α\alphaα. The fixed state ψ\psiψ is left implicit. Predictions are Bochner integrals, and all integrands are bounded and measurable.
  • The goal quantifies over all such models on all measurable spaces. Its hypothesis is the equality of all 4×84\times 84×8 predicted and Born probabilities. Because it uses the actual Born probabilities rather than an arbitrary list of constraints, it cannot hold vacuously.
  • Needed infrastructure: finite-dimensional matrix computations over C\mathbb CC with 2\sqrt22​, and basic measure theory (a.e. vanishing of nonnegative integrands).

Selected references

  • A. Drezet, An Elementary Proof That Everett's Quantum Multiverse Is Nonlocal: Bell-Locality and Branch-Symmetry in the Many-Worlds Interpretation, arXiv:2306.07794, 2023. https://arxiv.org/abs/2306.07794
  • D. M. Greenberger, M. A. Horne, A. Zeilinger, Going beyond Bell's theorem, in Bell's Theorem, Quantum Theory and Conceptions of the Universe, Kluwer, 1989, pp. 69–72. https://arxiv.org/abs/0712.0921
  • J. S. Bell, Speakable and Unspeakable in Quantum Mechanics, 2nd ed., Cambridge University Press, 2004. https://doi.org/10.1017/CBO9780511815676
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AnalysisMathematical Physics·Captain: Lucas

Verlinde 2016: Apparent Dark Matter and the Baryonic Tully-Fisher RelationResearch Paper

Motivation

In emergent gravity proposals, spacetime and gravity are macroscopic descriptions of an underlying quantum system. In Verlinde (2016) it is argued that in de Sitter space (a universe with positive dark energy) the entanglement entropy has, besides the usual area-law part, a thermal volume-law part, and that matter displaces this volume-law entropy. The elastic response of the medium to this displacement produces an extra gravitational pull, which the paper proposes as the origin of the effects usually ascribed to dark matter in galaxies and clusters.

The physical derivation is heuristic, but it ends in explicit mathematical relations between the baryonic (visible) mass profile MB(r)M_B(r)MB​(r) and the apparent dark matter mass profile MD(r)M_D(r)MD​(r) of a spherically symmetric, isolated system. The central one is the integral relation (7.40),

∫0rG MD(r′)2r′2 dr′=MB(r) a0 r6,\int_0^r \frac{G\,M_D(r')^2}{r'^2}\,dr' = \frac{M_B(r)\,a_0\,r}{6},∫0r​r′2GMD​(r′)2​dr′=6MB​(r)a0​r​,

where GGG is Newton's constant and a0=cH0a_0 = cH_0a0​=cH0​ is the Hubble acceleration scale. For a point mass it reproduces Milgrom's empirical acceleration relation and the baryonic Tully-Fisher relation vf4=aMGMBv_f^4 = a_M G M_Bvf4​=aM​GMB​ with aM=a0/6a_M = a_0/6aM​=a0​/6.

Setting

All quantities are real numbers or real functions of the radius r>0r>0r>0. G>0G>0G>0 and a0>0a_0>0a0​>0 are constants. MB(r)M_B(r)MB​(r) and MD(r)M_D(r)MD​(r) are the baryonic and apparent dark masses inside radius rrr. The Newtonian accelerations are gB(r)=GMB(r)/r2g_B(r) = GM_B(r)/r^2gB​(r)=GMB​(r)/r2 and gD(r)=GMD(r)/r2g_D(r) = GM_D(r)/r^2gD​(r)=GMD​(r)/r2 (7.42). The averaged density inside radius rrr is ρˉ(r)=3M(r)/(4πr3)\bar\rho(r) = 3M(r)/(4\pi r^3)ρˉ​(r)=3M(r)/(4πr3) (7.45), and the slope parameter is β(r)=− dlog⁡ρˉ/dlog⁡r=−rρˉ′(r)/ρˉ(r)\beta(r) = -\,d\log\bar\rho/d\log r = -r\bar\rho'(r)/\bar\rho(r)β(r)=−dlogρˉ​/dlogr=−rρˉ​′(r)/ρˉ​(r) (7.46). A local density ρ\rhoρ gives M(r)=∫0rρ(r′) 4πr′2 dr′M(r) = \int_0^r \rho(r')\,4\pi r'^2\,dr'M(r)=∫0r​ρ(r′)4πr′2dr′ (7.39).

The paper also uses two purely mathematical facts about elastic strain in d−1d-1d−1 spatial dimensions: a bound (7.30) on the largest principal strain of a traceless symmetric strain tensor, and the integration-by-parts identity (7.31) between the squared Hessian and the squared Laplacian.

Formalization targets

Goal: Milgrom relation for a central point mass (7.43)

If MB>0M_B>0MB​>0 is constant and (7.40) holds for every r>0r>0r>0, then for every r>0r>0r>0

gD(r)=aM gB(r),aM=a06.g_D(r) = \sqrt{a_M\, g_B(r)}, \qquad a_M = \frac{a_0}{6}.gD​(r)=aM​gB​(r)​,aM​=6a0​​.

Milestones

  • (4.44) the strain-squared integral outside a spherical inclusion: ∫c∞(c/V)2 dV=c\int_{c}^{\infty} (c/V)^2\,dV = c∫c∞​(c/V)2dV=c.
  • (7.30) ε2≤d−2d−1∑ijεij′2\varepsilon^2 \le \frac{d-2}{d-1}\sum_{ij}\varepsilon'^2_{ij}ε2≤d−1d−2​∑ij​εij′2​ for a traceless symmetric ε′\varepsilon'ε′ with eigenvalue ε\varepsilonε.
  • (7.31) ∫(∇i∇jχ)2 dV=∫(∇2χ)2 dV\int (\nabla_i\nabla_j\chi)^2\,dV = \int (\nabla^2\chi)^2\,dV∫(∇i​∇j​χ)2dV=∫(∇2χ)2dV.
  • (7.47) ρˉD(r)2=(4−βB(r)) a0 ρˉB(r)8πGr\bar\rho_D(r)^2 = (4-\beta_B(r))\,\frac{a_0\,\bar\rho_B(r)}{8\pi G r}ρˉ​D​(r)2=(4−βB​(r))8πGra0​ρˉ​B​(r)​ for a general baryonic profile.
  • (7.48) ρD(r)=(1−13βD(r)) ρˉD(r)\rho_D(r) = (1-\tfrac13\beta_D(r))\,\bar\rho_D(r)ρD​(r)=(1−31​βD​(r))ρˉ​D​(r).
  • (7.51) ΩD2=43 ΩB\Omega_D^2 = \tfrac43\,\Omega_BΩD2​=34​ΩB​ when (7.47) is applied at the Hubble radius with βB=0\beta_B = 0βB​=0.

Significance

The point-mass relation (7.43) is what connects the paper to the observed flat rotation curves and the baryonic Tully-Fisher law; (7.47) is its extension to extended mass profiles, which the paper uses to discuss galaxy clusters. The formal content checked here is the mathematics that turns the stated relation (7.40) into these observable consequences, plus the two elasticity lemmas used on the way to (7.40). The physical steps that lead to (7.40) (the entropy-displacement picture, the elastic interpretation, the identification (7.35)) are not mathematical statements and are outside the scope of this mission. None of these relations is known to have been machine-checked before.

Difficulty

The algebra is short once one may differentiate; the work is in the analysis. (7.40) is an integral identity with a singular weight 1/r′21/r'^21/r′2 at the origin, so recovering pointwise information requires the fundamental theorem of calculus on (0,∞)(0,\infty)(0,∞) with only continuity there, and care with integrability at 000. (7.31) is a double integration by parts in Rn\mathbb R^nRn that must be justified for the chosen decay class. (7.30) needs the spectral theorem for symmetric matrices, or an equivalent argument.

Formalization scope

All statements live in the namespace Verlinde2016. Radial profiles are functions R→R\mathbb R\to\mathbb RR→R, with hypotheses only on (0,∞)(0,\infty)(0,∞). The integral in (7.40) is Lean's interval integral over [0,r][0,r][0,r]; since it would be 000 for a non-integrable integrand, the positivity of the right-hand side forces integrability. Continuity of MDM_DMD​ (and of densities) on (0,∞)(0,\infty)(0,∞) is taken as a standing physical convention. In (7.31) the paper's condition that "χ\chiχ falls off rapidly enough" is formalized as χ\chiχ smooth with compact support. Spatial dimension is d−1d-1d−1 in (7.30); (7.40)–(7.51) use d=4d=4d=4 as in Section 7.2. Definitions: avgDensity, slopeParam, partialDeriv.

Selected references

  • E. Verlinde, Emergent Gravity and the Dark Universe, SciPost Phys. 2, 016 (2017). https://arxiv.org/abs/1611.02269
  • M. Milgrom, A modification of the Newtonian dynamics as a possible alternative to the hidden mass hypothesis, Astrophys. J. 270 (1983) 365. https://doi.org/10.1086/161130
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Mathematical Physics·Captain: Lucas

Liouville Field Theory I: Spectrum and Kinematic SymmetriesTextbook

Motivation

Liouville field theory is a two-dimensional conformal field theory whose classical equation of motion generalizes Liouville's equation. It is one of the few interacting conformal field theories with a continuous spectrum that has been solved: its three-point function on the sphere is given by the DOZZ formula (Dorn–Otto 1994; Zamolodchikov–Zamolodchikov 1996), proved rigorously from a probabilistic construction of the path integral by Kupiainen–Rhodes–Vargas (2017), with the conformal bootstrap established by Guillarmou–Kupiainen–Rhodes–Vargas (2020). It appears in two-dimensional quantum gravity, non-critical string theory, and log-correlated random energy models.

This mission is the first in a series following the Wikipedia article on Liouville field theory. It formalizes the kinematic layer of the theory: the algebraic relations between the coupling constant, the background charge, the central charge, the conformal dimensions and the reflection coefficient, on which every later statement (the spectrum, the DOZZ formula, the bootstrap) is phrased.

Setting

Fix a complex number bbb, the coupling constant. Define

  • the background charge Q=b+1bQ = b + \dfrac1bQ=b+b1​;
  • the central charge c=1+6Q2c = 1 + 6Q^2c=1+6Q2;
  • the conformal dimension of the primary field with momentum α∈C\alpha \in \mathbb Cα∈C,  Δ(α)=α(Q−α)\ \Delta(\alpha) = \alpha(Q - \alpha) Δ(α)=α(Q−α);
  • the reflection coefficient with normalization parameter λ\lambdaλ,
RP=± λ−2iP Γ(2ibP) Γ(2ib−1P)Γ(−2ibP) Γ(−2ib−1P),R_P = \pm\,\lambda^{-2iP}\,\frac{\Gamma(2ibP)\,\Gamma(2ib^{-1}P)}{\Gamma(-2ibP)\,\Gamma(-2ib^{-1}P)},RP​=±λ−2iPΓ(−2ibP)Γ(−2ib−1P)Γ(2ibP)Γ(2ib−1P)​,

with sign +1+1+1 if c∈(−∞,1)c \in (-\infty,1)c∈(−∞,1) and −1-1−1 otherwise.

Two symmetries act on these data: the reflection α→Q−α\alpha \to Q - \alphaα→Q−α and the duality b→1/bb \to 1/bb→1/b. The spectrum of the theory is the set of dimensions Δ∈c−124+R+\Delta \in \frac{c-1}{24} + \mathbb R_+Δ∈24c−1​+R+​, parametrized by momenta α=Q2+iP\alpha = \frac Q2 + iPα=2Q​+iP, P∈R+P \in \mathbb R_+P∈R+​.

Formalization targets

Goal: the spectrum parametrization

For every b∈Cb \in \mathbb Cb∈C, the map P↦Δ(Q2+iP)P \mapsto \Delta\left(\frac Q2 + iP\right)P↦Δ(2Q​+iP) is a bijection

[0,∞)  → ∼   c−124+[0,∞)⊂C.[0,\infty) \;\xrightarrow{\ \sim\ }\; \frac{c-1}{24} + [0,\infty) \subset \mathbb C.[0,∞) ∼ ​24c−1​+[0,∞)⊂C.

Milestones

  1. Reflection invariance: Δ(Q−α)=Δ(α)\Delta(Q-\alpha) = \Delta(\alpha)Δ(Q−α)=Δ(α).
  2. Duality: ccc and Δ\DeltaΔ are invariant under b→1/bb \to 1/bb→1/b.
  3. Marginality: for b≠0b \ne 0b=0, b(Q−b)=1  ⟺  Q=b+1/bb(Q - b) = 1 \iff Q = b + 1/bb(Q−b)=1⟺Q=b+1/b.
  4. Dimension in terms of momentum: Δ(Q2+iP)=c−124+P2\Delta\left(\frac Q2 + iP\right) = \frac{c-1}{24} + P^2Δ(2Q​+iP)=24c−1​+P2.
  5. Imaginary coupling: real nonzero β=ib\beta = ibβ=ib produce exactly the real central charges c≤1c \le 1c≤1.
  6. Reflection relation consistency: RPR−P=1R_P R_{-P} = 1RP​R−P​=1 for λ≠0\lambda \ne 0λ=0 and 2ibP,2ib−1P∉Z2ibP, 2ib^{-1}P \notin \mathbb Z2ibP,2ib−1P∈/Z.

Significance

These identities are the dictionary between the parameters used in the physics literature (bbb, QQQ, ccc, α\alphaα, PPP, Δ\DeltaΔ) and fix the conventions for later missions in the series: the DOZZ formula, the pole structure of correlation functions, and the reflection and duality covariance of structure constants are all stated in these variables. None of these statements is open; the value of the mission is a vetted, reusable definition layer with machine-checked consistency of the conventions.

Difficulty

The individual statements are elementary algebra over C\mathbb CC, together with the non-vanishing of the Gamma function away from its poles. The care needed is in conventions: total functions in Lean (0−1=00^{-1} = 00−1=0, principal-branch complex powers, Gamma at its poles) must be handled by explicit hypotheses exactly where the source implicitly excludes degenerate values.

Formalization scope

All quantities are complex-valued functions of b∈Cb \in \mathbb Cb∈C, defined in the namespace LiouvilleFieldTheory. 1/b1/b1/b is Lean's inverse (so b=0b = 0b=0 gives junk values Q=0Q = 0Q=0, c=1c = 1c=1; statements that need b≠0b \ne 0b=0 assume it). R+\mathbb R_+R+​ is taken to be [0,∞)[0,\infty)[0,∞). The condition c∈(−∞,1)c \in (-\infty,1)c∈(−∞,1) is encoded as Im⁡c=0∧Re⁡c<1\operatorname{Im} c = 0 \wedge \operatorname{Re} c < 1Imc=0∧Rec<1. λ−2iP\lambda^{-2iP}λ−2iP is the principal-branch complex power. The analytic content of the article (Virasoro representations, conformal blocks, the special function Υb\Upsilon_bΥb​, the path integral, the DOZZ formula) is out of scope for this mission.

Selected references

  • Wikipedia, Liouville field theory, revision 1376996606. https://en.wikipedia.org/w/index.php?title=Liouville_field_theory&oldid=1376996606
  • S. Ribault, Conformal field theory on the plane, 2014. https://arxiv.org/abs/1406.4290
  • H. Dorn, H.-J. Otto, Two and three point functions in Liouville theory, Nucl. Phys. B 429 (1994). https://arxiv.org/abs/hep-th/9403141
  • A. Zamolodchikov, Al. Zamolodchikov, Conformal bootstrap in Liouville field theory, Nucl. Phys. B 477 (1996). https://arxiv.org/abs/hep-th/9506136
  • A. Kupiainen, R. Rhodes, V. Vargas, Integrability of Liouville theory: proof of the DOZZ formula, 2017. https://arxiv.org/abs/1707.08785
  • C. Guillarmou, A. Kupiainen, R. Rhodes, V. Vargas, Conformal bootstrap in Liouville theory, 2020. https://arxiv.org/abs/2005.11530
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Machine LearningOptimal TransportOptimization·Captain: mikedeng1

Computational Optimal Transport XII: The Entropic Barycenter Scalings Are (e^{f_s/ε}, e^{g_s/ε}) for the Solutions of a Dual Program with Σ_s λ_s f_s = 0Textbook

Why barycenters of histograms matter

A barycenter summarizes several objects by minimizing a weighted sum of distances to them. For probability histograms, ordinary coordinatewise averages depend strongly on how the bins are labeled. Optimal transport instead lets mass move between bins and charges for those moves through a cost matrix. The resulting Wasserstein barycenter can reflect the geometry of the bins: nearby bins can exchange mass at a lower cost than distant ones. Peyré and Cuturi use this construction in their treatment of variational transport problems, including shape interpolation and the representation of a measure as a barycenter of other measures. The finite histogram problem in this mission is the entropically regularized version developed in §9.2 of their textbook.

The computational question is how to connect three ways of describing the same solution: a histogram that is the barycenter, transport matrices coupling it to the inputs, and vectors of dual potentials. Proposition 9.1 on p. 530 identifies the dual potentials behind the scaling factors of the optimal transport matrices. This is the second proposition numbered 9.1 in the chapter; the one on p. 519 concerns derivatives with respect to histograms.

Finite histogram setting

There are SSS input histograms. Source sss has nsn_sns​ bins and a probability vector bsb_sbs​, while the barycenter has nnn bins. The real matrix Cs∈Rn×nsC_s\in\mathbb R^{n\times n_s}Cs​∈Rn×ns​ gives the cost of transporting a unit of mass from barycenter bin iii to input bin jjj. A weight vector λ∈ΣS\lambda\in\Sigma_Sλ∈ΣS​ has nonnegative entries summing to one; Σk\Sigma_kΣk​ denotes the probability simplex on kkk bins. The unregularized barycenter problem (9.10) minimizes ∑sλsLCs(a,bs)\sum_s\lambda_s L_{C_s}(a,b_s)∑s​λs​LCs​​(a,bs​) over a∈Σna\in\Sigma_na∈Σn​, where LCsL_{C_s}LCs​​ is the minimum transport cost with marginals aaa and bsb_sbs​.

For a regularization parameter ε>0\varepsilon>0ε>0, the entropic transport cost LCsε(a,bs)L^\varepsilon_{C_s}(a,b_s)LCs​ε​(a,bs​) minimizes ⟨Cs,Ps⟩−εH(Ps)\langle C_s,P_s\rangle-\varepsilon H(P_s)⟨Cs​,Ps​⟩−εH(Ps​) over nonnegative coupling matrices PsP_sPs​ with row marginal aaa and column marginal bsb_sbs​. Here ⟨Cs,Ps⟩=∑i,jCs,ijPs,ij\langle C_s,P_s\rangle=\sum_{i,j}C_{s,ij}P_{s,ij}⟨Cs​,Ps​⟩=∑i,j​Cs,ij​Ps,ij​ and H(Ps)=∑i,j(−Ps,ijlog⁡Ps,ij+Ps,ij)H(P_s)=\sum_{i,j}(-P_{s,ij}\log P_{s,ij}+P_{s,ij})H(Ps​)=∑i,j​(−Ps,ij​logPs,ij​+Ps,ij​), with 0log⁡0=00\log0=00log0=0. The Gibbs kernel is Ks,ij=e−Cs,ij/εK_{s,ij}=e^{-C_{s,ij}/\varepsilon}Ks,ij​=e−Cs,ij​/ε.

The book also writes the regularized barycenter as a weighted KL projection. Its variables are the matrices (Ps)s(P_s)_s(Ps​)s​; each matrix has column marginal bsb_sbs​, and every matrix has the same row marginal. That shared row marginal is aaa, so the optimization can be expressed without carrying aaa as a separate variable. The generalized matrix divergence is KL(Ps∣Ks)=∑i,j[Ps,ijlog⁡(Ps,ij/Ks,ij)−Ps,ij+Ks,ij]\mathrm{KL}(P_s\mid K_s)=\sum_{i,j}[P_{s,ij}\log(P_{s,ij}/K_{s,ij})-P_{s,ij}+K_{s,ij}]KL(Ps​∣Ks​)=∑i,j​[Ps,ij​log(Ps,ij​/Ks,ij​)−Ps,ij​+Ks,ij​], using the continuous value at Ps,ij=0P_{s,ij}=0Ps,ij​=0. See (9.15)–(9.17) in Peyré and Cuturi.

Formalization targets

The goal is Proposition 9.1 on p. 530. The dual variables are row potentials fs∈Rnf_s\in\mathbb R^nfs​∈Rn and column potentials gs∈Rnsg_s\in\mathbb R^{n_s}gs​∈Rns​. They maximize

∑sλs[⟨gs,bs⟩−ε∑i,jefs,i/εKs,ijegs,j/ε]subject to∑sλsfs=0.\sum_s\lambda_s\left[\langle g_s,b_s\rangle-\varepsilon\sum_{i,j}e^{f_{s,i}/\varepsilon}K_{s,ij}e^{g_{s,j}/\varepsilon}\right] \quad\text{subject to}\quad \sum_s\lambda_s f_s=0.s∑​λs​[⟨gs​,bs​⟩−εi,j∑​efs,i​/εKs,ij​egs,j​/ε]subject tos∑​λs​fs​=0.

The theorem asserts that the primal and dual optima are attained, that every optimal primal family and every optimal dual family satisfy

Ps,ij=efs,i/εKs,ijegs,j/ε,P_{s,ij}=e^{f_{s,i}/\varepsilon}K_{s,ij}e^{g_{s,j}/\varepsilon},Ps,ij​=efs,i​/εKs,ij​egs,j​/ε,

and that the dual maximum is the minimum value of the entropic barycenter objective (9.15). The milestone targets are the scalar and matrix versions of the KL conjugate (9.22), followed by the closed form maximizer of one gsg_sgs​ block, corresponding to the update (9.18). These statements retain the source's finite matrix setting and are listed in the order in which their concepts enter the dual formulation.

What the result supplies

The scaling formula turns the optimal couplings into a product of a row factor, a fixed positive kernel, and a column factor. It identifies those factors with exponentials of dual potentials and gives a constrained maximization problem whose value is the entropic barycenter cost. Thus a solver can reason about the couplings, the barycenter marginal, or the potentials while referring to one theorem that connects them. The book uses the same variables to describe the iterative scaling updates on pp. 529–531 and later illustrates barycenters of shapes and surface measures; those applications depend on interpreting the matrix factors correctly. Peyré and Cuturi give the mathematical proposition and its argument. A complete Lean proof of the proposition and its supporting KL identities is the work this mission calls for; the statements here are draft targets rather than machine-checked solutions.

Where the difficulty lies

The potential program is a constrained optimization problem, while the transport problem constrains nonnegative matrices by two kinds of marginals. Equality of their optimum values must preserve the normalization constants in generalized KL. The exponential form alone does not establish that a proposed matrix has the required marginals, nor does a feasible matrix alone identify dual potentials. In addition, the assertion concerns every optimizer: a choice of zero source weight would leave that source's coupling unconstrained by the objective, and a zero target entry would put a logarithmic column update on the boundary. These are substantive edge cases of the formulas, not merely notation.

Formalization scope

Lean uses Fin S, Fin n, and Fin (n_s) for the finite index sets; these are zero based versions of the book's one based indices. Histograms and potentials are real vectors, costs and couplings are real matrices, and the Gibbs kernel is defined entrywise. The theorem requires S,n,ns>0S,n,n_s>0S,n,ns​>0, ε>0\varepsilon>0ε>0, strictly positive weights λs\lambda_sλs​ that sum to one, and strictly positive input histograms bsb_sbs​ that each sum to one. The strict positivity of weights and input entries is an explicit restriction beyond (9.15)–(9.21), needed for a statement about every optimizer and finite logarithmic potentials. Entropy uses 0log⁡0=00\log0=00log0=0 on nonnegative couplings. The shared row marginal is constructed by the constraints on the PsP_sPs​; it is not a free histogram unrelated to them.

The definition layer includes finite matrix pairing, entropy, generalized KL, the Gibbs kernel, feasibility, and primal and dual objective functions. The KL conjugate identities and the block optimizer are separate theorem targets so they can be reused in other entropic transport developments. The main theorem asserts primal and dual attainment as well as their relationship; it does not assume strong duality or an optimal scaling as an input. The page has slips in the dimensions of the marginal equations and a missing subscript on KKK in (9.17); the Lean statements use the dimensions specified by Ps∈Rn×nsP_s\in\mathbb R^{n\times n_s}Ps​∈Rn×ns​. The optional cost-gradient Proposition 9.2 on pp. 519–520 and the fsf_sfs​ block update (9.19)–(9.20) lie outside this proposal.

Selected references

  • Gabriel Peyré and Marco Cuturi, Computational Optimal Transport, Foundations and Trends in Machine Learning 11(5–6):355–607, 2019. DOI: 10.1561/2200000073.
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Computational Optimal Transport IX: The Entropic Cost L^ε_C(a, b) Equals max ⟨f, a⟩ + ⟨g, b⟩ − ε⟨e^{f/ε}, K e^{g/ε}⟩, with Optimal Scalings (e^{f/ε}, e^{g/ε})Textbook

Motivation

Optimal transport compares two distributions by finding the least expensive way to move mass between them. For finite histograms, the resulting linear program is precise but its optimal coupling can be sparse. In applications where the coupling represents traffic, matching, or a differentiable loss, a sparse plan may be undesirable or expensive to compute repeatedly. Peyré and Cuturi describe how adding an entropy term selects a diffuse coupling and leads to matrix scaling algorithms that can be used on large finite problems (Peyré and Cuturi, 2019, Chapter 4).

The entropic dual gives a second view of the same regularized problem. Its variables are real vectors attached to the two marginal constraints. Unlike the ordinary Kantorovich dual, the objective has no explicit feasibility constraints and is smooth. This permits calculations in the log domain, which the book introduces because direct scaling with exponentials can suffer from numerical overflow or underflow when the regularization parameter is small relative to the costs (Peyré and Cuturi, 2019, §4.4). This mission targets the exact relationship between the regularized minimum, the smooth dual maximum, and the scaling factors.

Setting

Fix positive integers n,mn,mn,m. A histogram a∈Σna\in\Sigma_na∈Σn​ is a vector with nonnegative entries summing to one; b∈Σmb\in\Sigma_mb∈Σm​ is defined similarly. Strict positivity is required for the attained dual maximum and log-domain updates; the primal scaling and Kantorovich-feasibility statements also cover zero-entry histograms. A coupling P∈U(a,b)P\in U(a,b)P∈U(a,b) is an n×mn\times mn×m matrix with nonnegative entries, row sums aaa, and column sums bbb. A real matrix CCC assigns cost CijC_{ij}Cij​ to each transfer from row iii to column jjj, and ⟨C,P⟩=∑i,jCijPij\langle C,P\rangle=\sum_{i,j}C_{ij}P_{ij}⟨C,P⟩=∑i,j​Cij​Pij​ is its transport cost.

The book uses the entropy H(P)=−∑i,jPij(log⁡Pij−1)H(P)=-\sum_{i,j}P_{ij}(\log P_{ij}-1)H(P)=−∑i,j​Pij​(logPij​−1), with 0log⁡0=00\log0=00log0=0. For ε>0\varepsilon>0ε>0, the regularized cost is LCε(a,b)=min⁡P∈U(a,b){⟨C,P⟩−εH(P)}L_C^\varepsilon(a,b)=\min_{P\in U(a,b)}\{\langle C,P\rangle-\varepsilon H(P)\}LCε​(a,b)=minP∈U(a,b)​{⟨C,P⟩−εH(P)}. The Gibbs kernel has entries Kij=exp⁡(−Cij/ε)K_{ij}=\exp(-C_{ij}/\varepsilon)Kij​=exp(−Cij​/ε). Given potentials f∈Rnf\in\mathbb R^nf∈Rn and g∈Rmg\in\mathbb R^mg∈Rm, the smooth dual objective is Q(f,g)=⟨f,a⟩+⟨g,b⟩−ε∑i,jefi/εKijegj/εQ(f,g)=\langle f,a\rangle+\langle g,b\rangle-\varepsilon\sum_{i,j}e^{f_i/\varepsilon}K_{ij}e^{g_j/\varepsilon}Q(f,g)=⟨f,a⟩+⟨g,b⟩−ε∑i,j​efi​/εKij​egj​/ε. These are the finite-dimensional objects of (4.1), (4.2), and (4.30) in the source (Peyré and Cuturi, 2019, pp. 425, 428, 448).

Formalization targets

Attained entropic duality

The goal is Proposition 4.4. There are an optimal coupling PPP and potentials (f,g)(f,g)(f,g) at which the dual reaches its maximum, with the common value

LCε(a,b)=⟨C,P⟩−εH(P)=max⁡f′,g′Q(f′,g′).L_C^\varepsilon(a,b) =\langle C,P\rangle-\varepsilon H(P) =\max_{f',g'} Q(f',g').LCε​(a,b)=⟨C,P⟩−εH(P)=f′,g′max​Q(f′,g′).

For every maximizing pair (f′,g′)(f',g')(f′,g′), the same optimal coupling satisfies

Pij=efi′/εKijegj′/ε.P_{ij}=e^{f'_i/\varepsilon}K_{ij}e^{g'_j/\varepsilon}.Pij​=efi′​/εKij​egj′​/ε.

Thus the dual potentials determine the scaling vectors u=ef′/εu=e^{f'/\varepsilon}u=ef′/ε and v=eg′/εv=e^{g'/\varepsilon}v=eg′/ε in (4.12). The maximum is an attained maximum of a real function, as stated in the book, rather than an unrestricted real infimum or supremum that might take a default value on a bad input (Peyré and Cuturi, 2019, Proposition 4.4).

Supporting results

The milestones follow the source's statements. Proposition 4.3 gives existence, uniqueness, and the Gibbs scaling of the primal optimizer. Remark 4.21 gives the two partial-gradient formulas and the closed log-domain block updates. Proposition 4.5 states that an entropic dual maximizer is a feasible pair of ordinary Kantorovich potentials, so its linear objective is bounded above by the unregularized cost LC(a,b)L_C(a,b)LC​(a,b) (Peyré and Cuturi, 2019, pp. 432, 449, 452–453).

Significance

The equality establishes that the matrix scaling variables and the dual potentials describe the same optimal coupling. It lets a computation based on scaling be interpreted as maximizing a smooth objective, and it gives exact marginal-error expressions through the dual gradients. Feasible Kantorovich potentials obtained at the optimum also provide a lower bound on the ordinary transport cost. These statements explain why log-domain updates represent the same mathematical problem as entropic transport, even when direct exponentiation is numerically fragile (Peyré and Cuturi, 2019, §§4.4–4.5).

The results are established in the textbook; the open work here is their Lean formalization. A completed development would provide reusable finite coupling, entropy, Gibbs-kernel, and smooth-dual interfaces, together with the existence and differentiability results needed to connect them. It would also distinguish a primal optimum, a dual maximum, and a feasible potential without relying on informal convention. The mission does not assert that these textbook results are new, and no machine-checked proof of these exact statements is claimed here.

Difficulty

The unconstrained dual objective has a symmetry: shifting every coordinate of fff by one constant and every coordinate of ggg by its negative leaves the value unchanged. Consequently, the set of maximizers is not bounded in the ordinary product space. Positivity of the marginals matters for attaining a maximum with finite real potentials; if a marginal entry is zero, the corresponding potential can escape toward negative infinity. On the primal side, the entropy formula must be valid at zero entries even though optimality links it to strictly positive exponential scalings. The formal argument must connect these boundary conventions and the finite-dimensional optimization statements without turning the maximum into a junk real supremum (Peyré and Cuturi, 2019, Proposition 4.4 and Remark 4.21).

Formalization scope

The Lean development represents indices by Fin n and Fin m, matrices by Matrix (Fin n) (Fin m) ℝ, and histograms by Mathlib's stdSimplex. Positive dimensions and ε>0\varepsilon>0ε>0 appear throughout. Strictly positive marginal entries are required for the attained dual maximum and log-domain updates; the other statements permit zero entries. The book assumes positive weights for discrete measures in Remark 2.1; the marginal positivity is also required for the attained dual maximum and the logarithmic updates. The cost matrix itself may have arbitrary real entries. Entropy uses Real.negMulLog, which implements 0log⁡0=00\log0=00log0=0 on the nonnegative couplings where it is applied.

The primal optimum is a predicate on a coupling and its objective, rather than a chosen matrix. The unregularized LCL_CLC​ is a real infimum used only when simplex marginals make its feasible set nonempty and bounded. The dual objective is a finite sum. Its gradients are stated as one-variable derivatives in each coordinate, and each block update is characterized as the unique maximizer with the other block fixed. The complete development needs Mathlib's finite sums, real exponential and logarithm, calculus, finite-dimensional topology, and optimization facts. Contributions that establish these shared interfaces or the four milestone statements are in scope.

Propositions 4.7 and 4.8 are excluded from this proposal because their printed bounds compare a dual linear objective with LCεL_C^\varepsilonLCε​ where the accompanying arguments instead support a comparison with the unregularized LCL_CLC​; (4.47) also has a sign discrepancy. The milestone list therefore avoids encoding a false inequality (Peyré and Cuturi, 2019, pp. 453–454).

Selected references

  • Gabriel Peyré and Marco Cuturi, Computational Optimal Transport, Foundations and Trends in Machine Learning 11(5–6), 2019, pp. 355–607. DOI: 10.1561/2200000073.
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Generalising the Scattered Property of Subspaces 2: If h + 1 Divides r and n ≥ h + 1, Maximum h-Scattered 𝔽_q-Subspaces of V(r, qⁿ) of Dimension rn/(h + 1) ExistResearch Paper

Motivation

A scattered subspace is an Fq\mathbb F_qFq​-subspace UUU of an Fqn\mathbb F_{q^n}Fqn​-vector space VVV that meets every one-dimensional Fqn\mathbb F_{q^n}Fqn​-subspace of VVV in an Fq\mathbb F_qFq​-subspace of dimension at most one. Scattered subspaces produce scattered linear sets in projective spaces over finite fields, and through them two-intersection sets, two-weight codes, translation caps and maximum rank distance (MRD) codes. Blokhuis and Lavrauw (Geom. Dedicata 2000) proved that a scattered subspace of V(r,qn)V(r,q^n)V(r,qn) has dimension at most rn/2rn/2rn/2, and constructions of that dimension are known whenever rnrnrn is even (see Bartoli, Giulietti, Marino, Polverino 2018 and the references there).

Csajbók, Marino, Polverino and Zullo (arXiv:1906.10590v2, Combinatorica 41, 2021) replace "one-dimensional" by "hhh-dimensional". The resulting hhh-scattered subspaces interpolate between scattered subspaces (h=1h=1h=1) and subspaces meeting every hyperplane in small dimension (h=r−1h=r-1h=r−1), which Sheekey and Van de Voorde (Des. Codes Cryptogr. 2020) showed to be equivalent to MRD codes with an idealiser isomorphic to Fqn\mathbb F_{q^n}Fqn​. For the new notion the paper proves an upper bound on the dimension (Theorem 2.3) and shows that the bound is attained when h+1h+1h+1 divides rrr (Theorem 2.6). This mission is the attainment half.

Setting

Let Fq⊆Fqn\mathbb F_q\subseteq\mathbb F_{q^n}Fq​⊆Fqn​ be finite fields, so n=[Fqn:Fq]n=[\mathbb F_{q^n}:\mathbb F_q]n=[Fqn​:Fq​], and let V=V(r,qn)V=V(r,q^n)V=V(r,qn) be an rrr-dimensional Fqn\mathbb F_{q^n}Fqn​-vector space with r≥1r\ge1r≥1. Every Fqn\mathbb F_{q^n}Fqn​-space is also an Fq\mathbb F_qFq​-space of dimension rnrnrn. For an integer hhh with 0<h≤r−10<h\le r-10<h≤r−1, an Fq\mathbb F_qFq​-subspace U≤VU\le VU≤V is hhh-scattered (Definition 1.1) if

  1. UUU spans VVV over Fqn\mathbb F_{q^n}Fqn​, ⟨U⟩Fqn=V\langle U\rangle_{\mathbb F_{q^n}}=V⟨U⟩Fqn​​=V, and
  2. dim⁡Fq(S∩U)≤h\dim_{\mathbb F_q}(S\cap U)\le hdimFq​​(S∩U)≤h for every hhh-dimensional Fqn\mathbb F_{q^n}Fqn​-subspace SSS of VVV.

An hhh-scattered subspace of largest possible Fq\mathbb F_qFq​-dimension is maximum hhh-scattered. Theorem 2.3 of the paper says that an hhh-scattered UUU either has dimension rrr and defines a subgeometry (an Fq\mathbb F_qFq​-basis of UUU is an Fqn\mathbb F_{q^n}Fqn​-basis of VVV), or satisfies

dim⁡FqU≤rnh+1.(1)\dim_{\mathbb F_q}U\le\frac{rn}{h+1}.\tag{1}dimFq​​U≤h+1rn​.(1)

Two building blocks appear in the statements. If V=V1⊕⋯⊕VtV=V_1\oplus\dots\oplus V_tV=V1​⊕⋯⊕Vt​ with Vi=V(ri,qn)V_i=V(r_i,q^n)Vi​=V(ri​,qn) and Ui≤ViU_i\le V_iUi​≤Vi​ are Fq\mathbb F_qFq​-subspaces, then U=U1⊕⋯⊕UtU=U_1\oplus\dots\oplus U_tU=U1​⊕⋯⊕Ut​ is the direct sum of the UiU_iUi​. For r≤nr\le nr≤n the Gabidulin-type subspace of Fqn r\mathbb F_{q^n}^{\,r}Fqnr​ is

Gr={(x,xq,xq2,…,xqr−1):x∈Fqn},G_r=\{(x,x^{q},x^{q^2},\dots,x^{q^{r-1}}) : x\in\mathbb F_{q^n}\},Gr​={(x,xq,xq2,…,xqr−1):x∈Fqn​},

the image of an Fq\mathbb F_qFq​-linear map, since x↦xqjx\mapsto x^{q^j}x↦xqj fixes Fq\mathbb F_qFq​.

Formalization targets

Goal: Theorem 2.6 (p. 7)

If h≥1h\ge1h≥1, h+1h+1h+1 divides rrr and n≥h+1n\ge h+1n≥h+1, then V(r,qn)V(r,q^n)V(r,qn) contains an Fq\mathbb F_qFq​-subspace UUU that is maximum hhh-scattered and satisfies

dim⁡FqU=rnh+1.\dim_{\mathbb F_q}U=\frac{rn}{h+1}.dimFq​​U=h+1rn​.

Milestones, in the order the paper uses them

  • Proposition 2.1 (p. 3): for h>1h>1h>1, every hhh-scattered subspace is iii-scattered for all 0<i<h0<i<h0<i<h.
  • Theorem 2.3 (p. 4): the dichotomy above, subgeometry or bound (1).
  • Theorem 2.4 (p. 6): if UiU_iUi​ is hih_ihi​-scattered in ViV_iVi​, then U1⊕⋯⊕UtU_1\oplus\dots\oplus U_tU1​⊕⋯⊕Ut​ is min⁡ihi\min_i h_imini​hi​-scattered in V1⊕⋯⊕VtV_1\oplus\dots\oplus V_tV1​⊕⋯⊕Vt​.
  • Theorem 2.4, second sentence (p. 6): if every UiU_iUi​ is hhh-scattered with dim⁡FqUi=rin/(h+1)\dim_{\mathbb F_q}U_i=r_in/(h+1)dimFq​​Ui​=ri​n/(h+1), then the direct sum is hhh-scattered of dimension rn/(h+1)rn/(h+1)rn/(h+1).
  • Example 2.5 (p. 7): for 2≤r≤n2\le r\le n2≤r≤n, GrG_rGr​ is maximum (r−1)(r-1)(r−1)-scattered of dimension nnn.

Significance

Together with Theorem 2.3, the goal settles the largest dimension of an hhh-scattered subspace of V(r,qn)V(r,q^n)V(r,qn) whenever h+1∣rh+1\mid rh+1∣r and n≥h+1n\ge h+1n≥h+1: it is exactly rn/(h+1)rn/(h+1)rn/(h+1). The subspaces it produces are the input of the paper's Delsarte duality (Theorem 3.3), which turns maximum hhh-scattered subspaces of V(r,qn)V(r,q^n)V(r,qn) reaching bound (1) into maximum (n−h−2)(n-h-2)(n−h−2)-scattered subspaces of V(rn/(h+1)−r,qn)V(rn/(h+1)-r,q^n)V(rn/(h+1)−r,qn) and so gives constructions also when h+1∤rh+1\nmid rh+1∤r. The direct-sum theorem is also of independent use: it extends the direct-sum construction for scattered linear sets of Bartoli, Giulietti, Marino and Polverino to every hhh, and Example 2.5 gives the subspace counterpart of Gabidulin codes.

All results in this mission are proved in the paper. To our knowledge none of them has a machine-checked proof; no formal library contains scattered subspaces, linear sets or Gabidulin-type subspaces. The remaining work is formalizing the paper's arguments: the counting of roots of linearized polynomials behind Example 2.5, the Grassmann-formula and quotient-space argument of Theorem 2.4, and the bound (1) of Theorem 2.3, which the companion mission on the dimension bound states as its goal.

Difficulty

The construction itself is short; the work lies in the three ingredients. The obvious approach to Theorem 2.4 fails: an hhh-dimensional Fqn\mathbb F_{q^n}Fqn​-subspace WWW of V1⊕V2V_1\oplus V_2V1​⊕V2​ need not be the sum of its intersections with V1V_1V1​ and V2V_2V2​, so the intersection W∩UW\cap UW∩U cannot be bounded summand by summand. Example 2.5 needs that a nonzero qqq-polynomial ∑j<rajxqj\sum_{j<r}a_jx^{q^j}∑j<r​aj​xqj has at most qr−1q^{r-1}qr−1 roots in Fqn\mathbb F_{q^n}Fqn​, and the root set is an Fq\mathbb F_qFq​-subspace. Maximality is not a property of a single subspace: it quantifies over every hhh-scattered subspace of VVV and so needs the full upper bound of Theorem 2.3, including the case n<h+1n<h+1n<h+1 handled there by Proposition 2.1.

Formalization scope

Fq\mathbb F_qFq​ is a finite field F, Fqn\mathbb F_{q^n}Fqn​ a finite field K with Algebra F K, so q=q=q= Fintype.card F and n=n=n= Module.finrank F K. VVV is a finite-dimensional K-module with a compatible F-module structure (IsScalarTower F K V), and r=r=r= Module.finrank K V. An Fq\mathbb F_qFq​-subspace is a Submodule F V; its intersection with an Fqn\mathbb F_{q^n}Fqn​-subspace S is S.restrictScalars F ⊓ U.

Conventions the statements commit to:

  • The range 0<h<r0<h<r0<h<r and the spanning condition are part of IsHScattered; without the range every spanning subspace would be hhh-scattered for h≥rh\ge rh≥r.
  • Every quotient rn/(h+1)rn/(h+1)rn/(h+1) is multiplied out, as (h+1)dim⁡FqU=rn(h+1)\dim_{\mathbb F_q}U=rn(h+1)dimFq​​U=rn or ≤rn\le rn≤rn; no natural-number division is used.
  • The direct sum in Theorem 2.4 is external: V=∏i<tViV=\prod_{i<t}V_iV=∏i<t​Vi​ ((i : Fin t) → V i) and U=U=U= Submodule.pi Set.univ U. The minimum h=min⁡ihih=\min_i h_ih=mini​hi​ is given by h≤hih\le h_ih≤hi​ for all iii and h=hih=h_ih=hi​ for some iii. The second part of Theorem 2.4 assumes t≥1t\ge1t≥1.
  • Example 2.5 is stated on Fin r → K, with GrG_rGr​ the range of the F-linear map x↦(xqj)j<rx\mapsto(x^{q^j})_{j<r}x↦(xqj)j<r​, and assumes r≥2r\ge2r≥2 so that (r−1)(r-1)(r−1)-scattered is within Definition 1.1's range.
  • The goal assumes r≥1r\ge1r≥1: h+1h+1h+1 divides 000, but the zero space has no hhh-scattered subspace. The upper end h<rh<rh<r follows from h+1∣rh+1\mid rh+1∣r.
  • "Defines a subgeometry" in Theorem 2.3 is: some subset of UUU spans UUU over Fq\mathbb F_qFq​ and is an Fqn\mathbb F_{q^n}Fqn​-basis of VVV.

The goal is not satisfied by a small subspace: both the dimension equality (h+1)dim⁡FqU=rn(h+1)\dim_{\mathbb F_q}U=rn(h+1)dimFq​​U=rn and maximality among all hhh-scattered subspaces of VVV are part of its conclusion.

Results the paper cites without a number (the Blokhuis–Lavrauw bound for h=1h=1h=1, the direct-sum theorem for scattered linear sets) are not items. Theorem 2.3 is restated here because the maximality in Theorem 2.6 rests on it; it duplicates the goal of the companion mission. Useful infrastructure beyond this mission: roots of linearized polynomials over finite fields, dimension formulas for subspaces under restriction of scalars, and direct sums of hhh-scattered subspaces. Contributions on any milestone are welcome, and the goal can be closed from the milestones alone.

Selected references

  • B. Csajbók, G. Marino, O. Polverino, F. Zullo, Generalising the scattered property of subspaces, arXiv:1906.10590v2, 2020; Combinatorica 41 (2021). https://arxiv.org/abs/1906.10590, https://doi.org/10.1007/s00493-020-4347-y
  • A. Blokhuis, M. Lavrauw, Scattered spaces with respect to a spread in PG(n, q), Geom. Dedicata 81 (2000), 231–243. https://doi.org/10.1023/A:1005283806897
  • D. Bartoli, M. Giulietti, G. Marino, O. Polverino, Maximum scattered linear sets and complete caps in Galois spaces, Combinatorica 38 (2018), 255–278. https://doi.org/10.1007/s00493-016-3531-6
  • J. Sheekey, G. Van de Voorde, Rank-metric codes, linear sets, and their duality, Des. Codes Cryptogr. 88 (2020), 655–675. https://doi.org/10.1007/s10623-019-00703-z
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Computational Optimal Transport VII: The Entropic Optimal Coupling P_ε Tends to the Maximum-Entropy Optimal Plan as ε → 0 and to a ⊗ b as ε → ∞Textbook

Motivation

The discrete optimal transport problem of Kantorovich asks for the cheapest way to move a probability histogram a\mathbf aa onto a histogram b\mathbf bb when moving a unit of mass from iii to jjj costs Ci,j\mathbf C_{i,j}Ci,j​. Its optimal couplings are vertices of a polytope and are typically sparse: they rely on a few routes. Two communities have reasons to blur them. In transportation planning, observed traffic is more diffuse than linear-programming predictions, which led to the "gravity" model of Wilson (1969) and Erlander (1980), as recounted in Peyré and Cuturi, §4.1. In machine learning and statistics, adding an entropy term to the objective turns the linear program into a strictly convex problem that Sinkhorn's matrix-scaling algorithm solves at scale (Cuturi, 2013).

Every use of the entropic problem raises the same question: what does the regularized solution approximate? Chapter 4 of Peyré and Cuturi's Computational Optimal Transport answers it in Proposition 4.1 for the two extreme regimes of the regularization strength ε\varepsilonε.

Timeline. Cominetti and San Martín (1994) studied the convergence of entropic penalties for linear programs, including first-order expansions near ε=0\varepsilon = 0ε=0 and ε=+∞\varepsilon = +\inftyε=+∞. Peyré and Cuturi (2019) record the transport case as Proposition 4.1, with a short compactness proof.

Setting

Fix sizes n,m≥1n, m \ge 1n,m≥1. A histogram is a vector of the probability simplex Σn={a∈R+n:∑iai=1}\Sigma_n = \{\mathbf a \in \mathbb R^n_+ : \sum_i \mathbf a_i = 1\}Σn​={a∈R+n​:∑i​ai​=1}. Given a∈Σn\mathbf a \in \Sigma_na∈Σn​, b∈Σm\mathbf b \in \Sigma_mb∈Σm​, the set of couplings is

U(a,b)={P∈R+n×m:P1m=a, P⊤1n=b},\mathbf U(\mathbf a,\mathbf b) = \{\mathbf P \in \mathbb R_+^{n\times m} : \mathbf P\mathbb 1_m = \mathbf a,\ \mathbf P^\top\mathbb 1_n = \mathbf b\},U(a,b)={P∈R+n×m​:P1m​=a, P⊤1n​=b},

nonnegative matrices with row sums a\mathbf aa and column sums b\mathbf bb. For a cost matrix C∈Rn×m\mathbf C \in \mathbb R^{n\times m}C∈Rn×m write ⟨C,P⟩=∑i,jCi,jPi,j\langle \mathbf C, \mathbf P\rangle = \sum_{i,j}\mathbf C_{i,j}\mathbf P_{i,j}⟨C,P⟩=∑i,j​Ci,j​Pi,j​. The Kantorovich problem is LC(a,b)=min⁡P∈U(a,b)⟨C,P⟩\mathrm L_{\mathbf C}(\mathbf a,\mathbf b) = \min_{\mathbf P \in \mathbf U(\mathbf a,\mathbf b)}\langle \mathbf C,\mathbf P\rangleLC​(a,b)=minP∈U(a,b)​⟨C,P⟩.

The discrete entropy of a coupling is

H(P)=−∑i,jPi,j(log⁡Pi,j−1),\mathbf H(\mathbf P) = -\sum_{i,j}\mathbf P_{i,j}(\log \mathbf P_{i,j} - 1),H(P)=−i,j∑​Pi,j​(logPi,j​−1),

with 0log⁡0=00\log 0 = 00log0=0. For ε>0\varepsilon > 0ε>0 the entropic problem is

LCε(a,b)=min⁡P∈U(a,b)⟨P,C⟩−εH(P),\mathrm L^\varepsilon_{\mathbf C}(\mathbf a,\mathbf b) = \min_{\mathbf P\in\mathbf U(\mathbf a,\mathbf b)}\langle \mathbf P,\mathbf C\rangle - \varepsilon\mathbf H(\mathbf P),LCε​(a,b)=P∈U(a,b)min​⟨P,C⟩−εH(P),

whose unique minimizer is Pε\mathbf P_\varepsilonPε​. The maximum-entropy optimal coupling P0⋆\mathbf P^\star_0P0⋆​ is the optimal coupling of the Kantorovich problem with the largest entropy. The Gibbs kernel is Ki,j=e−Ci,j/ε\mathbf K_{i,j} = e^{-\mathbf C_{i,j}/\varepsilon}Ki,j​=e−Ci,j​/ε and the Kullback–Leibler divergence between matrices is KL(P∣K)=∑i,jPi,jlog⁡(Pi,j/Ki,j)−Pi,j+Ki,j\mathrm{KL}(\mathbf P|\mathbf K) = \sum_{i,j}\mathbf P_{i,j}\log(\mathbf P_{i,j}/\mathbf K_{i,j}) - \mathbf P_{i,j} + \mathbf K_{i,j}KL(P∣K)=∑i,j​Pi,j​log(Pi,j​/Ki,j​)−Pi,j​+Ki,j​.

In Lean these are couplings, frob and IsOptimalCoupling in CompOT.Assignment, and entropy, entObjective, IsEntropicOptimal, IsMaxEntropyOptimal, gibbs and klMat in CompOT.EntropicLimit.

Formalization targets

Goal: Proposition 4.1

For a∈Σn\mathbf a\in\Sigma_na∈Σn​, b∈Σm\mathbf b \in \Sigma_mb∈Σm​ and any cost matrix C\mathbf CC,

Pε→ε→0+P0⋆,LCε(a,b)→ε→0+LC(a,b),Pε→ε→+∞a⊗b=(aibj)i,j.\mathbf P_\varepsilon \xrightarrow{\varepsilon\to 0^+} \mathbf P^\star_0,\qquad \mathrm L^\varepsilon_{\mathbf C}(\mathbf a,\mathbf b)\xrightarrow{\varepsilon\to 0^+}\mathrm L_{\mathbf C}(\mathbf a,\mathbf b),\qquad \mathbf P_\varepsilon\xrightarrow{\varepsilon\to+\infty}\mathbf a\otimes\mathbf b = (\mathbf a_i\mathbf b_j)_{i,j}.Pε​ε→0+​P0⋆​,LCε​(a,b)ε→0+​LC​(a,b),Pε​ε→+∞​a⊗b=(ai​bj​)i,j​.

Milestones

  1. For ε>0\varepsilon > 0ε>0, problem (4.2) has a unique optimal solution (§4.1, p. 425).
  2. The maximum-entropy optimal coupling (4.3) exists and is unique (proof of Proposition 4.1, p. 426).
  3. The sandwich (4.5): for an optimal P\mathbf PP and ε>0\varepsilon>0ε>0, 0≤⟨C,Pε⟩−⟨C,P⟩≤ε(H(Pε)−H(P))0 \le \langle\mathbf C,\mathbf P_\varepsilon\rangle - \langle \mathbf C,\mathbf P\rangle \le \varepsilon(\mathbf H(\mathbf P_\varepsilon) - \mathbf H(\mathbf P))0≤⟨C,Pε​⟩−⟨C,P⟩≤ε(H(Pε​)−H(P)).
  4. a⊗b\mathbf a \otimes \mathbf ba⊗b is the unique maximizer of H\mathbf HH on U(a,b)\mathbf U(\mathbf a,\mathbf b)U(a,b) (p. 426).
  5. (4.7): Pε\mathbf P_\varepsilonPε​ is the KL projection of the Gibbs kernel onto U(a,b)\mathbf U(\mathbf a,\mathbf b)U(a,b) (p. 428).

Significance

The result. Proposition 4.1 justifies the entropic regularization as an approximation scheme: for small ε\varepsilonε the regularized plan converges to an exact optimal plan, and specifically to a canonical one, the most diffuse among all optimal couplings, so the limit does not depend on any tie-breaking. The value LCε\mathrm L^\varepsilon_{\mathbf C}LCε​ converges to the transport cost, which is what makes Sinkhorn-based estimates of transport distances meaningful. For large ε\varepsilonε the plan becomes the independent coupling a⊗b\mathbf a\otimes\mathbf ba⊗b, which explains the interpolation between optimal transport and maximum mean discrepancy studied later in the book. The KL projection identity (4.7) is the starting point of Sinkhorn's algorithm, so this mission also supplies the bridge to the next missions of the series.

Formalizing it. The proposition is a known result with a complete proof in the source; the work is formalizing it in Lean 4 with Mathlib. To our knowledge none of these statements, nor the discrete entropic transport problem itself, has a machine-checked proof in Mathlib or on the platform. A formal proof also pins down the convention for zero entries, on which the statement silently depends.

Difficulty

The obvious argument passes to the limit in the optimality of Pε\mathbf P_\varepsilonPε​, but this only shows that limit points are optimal for the Kantorovich problem; it does not identify which optimal coupling is the limit when the optimal set is a face of the polytope rather than a vertex. Identifying it requires a second-order argument at the level of the entropy, and the convergence of the whole family (not just a subsequence) rests on the uniqueness of the maximum-entropy optimal coupling, which in turn needs strict concavity of H\mathbf HH on a set that may contain matrices with zero entries, where the logarithm is singular. Existence and uniqueness of Pε\mathbf P_\varepsilonPε​ face the same boundary issue: the minimizer may a priori sit on the boundary of the polytope.

Formalization scope

Indices are 0-based (Fin n, Fin m); histograms are functions Fin n → ℝ in Mathlib's stdSimplex; matrices are Matrix (Fin n) (Fin m) ℝ with the product topology, so convergence is entrywise. Entropy uses 0log⁡0=00\log 0 = 00log0=0 (Real.negMulLog); the book's remark that H=−∞\mathbf H = -\inftyH=−∞ at a zero entry is not used, since it would contradict the continuity of H\mathbf HH invoked in the book's proof and make (4.3) degenerate. Optimality is expressed by predicates (a minimizer over U(a,b)\mathbf U(\mathbf a,\mathbf b)U(a,b)), never by a real infimum, so no junk value of an empty infimum appears. The limit ε→0\varepsilon \to 0ε→0 is taken from the right. The values LCε\mathrm L^\varepsilon_{\mathbf C}LCε​ and LC\mathrm L_{\mathbf C}LC​ appear as the objectives at Pε\mathbf P_\varepsilonPε​ and P0⋆\mathbf P^\star_0P0⋆​.

In the goal, Pε\mathbf P_\varepsilonPε​ and P0⋆\mathbf P^\star_0P0⋆​ are given by hypotheses (any family solving (4.2) for every ε>0\varepsilon > 0ε>0, any solution of (4.3)). This is not a vacuous formalization: milestones 1 and 2 assert that such objects exist and are unique, so the hypotheses are satisfiable and determine them. Standing assumptions are exactly those of the book: a∈Σn\mathbf a \in \Sigma_na∈Σn​, b∈Σm\mathbf b \in \Sigma_mb∈Σm​, ε>0\varepsilon > 0ε>0; no positivity of a\mathbf aa, b\mathbf bb or C\mathbf CC is assumed.

A complete development needs: compactness of the transport polytope, continuity and strict concavity of x↦−xlog⁡xx \mapsto -x\log xx↦−xlogx on [0,∞)[0,\infty)[0,∞), and the Gibbs inequality on finite sums. These are reusable for the Sinkhorn and entropic-duality missions of this series. Proofs of any milestone are welcome independently.

Selected references

  • G. Peyré and M. Cuturi, Computational Optimal Transport, Foundations and Trends in Machine Learning 11(5–6):355–607, 2019. https://doi.org/10.1561/2200000073 (§4.1, pp. 425–430)
  • R. Cominetti and J. San Martín, Asymptotic analysis of the exponential penalty trajectory in linear programming, Mathematical Programming 67(1–3):169–187, 1994. https://doi.org/10.1007/BF01582220
  • M. Cuturi, Sinkhorn Distances: Lightspeed Computation of Optimal Transport, NeurIPS 2013. https://arxiv.org/abs/1306.0895
  • A. G. Wilson, The use of entropy maximizing models, in the theory of trip distribution, mode split and route split, Journal of Transport Economics and Policy, 108–126, 1969 (bibliography of Peyré and Cuturi). https://doi.org/10.1561/2200000073
  • S. Erlander, Optimal Spatial Interaction and the Gravity Model, Vol. 173, Springer-Verlag, 1980 (bibliography of Peyré and Cuturi). https://doi.org/10.1561/2200000073
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Information TheoryMachine LearningProbability+1·Captain: mikedeng1

Information-Theoretic Analysis of Generalization Capability of Learning Algorithms III: The Gibbs Algorithm Has Excess Risk at Most (1/β)log(1/Q(wₒ)) + β/(2n) (Corollary 2)Research Paper

Motivation

Statistical learning algorithms fit hypotheses to data that are available during training, while their performance is judged on new data. A rule that minimizes training loss can fit peculiarities of the sample, so its training loss and population loss need not agree. Xu and Raginsky study this difference through information quantities associated with the sample and the algorithm's output. Their Gibbs example shows how a probability distribution chosen before seeing the data can guide a randomized learner and yield a population-risk guarantee with explicit dependence on the sample size and the prior's mass at a good hypothesis. This mission formalizes their countable-hypothesis result, Corollary 2, and the steps of Section 4.3 and Appendix D that support it. Xu and Raginsky, 2017

Setting

Let Z\mathcal ZZ be an instance space and W\mathcal WW a hypothesis space, each with a measurable structure. A probability measure μ\muμ on Z\mathcal ZZ generates a sample S=(Z1,…,Zn)S=(Z_1,\ldots,Z_n)S=(Z1​,…,Zn​) of nnn independent observations. A loss ℓ(w,z)\ell(w,z)ℓ(w,z) measures the cost of hypothesis www on instance zzz. Its population risk is Lμ(w)=EZ∼μℓ(w,Z)L_\mu(w)=\mathbb E_{Z\sim\mu}\ell(w,Z)Lμ​(w)=EZ∼μ​ℓ(w,Z); its empirical risk on a realized sample sss is Ls(w)=n−1∑i=1nℓ(w,zi)L_s(w)=n^{-1}\sum_{i=1}^n\ell(w,z_i)Ls​(w)=n−1∑i=1n​ℓ(w,zi​). These definitions match equations (1)–(3) of the paper. Xu and Raginsky, 2017, p. 2

A randomized learning rule is a Markov kernel PW∣SP_{W|S}PW∣S​: for each observed sample sss, it gives a probability distribution for an output hypothesis WWW. Together with the sample law μ⊗n\mu^{\otimes n}μ⊗n, it determines the joint law of (S,W)(S,W)(S,W). The expected generalization error is gen⁡(μ,PW∣S)=E[Lμ(W)−LS(W)]\operatorname{gen}(\mu,P_{W|S})=\mathbb E[L_\mu(W)-L_S(W)]gen(μ,PW∣S​)=E[Lμ​(W)−LS​(W)], as in equation (4). A probability distribution QQQ on W\mathcal WW serves as a prior. The Gibbs rule at inverse temperature β>0\beta>0β>0 weights a hypothesis by its prior mass times e−βLs(w)e^{-\beta L_s(w)}e−βLs​(w), then normalizes across the hypothesis space. Thus it favors low empirical risk without discarding the prior. Xu and Raginsky, 2017, pp. 2, 6

The associated optimization problem balances expected empirical risk against the conditional Kullback–Leibler divergence from the learning kernel to QQQ: D(PW∣S∥Q∣PS)=∫D(PW∣S=s∥Q) μ⊗n(ds)D(P_{W|S}\Vert Q\mid P_S)=\int D(P_{W|S=s}\Vert Q)\,\mu^{\otimes n}(ds)D(PW∣S​∥Q∣PS​)=∫D(PW∣S=s​∥Q)μ⊗n(ds). This penalty measures how much the output distribution changes from the prior after observing the sample. The paper's Theorem 5 identifies the Gibbs kernel as an optimizer of this relaxed problem. Xu and Raginsky, 2017, p. 6

Formalization targets

Gibbs optimization

For nonnegative loss, Theorem 5 asserts that the Gibbs kernel is a Markov learning rule, has the normalized exponential distribution at each sample, and minimizes the relaxed objective over learning kernels:

PW∣S=s∗(dw)=e−βLs(w)Q(dw)EQ[e−βLs(W)].P^*_{W|S=s}(dw)=\frac{e^{-\beta L_s(w)}Q(dw)}{\mathbb E_Q[e^{-\beta L_s(W)}]}.PW∣S=s∗​(dw)=EQ​[e−βLs​(W)]e−βLs​(w)Q(dw)​.

The milestone list also records the Appendix D empirical-risk comparison with a fixed hypothesis, its identity D(δw∥Q)=−log⁡Q({w})D(\delta_w\Vert Q)=-\log Q(\{w\})D(δw​∥Q)=−logQ({w}) when the prior has positive mass at www, and the Gibbs generalization bound quoted in equation (28). Xu and Raginsky, 2017, pp. 6–7, 13

Expected population risk

The goal is Corollary 2. If W\mathcal WW is countable, 0≤ℓ≤10\le\ell\le10≤ℓ≤1, wow_owo​ achieves the minimum population risk, and Q({wo})>0Q(\{w_o\})>0Q({wo​})>0, then

E[Lμ(W)]≤inf⁡w∈WLμ(w)+1βlog⁡1Q({wo})+β2n.\mathbb E[L_\mu(W)]\le\inf_{w\in\mathcal W}L_\mu(w) +\frac1\beta\log\frac1{Q(\{w_o\})}+\frac\beta{2n}.E[Lμ​(W)]≤w∈Winf​Lμ​(w)+β1​logQ({wo​})1​+2nβ​.

The first excess term reflects the prior mass on a population-risk minimizer; the second is controlled by sample size and inverse temperature. The target keeps the infimum over the whole hypothesis space and the constants of equation (29). Xu and Raginsky, 2017, p. 7

Significance

Corollary 2 gives a quantitative way to choose a prior and temperature: a prior that puts more mass on a good hypothesis reduces the logarithmic term, while changing β\betaβ changes both terms in opposite directions. It also connects the optimization characterization of a randomized algorithm to the population risk that matters for learning. Without the empirical-risk comparison and the separate generalization estimate, optimizing the training objective alone would not give this population statement. Xu and Raginsky, 2017, pp. 6–7, 13

The cited paper proves Corollary 2 and Theorem 5; the present task is to supply machine-checked proofs of their faithful Lean statements. The Donsker–Varadhan inequality and a tilted-measure identity are already published and proved on the platform, and the basic sample-law and risk definitions are already published. The Gibbs optimization, the Appendix D comparisons, and the quoted bound (28) are posed here as new targets. This work would leave reusable definitions for randomized learning kernels, their Gibbs specialization, and objectives with potentially infinite divergence.

Difficulty

The Gibbs rule is defined separately at every sample, but a learning algorithm must be a measurable kernel on the entire sample space. Establishing the normalized density pointwise does not by itself establish this kernel property. The relaxed objective also combines an integral of empirical risk with a conditional divergence, either of which can be infinite before bounded-loss assumptions are imposed. Equation (28), which the paper attributes to earlier work, needs a sharp β/(2n)\beta/(2n)β/(2n) constant; the looser information bound discussed immediately before it has a different dependence on β\betaβ. These are separate mathematical obligations even though the final displayed inequality is short. Xu and Raginsky, 2017, pp. 6–7, 13

Formalization scope

The Lean development represents a sample as Fin n → Z, its law as Measure.pi, and a randomized learner as Kernel (Fin n → Z) W. Population and empirical risks come from the published LearnStability.Characterization.Setting definition. The Gibbs kernel is built by a normalized density over the probability prior QQQ; Theorem 5 explicitly asserts it is a Markov kernel. The relaxed objective takes values in R≥0∪{∞}\mathbb R_{\ge0}\cup\{\infty\}R≥0​∪{∞}, so an infinite KL cost is not lost through real conversion. Generalization error remains the real expectation of the population-risk minus empirical-risk difference. The stated loss bounds ensure those real expectations are meaningful in the bounded-loss results.

The paper assumes a nonnegative loss in its general setting and requires ℓ∈[0,1]\ell\in[0,1]ℓ∈[0,1] for the later bounds. The Lean statements add joint measurability of ℓ\ellℓ and n>0n>0n>0 explicitly. Corollary 2 also makes the named minimizer's defining property and positive prior mass explicit: countability alone does not ensure an attained infimum, and a zero prior mass makes the printed logarithmic bound infinite. Theorem 5 retains its general measurable hypothesis space and nonnegative loss; countability appears only where the paper invokes it. Theorem 5 states optimality over all Markov kernels, the displayed Gibbs formula, and, for each sample, uniqueness of the Gibbs distribution as the minimizer of the samplewise problem (C.12) of Appendix C. The goal concerns the Gibbs kernel's actual population risk, rather than only the existence of a minimizer of the relaxed objective.

Useful contributions include probability-kernel measurability, control of conditional divergence and its finite conversions, the fixed-hypothesis comparison, and the sharp bounded-loss generalization estimate. The point-mass KL identity and the extended objective can be reused in other Gibbs and PAC-Bayesian formalizations. Xu and Raginsky, 2017, pp. 2, 6–7, 13

Selected references

  • A. Xu and M. Raginsky, Information-Theoretic Analysis of Generalization Capability of Learning Algorithms, Advances in Neural Information Processing Systems 30, 2017. arXiv:1705.07809v2
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Optimization·Captain: mikedeng1

A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization III: Every Limit Point of BSCA with Armijo Steps Is a Stationary PointResearch Paper

Motivation

Large optimization models often split their variables into blocks that can be handled separately. A method can then solve one smaller approximation problem per iteration instead of the full problem. The block successive convex approximation (BSCA) method of Razaviyayn, Hong and Luo allows the block approximation to guide the search without requiring it to be a global upper bound on the original objective. This matters when an upper bound is awkward to construct but a locally accurate convex model is available. The question is what such a run can approach: a small objective value by itself does not certify first-order optimality.

The paper develops this result after its successive upper-bound minimization methods. Theorem 4 of the pinned preprint gives the limit-point guarantee for BSCA with an Armijo step search. The mission records that result and the numbered relations (31)–(34) and (39) that organize the argument, using the 2012 preprint as the sole source for statement indices and page numbers.

Setting

The decision vector is a block vector x=(x1,…,xN)x=(x_1,\ldots,x_N)x=(x1​,…,xN​), with block xix_ixi​ in a nonempty, closed, convex set Xi⊆RmiX_i\subseteq\mathbb R^{m_i}Xi​⊆Rmi​. The feasible set is X=∏i=1NXiX=\prod_{i=1}^{N}X_iX=∏i=1N​Xi​. A continuously differentiable objective fff assigns each feasible vector a real value. A point z∈Xz\in Xz∈X is stationary when f′(z;d)≥0f'(z;d)\ge0f′(z;d)≥0 for every direction ddd whose full step z+dz+dz+d lies in XXX. For a smooth objective, f′(z;d)f'(z;d)f′(z;d) is the derivative of fff in direction ddd.

For block iii, the function hi(yi,x)h_i(y_i,x)hi​(yi​,x) approximates the objective as the trial block yiy_iyi​ varies around a current point xxx. Its directional derivative at yi=xiy_i=x_iyi​=xi​ agrees with the objective derivative in the corresponding one-block direction, as in equation (30). Each hi(⋅,x)h_i(\cdot,x)hi​(⋅,x) is strictly convex on XiX_iXi​, and hih_ihi​ varies continuously with its trial block and feasible base point. Strict convexity is a condition in the trial block for each fixed xxx; it does not say that hih_ihi​ is jointly convex in both arguments.

At iteration rrr, a cyclic schedule chooses one block iii. A minimizer yiry_i^ryir​ of hi(⋅,xr)h_i(\cdot,x^r)hi​(⋅,xr) over XiX_iXi​ replaces that block in yry^ryr; all other blocks of yry^ryr equal those of xrx^rxr. The search direction is dr=yr−xrd^r=y^r-x^rdr=yr−xr. With σ,β∈(0,1)\sigma,\beta\in(0,1)σ,β∈(0,1) and an initial trial length αinit>0\alpha^{\rm init}>0αinit>0, the Armijo rule chooses the largest member αr\alpha^rαr of {αinitβj:j=0,1,…}\{\alpha^{\rm init}\beta^j:j=0,1,\ldots\}{αinitβj:j=0,1,…} satisfying

f(xr)−f(xr+αrdr)≥−σαrf′(xr;dr),f(x^r)-f(x^r+\alpha^r d^r)\ge-\sigma\alpha^r f'(x^r;d^r),f(xr)−f(xr+αrdr)≥−σαrf′(xr;dr),

then sets xr+1=xr+αrdrx^{r+1}=x^r+\alpha^r d^rxr+1=xr+αrdr. The selected minimizer can be any one of the block minimizers; the definition does not prescribe a hidden tie-breaking rule.

Formalization targets

The first targets identify descent and line-search existence. Equation (31) says the chosen block direction has a nonpositive derivative. Equation (32) says a strict descent direction admits a trial index. The later milestones state the vanishing scaled derivative (33), a subsequence with vanishing search directions (34), and limiting block optimality following (39).

The goal is Theorem 4: for every limit point zzz of the sequence generated by cyclic BSCA,

z∈X,f′(z;d)≥0for every d with z+d∈X.z\in X,\qquad f'(z;d)\ge0\quad\text{for every }d\text{ with }z+d\in X.z∈X,f′(z;d)≥0for every d with z+d∈X.

This is a limit-point assertion. It does not assert convergence of the entire iterate sequence, a convergence rate, or global optimality of a stationary point.

Significance

Theorem 4 identifies what an accumulation point of BSCA must satisfy even though its block models need only local first-order agreement rather than global upper bounds. In a nonconvex problem, stationarity is a meaningful first-order conclusion but need not be a global minimum. The exact role of the step search is important: accepting some Armijo step gives decrease, whereas the theorem's limit-point claim uses the largest acceptable member of the geometric trial sequence.

This mission asks solvers to supply machine-checked proofs for a known paper theorem and its stated supporting relations. The draft statements compile as open theorems; they are not proofs. The reusable output includes a block run predicate, an explicit first-order agreement predicate, and a bridge between feasible-direction stationarity and an extended-real objective on the full block space. These objects also make subsequent variants of cyclic block methods easier to state precisely.

Difficulty

A simple decrease argument shows that objective values do not increase, but the accepted step sizes may approach zero. Therefore decrease alone does not imply dr→0d^r\to0dr→0 or that a limit point minimizes every block model. The paper's key limit statement singles out a convergent subsequence on which a fixed block is updated and finds a further subsequence with vanishing directions. Passing block optimality to the limit then requires continuity of the approximation and feasible limit points. The cyclic schedule is needed to cover all blocks, while first-order agreement turns block optimality into stationarity for the original smooth objective.

Formalization scope

Lean uses the published TsengBCD.Stationary.X for the finite product of Euclidean block spaces, its extended-real lower directional derivative, and its stationary-point predicate. The product has the sup norm rather than the paper's Euclidean product norm; in finite dimension the notions of convergence used here agree. Blocks and iterations are indexed from zero. The paper's cyclic choice i=(r mod N)+1i=(r\bmod N)+1i=(rmodN)+1 is reindexed so the step from xrx^rxr to xr+1x^{r+1}xr+1 updates block r mod Nr\bmod NrmodN. The existence of the schedule already rules out zero blocks.

The objective is a real function on the full block space, with ContDiff ℝ 1 expressing the paper's continuous differentiability. This makes the Fréchet derivative used in the Armijo test equal to the paper's lower directional derivative at the points in question. For stationarity, the objective is extended by +∞+\infty+∞ outside XXX, so infeasible directions cannot silently become admissible. The approximation derivative remains extended-real because the paper assumes only convexity for that function. ContinuousOn is taken on the product of a feasible trial block and a feasible base point.

Two points are explicit in the formalization. First, 0<αinit≤10<\alpha^{\rm init}\le10<αinit≤1 ensures that a convex combination of xrx^rxr and yry^ryr stays in XXX; the paper prints only positivity. Whether Theorem 4 holds without this restriction under a different global interpretation of fff and hih_ihi​ remains a separate question. Second, Figure 4 mixes rrr and r−1r-1r−1 in the approximation, direction, and update lines. The run follows the adjacent prose and proof, all of which construct the direction at the current iterate. For (33), the existence of a limit point makes explicit the proof's standing situation and gives the decreasing objective a finite limit. An arbitrary unrelated sequence, an Armijo step that is not the largest accepted trial, or an objective extension that gives a default zero derivative off XXX is outside this scope.

The remaining proof work includes feasibility of every iterate, first-order agreement along the selected block, the Armijo existence statement, the subsequence claim, passage of block minimization to the limit, and the final stationary-point equivalence. The finite-dimensional convex analysis and topology involved are reusable beyond this one algorithm.

Selected references

  • M. Razaviyayn, M. Hong and Z.-Q. Luo, A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization, SIAM Journal on Optimization 23(2), 2013, 1126–1153. arXiv:1209.2385v1; DOI:10.1137/120891009.
  • P. Tseng, Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization, Journal of Optimization Theory and Applications 109(3), 2001, 475–494. DOI:10.1023/A:1017501703105.
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AlgebraCombinatoricsDiscrete Geometry+1·Captain: mikedeng1

Generalising the Scattered Property of Subspaces 1: An h-Scattered 𝔽_q-Subspace of V(r, qⁿ) Either Defines a Subgeometry or Has Dimension at Most rn/(h + 1)Research Paper

Motivation

Let VVV be an rrr-dimensional vector space over the finite field Fqn\mathbb F_{q^n}Fqn​. Viewed over the subfield Fq\mathbb F_qFq​, VVV has dimension rnrnrn, and its one-dimensional Fqn\mathbb F_{q^n}Fqn​-subspaces form the Desarguesian spread. An Fq\mathbb F_qFq​-subspace UUU of VVV is scattered if it meets every element of this spread in an Fq\mathbb F_qFq​-subspace of dimension at most one. Scattered subspaces define scattered Fq\mathbb F_qFq​-linear sets in PG(r−1,qn)\mathrm{PG}(r-1,q^n)PG(r−1,qn). Through these linear sets they are connected to projective two-weight codes, strongly regular graphs and maximum rank distance (MRD) codes. In 2000 Blokhuis and Lavrauw proved that a scattered subspace has dimension at most rn/2rn/2rn/2, and a sequence of papers showed that this bound is attained whenever 2∣rn2 \mid rn2∣rn.

Csajbók, Marino, Polverino and Zullo (arXiv:1906.10590v2; Combinatorica 41, 2021) replace the one-dimensional spread elements by all hhh-dimensional Fqn\mathbb F_{q^n}Fqn​-subspaces. This gives a hierarchy of conditions, the hhh-scattered subspaces, that refines the scattered case. For h=r−1h = r-1h=r−1 and dimension nnn these subspaces had already been identified with Fqn\mathbb F_{q^n}Fqn​-linear MRD codes by Sheekey and Van de Voorde. This mission formalizes the paper's main theorem, the dimension bound for hhh-scattered subspaces.

Timeline.

  • 2000: Blokhuis and Lavrauw, Scattered spaces with respect to a spread in PG(n, q), prove dim⁡FqU≤rn/2\dim_{\mathbb F_q} U \le rn/2dimFq​​U≤rn/2 for scattered UUU (the case h=1h = 1h=1).
  • 2000–2018: Ball–Blokhuis–Lavrauw (2000), Blokhuis–Lavrauw (2000), Csajbók–Marino–Polverino–Zullo (2017) and Bartoli–Giulietti–Marino–Polverino (2018) together show that scattered subspaces of dimension rn/2rn/2rn/2 exist whenever rnrnrn is even.
  • 2020: Sheekey and Van de Voorde study subspaces scattered with respect to hyperplanes (h=r−1h = r-1h=r−1) and relate them to MRD codes.
  • 2019–2021: Csajbók, Marino, Polverino and Zullo introduce hhh-scattered subspaces and prove the bound rn/(h+1)rn/(h+1)rn/(h+1) for every hhh (Theorem 2.3).

Setting

Let Fq⊆Fqn\mathbb F_q \subseteq \mathbb F_{q^n}Fq​⊆Fqn​ be finite fields, so n=dim⁡FqFqnn = \dim_{\mathbb F_q}\mathbb F_{q^n}n=dimFq​​Fqn​, and let VVV be a vector space over Fqn\mathbb F_{q^n}Fqn​ of dimension rrr. Every Fqn\mathbb F_{q^n}Fqn​-subspace of VVV is also an Fq\mathbb F_qFq​-subspace. For an Fq\mathbb F_qFq​-subspace UUU of VVV, ⟨U⟩Fqn\langle U\rangle_{\mathbb F_{q^n}}⟨U⟩Fqn​​ denotes its Fqn\mathbb F_{q^n}Fqn​-span.

Definition 1.1. Let 0<h≤r−10 < h \le r-10<h≤r−1. An Fq\mathbb F_qFq​-subspace UUU of VVV is hhh-scattered if ⟨U⟩Fqn=V\langle U\rangle_{\mathbb F_{q^n}} = V⟨U⟩Fqn​​=V and every hhh-dimensional Fqn\mathbb F_{q^n}Fqn​-subspace SSS of VVV satisfies dim⁡Fq(S∩U)≤h\dim_{\mathbb F_q}(S\cap U) \le hdimFq​​(S∩U)≤h. In Lean this is HScattered.Bound.IsHScattered F K h U, with F =Fq=\mathbb F_q=Fq​, K =Fqn=\mathbb F_{q^n}=Fqn​, and rrr = Module.finrank K V.

An Fq\mathbb F_qFq​-subspace UUU defines a subgeometry of PG(V,Fqn)\mathrm{PG}(V,\mathbb F_{q^n})PG(V,Fqn​) if some Fq\mathbb F_qFq​-basis of UUU is also an Fqn\mathbb F_{q^n}Fqn​-basis of VVV (HScattered.Construction.DefinesSubgeometry F K U, a definition shared with the other missions of this series). In coordinates, UUU is then Fq r\mathbb F_q^{\,r}Fqr​ inside Fqn r\mathbb F_{q^n}^{\,r}Fqnr​.

A rank distance code of Fqn×m\mathbb F_q^{n\times m}Fqn×m​, n≤mn \le mn≤m, is a set C\mathcal CC of Fq\mathbb F_qFq​-linear maps from an mmm-dimensional to an nnn-dimensional Fq\mathbb F_qFq​-space, with distance d(f,g)=rk⁡(f−g)d(f,g) = \operatorname{rk}(f-g)d(f,g)=rk(f−g).

Formalization targets

Goal: Theorem 2.3

If UUU is an hhh-scattered Fq\mathbb F_qFq​-subspace of VVV, then either dim⁡FqU=r\dim_{\mathbb F_q}U = rdimFq​​U=r, UUU defines a subgeometry and UUU is (r−1)(r-1)(r−1)-scattered, or

dim⁡FqU≤rnh+1.\dim_{\mathbb F_q} U \le \frac{rn}{h+1}.dimFq​​U≤h+1rn​.

The disjunction is inclusive. The statement holds for every hhh in the range 0<h≤r−10 < h \le r-10<h≤r−1, including h=1h = 1h=1.

Milestones

  • Proposition 2.1. For h>1h > 1h>1, an hhh-scattered subspace is iii-scattered for every 0<i<h0 < i < h0<i<h.
  • Lemma 2.2. If r≥2r \ge 2r≥2 and r≤i≤nr \le i \le nr≤i≤n, then VVV contains an (r−1)(r-1)(r−1)-scattered Fq\mathbb F_qFq​-subspace of dimension iii.
  • Result 4.6 (Delsarte). A rank distance code of Fqn×m\mathbb F_q^{n\times m}Fqn×m​, n≤mn\le mn≤m, with minimum distance ddd has ∣C∣≤qm(n−d+1)|\mathcal C| \le q^{m(n-d+1)}∣C∣≤qm(n−d+1).

Significance

The result. Theorem 2.3 is the upper end of the theory of hhh-scattered subspaces. It shows that the bound rn/(h+1)rn/(h+1)rn/(h+1) decreases with hhh: a stronger intersection condition forces a smaller subspace. Every subspace reaching the bound is a maximum hhh-scattered subspace. The rest of the paper builds on such subspaces. Its constructions show the bound is sharp when h+1∣rh+1 \mid rh+1∣r. Its intersection numbers with hyperplanes lie in [rn/(h+1)−n, rn/(h+1)−n+h][rn/(h+1)-n,\ rn/(h+1)-n+h][rn/(h+1)−n, rn/(h+1)−n+h]. Its Delsarte-type duality sends maximum hhh-scattered subspaces to maximum (n−h−2)(n-h-2)(n−h−2)-scattered ones. Each of these statements presupposes the bound. For h=r−1h = r-1h=r−1, the bound dim⁡U≤n\dim U \le ndimU≤n is the geometric counterpart of the Singleton bound for MRD codes.

Formalizing it. The theorem is proved in the paper; it has no machine-checked proof. A formalization would provide a Lean model of hhh-scattered subspaces over a field tower and a rank-metric Singleton bound, and the bound itself as a reusable lemma for the companion missions on constructions, hyperplane intersections and duality. The case h=1h = 1h=1 is attributed in the paper to Blokhuis–Lavrauw and is not reproved there, so a complete formal proof must also supply that case.

Difficulty

The obvious approach is to count. Every hhh-dimensional Fqn\mathbb F_{q^n}Fqn​-subspace meets UUU in at most qhq^hqh vectors. But these subspaces overlap heavily, and a double count of incidences between vectors of UUU and hhh-dimensional subspaces does not produce a bound of the form rn/(h+1)rn/(h+1)rn/(h+1). The hhh-scattered condition must be used on subspaces of every dimension t<ht < ht<h at once, which is the content of Proposition 2.1. The argument for h=1h = 1h=1 does not carry over directly either: the paper treats h=r−1h = r-1h=r−1, 1<h<r−11 < h < r-11<h<r−1 with n≥h+1n \ge h+1n≥h+1, and n<h+1n < h+1n<h+1 as separate cases. The case h=r−1h = r-1h=r−1 rests on a coding-theoretic input (Result 4.6), and the middle case needs auxiliary subspaces whose existence (Lemma 2.2) holds only when n≥h+1n \ge h+1n≥h+1.

Formalization scope

  • Field tower. Fq\mathbb F_qFq​ and Fqn\mathbb F_{q^n}Fqn​ are finite fields F, K with [Algebra F K]. VVV is a finite-dimensional K-module with a compatible F-module structure ([IsScalarTower F K V]). The parameters are qqq = Fintype.card F, nnn = Module.finrank F K and rrr = Module.finrank K V.
  • Subspaces. Fq\mathbb F_qFq​-subspaces are Submodule F V and Fqn\mathbb F_{q^n}Fqn​-subspaces are Submodule K V. The intersection S∩US\cap US∩U is S.restrictScalars F ⊓ U.
  • The definition. The range 0<h<r0 < h < r0<h<r and the spanning condition ⟨U⟩Fqn=V\langle U\rangle_{\mathbb F_{q^n}} = V⟨U⟩Fqn​​=V are clauses of IsHScattered.
  • No division. rn/(h+1)rn/(h+1)rn/(h+1) never appears with division: "dim⁡U≤rn/(h+1)\dim U \le rn/(h+1)dimU≤rn/(h+1)" is (h+1)dim⁡U≤rn(h+1)\dim U \le rn(h+1)dimU≤rn in N\mathbb NN.
  • Result 4.6. The code is a Finset of linear maps. The minimum distance ddd enters as a lower bound on all pairwise rank distances, with 1≤d≤n1 \le d \le n1≤d≤n.
  • Lemma 2.2. It carries r≥2r \ge 2r≥2, which Definition 1.1 forces for (r−1)(r-1)(r−1)-scattered subspaces.

Ruling out trivial readings. Without the range h<rh < rh<r, every spanning subspace would be "hhh-scattered" for h≥rh \ge rh≥r, and U=VU = VU=V of dimension rnrnrn would refute the goal. Without the spanning condition, U=0U = 0U=0 would satisfy the bound vacuously. Both conditions are part of the definition, and the goal is the inclusive disjunction for every hhh-scattered UUU.

Cited inputs. Result 4.6 is Delsarte's theorem [13], numbered in this paper and posed here with sorry. The case h=1h = 1h=1 of the goal is the Blokhuis–Lavrauw bound [4], which the paper cites without proof and says can be obtained by adapting its argument (Zullo's thesis [30]). It is not an item: a complete proof of the goal must cover it.

Infrastructure. A proof needs: dimension arithmetic for restriction of scalars in a tower; the rank–nullity theorem for Fq\mathbb F_qFq​-linear maps; the count of roots of a qqq-polynomial ∑jajxqj\sum_j a_j x^{q^j}∑j​aj​xqj, for Lemma 2.2; and a rank-metric Singleton bound. The Singleton bound and Lemma 2.2 are reusable beyond this mission. Contributions are welcome on any milestone, on the h=1h = 1h=1 case, and on the reduction for n<h+1n < h+1n<h+1.

Selected references

  • B. Csajbók, G. Marino, O. Polverino, F. Zullo, Generalising the scattered property of subspaces, arXiv:1906.10590v2 (2020); Combinatorica 41 (2021). https://arxiv.org/abs/1906.10590v2
  • A. Blokhuis, M. Lavrauw, Scattered spaces with respect to a spread in PG(n, q), Geometriae Dedicata 81 (2000) 231–243. https://doi.org/10.1023/A:1005283806897
  • P. Delsarte, Bilinear forms over a finite field, with applications to coding theory, J. Combin. Theory Ser. A 25 (1978) 226–241. https://doi.org/10.1016/0097-3165(78)90015-8
  • J. Sheekey, G. Van de Voorde, Rank-metric codes, linear sets, and their duality, Designs, Codes and Cryptography 88 (2020) 655–675. https://doi.org/10.1007/s10623-019-00703-z
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A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization II: Limit Points of Cyclic BSUM with Unique Block Minimizers Are Coordinatewise StationaryResearch Paper

Motivation

Many large optimization problems in signal processing, statistics and machine learning have a variable that splits naturally into blocks, and are solved by updating one block at a time. The classical method of this kind, block coordinate descent (BCD), minimises the objective exactly in one block while the others are held fixed. In practice the exact block subproblem is often too expensive, and one minimises instead a simpler function that upper-bounds the objective and touches it at the current point. The EM algorithm, DC (difference of convex) programming, proximal and linearised block updates and several alternating schemes for matrix factorisation and transceiver design are all of this form.

Razaviyayn, Hong and Luo (arXiv:1209.2385, SIAM J. Optim. 23 (2013)) gave one convergence analysis for this whole family, the block successive upper-bound minimization (BSUM) method, without convexity or smoothness of the objective. This mission formalizes the block part of that analysis: the cyclic BSUM method (Theorem 2) and its maximum-improvement variant MISUM (Theorem 3).

Timeline. Bertsekas (Nonlinear Programming, 1999, Prop. 2.7.1) proved that limit points of cyclic BCD are stationary for continuously differentiable objectives over a product of closed convex sets when each block minimiser is unique. Tseng (J. Optim. Theory Appl. 109, 2001) treated nondifferentiable objectives using regularity and coordinatewise minima. Chen, He, Li and Zhang (SIAM J. Optim. 22, 2012) introduced maximum block improvement, which drops the uniqueness assumption. The 2013 paper extends these results from exact block minimisation of fff to minimisation of block upper bounds.

Setting

The problem is

min⁡x f(x)s.t.x∈X=X1×⋯×Xn,(12)\min_x\ f(x)\quad\text{s.t.}\quad x\in\mathcal X=\mathcal X_1\times\cdots\times\mathcal X_n,\qquad (12)xmin​ f(x)s.t.x∈X=X1​×⋯×Xn​,(12)

where each Xi⊆Rmi\mathcal X_i\subseteq\mathbb R^{m_i}Xi​⊆Rmi​ is a closed convex set and fff is a continuous real function on X\mathcal XX. A point is x=(x1,…,xn)x=(x_1,\dots,x_n)x=(x1​,…,xn​) with xi∈Rmix_i\in\mathbb R^{m_i}xi​∈Rmi​. The directional derivative is f′(x;d)=lim inf⁡λ↓0 (f(x+λd)−f(x))/λf'(x;d)=\liminf_{\lambda\downarrow0}\,(f(x+\lambda d)-f(x))/\lambdaf′(x;d)=liminfλ↓0​(f(x+λd)−f(x))/λ, a value in [−∞,+∞][-\infty,+\infty][−∞,+∞]. A point z∈Xz\in\mathcal Xz∈X is stationary for (12) if f′(z;d)≥0f'(z;d)\ge0f′(z;d)≥0 for every ddd with z+d∈Xz+d\in\mathcal Xz+d∈X. The function fff is regular at zzz if f′(z;d)≥0f'(z;d)\ge0f′(z;d)≥0 holds for every d=(d1,…,dn)d=(d_1,\dots,d_n)d=(d1​,…,dn​) such that f′(z;(0,…,dk,…,0))≥0f'(z;(0,\dots,d_k,\dots,0))\ge0f′(z;(0,…,dk​,…,0))≥0 for every block kkk. A point z∈Xz\in\mathcal Xz∈X is coordinatewise stationary if f′(z;d)≥0f'(z;d)\ge0f′(z;d)≥0 for every d=(0,…,dk,…,0)d=(0,\dots,d_k,\dots,0)d=(0,…,dk​,…,0) with zk+dk∈Xkz_k+d_k\in\mathcal X_kzk​+dk​∈Xk​, for every kkk.

For each block iii an approximation function ui(xi,y)u_i(x_i,y)ui​(xi​,y) is given, with xi∈Rmix_i\in\mathbb R^{m_i}xi​∈Rmi​ and y∈Xy\in\mathcal Xy∈X. Assumption 2 requires:

  • (B1) ui(yi,y)=f(y)u_i(y_i,y)=f(y)ui​(yi​,y)=f(y) for y∈Xy\in\mathcal Xy∈X;
  • (B2) ui(xi,y)≥f(y1,…,yi−1,xi,yi+1,…,yn)u_i(x_i,y)\ge f(y_1,\dots,y_{i-1},x_i,y_{i+1},\dots,y_n)ui​(xi​,y)≥f(y1​,…,yi−1​,xi​,yi+1​,…,yn​) for xi∈Xix_i\in\mathcal X_ixi​∈Xi​, y∈Xy\in\mathcal Xy∈X;
  • (B3) ui′(xi,y;di)∣xi=yi=f′(y;(0,…,di,…,0))u_i'(x_i,y;d_i)\big|_{x_i=y_i}=f'(y;(0,\dots,d_i,\dots,0))ui′​(xi​,y;di​)​xi​=yi​​=f′(y;(0,…,di​,…,0)) whenever yi+di∈Xiy_i+d_i\in\mathcal X_iyi​+di​∈Xi​, the left side being the derivative in xix_ixi​ only;
  • (B4) uiu_iui​ is continuous in (xi,y)(x_i,y)(xi​,y).

The BSUM algorithm (Fig. 2) starts at x0∈Xx^0\in\mathcal Xx0∈X and, at each iteration, picks a block iii by the cyclic rule, replaces xix_ixi​ by any minimiser of ui(⋅,xr−1)u_i(\cdot,x^{r-1})ui​(⋅,xr−1) over Xi\mathcal X_iXi​, and keeps the other blocks. The MISUM algorithm (Fig. 3) instead updates a block kkk attaining min⁡imin⁡xi∈Xiui(xi,xr−1)\min_i\min_{x_i\in\mathcal X_i}u_i(x_i,x^{r-1})mini​minxi​∈Xi​​ui​(xi​,xr−1).

Formalization targets

Goal: Theorem 2(a)

If each ui(xi,y)u_i(x_i,y)ui​(xi​,y) is quasi-convex in xix_ixi​, Assumption 2 holds and every block subproblem min⁡xi∈Xiui(xi,y)\min_{x_i\in\mathcal X_i}u_i(x_i,y)minxi​∈Xi​​ui​(xi​,y), y∈Xy\in\mathcal Xy∈X, has a unique solution, then every limit point zzz of the cyclic BSUM iterates satisfies

f′(z;d)≥0∀ d=(0,…,dk,…,0), zk+dk∈Xk, k=1,…,n,f'(z;d)\ge0\qquad\forall\,d=(0,\dots,d_k,\dots,0),\ z_k+d_k\in\mathcal X_k,\ k=1,\dots,n,f′(z;d)≥0∀d=(0,…,dk​,…,0), zk​+dk​∈Xk​, k=1,…,n,

and zzz is a stationary point of (12) if fff is regular at zzz.

Milestones

The proof passes through the monotonicity f(x0)≥f(x1)≥⋯f(x^0)\ge f(x^1)\ge\cdotsf(x0)≥f(x1)≥⋯ (14); the limit lim⁡rf(xr)=f(z)\lim_r f(x^r)=f(z)limr​f(xr)=f(z) at a limit point zzz (15); the claim that xrj→zx^{r_j}\to zxrj​→z forces xrj+1→zx^{r_j+1}\to zxrj​+1→z; block minimality uk(zk,z)≤uk(xk,z)u_k(z_k,z)\le u_k(x_k,z)uk​(zk​,z)≤uk​(xk​,z) for all xk∈Xkx_k\in\mathcal X_kxk​∈Xk​ and every kkk; and the first-order condition uk′(xk,z;dk)∣xk=zk≥0u_k'(x_k,z;d_k)|_{x_k=z_k}\ge0uk′​(xk​,z;dk​)∣xk​=zk​​≥0 for feasible dkd_kdk​ (22).

Companions

Theorem 2(b): if the level set X0={x∈X:f(x)≤f(x0)}\mathcal X^0=\{x\in\mathcal X: f(x)\le f(x^0)\}X0={x∈X:f(x)≤f(x0)} is compact, block minimisers are unique in at least n−1n-1n−1 blocks and fff is regular on X0\mathcal X^0X0, then d(xr,X∗)→0d(x^r,\mathcal X^*)\to0d(xr,X∗)→0, where X∗\mathcal X^*X∗ is the set of stationary points. Theorem 3: under Assumption 2 alone, every limit point of the MISUM iterates is coordinatewise stationary, and stationary where fff is regular.

Significance

Theorem 2 is the stationarity guarantee for every method that can be written as cyclic block minimisation of tight upper bounds: block EM, alternating DC iterations, proximal block updates, and the matrix-factorisation and beamforming schemes of Section VIII of the paper. Each such method needs only a check of (B1)–(B4) to inherit it. Theorem 3 shows that the uniqueness assumption, which part (a) needs and BCD needs too, can be traded for a greedy block choice.

The results are proved in the paper. As far as the platform's catalogue shows, none of them has been formalized; the related published work is the formalization of Tseng's 2001 setting (directional derivative, stationarity, regularity, cyclic rule), which this mission reuses. Formalizing the analysis checks the "without loss of generality" and "by further restricting to a subsequence" steps, which carry most of the argument, and gives reusable statements about limit points of block methods.

Difficulty

The obvious argument shows that f(xr)f(x^r)f(xr) decreases and that a limit point zzz satisfies the block optimality condition for the block updated along the convergent subsequence. It does not say anything about the other blocks: consecutive iterates may stay a fixed distance apart, so the next iterate need not converge to zzz, and then nothing is known about zzz in the next block. Showing that xrj+1→zx^{r_j+1}\to zxrj​+1→z is the central step, and it is exactly where quasi-convexity and uniqueness of the block minimisers are used. Without uniqueness, limit points of even exact cyclic BCD need not be stationary (Powell's examples, which the paper cites), which is why Theorem 2(b) needs compactness and Theorem 3 needs a different update rule.

The step from block minimality to stationarity is a second gap: (B3) only matches derivatives in coordinate directions, and passing to all directions requires regularity of fff.

Formalization scope

The space Rm1×⋯×Rmn\mathbb R^{m_1}\times\cdots\times\mathbb R^{m_n}Rm1​×⋯×Rmn​ is the dependent product X n of the referenced Tseng setting, with blocks indexed 0,…,N−10,\dots,N-10,…,N−1 (paper block kkk is index k−1k-1k−1). It carries the sup norm over blocks, which matters only for distances; convergence statements do not depend on the norm. The block sets are closed and convex, fff is a real function continuous on X\mathcal XX, and x0∈Xx^0\in\mathcal Xx0∈X; these standing hypotheses of Section IV appear in every statement.

The paper assumes dom⁡f=X\operatorname{dom}f=\mathcal Xdomf=X. Accordingly fff is extended by +∞+\infty+∞ off X\mathcal XX, and stationarity and regularity are those of the extension. Directional derivatives are extended-real liminfs, never real-valued limits with a junk value. A run is a predicate on a sequence: each new block is a minimiser of the block subproblem, but existence of minimisers is not assumed. The cyclic rule updates block r mod Nr \bmod NrmodN on the step xr→xr+1x^r\to x^{r+1}xr→xr+1, a relabelling of Fig. 2's i=(r mod n)+1i=(r\bmod n)+1i=(rmodn)+1. Limit points are cluster points of the sequence.

Deviations from the printed statements, each disclosed in the items:

  • Theorems 2(a) and 3 say "coordinatewise minimum". Their proofs establish coordinatewise stationarity and call it that, and the literal minimum claim is false (one block, X=[−1,1]\mathcal X=[-1,1]X=[−1,1], f(x)=−x2f(x)=-x^2f(x)=−x2, a quadratic upper bound tight at the iterate; the run stays at 000 while f(1)<f(0)f(1)<f(0)f(1)<f(0)). The statements assert coordinatewise stationarity.
  • Theorem 2(b) assumes regularity "at every point in the set of stationary points"; the proof applies it at a limit point not yet known to be stationary. The statement assumes regularity on the level set X0\mathcal X^0X0, as Tseng's Theorem 4.1 does.
  • d(xr,X∗)→0d(x^r,\mathcal X^*)\to0d(xr,X∗)→0 is stated as "for every ε>0\varepsilon>0ε>0, eventually xrx^rxr is within ε\varepsilonε of a stationary point", so an empty X∗\mathcal X^*X∗ cannot make it true.

Trivializing formalizations are ruled out: hypotheses on uiu_iui​ are required on all of X\mathcal XX, not just along the run; uniqueness is required at every y∈Xy\in\mathcal Xy∈X; and a sorry-free check shows that all hypotheses of Theorem 2(a) hold together for a one-block instance.

Not formalized: Proposition 2 (a sufficient condition for (B3)), Corollaries 2 and 3 (essentially cyclic and overlapping rules) and the applications of Section VIII. Contributions of reusable lemmas about cluster points of block methods and first-order conditions for constrained minimisers in a single block are welcome.

Selected references

  • M. Razaviyayn, M. Hong, Z.-Q. Luo, A unified convergence analysis of block successive minimization methods for nonsmooth optimization, arXiv:1209.2385v1, 2012; SIAM J. Optim. 23(2) (2013) 1126–1153. https://arxiv.org/abs/1209.2385
  • P. Tseng, Convergence of a block coordinate descent method for nondifferentiable minimization, J. Optim. Theory Appl. 109 (2001) 475–494. https://doi.org/10.1023/A:1017501703105
  • D. P. Bertsekas, Nonlinear Programming, 2nd ed., Athena Scientific, 1999.
  • B. Chen, S. He, Z. Li, S. Zhang, Maximum block improvement and polynomial optimization, SIAM J. Optim. 22(1) (2012) 87–107. https://doi.org/10.1137/110834858
  • M. J. D. Powell, On search directions for minimization algorithms, Math. Program. 4 (1973) 193–201. https://doi.org/10.1007/BF01584660
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Algorithmic Game TheoryOperations ResearchOptimization·Captain: mikedeng1

Quality in Supply Chain Encroachment I: With Endogenous Uniform Quality, an Encroaching Manufacturer Gains and the Retailer Always LosesResearch Paper

Motivation

Manufacturers increasingly sell directly to consumers through their own stores and websites, alongside the independent retailers that carry their products. This practice is called supply chain encroachment. Retailers routinely resent it, and examples range from beer brewers to personal computers. Arya, Mittendorf and Sappington (Marketing Science, 2007) showed that, when product quality is fixed, encroachment can benefit the retailer too: the manufacturer lowers the wholesale price to keep the retailer channel competitive, and this wholesale price effect can outweigh the lost sales.

Ha, Long and Nasiry (Manufacturing & Service Operations Management, 2016; authors' manuscript SSRN 3970373) ask what changes when the manufacturer also chooses product quality. Their first result, formalized in this mission, is that the win–win outcome disappears: with endogenous quality and a single product sold in both channels, encroachment always helps the manufacturer and always hurts the retailer.

Setting

Consumers have a quality sensitivity θ\thetaθ uniformly distributed on [0,1][0,1][0,1], and a consumer who buys a product of quality u>0u>0u>0 at price ppp obtains surplus θu−p\theta u-pθu−p. When one product of quality uuu is sold in total quantity qqq, the market-clearing price is p=u(1−q)p=u(1-q)p=u(1−q). The manufacturer's unit production cost for quality uuu is ku2ku^2ku2, with k>0k>0k>0 the cost of quality. Selling one unit through her own direct channel costs her an additional c≥0c\ge 0c≥0; the retailer's selling cost is 000.

Benchmark (no direct channel). The manufacturer chooses a wholesale price www and a quality uuu; after observing them the retailer orders qR≥0q_R\ge 0qR​≥0. The profits are

ΠRN=(u(1−qR)−w) qR,ΠMN=(w−ku2) qR.\Pi^N_R=(u(1-q_R)-w)\,q_R,\qquad \Pi^N_M=(w-ku^2)\,q_R .ΠRN​=(u(1−qR​)−w)qR​,ΠMN​=(w−ku2)qR​.

Encroachment with uniform quality. The game has three stages and perfect information:

  1. the manufacturer chooses www and uuu;
  2. the retailer, having observed them, orders qR≥0q_R\ge 0qR​≥0;
  3. the manufacturer, having observed qRq_RqR​, sells qM≥0q_M\ge 0qM​≥0 directly.

Both channels sell the same product at the price u(1−qR−qM)u(1-q_R-q_M)u(1−qR​−qM​), so

ΠRU=(u(1−qR−qM)−w) qR,ΠMU=(w−ku2) qR+(u−uqM−uqR−c−ku2) qM.\Pi^U_R=(u(1-q_R-q_M)-w)\,q_R,\qquad \Pi^U_M=(w-ku^2)\,q_R+(u-uq_M-uq_R-c-ku^2)\,q_M .ΠRU​=(u(1−qR​−qM​)−w)qR​,ΠMU​=(w−ku2)qR​+(u−uqM​−uqR​−c−ku2)qM​.

The solution concept is subgame perfect equilibrium: at every decision node, on or off the equilibrium path, the mover's rule picks a feasible action that no feasible alternative beats. The manufacturer encroaches when qMU>0q^U_M>0qMU​>0 on the equilibrium path.

In the Lean development these are the structures Outcome and Profile, the payoffs retailerPayoff and mfrPayoff k c, and the predicate IsSPE k c σ of the definition QualityEncroach.Uniform.Game, with the benchmark counterparts BenchProfile and IsBenchSPE k τ.

Formalization targets

Goal: Proposition 1(iii)

In the benchmark, every equilibrium has uN=13ku^N=\tfrac1{3k}uN=3k1​ and profits ΠMN=154k\Pi^N_M=\tfrac1{54k}ΠMN​=54k1​, ΠRN=1108k\Pi^N_R=\tfrac1{108k}ΠRN​=108k1​. The goal states that, for every k>0k>0k>0, c≥0c\ge0c≥0 and every subgame perfect equilibrium of the encroachment game in which the manufacturer encroaches,

ΠMU>154k=ΠMNandΠRU<1108k=ΠRN.\Pi^U_M>\frac{1}{54k}=\Pi^N_M\qquad\text{and}\qquad \Pi^U_R<\frac1{108k}=\Pi^N_R .ΠMU​>54k1​=ΠMN​andΠRU​<108k1​=ΠRN​.

The statement fixes no threshold value, and it holds for every equilibrium.

Milestones

The milestones follow the paper's backward induction:

  1. the benchmark subgame, equations (1)–(2), and the benchmark equilibrium (§3.2);
  2. the manufacturer's stage-3 best response qMU(qR,w,u)=(12−qR2−c2u−ku2)+q^U_M(q_R,w,u)=\big(\tfrac12-\tfrac{q_R}{2}-\tfrac{c}{2u}-\tfrac{ku}{2}\big)^+qMU​(qR​,w,u)=(21​−2qR​​−2uc​−2ku​)+;
  3. the quantity subgame (3) and the optimal wholesale price and profits (4)–(5) for a given quality;
  4. the three-case optimal profit ΠM(u)\Pi_M(u)ΠM​(u) of the Appendix, covering three regimes: the manufacturer sells directly, the retailer deters direct sales exactly, or the direct channel is idle;
  5. Proposition 1(i): there is a threshold c~>0\tilde c>0c~>0 such that
c<c~ ⇒ qMU>0,c>c~ ⇒ qMU=0.c<\tilde c\ \Rightarrow\ q^U_M>0,\qquad c>\tilde c\ \Rightarrow\ q^U_M=0 .c<c~ ⇒ qMU​>0,c>c~ ⇒ qMU​=0.

Further statements

The mission also contains:

  • condition (6) for a fixed quality, together with the identity wN(u)−wU(u)=c/6w^N(u)-w^U(u)=c/6wN(u)−wU(u)=c/6;
  • Proposition 1(ii): the equilibrium quality first rises and then falls in ccc, and it is distorted upward exactly below a threshold cuc^ucu;
  • Corollary 1: c~\tilde cc~ is decreasing in kkk, and for c>0c>0c>0 the retailer's profit under encroachment is increasing in kkk;
  • existence of a subgame perfect equilibrium.

Significance

The result separates two regimes that look alike. With quality fixed, a retailer facing an encroaching manufacturer is better off exactly when condition (6) holds. With quality chosen by the manufacturer, that region disappears. The manufacturer distorts quality, upward when ccc is small and downward when it is large, and so relies less on the wholesale price to steer the retailer's order. The retailer then loses in every equilibrium in which encroachment occurs. The paper's later sections (quality differentiation, a fixed cost of quality, no quality commitment, a general cost function) all measure against this base case.

The result is proved on paper, by backward induction with closed-form profits, an envelope-theorem convexity argument in ccc, and a numerically located threshold c~≈0.1019/k\tilde c\approx0.1019/kc~≈0.1019/k. None of it is formalized. A machine-checked proof would certify:

  • the closed-form reduced profits;
  • the case analysis of the Appendix, whose boundaries are given by roots of polynomial equations in uuu;
  • the claim, stated in the paper only for the reduced problem, that it holds for every subgame perfect equilibrium of the game.

Difficulty

Each step of the backward induction is a one-variable concave problem, but the steps do not compose into a single smooth problem. The retailer's best order depends on whether his order leaves room for direct sales. As a result the manufacturer's profit given uuu is the piecewise function ΠM(u)\Pi_M(u)ΠM​(u). Its middle piece describes a retailer who orders exactly enough to keep the manufacturer out, and that piece is not a stationary point of anything. The global optimum over uuu switches between the first piece and the other two at a threshold c~\tilde cc~ that the paper locates only numerically.

The goal compares the optimum of this piecewise problem with a constant, for every equilibrium. The natural first idea, comparing the reduced profits with the benchmark ones pointwise in uuu, does not work for the retailer: for a fixed quality his encroachment profit 2c2/(9u)2c^2/(9u)2c2/(9u) exceeds the benchmark profit u(1−ku)2/16u(1-ku)^2/16u(1−ku)2/16 on part of the range, which is exactly condition (6). The comparison has to use the quality the manufacturer actually chooses, and that quality is known only through the case analysis above.

Formalization scope

All quantities are real numbers. The following conventions are fixed:

  • the wholesale price ranges over all of R\mathbb RR, since the paper states no sign restriction;
  • qualities are u>0u>0u>0 and quantities qR,qM≥0q_R,q_M\ge0qR​,qM​≥0;
  • the inverse demand u(1−qR−qM)u(1-q_R-q_M)u(1−qR​−qM​) is used for all nonnegative quantities, as in the paper.

Subgame perfection is written in one-shot-deviation form at every history, which in this three-stage game with perfect information is subgame perfection. It constrains the retailer's rule at every (w,u)(w,u)(w,u) and the manufacturer's stage-3 rule at every (w,u,qR)(w,u,q_R)(w,u,qR​), not only on the path.

The benchmark profits in the goal are the paper's constants 1/(54k)1/(54k)1/(54k) and 1/(108k)1/(108k)1/(108k). The milestone benchmark_equilibrium proves that a benchmark equilibrium exists and that these are its profits.

Two formalizations would trivialize the goal, and both are ruled out:

  • stating the goal about the reduced form ΠMU\Pi^U_MΠMU​ at a maximizer of ΠM(u)\Pi_M(u)ΠM​(u) instead of about the game would assume the backward induction;
  • dropping the off-path optimality conditions would let the goal range over non-equilibria.

The encroachment threshold of Proposition 1(i) is stated in both directions, with c~>0\tilde c>0c~>0 chosen before ccc. The boundary point c=c~c=\tilde cc=c~ is left open, because the paper's statements disagree there.

The closed forms qRNq^N_RqRN​, wNw^NwN, ΠMN\Pi^N_MΠMN​, ΠRN\Pi^N_RΠRN​, qMUq^U_MqMU​, qRUq^U_RqRU​, wUw^UwU, ΠMU\Pi^U_MΠMU​, ΠRU\Pi^U_RΠRU​, ΠMUZ\Pi^{UZ}_MΠMUZ​ and ΠM\Pi_MΠM​ are definitions that cite their page. Every statement that uses one with uuu in a denominator assumes u>0u>0u>0.

A complete development needs:

  • concave quadratic maximization;
  • a piecewise-concave best-response lemma;
  • the envelope argument of the Appendix, or a direct polynomial comparison;
  • some way to certify the numerically located thresholds, for which interval arithmetic or explicit polynomial sign certificates are both acceptable.

The game definitions are reusable for the other missions of this series, which keep this cost structure. Proofs of any milestone are welcome, as are alternative arguments for the goal that bypass the threshold computation.

Selected references

  • A. Ha, X. Long, J. Nasiry, Quality in Supply Chain Encroachment, Manufacturing & Service Operations Management 18(2), 2016. https://doi.org/10.1287/msom.2015.0562 (authors' manuscript: https://ssrn.com/abstract=3970373)
  • A. Arya, B. Mittendorf, D. E. M. Sappington, The Bright Side of Supplier Encroachment, Marketing Science 26(5): 651–659, 2007. https://doi.org/10.1287/mksc.1070.0280
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Convex OptimizationOperations ResearchOptimization+1·Captain: mikedeng1

Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization 4: Lagrangian Duals of the Reduced Relaxation and Their Closed-Form Optimal Dual VariablesResearch Paper

Motivation

Best subset selection with ridge shrinkage, the ℓ0ℓ2\ell_0\ell_2ℓ0​ℓ2​-regularized least squares problem

min⁡β∈Rp 12∥y−Xβ∥22+λ0∥β∥0+λ2∥β∥22,\min_{\beta\in\mathbb R^p}\ \tfrac12\|y-X\beta\|_2^2+\lambda_0\|\beta\|_0+\lambda_2\|\beta\|_2^2,β∈Rpmin​ 21​∥y−Xβ∥22​+λ0​∥β∥0​+λ2​∥β∥22​,

is a mixed integer program. Exact solvers for it rely on branch-and-bound: at every node of the search tree a convex relaxation is solved, and the node is discarded if a lower bound on that relaxation already exceeds the best objective value found so far. Hazimeh, Mazumder and Saab (arXiv:2004.06152v2, Mathematical Programming 2022) build such a solver, L0BnB, for instances with ppp in the millions. Its relaxations are solved only approximately, by first-order methods, and an approximate primal solution does not by itself certify a lower bound. The bound has to come from a dual feasible point. Theorem 2 of the paper supplies the duals of the node relaxation in closed form, together with explicit formulas for the optimal dual variables as functions of the primal optimum. Everything the paper later proves about the quality of its dual bounds (Section 3.2, Theorem 3) starts from these formulas.

Setting

Data are a matrix X∈Rn×pX\in\mathbb R^{n\times p}X∈Rn×p with columns X1,…,XpX_1,\dots,X_pX1​,…,Xp​, a response y∈Rny\in\mathbb R^ny∈Rn, and parameters λ0,λ2,M>0\lambda_0,\lambda_2,M>0λ0​,λ2​,M>0, where MMM bounds the coefficients. Write [p]={1,…,p}[p]=\{1,\dots,p\}[p]={1,…,p} and [a]+=max⁡{a,0}[a]_+=\max\{a,0\}[a]+​=max{a,0}. The reverse Huber penalty is B(t)=∣t∣\mathcal B(t)=|t|B(t)=∣t∣ for ∣t∣≤1|t|\le1∣t∣≤1 and B(t)=(t2+1)/2\mathcal B(t)=(t^2+1)/2B(t)=(t2+1)/2 for ∣t∣≥1|t|\ge1∣t∣≥1. Define

ψ1(b)=2λ0 B(bλ2/λ0),ψ2(b)=(λ0M+λ2M)∣b∣,\psi_1(b)=2\lambda_0\,\mathcal B\big(b\sqrt{\lambda_2/\lambda_0}\big),\qquad \psi_2(b)=\Big(\frac{\lambda_0}{M}+\lambda_2M\Big)|b|,ψ1​(b)=2λ0​B(bλ2​/λ0​​),ψ2​(b)=(Mλ0​​+λ2​M)∣b∣,

and let ψ=ψ1\psi=\psi_1ψ=ψ1​ if λ0/λ2≤M\sqrt{\lambda_0/\lambda_2}\le Mλ0​/λ2​​≤M and ψ=ψ2\psi=\psi_2ψ=ψ2​ if λ0/λ2>M\sqrt{\lambda_0/\lambda_2}>Mλ0​/λ2​​>M. The reduced relaxation (5) is

min⁡β∈Rp F(β)=12∥y−Xβ∥22+∑i∈[p]ψ(βi)s.t.∥β∥∞≤M.\min_{\beta\in\mathbb R^p}\ F(\beta)=\tfrac12\|y-X\beta\|_2^2+\sum_{i\in[p]}\psi(\beta_i)\quad\text{s.t.}\quad\|\beta\|_\infty\le M.β∈Rpmin​ F(β)=21​∥y−Xβ∥22​+i∈[p]∑​ψ(βi​)s.t.∥β∥∞​≤M.

By Theorem 1 of the paper it is the interval relaxation of the perspective formulation, projected onto β\betaβ.

The two dual objectives of Theorem 2 are, for α,ρ∈Rn\alpha,\rho\in\mathbb R^nα,ρ∈Rn and γ,μ∈Rp\gamma,\mu\in\mathbb R^pγ,μ∈Rp,

h1(α,γ)=−12∥α∥22−αTy−∑i∈[p]v(α,γi),v(α,γi)=[(αTXi−γi)24λ2−λ0]++M∣γi∣,h_1(\alpha,\gamma)=-\tfrac12\|\alpha\|_2^2-\alpha^Ty-\sum_{i\in[p]}v(\alpha,\gamma_i),\qquad v(\alpha,\gamma_i)=\Big[\frac{(\alpha^TX_i-\gamma_i)^2}{4\lambda_2}-\lambda_0\Big]_++M|\gamma_i|,h1​(α,γ)=−21​∥α∥22​−αTy−i∈[p]∑​v(α,γi​),v(α,γi​)=[4λ2​(αTXi​−γi​)2​−λ0​]+​+M∣γi​∣, h2(ρ,μ)=−12∥ρ∥22−ρTy−M∥μ∥1,h_2(\rho,\mu)=-\tfrac12\|\rho\|_2^2-\rho^Ty-M\|\mu\|_1,h2​(ρ,μ)=−21​∥ρ∥22​−ρTy−M∥μ∥1​,

where h1h_1h1​ is maximized over all of Rn×Rp\mathbb R^n\times\mathbb R^pRn×Rp (problem (20)) and h2h_2h2​ subject to ∣ρTXi∣−μi≤λ0/M+λ2M|\rho^TX_i|-\mu_i\le\lambda_0/M+\lambda_2M∣ρTXi​∣−μi​≤λ0​/M+λ2​M for i∈[p]i\in[p]i∈[p] (problem (22)). For an optimal β∗\beta^*β∗ of (5), with residual r∗=y−Xβ∗r^*=y-X\beta^*r∗=y−Xβ∗, the candidate dual variables are

α∗=ρ∗=−r∗,γi∗=1[∣βi∗∣=M](α∗TXi−2Mλ2 sign(α∗TXi)),μi∗=1[∣βi∗∣=M](∣ρ∗TXi∣−λ0/M−λ2M).\alpha^*=\rho^*=-r^*,\qquad \gamma^*_i=\mathbb 1_{[|\beta^*_i|=M]}\big(\alpha^{*T}X_i-2M\lambda_2\,\mathrm{sign}(\alpha^{*T}X_i)\big),\qquad \mu^*_i=\mathbb 1_{[|\beta^*_i|=M]}\big(|\rho^{*T}X_i|-\lambda_0/M-\lambda_2M\big).α∗=ρ∗=−r∗,γi∗​=1[∣βi∗​∣=M]​(α∗TXi​−2Mλ2​sign(α∗TXi​)),μi∗​=1[∣βi∗​∣=M]​(∣ρ∗TXi​∣−λ0​/M−λ2​M).

In the Lean development these objects are F, box, h1, v, h2, Feas22, alphaStar, gammaStar, rhoStar, muStar in the namespace L0BnB.Duality.

Formalization targets

Goal: Theorem 2 (pp. 13–14)

Let β∗\beta^*β∗ minimize FFF over ∥β∥∞≤M\|\beta\|_\infty\le M∥β∥∞​≤M.

  • If λ0/λ2≤M\sqrt{\lambda_0/\lambda_2}\le Mλ0​/λ2​​≤M:  h1(α,γ)≤F(β)\ h_1(\alpha,\gamma)\le F(\beta) h1​(α,γ)≤F(β) for all α,γ\alpha,\gammaα,γ and all feasible β\betaβ, and h1(α∗,γ∗)=F(β∗)h_1(\alpha^*,\gamma^*)=F(\beta^*)h1​(α∗,γ∗)=F(β∗).
  • If λ0/λ2>M\sqrt{\lambda_0/\lambda_2}>Mλ0​/λ2​​>M:  h2(ρ,μ)≤F(β)\ h_2(\rho,\mu)\le F(\beta) h2​(ρ,μ)≤F(β) for all (ρ,μ)(\rho,\mu)(ρ,μ) feasible for (22) and all feasible β\betaβ; (ρ∗,μ∗)(\rho^*,\mu^*)(ρ∗,μ∗) is feasible for (22) and h2(ρ∗,μ∗)=F(β∗)h_2(\rho^*,\mu^*)=F(\beta^*)h2​(ρ∗,μ∗)=F(β∗).

This is "(20), respectively (22), is a dual of (5), and (23), respectively (24), are optimal dual variables", with the paper's remark that strong duality holds.

Milestones

  1. (45) For a∈Ra\in\mathbb Ra∈R, η≥0\eta\ge0η≥0 and D(b)=ψ1(b)+ab+η∣b∣D(b)=\psi_1(b)+ab+\eta|b|D(b)=ψ1​(b)+ab+η∣b∣, the point 000 (if 2λ0λ2+η−∣a∣≥02\sqrt{\lambda_0\lambda_2}+\eta-|a|\ge02λ0​λ2​​+η−∣a∣≥0) or −λ0/λ2 sign(a)-\sqrt{\lambda_0/\lambda_2}\,\mathrm{sign}(a)−λ0​/λ2​​sign(a) (otherwise) minimizes DDD on ∣b∣≤λ0/λ2|b|\le\sqrt{\lambda_0/\lambda_2}∣b∣≤λ0​/λ2​​.
  2. (47) If ∣a∣−η≥2λ0λ2|a|-\eta\ge2\sqrt{\lambda_0\lambda_2}∣a∣−η≥2λ0​λ2​​, then min⁡b∈RD(b)=−14λ2(∣a∣−η)2+λ0\min_{b\in\mathbb R}D(b)=-\frac{1}{4\lambda_2}(|a|-\eta)^2+\lambda_0minb∈R​D(b)=−4λ2​1​(∣a∣−η)2+λ0​.
  3. Weak duality, the first halves of both bullets of the goal.
  4. (23) h1(α∗,γ∗)=F(β∗)h_1(\alpha^*,\gamma^*)=F(\beta^*)h1​(α∗,γ∗)=F(β∗) when λ0/λ2≤M\sqrt{\lambda_0/\lambda_2}\le Mλ0​/λ2​​≤M.
  5. (24) (ρ∗,μ∗)(\rho^*,\mu^*)(ρ∗,μ∗) feasible for (22) and h2(ρ∗,μ∗)=F(β∗)h_2(\rho^*,\mu^*)=F(\beta^*)h2​(ρ∗,μ∗)=F(β∗) when λ0/λ2>M\sqrt{\lambda_0/\lambda_2}>Mλ0​/λ2​​>M.

Significance

The result. Theorem 2 turns the node relaxation of L0BnB into a pair of explicit concave maximization problems, one per regime of λ0/λ2\sqrt{\lambda_0/\lambda_2}λ0​/λ2​​ versus MMM. The dual (20) is unconstrained, so any (α,γ)(\alpha,\gamma)(α,γ) gives a valid lower bound; the paper's dual bounds (25)–(28) evaluate h1h_1h1​ or h2h_2h2​ at α^=−r^\hat\alpha=-\hat rα^=−r^ built from an inexact primal solution β^\hat\betaβ^​, with the remaining variable chosen in closed form. The formulas (23)–(24) explain why this choice is right: at the exact optimum it recovers the primal value. Theorem 3 of the paper, which bounds the loss of this dual bound by a quantity depending on the support size rather than on ppp, compares v(α^,γ^i)v(\hat\alpha,\hat\gamma_i)v(α^,γ^​i​) to v(α∗,γi∗)v(\alpha^*,\gamma^*_i)v(α∗,γi∗​) term by term and therefore depends on (23).

Formalizing it. The paper proves the case λ0/λ2≤M\sqrt{\lambda_0/\lambda_2}\le Mλ0​/λ2​​≤M in Appendix A and omits the proof of the case λ0/λ2>M\sqrt{\lambda_0/\lambda_2}>Mλ0​/λ2​​>M ("follows along the lines similar to what was shown above"). The appendix also contains an intermediate formula for the coordinate minimum, −[(∣αTXi∣−ηi)2/(4λ2)−λ0]+-[(|\alpha^TX_i|-\eta_i)^2/(4\lambda_2)-\lambda_0]_+−[(∣αTXi​∣−ηi​)2/(4λ2​)−λ0​]+​, that is incorrect when ηi\eta_iηi​ is large; the final dual (49) = (20) is nevertheless correct. A machine-checked proof supplies the omitted case and settles the correct statement. To our knowledge neither half has been formalized.

Difficulty

The dual objective (20) hides the box constraint inside the term M∣γi∣M|\gamma_i|M∣γi​∣ and the penalty inside [ ⋅ ]+[\,\cdot\,]_+[⋅]+​, and the reverse Huber penalty is piecewise, so even the weak-duality half has to handle both pieces of ψ1\psi_1ψ1​ and the junction ∣b∣=λ0/λ2|b|=\sqrt{\lambda_0/\lambda_2}∣b∣=λ0​/λ2​​. The attainment half is where the work lies: it needs the optimality conditions of a nonsmooth convex problem over a box, at coordinates where βi∗=0\beta^*_i=0βi∗​=0 (where ψ\psiψ is not differentiable), where ∣βi∗∣=M|\beta^*_i|=M∣βi∗​∣=M (where the box is active), and where both may interact with the regime boundary λ0/λ2=M\sqrt{\lambda_0/\lambda_2}=Mλ0​/λ2​​=M. The natural first idea, to read (23) off the Lagrangian derivation in the paper, does not yield a proof: that derivation assumes a dual optimum (α∗,η∗)(\alpha^*,\eta^*)(α∗,η∗) exists and relates it to β∗\beta^*β∗ by complementary slackness, whereas the theorem asserts an identity for the explicit (α∗,γ∗)(\alpha^*,\gamma^*)(α∗,γ∗) built from β∗\beta^*β∗ alone.

Formalization scope

Data are X : Matrix (Fin n) (Fin p) ℝ, y : Fin n → ℝ and reals lam0 lam2 M with 0 < lam0, 0 < lam2, 0 < M as hypotheses; [p][p][p] is Fin p. Norms and inner products are explicit sums: αTXi=∑rαrXri\alpha^TX_i=\sum_r\alpha_rX_{ri}αTXi​=∑r​αr​Xri​, ∥α∥22=∑rαr2\|\alpha\|_2^2=\sum_r\alpha_r^2∥α∥22​=∑r​αr2​, ∥μ∥1=∑i∣μi∣\|\mu\|_1=\sum_i|\mu_i|∥μ∥1​=∑i​∣μi​∣, [a]+=max⁡{a,0}[a]_+=\max\{a,0\}[a]+​=max{a,0}, and ∥β∥∞≤M\|\beta\|_\infty\le M∥β∥∞​≤M is ∣βi∣≤M|\beta_i|\le M∣βi​∣≤M for every iii. The regime split is written λ0/λ2≤M\sqrt{\lambda_0/\lambda_2}\le Mλ0​/λ2​​≤M versus M<λ0/λ2M<\sqrt{\lambda_0/\lambda_2}M<λ0​/λ2​​. sign is Real.sign, with sign(0)=0\mathrm{sign}(0)=0sign(0)=0; where it is evaluated in (23) the argument is nonzero at an optimum. The optimal solution β∗\beta^*β∗ is a hypothesis (feasible and minimizing FFF over the box); its existence is not part of the statements. The coordinate milestones (45) and (47) are stated for a scalar aaa standing for αTXi\alpha^TX_iαTXi​ and a multiplier η≥0\eta\ge0η≥0. No normalization of XXX or yyy is assumed: the unit-norm convention that opens Section 3 is not used by Theorem 2. The paper writes no O(⋅)O(\cdot)O(⋅) in these results, so no constants are instantiated.

"A dual is given by" is formalized as weak duality over all dual-feasible points together with equality at the explicit dual variables. Weak duality alone would not be Theorem 2, and neither would the equality alone; the goal requires both in both regimes. Uniqueness of the dual optimum is not claimed.

A complete development needs elementary convex analysis of the scalar penalty ψ1\psi_1ψ1​ (its conjugate and subdifferential), first-order optimality conditions for a convex function over a box in Rp\mathbb R^pRp, and finite-sum bookkeeping. The scalar facts about the reverse Huber penalty are reusable in the companion missions on the reduced relaxation and on dual-bound quality. Contributions to any milestone, or a direct proof of the goal, are welcome.

Selected references

  • H. Hazimeh, R. Mazumder, A. Saab, Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization, arXiv:2004.06152v2 (2021); Mathematical Programming (2022). https://arxiv.org/abs/2004.06152v2
  • S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. https://web.stanford.edu/~boyd/cvxbook/
  • A. B. Owen, A robust hybrid of lasso and ridge regression, Contemporary Mathematics 443, 59–72, 2007. https://doi.org/10.1090/conm/443/08555
  • D. Bertsimas, A. King, R. Mazumder, Best subset selection via a modern optimization lens, Annals of Statistics 44(2), 813–852, 2016. https://doi.org/10.1214/15-AOS1388
  • H. Hazimeh, R. Mazumder, Fast best subset selection: coordinate descent and local combinatorial optimization algorithms, Operations Research 68(5), 1517–1537, 2020. https://arxiv.org/abs/1803.01454
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Operations ResearchOptimization·Captain: mikedeng1

Approximation Algorithms for Product Framing and Pricing 3: Under MNL Pricing, Optimal Framing Fills Pages in Descending Quality Order with One Price per PageResearch Paper

Motivation

Online retailers show their catalogue on a sequence of pages, and most visitors never reach the last one. Which products appear on the first page, and at what prices, therefore decides what a typical consumer can buy at all. Gallego, Li, Truong and Wang (Operations Research, 2020) model this as the product framing problem: the consideration set of a consumer is the set of products on the pages that consumer is willing to view, and the number of pages viewed is random. Their Section 7 adds prices to the problem and asks how a retailer should frame and price jointly when consumers choose by the multinomial logit (MNL) model.

The question connects two literatures. In assortment pricing under MNL with a common price sensitivity, the optimal prices of all offered products are known to be equal (Anderson, de Palma and Thisse 1992; Gallego and Stefanescu, as cited on p. 16 of the paper), and the nested-logit extension of Gallego and Wang (Operations Research, 2014) keeps an adjusted markup constant across nests. In search and ranking models, the order in which products are shown changes which ones are bought. The paper's structural results say what survives of the equal-price rule once consideration sets are nested by page, and which products belong on the early pages.

Setting

There are nnn products, i∈[n]={1,…,n}i \in [n] = \{1,\dots,n\}i∈[n]={1,…,n}, and mmm pages, each holding at most ppp products. A framing places each product on one page x(i)∈[m]x(i)\in[m]x(i)∈[m] or leaves it undisplayed. A consumer views the first XXX pages, where X∈[m]X\in[m]X∈[m] is random with law λ(x)=P[X=x]\lambda(x)=\mathbf P[X=x]λ(x)=P[X=x], independent of the framing. A consumer who views xxx pages considers S(x)S(x)S(x), the products on pages 1,…,x1,\dots,x1,…,x, so S(1)⊆S(2)⊆⋯⊆S(m)S(1)\subseteq S(2)\subseteq\cdots\subseteq S(m)S(1)⊆S(2)⊆⋯⊆S(m).

Product iii has a quality ai∈Ra_i\in\mathbb Rai​∈R and a price ri∈Rr_i\in\mathbb Rri​∈R, and the price sensitivity is β>0\beta>0β>0. The mean utility of product iii is ui=ai−βriu_i=a_i-\beta r_iui​=ai​−βri​, and the outside option has utility u0=0u_0=0u0​=0. Under the MNL model a consumer with consideration set SSS buys i∈Si\in Si∈S with probability

P(i,S)=eui1+∑k∈Seuk,P(i,S)=\frac{e^{u_i}}{1+\sum_{k\in S}e^{u_k}},P(i,S)=1+∑k∈S​euk​eui​​,

and P(i,S)=0P(i,S)=0P(i,S)=0 for i∉Si\notin Si∈/S. The expected revenue from that consumer is R(r∣S)=∑i∈SriP(i,S)R(r\mid S)=\sum_{i\in S}r_iP(i,S)R(r∣S)=∑i∈S​ri​P(i,S), and the total expected revenue of a framing and a price vector is

E[R(r∣S(X))]=∑x=1mλ(x) R(r∣S(x)).\mathbf E\big[R(r\mid S(X))\big]=\sum_{x=1}^m\lambda(x)\,R\big(r\mid S(x)\big).E[R(r∣S(X))]=x=1∑m​λ(x)R(r∣S(x)).

For a fixed framing, R(a)R(a)R(a) denotes the optimal value of this quantity over all price vectors, as a function of the quality vector aaa.

Formalization targets

Goal: Theorem 6 (p. 17)

The goal is the existence of an optimal joint solution with the paper's structure: there are a feasible framing and prices r∈Rnr\in\mathbb R^nr∈Rn such that no feasible framing with any prices earns more, and

∣S(x)∣=min⁡(n,  x p)for all x∈[m],ak≤ai  whenever i is displayed and k is undisplayed or on a later page.|S(x)|=\min(n,\;x\,p)\quad\text{for all }x\in[m],\qquad a_k\le a_i\ \text{ whenever } i \text{ is displayed and } k \text{ is undisplayed or on a later page.}∣S(x)∣=min(n,xp)for all x∈[m],ak​≤ai​  whenever i is displayed and k is undisplayed or on a later page.

Pages are filled in order until all products are displayed, and the products appear in descending order of quality.

Milestones

  1. (15), p. 43. The partial derivative of the total expected revenue in the price of a displayed product:
∂ E[R(r∣S(X))]∂ri=β∑l=x(i)mλ(l)P(i,S(l)){1β+R(r∣S(l))−ri}.\frac{\partial\,\mathbf E[R(r\mid S(X))]}{\partial r_i}=\beta\sum_{l=x(i)}^m\lambda(l)P\big(i,S(l)\big)\Big\{\tfrac1\beta+R\big(r\mid S(l)\big)-r_i\Big\}.∂ri​∂E[R(r∣S(X))]​=βl=x(i)∑m​λ(l)P(i,S(l)){β1​+R(r∣S(l))−ri​}.
  1. (17), p. 43. At every optimal price vector, each displayed price is 1/β1/\beta1/β plus a weighted average of the revenues R(r∣S(l))R(r\mid S(l))R(r∣S(l)), l≥x(i)l\ge x(i)l≥x(i).
  2. Theorem 5, p. 16. For a fixed framing, every optimal price vector is constant on each page, ri=θx(i)r_i=\theta_{x(i)}ri​=θx(i)​, with θ1≤θ2≤⋯≤θm\theta_1\le\theta_2\le\cdots\le\theta_mθ1​≤θ2​≤⋯≤θm​.
  3. (19), p. 44. The optimal value R(a)R(a)R(a) is nondecreasing in the quality of any displayed product.

Significance

Theorem 5 reduces the pricing problem of a fixed framing from nnn prices to mmm page prices and says that later pages carry higher prices; this is the page-level analogue of the constant-markup property of MNL pricing, and it runs opposite to the ordering found in oligopoly search models (Arbatskaya 2007). Theorem 6 removes the framing decision almost entirely: a joint optimum is obtained by sorting the products by quality and filling pages in that order, leaving only page prices to choose. The paper's approximation algorithm for joint pricing and framing (Theorem 7, NEST-P, with guarantee 6/π26/\pi^26/π2) starts from this structure.

None of these statements has a machine-checked proof. A complete development would give the first formal treatment of MNL price optimization with nested consideration sets, including the existence of optimal prices, which the paper takes for granted. The paper's argument for the monotonicity θ1≤⋯≤θm\theta_1\le\cdots\le\theta_mθ1​≤⋯≤θm​ and its envelope-theorem step (19) are informal; a formal proof has to supply both.

Difficulty

The expected revenue is not concave in the prices, even with two products on two pages (Example 1, p. 17), so the first-order condition alone does not identify the optimum, and existence of an optimal price vector has to be argued from the behaviour of the revenue as prices tend to ±∞\pm\infty±∞. The within-page equality of prices follows from the first-order condition, but the ordering of page prices needs a global comparison of revenues across consideration sets. In Theorem 6, the obvious exchange argument (swap a higher-quality product forward) does not obviously keep revenue from decreasing when the swapped products have different prices; the paper splits into two cases, and in one of them the improvement comes from continuously moving qualities, which requires control of the optimal value as a function of aaa rather than of a fixed price vector.

Formalization scope

Products are Fin n, pages are the natural numbers 1,…,m1,\dots,m1,…,m with m≥1m\ge1m≥1, and capacities satisfy p≥1p\ge1p≥1. A framing is f : Fin n → ℕ with page f i ∈ [m] for displayed products and f i = 0 for undisplayed ones (the paper's x(i)=m+1x(i)=m+1x(i)=m+1); feasibility means at most ppp products per page. The law of XXX is a nonnegative function on [m][m][m] summing to one. Prices are finite reals: the paper's priced-out products (ri=+∞r_i=+\inftyri​=+∞) coincide with undisplayed ones, which the framing already allows. "Descending order" and "increases" are read weakly.

Theorem 5 and (17) are stated for every maximizing price vector and only for products on pages that some consumer reaches (Λ(x)=P[X≥x]>0\Lambda(x)=\mathbf P[X\ge x]>0Λ(x)=P[X≥x]>0), a restriction the paper never discusses: a product on a page no consumer reaches can carry any price at an optimum. They do not assert that a maximizer exists. Theorem 6 is read existentially ("some optimal solution has this structure"); the universal reading is false when some λ(x)=0\lambda(x)=0λ(x)=0, because exchanging products between pages xxx and x+1x+1x+1 then changes no revenue. (19) is formalized as its stated consequence, the monotonicity of the optimal value R(a)=sup⁡rE[R(r∣S(X))]R(a)=\sup_r\mathbf E[R(r\mid S(X))]R(a)=supr​E[R(r∣S(X))], together with the boundedness of the revenues; the envelope identity itself would need a differentiable selection of optimal prices that the paper does not establish.

A trivializing formalization of the goal would take the optimum over framings that are already sorted, over a single fixed framing, or over prices fixed in advance; the goal's optimality clause ranges over all feasible framings and all real price vectors. The development needs elementary calculus of the MNL revenue (HasDerivAt, Real.exp), finite sums over pages, and a compactness or limiting argument for the existence of optimal prices. Pages are those of the authors' accepted manuscript, which differ from the journal typesetting. Proofs of any milestone, and lemmas on MNL pricing reusable beyond this paper (boundedness of R(r∣S)R(r\mid S)R(r∣S), existence of optimal MNL prices, the constant-price property for a single assortment), are welcome.

Selected references

  • G. Gallego, A. Li, V.-A. Truong, X. Wang, Approximation Algorithms for Product Framing and Pricing, Operations Research 68(1), 2020. https://doi.org/10.1287/opre.2019.1875
  • S. P. Anderson, A. de Palma, J.-F. Thisse, Discrete Choice Theory of Product Differentiation, MIT Press, 1992 (book; cited by the paper for the equal-price property of MNL pricing).
  • G. Gallego, R. Wang, Multiproduct Price Optimization and Competition under the Nested Logit Model with Product-Differentiated Price Sensitivities, Operations Research 62(2), 2014. https://doi.org/10.1287/opre.2013.1249
  • M. Arbatskaya, Ordered Search, The RAND Journal of Economics 38(1), 2007. http://www.jstor.org/stable/25046295
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Convex OptimizationDynamical SystemsFunctional Analysis+2·Captain: mikedeng1

Tikhonov Regularization of a Second Order Dynamical System with Hessian Driven Damping 1: For α > 3 and ∫ tε(t) dt < +∞ the Trajectory Converges Weakly to a Minimizer of gResearch Paper

Motivation

Minimizing a convex function ggg on a real Hilbert space H\mathcal HH by first-order methods has a continuous-time counterpart: second-order differential equations whose trajectories descend ggg. Su, Boyd and Candès (20) showed that Nesterov's accelerated gradient method is a discretization of x¨+αtx˙+∇g(x)=0\ddot x+\frac{\alpha}{t}\dot x+\nabla g(x)=0x¨+tα​x˙+∇g(x)=0 with α=3\alpha=3α=3, along which g(x(t))−min⁡g=O(1/t2)g(x(t))-\min g=O(1/t^2)g(x(t))−ming=O(1/t2). Two modifications of this equation have been studied separately because each adds a property the plain system lacks.

  • A Hessian-driven damping term β∇2g(x(t))x˙(t)\beta\nabla^2g(x(t))\dot x(t)β∇2g(x(t))x˙(t) (Attouch, Peypouquet, Redont, J. Differential Equations 2016) damps oscillations and, for β>0\beta>0β>0, makes t2∥∇g(x(t))∥2t^2\|\nabla g(x(t))\|^2t2∥∇g(x(t))∥2 integrable, while keeping fast values and weak convergence of trajectories.
  • A Tikhonov regularization term ϵ(t)x(t)\epsilon(t)x(t)ϵ(t)x(t) with ϵ(t)↓0\epsilon(t)\downarrow0ϵ(t)↓0 (Attouch, Chbani, Riahi, J. Math. Anal. Appl. 2018) can steer trajectories to the minimum-norm minimizer.

Boţ, Csetnek and László (arXiv:1911.12845v2, 2020) combine both terms and ask which properties of the two parent systems survive. This mission covers the first of their answers: when ϵ\epsilonϵ decays fast enough, the fast rates and the weak convergence of trajectories are preserved.

Timeline. 2016 (published 2018): weak convergence of trajectories of x¨+αtx˙+∇g(x)=0\ddot x+\frac\alpha t\dot x+\nabla g(x)=0x¨+tα​x˙+∇g(x)=0 for α>3\alpha>3α>3 (Attouch, Chbani, Peypouquet, Redont, Math. Program. 168). 2016: the same with Hessian-driven damping, β>0\beta>0β>0 (Attouch, Peypouquet, Redont). 2018: Tikhonov regularization of that system, with the O(1/t2)O(1/t^2)O(1/t2) rate kept and strong convergence to the minimum-norm minimizer under slow decay of ϵ\epsilonϵ (Attouch, Chbani, Riahi). 2020: both terms together (Boţ, Csetnek, László, this paper).

Setting

Fix a real Hilbert space H\mathcal HH, a starting time t0>0t_0>0t0​>0, parameters α≥3\alpha\ge3α≥3 and β≥0\beta\ge0β≥0, and initial data u0,v0∈Hu_0,v_0\in\mathcal Hu0​,v0​∈H. The General assumption of the paper is:

  • g:H→Rg:\mathcal H\to\mathbb Rg:H→R is convex and twice Fréchet differentiable, ∇g\nabla g∇g is Lipschitz continuous on bounded sets, and argmin⁡g≠∅\operatorname{argmin} g\ne\emptysetargming=∅; min⁡g\min gming denotes its minimal value;
  • ϵ:[t0,+∞)→[0,+∞)\epsilon:[t_0,+\infty)\to[0,+\infty)ϵ:[t0​,+∞)→[0,+∞) is nonincreasing, of class C1C^1C1, and ϵ(t)→0\epsilon(t)\to0ϵ(t)→0 as t→+∞t\to+\inftyt→+∞.

The dynamical system is

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.(5)\ddot x(t)+\frac{\alpha}{t}\dot x(t)+\beta\nabla^2g(x(t))\dot x(t)+\nabla g(x(t))+\epsilon(t)x(t)=0,\quad t\ge t_0,\quad x(t_0)=u_0,\ \dot x(t_0)=v_0. \tag{5}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​.(5)

A global C2C^2C2-solution is a twice continuously differentiable x:[t0,+∞)→Hx:[t_0,+\infty)\to\mathcal Hx:[t0​,+∞)→H satisfying (5) for every t≥t0t\ge t_0t≥t0​. The decay of ϵ\epsilonϵ is controlled by ∫t0+∞tϵ(t) dt<+∞\int_{t_0}^{+\infty}t\epsilon(t)\,dt<+\infty∫t0​+∞​tϵ(t)dt<+∞ together with one of two conditions:

  • (a) there exist a>1a>1a>1 and t1≥t0t_1\ge t_0t1​≥t0​ with ϵ˙(t)≤−aβ2ϵ2(t)\dot\epsilon(t)\le-\frac{a\beta}{2}\epsilon^2(t)ϵ˙(t)≤−2aβ​ϵ2(t) for all t≥t1t\ge t_1t≥t1​;
  • (b) there exist a>0a>0a>0 and t1≥t0t_1\ge t_0t1​≥t0​ with ϵ(t)≤at\epsilon(t)\le\frac atϵ(t)≤ta​ for all t≥t1t\ge t_1t≥t1​.

A curve x(t)x(t)x(t) converges weakly to xˉ\bar xxˉ if ⟨x(t),v⟩→⟨xˉ,v⟩\langle x(t),v\rangle\to\langle\bar x,v\rangle⟨x(t),v⟩→⟨xˉ,v⟩ for every v∈Hv\in\mathcal Hv∈H. The Lean development lives in the namespace TikhonovHDD.Weak, with IsSolution, GenAssumptionG, GenAssumptionEps, CondA, CondB, argmin, minVal and WeakTendstoAtTop for these objects.

Formalization targets

Goal: Theorem 3.5 (p. 15)

Under the General assumption, ∫t0+∞tϵ(t) dt<+∞\int_{t_0}^{+\infty}t\epsilon(t)\,dt<+\infty∫t0​+∞​tϵ(t)dt<+∞ and (a) or (b), if α>3\alpha>3α>3 then every global C2C^2C2-solution of (5) satisfies

∃ xˉ∈argmin⁡g:x(t)⇀xˉ(t→+∞).\exists\,\bar x\in\operatorname{argmin} g:\qquad x(t)\rightharpoonup\bar x\quad(t\to+\infty).∃xˉ∈argming:x(t)⇀xˉ(t→+∞).

Milestones

In the order the argument uses them:

  1. Theorem 2.1 (p. 4): existence and uniqueness of a global C2C^2C2-solution of (5).
  2. Lemma A.2 (p. 29): a bounded-below, locally absolutely continuous FFF with F˙≤G∈L1\dot F\le G\in L^1F˙≤G∈L1 a.e. has a finite limit.
  3. Theorem 3.3 (p. 11), split three ways: for α≥3\alpha\ge3α≥3, g(x(t))−min⁡g=O(1/t2)g(x(t))-\min g=O(1/t^2)g(x(t))−ming=O(1/t2); for α>3\alpha>3α>3, xxx is bounded and t(g(x(t))−min⁡g)t(g(x(t))-\min g)t(g(x(t))−ming), t∥x˙∥2t\|\dot x\|^2t∥x˙∥2, tϵ∥x−x∗∥2t\epsilon\|x-x^*\|^2tϵ∥x−x∗∥2, tϵ∥x∥2∈L1t\epsilon\|x\|^2\in L^1tϵ∥x∥2∈L1; and separately t2∥∇g(x(t))∥2∈L1t^2\|\nabla g(x(t))\|^2\in L^1t2∥∇g(x(t))∥2∈L1.
  4. Lemma A.3 (p. 30): u(t)+tαu˙(t)→uˉu(t)+\frac t\alpha\dot u(t)\to\bar uu(t)+αt​u˙(t)→uˉ with α>0\alpha>0α>0 implies u(t)→uˉu(t)\to\bar uu(t)→uˉ.
  5. Theorem 3.4 (p. 12): for α>3\alpha>3α>3 and every x∗∈argmin⁡gx^*\in\operatorname{argmin} gx∗∈argming, t⟨∇g(x),x−x∗⟩∈L1t\langle\nabla g(x),x-x^*\rangle\in L^1t⟨∇g(x),x−x∗⟩∈L1, lim⁡∥x(t)−x∗∥\lim\|x(t)-x^*\|lim∥x(t)−x∗∥ and lim⁡t⟨x˙+β∇g(x),x−x∗⟩\lim t\langle\dot x+\beta\nabla g(x),x-x^*\ranglelimt⟨x˙+β∇g(x),x−x∗⟩ exist, g(x(t))−min⁡g=o(1/t2)g(x(t))-\min g=o(1/t^2)g(x(t))−ming=o(1/t2), ∥x˙+β∇g(x)∥=o(1/t)\|\dot x+\beta\nabla g(x)\|=o(1/t)∥x˙+β∇g(x)∥=o(1/t), and t2ϵ(t)∥x(t)∥2→0t^2\epsilon(t)\|x(t)\|^2\to0t2ϵ(t)∥x(t)∥2→0.

Significance

The result. Theorem 3.5 shows that the Tikhonov term, when it decays at least as fast as ∫tϵ<∞\int t\epsilon<\infty∫tϵ<∞ requires, does not disturb the asymptotics of the Hessian-damped system: values converge at rate o(1/t2)o(1/t^2)o(1/t2) and trajectories converge weakly to a minimizer. With β=0\beta=0β=0 it recovers the corresponding results for the regularized system without Hessian damping, and with ϵ≡0\epsilon\equiv0ϵ≡0 those for the Hessian-damped system. It is also the boundary case of the paper's second half, where slower decay of ϵ\epsilonϵ is shown to give strong convergence to the minimum-norm minimizer instead.

Formalizing it. The results are proved in the paper; none of them is machine-checked. A formal development would produce reusable pieces: a Lyapunov-energy analysis of second-order systems with time-dependent coefficients, the asymptotic Lemmas A.2 and A.3, and the bridge from "the distance to every minimizer converges and weak cluster points are minimizers" to weak convergence of a curve (the continuous Opial lemma).

Difficulty

The obvious approach, a single Lyapunov function that decreases along trajectories, fails here: the Tikhonov term makes the natural energy Eb\mathcal E_bEb​ only almost nonincreasing, with an error ∝tϵ(t)∥x∗∥2\propto t\epsilon(t)\|x^*\|^2∝tϵ(t)∥x∗∥2 that must be integrated, and the Hessian term produces a cross term −βϵ(t)t2⟨∇g(x),x⟩-\beta\epsilon(t)t^2\langle\nabla g(x),x\rangle−βϵ(t)t2⟨∇g(x),x⟩ that is controlled only by condition (a) or (b). The step from O(1/t2)O(1/t^2)O(1/t2) to o(1/t2)o(1/t^2)o(1/t2) and to the existence of lim⁡∥x(t)−x∗∥\lim\|x(t)-x^*\|lim∥x(t)−x∗∥ cannot be read off a single bounded energy: boundedness of Eb\mathcal E_bEb​ gives rates in OOO, not in ooo, and says nothing directly about ∥x(t)−x∗∥\|x(t)-x^*\|∥x(t)−x∗∥. Weak convergence then requires Opial's lemma, which is not in Mathlib in continuous form. Existence of solutions is itself nontrivial in infinite dimension: the right-hand side involves ∇2g\nabla^2g∇2g, so the system has to be rewritten as a first-order system before the Cauchy–Lipschitz theorem applies.

Formalization scope

  • H\mathcal HH is any real Hilbert space (InnerProductSpace ℝ H, CompleteSpace H); nothing is finite-dimensional, so weak convergence is genuinely weaker than norm convergence. It is stated through inner products with every v∈Hv\in\mathcal Hv∈H; stating it as norm convergence, or testing only some vvv, would change the theorem.
  • Trajectories are maps R→H\mathbb R\to\mathcal HR→H with velocity and acceleration maps; derivatives are one-sided at t0t_0t0​ and values before t0t_0t0​ play no role. Every theorem is stated for every C2C^2C2-solution; Theorem 2.1 shows this class is a singleton, so the hypothesis is not vacuous. A sorry-free check that the hypotheses of the goal can be met (g=0g=0g=0, ϵ=0\epsilon=0ϵ=0, x=0x=0x=0) accompanies the drafts.
  • ∇2g(x)v\nabla^2g(x)v∇2g(x)v is the derivative of ∇g\nabla g∇g at xxx applied to vvv; ϵ˙\dot\epsilonϵ˙ is a derivative map tied to ϵ\epsilonϵ on [t0,+∞)[t_0,+\infty)[t0​,+∞); min⁡g=inf⁡g\min g=\inf gming=infg, attained since argmin⁡g≠∅\operatorname{argmin} g\ne\emptysetargming=∅; L1L^1L1 membership is Lebesgue integrability on [t0,+∞)[t_0,+\infty)[t0​,+∞) (never a comparison of a Bochner integral with a number); OOO, ooo are Asymptotics relations at +∞+\infty+∞.
  • Repair. Theorem 2.1 carries the extra hypothesis "β=0\beta=0β=0 or g∈C2g\in C^2g∈C2". As printed (only twice differentiable ggg) it is false for β>0\beta>0β>0: on R\mathbb RR, g′(x)=2x+x2sin⁡(1/x)g'(x)=2x+x^2\sin(1/x)g′(x)=2x+x2sin(1/x) gives a strongly convex ggg with bounded but discontinuous g′′g''g′′, and the solution from u0=0u_0=0u0​=0, v0=1v_0=1v0​=1 is not C2C^2C2. The paper's own proof uses continuity of ∇2g\nabla^2 g∇2g.
  • Flagged, not repaired. The last claim of Theorem 3.3, t2∥∇g(x(t))∥2∈L1t^2\|\nabla g(x(t))\|^2\in L^1t2∥∇g(x(t))∥2∈L1, is its own item: the paper's argument for it uses a term proportional to β\betaβ, so for β=0\beta=0β=0 the paper gives no proof. No counterexample is known, and the statement is drafted as printed.
  • Referenced, not re-posed. Lemma A.4 (continuous Opial lemma, p. 30), the last step of the goal's proof, is the published InertialAVD.Traj.lemma_A_2 (same hypotheses: SSS nonempty, lim⁡∥x(t)−z∥\lim\|x(t)-z\|lim∥x(t)−z∥ exists for every z∈Sz\in Sz∈S, every weak sequential limit point lies in SSS; same conclusion). It is a reference milestone of this mission; its weak convergence InertialAVD.Traj.WeakTendstoAtTop has the same body as TikhonovHDD.Weak.WeakTendstoAtTop.
  • The model is defined from scratch: the published HessianDamping.DINAVD.Setting concerns a different equation (no ϵx\epsilon xϵx term) and is not reused.
  • Welcome contributions: a general Cauchy–Lipschitz existence theorem for locally Lipschitz first-order systems in Banach spaces on [t0,+∞)[t_0,+\infty)[t0​,+∞) with a blow-up alternative, the continuous Opial lemma, and Lemma A.2 for absolutely continuous functions.

Selected references

  • R.I. Boţ, E.R. Csetnek, S.C. László, Tikhonov regularization of a second order dynamical system with Hessian driven damping, arXiv:1911.12845v2, 2020; published in Math. Program., https://doi.org/10.1007/s10107-020-01528-8. https://arxiv.org/abs/1911.12845v2
  • H. Attouch, J. Peypouquet, P. Redont, Fast convex optimization via inertial dynamics with Hessian driven damping, J. Differential Equations 261(10), 5734–5783, 2016. https://doi.org/10.1016/j.jde.2016.08.020
  • H. Attouch, Z. Chbani, H. Riahi, Combining fast inertial dynamics for convex optimization with Tikhonov regularization, J. Math. Anal. Appl. 457(2), 1065–1094, 2018. https://doi.org/10.1016/j.jmaa.2016.12.017
  • H. Attouch, Z. Chbani, J. Peypouquet, P. Redont, Fast convergence of inertial dynamics and algorithms with asymptotic vanishing viscosity, Math. Program. 168, 123–175, 2018. https://doi.org/10.1007/s10107-016-0992-8
  • W. Su, S. Boyd, E.J. Candès, A differential equation for modeling Nesterov's accelerated gradient method: theory and insights, J. Mach. Learn. Res. 17(153), 1–43, 2016. https://jmlr.org/papers/v17/15-084.html
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Bandit AlgorithmsInformation TheoryMachine Learning+1·Captain: mikedeng1

A Tutorial on Thompson Sampling I: Any Algorithm Whose Information Ratio Is at Most Γ̄ Has Expected Regret at Most √(Γ̄·H(x*)·T)Research Paper

Motivation

Thompson sampling (TS) chooses each action with the posterior probability that it is optimal. Its practical success in online advertising, recommendation and experimentation raised the question of why it works and when it does not. The tutorial of Russo, Van Roy, Kazerouni, Osband and Wen (Found. Trends Mach. Learn. 11(1), 2018) answers part of that question in §8.1 with an information-theoretic regret bound due to Russo and Van Roy (JMLR 17, 2016). The bound applies to every algorithm, not only TS. It reduces the analysis of a particular algorithm to one quantity, the information ratio: the squared expected regret of a period divided by the information the period yields about the optimal action.

Timeline:

  • 2016: Russo and Van Roy, An information-theoretic analysis of Thompson sampling (JMLR 17), introduce the information ratio with the regret and the mutual information both conditioned on a realised history, and prove the regret bound and bounds on the ratio for TS (for example ∣X∣/2|\mathcal X|/2∣X∣/2 for ∣X∣|\mathcal X|∣X∣-armed bandits and d/2d/2d/2 for linear bandits).
  • 2018: Russo and Van Roy, Learning to optimize via information-directed sampling (Oper. Res. 66), design algorithms that minimise the information ratio directly.
  • 2018: Russo and Van Roy, Satisficing in time-sensitive bandit learning (arXiv:1803.02855), observe that the same bounds hold with the information ratio averaged over histories. This is the form (8.7) the tutorial uses (footnote 1, p. 75).
  • 2018: the tutorial gives the bound as (8.8), with a three-line proof on p. 76 whose last step is cited from the 2016 paper.

Setting

An agent acts in periods t=1,2,…t=1,2,\dotst=1,2,…. In each period it selects an action xtx_txt​ from a finite set X\mathcal XX and observes an outcome yty_tyt​ from a finite set Y\mathcal YY. An unknown parameter θ\thetaθ, taking values in a measurable space Θ\ThetaΘ, has a prior distribution PPP. Given θ\thetaθ and the action xxx, the outcome is drawn from a probability vector qθ(⋅∣x)q_\theta(\cdot\mid x)qθ​(⋅∣x) that depends measurably on θ\thetaθ. The agent receives the reward r(yt)r(y_t)r(yt​) for a known function rrr. The mean reward of action xxx is

μ(x,θ)=∑yqθ(y∣x) r(y),\mu(x,\theta)=\sum_{y}q_\theta(y\mid x)\,r(y),μ(x,θ)=y∑​qθ​(y∣x)r(y),

and the optimal action x∗=x∗(θ)x^*=x^*(\theta)x∗=x∗(θ) is a measurable selector of arg⁡max⁡xμ(x,θ)\arg\max_x\mu(x,\theta)argmaxx​μ(x,θ). Ties may be broken in any measurable way, and every result holds for every selector.

The history Ht\mathbb H_tHt​ is the list (x1,y1),…,(xt,yt)(x_1,y_1),\dots,(x_t,y_t)(x1​,y1​),…,(xt​,yt​). An algorithm π\piπ assigns to every history a probability vector over X\mathcal XX, from which the next action is drawn. The joint law of (θ,Ht)(\theta,\mathbb H_t)(θ,Ht​) is P(dθ)∏s≤tπ(xs∣Hs−1) qθ(ys∣xs)P(d\theta)\prod_{s\le t}\pi(x_s\mid\mathbb H_{s-1})\,q_\theta(y_s\mid x_s)P(dθ)∏s≤t​π(xs​∣Hs−1​)qθ​(ys​∣xs​), and every expectation below is taken under it. The expected cumulative regret, or Bayesian regret, is

E[Regret(T)]=E[∑t=1T(μ(x∗,θ)−μ(xt,θ))].\mathbb E[\mathrm{Regret}(T)]=\mathbb E\Big[\sum_{t=1}^T\big(\mu(x^*,\theta)-\mu(x_t,\theta)\big)\Big].E[Regret(T)]=E[t=1∑T​(μ(x∗,θ)−μ(xt​,θ))].

The entropy of the prior distribution of x∗x^*x∗ is H(x∗)=−∑aP(x∗=a)log⁡P(x∗=a)H(x^*)=-\sum_a\mathbb P(x^*=a)\log\mathbb P(x^*=a)H(x∗)=−∑a​P(x∗=a)logP(x∗=a). The conditional mutual information I(x∗;(xt,yt)∣Ht−1)I(x^*;(x_t,y_t)\mid\mathbb H_{t-1})I(x∗;(xt​,yt​)∣Ht−1​) is the expected information that the period-ttt observation carries about x∗x^*x∗ given the past, averaged over the past. The information ratio of period ttt is (8.7):

Γt=(E[μ(x∗,θ)−μ(xt,θ)])2I(x∗;(xt,yt)∣Ht−1).\Gamma_t=\frac{\big(\mathbb E[\mu(x^*,\theta)-\mu(x_t,\theta)]\big)^2}{I(x^*;(x_t,y_t)\mid\mathbb H_{t-1})}.Γt​=I(x∗;(xt​,yt​)∣Ht−1​)(E[μ(x∗,θ)−μ(xt​,θ)])2​.

Formalization targets

Goal: (8.8)

For every model and every algorithm, every horizon TTT and every Γˉ≥0\bar\Gamma\ge0Γˉ≥0 with (E[μ(x∗,θ)−μ(xt,θ)])2≤Γˉ I(x∗;(xt,yt)∣Ht−1)\big(\mathbb E[\mu(x^*,\theta)-\mu(x_t,\theta)]\big)^2\le\bar\Gamma\,I(x^*;(x_t,y_t)\mid\mathbb H_{t-1})(E[μ(x∗,θ)−μ(xt​,θ)])2≤ΓˉI(x∗;(xt​,yt​)∣Ht−1​) for t=1,…,Tt=1,\dots,Tt=1,…,T,

E[Regret(T)]≤Γˉ H(x∗) T.\mathbb E[\mathrm{Regret}(T)]\le\sqrt{\bar\Gamma\,H(x^*)\,T}.E[Regret(T)]≤ΓˉH(x∗)T​.

When every mutual information is positive, the least such Γˉ\bar\GammaΓˉ is max⁡t≤TΓt\max_{t\le T}\Gamma_tmaxt≤T​Γt​, which is the tutorial's Γˉ\bar\GammaΓˉ. A companion theorem states that literal form under the positivity hypothesis.

Milestones, in the order of the p. 76 proof

  1. Regret is additive over periods:
E[Regret(T)]=∑t=1TE[μ(x∗,θ)−μ(xt,θ)].\mathbb E[\mathrm{Regret}(T)]=\sum_{t=1}^T\mathbb E[\mu(x^*,\theta)-\mu(x_t,\theta)].E[Regret(T)]=t=1∑T​E[μ(x∗,θ)−μ(xt​,θ)].
  1. The Jensen (Cauchy–Schwarz) step: if δt2≤ΓˉIt\delta_t^2\le\bar\Gamma I_tδt2​≤ΓˉIt​ for every ttt and ∑tIt≤H\sum_tI_t\le H∑t​It​≤H, then ∑tδt≤TΓˉH\sum_t\delta_t\le\sqrt{T\bar\Gamma H}∑t​δt​≤TΓˉH​. This milestone is the published, proved theorem BanditAlgorithm.information_ratio_cumulative_bound_signed. It states the step in the form the proof uses, with the entropy budget as a hypothesis.
  2. The entropy budget:
∑t=1TI(x∗;(xt,yt)∣Ht−1)≤H(x∗).\sum_{t=1}^T I\big(x^*;(x_t,y_t)\mid\mathbb H_{t-1}\big)\le H(x^*).t=1∑T​I(x∗;(xt​,yt​)∣Ht−1​)≤H(x∗).

Significance

The result. (8.8) separates the regret analysis of an algorithm into two independent problems. One is a per-period bound on the information ratio, which depends on the structure of the problem and the algorithm. The other is the entropy of the optimal action, which depends only on the prior. Combined with the bound Γˉ≤∣X∣/2\bar\Gamma\le|\mathcal X|/2Γˉ≤∣X∣/2 that Russo and Van Roy (2016) prove for TS on ∣X∣|\mathcal X|∣X∣-armed bandits, and with H(x∗)≤log⁡∣X∣H(x^*)\le\log|\mathcal X|H(x∗)≤log∣X∣, it gives ∣X∣ Tlog⁡∣X∣/2\sqrt{|\mathcal X|\,T\log|\mathcal X|/2}∣X∣Tlog∣X∣/2​. On the shortest-path problem of p. 76, Γˉ≤d/2\bar\Gamma\le d/2Γˉ≤d/2 makes the regret depend on the number ddd of edges and on H(x∗)H(x^*)H(x∗), not on the exponentially large number of paths. The same inequality is the design criterion of information-directed sampling, which chooses actions to minimise the ratio.

Formalizing it. The bound and its proof are known. The work here is to make the probabilistic model explicit, with a general parameter space, an outcome kernel and randomized algorithms; to formalize the per-period decomposition of the regret and the chain-rule argument behind the entropy budget, which the tutorial cites rather than proves; and to obtain (8.8) for every algorithm in one statement. The Proved platform results for Lattimore and Szepesvári's Bayesian adversarial bandit (Theorem 36.5 and its chain-rule lemmas) are about a different model: one algorithm (TS), the best arm in hindsight of a reward matrix, and the budget log⁡k\log klogk. To our knowledge, (8.8) itself has not been formalized.

Difficulty

The Cauchy–Schwarz step is elementary and already proved on the platform. Milestone 1 is bookkeeping: the cumulative regret integrates over histories of length TTT, while the period-ttt term integrates over histories of length ttt, and later periods must be integrated out.

The substance is the entropy budget. The obvious attempt bounds each period's information by H(x∗)H(x^*)H(x∗), which loses a factor TTT. The bound is uniform in TTT only because the per-period informations telescope: their sum is the information that the whole history carries about x∗x^*x∗, which is at most the entropy of x∗x^*x∗. Each step requires conditional distributions of x∗x^*x∗ given histories, where θ\thetaθ lives in a general measurable space and is integrated against the prior. Zero-probability histories must also be handled consistently with the convention 0log⁡0=00\log0=00log0=0.

Formalization scope

  • X\mathcal XX and Y\mathcal YY are finite types; Θ\ThetaΘ is an arbitrary measurable space with a probability measure PPP. The tutorial allows infinite action sets and general outcomes. The finite specialization makes H(x∗)H(x^*)H(x∗) and every mutual information a finite sum. The kernel qθ(⋅∣x)q_\theta(\cdot\mid x)qθ​(⋅∣x) of §4 replaces the noise representation yt=g(xt,θ,wt)y_t=g(x_t,\theta,w_t)yt​=g(xt​,θ,wt​) of p. 70; both describe the same laws.
  • Standing hypotheses: qθ(y∣x)≥0q_\theta(y\mid x)\ge0qθ​(y∣x)≥0, ∑yqθ(y∣x)=1\sum_yq_\theta(y\mid x)=1∑y​qθ​(y∣x)=1, measurability of θ↦qθ(y∣x)\theta\mapsto q_\theta(y\mid x)θ↦qθ​(y∣x), measurability of the level sets of x∗x^*x∗, and μ(x,θ)≤μ(x∗(θ),θ)\mu(x,\theta)\le\mu(x^*(\theta),\theta)μ(x,θ)≤μ(x∗(θ),θ). They make every integrand bounded and measurable, so no integrability hypothesis is added.
  • An algorithm is a behavioural policy, giving a probability vector over actions for each history. Under perfect recall this is equivalent to an algorithm with internal randomization. Periods are 0-based in Lean: Lean period sss is tutorial period t=s+1t=s+1t=s+1.
  • Logarithms are natural in both III and HHH. The conditional mutual information is defined directly as an average of Kullback–Leibler divergences, with joint probabilities. It is not defined as a difference of unconditional mutual informations: that would make the chain rule true by definition and empty milestone 3. Γt\Gamma_tΓt​ is never written as a quotient in the goal, so a period with zero information cannot turn the bound into one with Γˉ=0\bar\Gamma=0Γˉ=0.
  • Needed infrastructure: marginalisation over later periods of the history law, the chain rule of conditional mutual information for finite variables averaged against a general prior, and I(x∗;HT)≤H(x∗)I(x^*;\mathbb H_T)\le H(x^*)I(x∗;HT​)≤H(x∗). The Proved platform results InformationTheory.kernel_average_categorical_kl_le_marginal_entropy and its _general variant (average KL to the mixture is at most the mixture's entropy), and the adversarial-bandit lemmas BanditAlgorithm.bayesian_ts_conditional_information_gain_chain_rule and bayesian_history_mutualInformation_endpoint_bound, are related tools. A chain-rule library for this history model would be reusable beyond this mission, for example for information-directed sampling. Proofs of auxiliary lemmas are welcome.

The tutorial's argument is on pp. 75–76. The tutorial cites Russo and Van Roy (2016) for the original results, including the entropy budget.

Selected references

  • D. J. Russo, B. Van Roy, A. Kazerouni, I. Osband, Z. Wen, A Tutorial on Thompson Sampling, Foundations and Trends in Machine Learning 11(1):1–96, 2018. https://doi.org/10.1561/2200000070
  • D. Russo, B. Van Roy, An Information-Theoretic Analysis of Thompson Sampling, Journal of Machine Learning Research 17(68):1–30, 2016. https://jmlr.org/papers/v17/14-087.html (arXiv: https://arxiv.org/abs/1403.5341)
  • D. Russo, B. Van Roy, Learning to Optimize via Information-Directed Sampling, Operations Research 66(1):230–252, 2018. https://doi.org/10.1287/opre.2017.1663
  • D. Russo, B. Van Roy, Satisficing in Time-Sensitive Bandit Learning, arXiv preprint, 2018. https://arxiv.org/abs/1803.02855
  • T. Lattimore, C. Szepesvári, Bandit Algorithms, Cambridge University Press, 2020, Chapter 36. https://doi.org/10.1017/9781108571401
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Computational Optimal Transport VIII: Sinkhorn's Scalings Contract by λ(K)² per Iteration in Hilbert's Projective MetricTextbook

Why Sinkhorn's algorithm needs a convergence rate

Sinkhorn's algorithm is the standard method for computing entropically regularized optimal transport between two histograms. It is used throughout machine learning, statistics, imaging and graphics because each iteration is two matrix–vector products followed by entrywise divisions, which parallelize on GPUs and differentiate easily. The same iteration, under the names matrix scaling, RAS or iterative proportional fitting, has been used since the 1930s in transportation planning, economics and contingency-table analysis. Whether the iteration converges, and how fast, determines how many iterations a practitioner must run and which stopping criteria are reliable.

This mission formalizes the global convergence analysis of Sinkhorn's algorithm as presented in Chapter 4 (§4.2, Remarks 4.12–4.14) of G. Peyré and M. Cuturi, Computational Optimal Transport (2019).

Timeline. Sinkhorn (1964) proved that a positive matrix can be scaled to be doubly stochastic, and Sinkhorn and Knopp (1967) extended this to prescribed marginals. Birkhoff (1957) and Samelson (1957) independently showed that a positive linear map is a strict contraction of the positive cone for Hilbert's projective metric, with an explicit ratio. Franklin and Lorenz (1989) combined the two to prove that Sinkhorn's scalings converge linearly in Hilbert's metric at rate λ(K)2\lambda(K)^2λ(K)2. Linial, Samorodnitsky and Wigderson (1998) related the iteration to the permanent.

Setting

Fix n,m≥1n, m \ge 1n,m≥1, a matrix K∈Rn×mK \in \mathbb{R}^{n\times m}K∈Rn×m with positive entries (in the book, the Gibbs kernel Ki,j=e−Ci,j/εK_{i,j} = e^{-C_{i,j}/\varepsilon}Ki,j​=e−Ci,j​/ε), and vectors a∈Rna \in \mathbb{R}^na∈Rn, b∈Rmb \in \mathbb{R}^mb∈Rm with positive entries. Products ⊙\odot⊙ and quotients of vectors are entrywise. The regularized transport plan has the form P=diag(u)Kdiag(v)P = \mathrm{diag}(u)K\mathrm{diag}(v)P=diag(u)Kdiag(v), where the scalings u,vu, vu,v solve

u⊙(Kv)=a,v⊙(K⊤u)=b.(4.14)u \odot (Kv) = a, \qquad v \odot (K^\top u) = b. \tag{4.14}u⊙(Kv)=a,v⊙(K⊤u)=b.(4.14)

Sinkhorn's algorithm alternately enforces each equation:

v(0)=1m,u(ℓ+1)=aKv(ℓ),v(ℓ+1)=bK⊤u(ℓ+1).(4.15)v^{(0)} = \mathbb{1}_m,\qquad u^{(\ell+1)} = \frac{a}{Kv^{(\ell)}},\qquad v^{(\ell+1)} = \frac{b}{K^\top u^{(\ell+1)}}. \tag{4.15}v(0)=1m​,u(ℓ+1)=Kv(ℓ)a​,v(ℓ+1)=K⊤u(ℓ+1)b​.(4.15)

For vectors with positive entries, Hilbert's projective metric is

dH(u,u′)=log⁡max⁡i,juiuj′ujui′,d_{\mathcal H}(u,u') = \log\max_{i,j}\frac{u_i u'_j}{u_j u'_i},dH​(u,u′)=logi,jmax​uj​ui′​ui​uj′​​,

which vanishes exactly when uuu and u′u'u′ are proportional, and equals the variation seminorm ∥log⁡u−log⁡u′∥var\|\log u - \log u'\|_{\mathrm{var}}∥logu−logu′∥var​ with ∥f∥var=max⁡ifi−min⁡ifi\|f\|_{\mathrm{var}} = \max_i f_i - \min_i f_i∥f∥var​=maxi​fi​−mini​fi​. Birkhoff's constants of KKK are

η(K)=max⁡i,j,k,ℓKi,kKj,ℓKj,kKi,ℓ,λ(K)=η(K)−1η(K)+1.\eta(K) = \max_{i,j,k,\ell}\frac{K_{i,k}K_{j,\ell}}{K_{j,k}K_{i,\ell}},\qquad \lambda(K) = \frac{\sqrt{\eta(K)}-1}{\sqrt{\eta(K)}+1}.η(K)=i,j,k,ℓmax​Kj,k​Ki,ℓ​Ki,k​Kj,ℓ​​,λ(K)=η(K)​+1η(K)​−1​.

Write P(ℓ)=diag(u(ℓ))Kdiag(v(ℓ))P^{(\ell)} = \mathrm{diag}(u^{(\ell)})K\mathrm{diag}(v^{(\ell)})P(ℓ)=diag(u(ℓ))Kdiag(v(ℓ)) and P⋆=diag(u⋆)Kdiag(v⋆)P^\star = \mathrm{diag}(u^\star)K\mathrm{diag}(v^\star)P⋆=diag(u⋆)Kdiag(v⋆) for a positive solution (u⋆,v⋆)(u^\star, v^\star)(u⋆,v⋆) of (4.14).

Formalization targets

Goal: Theorem 4.2 (Franklin–Lorenz)

For every ℓ\ellℓ,

dH(v(ℓ),v⋆)≤λ(K)2ℓ dH(v(0),v⋆),dH(u(ℓ+1),u⋆)≤λ(K)2ℓ+1 dH(v(0),v⋆),d_{\mathcal H}(v^{(\ell)}, v^\star) \le \lambda(K)^{2\ell}\, d_{\mathcal H}(v^{(0)}, v^\star),\qquad d_{\mathcal H}(u^{(\ell+1)}, u^\star) \le \lambda(K)^{2\ell+1}\, d_{\mathcal H}(v^{(0)}, v^\star),dH​(v(ℓ),v⋆)≤λ(K)2ℓdH​(v(0),v⋆),dH​(u(ℓ+1),u⋆)≤λ(K)2ℓ+1dH​(v(0),v⋆),

so both distances tend to 000; for ℓ≥1\ell \ge 1ℓ≥1,

dH(u(ℓ),u⋆)≤dH(P(ℓ)1m,a)1−λ(K)2,max⁡i,j∣log⁡Pi,j(ℓ)−log⁡Pi,j⋆∣≤dH(u(ℓ),u⋆)+dH(v(ℓ),v⋆),d_{\mathcal H}(u^{(\ell)}, u^\star) \le \frac{d_{\mathcal H}(P^{(\ell)}\mathbb{1}_m, a)}{1-\lambda(K)^2},\qquad \max_{i,j}\big|\log P^{(\ell)}_{i,j} - \log P^\star_{i,j}\big| \le d_{\mathcal H}(u^{(\ell)},u^\star) + d_{\mathcal H}(v^{(\ell)},v^\star),dH​(u(ℓ),u⋆)≤1−λ(K)2dH​(P(ℓ)1m​,a)​,i,jmax​​logPi,j(ℓ)​−logPi,j⋆​​≤dH​(u(ℓ),u⋆)+dH​(v(ℓ),v⋆),

and for ℓ≥0\ell \ge 0ℓ≥0 the mirrored bound for v(ℓ)v^{(\ell)}v(ℓ) with the half-step coupling diag(u(ℓ+1))Kdiag(v(ℓ))\mathrm{diag}(u^{(\ell+1)})K\mathrm{diag}(v^{(\ell)})diag(u(ℓ+1))Kdiag(v(ℓ)). The book writes the rate as O(λ(K)2ℓ)O(\lambda(K)^{2\ell})O(λ(K)2ℓ); the constants stated are those its proof yields.

Milestones, in attack order

  1. (4.21): dH(u,u′)=∥log⁡u−log⁡u′∥vard_{\mathcal H}(u,u') = \|\log u - \log u'\|_{\mathrm{var}}dH​(u,u′)=∥logu−logu′∥var​.
  2. Remark 4.12: dHd_{\mathcal H}dH​ is a distance on rays (triangle inequality; zero exactly on proportional vectors).
  3. Invariances: dH(v,v′)=dH(v/v′,1)=dH(1/v,1/v′)d_{\mathcal H}(v,v') = d_{\mathcal H}(v/v',\mathbb{1}) = d_{\mathcal H}(\mathbb{1}/v,\mathbb{1}/v')dH​(v,v′)=dH​(v/v′,1)=dH​(1/v,1/v′) and invariance under positive diagonal scaling.
  4. Theorem 4.1 (Birkhoff–Hopf): dH(Kv,Kv′)≤λ(K) dH(v,v′)d_{\mathcal H}(Kv,Kv') \le \lambda(K)\,d_{\mathcal H}(v,v')dH​(Kv,Kv′)≤λ(K)dH​(v,v′) and λ(K)<1\lambda(K) < 1λ(K)<1.
  5. One half-step contracts by λ(K)\lambda(K)λ(K): dH(u(ℓ+1),u⋆)≤λ(K) dH(v(ℓ),v⋆)d_{\mathcal H}(u^{(\ell+1)},u^\star) \le \lambda(K)\,d_{\mathcal H}(v^{(\ell)},v^\star)dH​(u(ℓ+1),u⋆)≤λ(K)dH​(v(ℓ),v⋆), and the vvv-analogue.
  6. The display behind (4.23): dH(u(ℓ),u⋆)≤dH(a,u(ℓ)⊙Kv(ℓ))+λ(K)2dH(u(ℓ),u⋆)d_{\mathcal H}(u^{(\ell)},u^\star) \le d_{\mathcal H}(a, u^{(\ell)}\odot Kv^{(\ell)}) + \lambda(K)^2 d_{\mathcal H}(u^{(\ell)},u^\star)dH​(u(ℓ),u⋆)≤dH​(a,u(ℓ)⊙Kv(ℓ))+λ(K)2dH​(u(ℓ),u⋆), and its mirror.

Significance

The result. Theorem 4.2 gives a global linear rate that holds from the first iteration, independent of the starting point, with a ratio computable from KKK alone. Through (4.21) the rate transfers to the dual potentials εlog⁡u(ℓ)\varepsilon\log u^{(\ell)}εlogu(ℓ), and the bounds (4.23) turn the marginal violations P(ℓ)1mP^{(\ell)}\mathbb{1}_mP(ℓ)1m​ versus aaa — which are computed anyway — into certified stopping criteria. Theorem 4.1 alone also yields a quantitative Perron–Frobenius theorem (Remark 4.13).

Formalizing it. All results here are classical and proved in the literature (Birkhoff 1957; Franklin–Lorenz 1989); Theorem 4.1 is quoted by the book without proof. To our knowledge neither Hilbert's projective metric on the positive cone, the Birkhoff–Hopf theorem, nor the convergence rate of Sinkhorn's algorithm has a machine-checked proof in Mathlib. The mission produces a reusable development of dHd_{\mathcal H}dH​ and Birkhoff's contraction theorem, and a certified rate and stopping rule for the most widely used optimal-transport solver.

Difficulty

Milestones 1–3 and 5–6 are finite manipulations of maxima of ratios. The central difficulty is Theorem 4.1. The obvious argument — bound (Kv)i/(Kv′)i(Kv)_i/(Kv')_i(Kv)i​/(Kv′)i​ entrywise by max⁡kvk/vk′\max_k v_k/v'_kmaxk​vk​/vk′​ — only shows that KKK is non-expansive (λ≤1\lambda \le 1λ≤1); it loses the strict contraction entirely. The sharp ratio (η−1)/(η+1)(\sqrt{\eta}-1)/(\sqrt{\eta}+1)(η​−1)/(η​+1) depends on how KKK acts on the two vectors jointly, which the entrywise argument does not see. The book quotes the theorem without proof, so the argument has to be supplied from Birkhoff's original paper or later expositions.

Formalization scope

Lean namespace CompOT.Sinkhorn. Indices are 000-based (Fin n, Fin m); vectors are functions Fin n → ℝ, matrices Matrix (Fin n) (Fin m) ℝ, and ⊙\odot⊙ and /// are the pointwise operations on functions. dHd_{\mathcal H}dH​, η\etaη and ∥⋅∥var\|\cdot\|_{\mathrm{var}}∥⋅∥var​ use real iSup/iInf over the finite index sets. Every theorem assumes 0<n0 < n0<n, 0<m0 < m0<m and positive entries for KKK, aaa, bbb and every vector passed to dHd_{\mathcal H}dH​, which excludes Lean's junk values for division by zero and logarithms of non-positive numbers. Sinkhorn's algorithm is a run predicate (IsSinkhornRun) stating the recursion (4.15) with v(0)=1mv^{(0)} = \mathbb{1}_mv(0)=1m​; u(0)u^{(0)}u(0) is not an iterate and is unconstrained. The solution (u⋆,v⋆)(u^\star, v^\star)(u⋆,v⋆) is any positive solution of (4.14), taken as a hypothesis (it exists iff ∑iai=∑jbj\sum_i a_i = \sum_j b_j∑i​ai​=∑j​bj​); the book's histograms are probability vectors, but only positivity is used. P⋆P^\starP⋆ in (4.24) is diag(u⋆)Kdiag(v⋆)\mathrm{diag}(u^\star)K\mathrm{diag}(v^\star)diag(u⋆)Kdiag(v⋆), the solution of the regularized problem (4.2) by Proposition 4.3.

Deviations from the print, all disclosed in the items: the O(⋅)O(\cdot)O(⋅) of (4.22) is replaced by the proof's explicit constants; the limit (u(ℓ),v(ℓ))→(u⋆,v⋆)(u^{(\ell)}, v^{(\ell)}) \to (u^\star, v^\star)(u(ℓ),v(ℓ))→(u⋆,v⋆) is stated as dH→0d_{\mathcal H} \to 0dH​→0, since scalings are determined only up to (ru,v/r)(ru, v/r)(ru,v/r); the second inequality of (4.23) is false as printed (its right side is 000 for ℓ≥1\ell \ge 1ℓ≥1) and is stated with the half-step coupling the mirrored proof gives.

A definition of dHd_{\mathcal H}dH​ without the logarithm, or as a maximum of single-entry ratios, would make the statements different theorems; the definition here is the book's, and (4.21) is a milestone checking it against the variation seminorm. A run predicate not tied to KKK, aaa, bbb, or an unsatisfiable one, is ruled out: the predicate is the recursion itself and is inhabited for every KKK, aaa, bbb.

Contributions welcome: the metric properties of dHd_{\mathcal H}dH​ (reusable for Perron–Frobenius and nonlinear consensus), a proof of the Birkhoff–Hopf theorem, and positivity lemmas for Sinkhorn iterates.

Selected references

  • G. Peyré, M. Cuturi, Computational Optimal Transport, Foundations and Trends in Machine Learning 11(5–6):355–607, 2019. https://doi.org/10.1561/2200000073 (§4.2, pp. 431–443)
  • J. Franklin, J. Lorenz, On the scaling of multidimensional matrices, Linear Algebra and its Applications 114/115:717–735, 1989. https://doi.org/10.1016/0024-3795(89)90490-4
  • G. Birkhoff, Extensions of Jentzsch's theorem, Transactions of the AMS 85(1):219–227, 1957. https://doi.org/10.1090/S0002-9947-1957-0087058-6
  • H. Samelson, On the Perron–Frobenius theorem, Michigan Mathematical Journal 4(1):57–59, 1957. https://doi.org/10.1307/mmj/1028990177
  • R. Sinkhorn, A relationship between arbitrary positive matrices and doubly stochastic matrices, Annals of Mathematical Statistics 35(2):876–879, 1964. https://doi.org/10.1214/aoms/1177703591
  • N. Linial, A. Samorodnitsky, A. Wigderson, A deterministic strongly polynomial algorithm for matrix scaling and approximate permanents, STOC 1998. https://doi.org/10.1145/276698.276880
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Wasserstein Distributionally Robust Kalman Filtering 1: Minimax MMSE Estimation over a Gaussian Wasserstein Ball Is a Finite Convex SDP with an Affine Robust EstimatorResearch Paper

Motivation

The Kalman filter estimates the hidden state of a linear system from noisy observations. At every step it solves a minimum mean square error (MMSE) estimation problem: given a joint normal distribution of a signal xxx and an observation yyy, find the estimator ψ(y)\psi(y)ψ(y) minimizing E[∥x−ψ(y)∥2]\mathbb E[\|x-\psi(y)\|^2]E[∥x−ψ(y)∥2]. The answer, the conditional mean, is optimal only if the model distribution is correct, and in practice the system matrices and noise covariances are estimated and misspecified.

Shafieezadeh-Abadeh, Nguyen, Kuhn and Mohajerin Esfahani (arXiv:1809.08830, NeurIPS 2018) replace the single model by a set of plausible models: all normal distributions within type-2 Wasserstein distance ρ\rhoρ of a nominal normal distribution. The estimator is then chosen to minimize the worst-case mean square error over this set. Their Theorem 2.5 shows that this robust estimation problem, although posed over an infinite-dimensional space of estimators and a nonconvex set of distributions, has the same value as a finite convex program over covariance matrices, and that the robust estimator is affine. Applied at every time step, this yields the Wasserstein distributionally robust Kalman filter of the paper's §4.

This mission formalizes Theorem 2.5 together with the minimax theorem (Theorem 2.3) and the lemmas of its proof.

Setting

Let n,m≥0n, m \ge 0n,m≥0 and d=n+md = n+md=n+m. A point of Rd\mathbb R^dRd is z=[x;y]z = [x; y]z=[x;y] with signal x∈Rnx\in\mathbb R^nx∈Rn and observation y∈Rmy\in\mathbb R^my∈Rm. A symmetric matrix S∈Rd×dS \in \mathbb R^{d\times d}S∈Rd×d is partitioned as S=[SxxSxySyxSyy]S = \begin{bmatrix} S_{xx} & S_{xy}\\ S_{yx} & S_{yy}\end{bmatrix}S=[Sxx​Syx​​Sxy​Syy​​]. S+d\mathbb S^d_+S+d​ (S++d\mathbb S^d_{++}S++d​) denotes the positive semidefinite (definite) matrices, A⪰BA \succeq BA⪰B means A−B∈S+dA-B\in\mathbb S^d_+A−B∈S+d​, ⟨A,B⟩=Tr⁡[A⊤B]\langle A,B\rangle = \operatorname{Tr}[A^\top B]⟨A,B⟩=Tr[A⊤B], and A1/2A^{1/2}A1/2 is the positive semidefinite square root of A⪰0A\succeq 0A⪰0.

Normal distributions. Nd(c,S)\mathcal N_d(c,S)Nd​(c,S) is the normal distribution on Rd\mathbb R^dRd with mean ccc and covariance S∈S+dS\in\mathbb S^d_+S∈S+d​; singular SSS is allowed. Nd\mathcal N_dNd​ is the set of all of them.

Wasserstein distance. For distributions Q1,Q2\mathbb Q_1,\mathbb Q_2Q1​,Q2​ on Rd\mathbb R^dRd,

W2(Q1,Q2)=inf⁡π(∫∥z1−z2∥2 π(dz1,dz2))1/2,W_2(\mathbb Q_1,\mathbb Q_2) = \inf_{\pi} \Bigl(\int \|z_1-z_2\|^2\,\pi(dz_1,dz_2)\Bigr)^{1/2},W2​(Q1​,Q2​)=πinf​(∫∥z1​−z2​∥2π(dz1​,dz2​))1/2,

the infimum ranging over all distributions π\piπ on Rd×Rd\mathbb R^d\times\mathbb R^dRd×Rd with marginals Q1\mathbb Q_1Q1​ and Q2\mathbb Q_2Q2​.

Ambiguity set. Fix μ=[μx;μy]∈Rd\mu = [\mu_x;\mu_y]\in\mathbb R^dμ=[μx​;μy​]∈Rd, Σ∈S++d\Sigma\in\mathbb S^d_{++}Σ∈S++d​, the nominal distribution P=Nd(μ,Σ)\mathbb P=\mathcal N_d(\mu,\Sigma)P=Nd​(μ,Σ) and a radius ρ≥0\rho\ge 0ρ≥0. The Wasserstein ambiguity set is

P={Q∈Nd:W2(Q,P)≤ρ}.\mathcal P = \{\mathbb Q\in\mathcal N_d : W_2(\mathbb Q,\mathbb P)\le\rho\}.P={Q∈Nd​:W2​(Q,P)≤ρ}.

Robust estimation. Let L\mathcal LL be the set of all measurable functions ψ:Rm→Rn\psi:\mathbb R^m\to\mathbb R^nψ:Rm→Rn. The distributionally robust MMSE problem is

inf⁡ψ∈L sup⁡Q∈P EQ[∥x−ψ(y)∥2],(2)\inf_{\psi\in\mathcal L}\ \sup_{\mathbb Q\in\mathcal P}\ \mathbb E^{\mathbb Q}\bigl[\|x-\psi(y)\|^2\bigr], \tag{2}ψ∈Linf​ Q∈Psup​ EQ[∥x−ψ(y)∥2],(2)

and a distributionally robust MMSE estimator is a ψ\psiψ attaining the outer infimum. Nature's problem exchanges the two optimizations; a Q⋆∈P\mathbb Q^\star\in\mathcal PQ⋆∈P attaining its outer supremum is a least favorable prior.

The finite program. With σ‾=λmin⁡(Σ)\underline\sigma = \lambda_{\min}(\Sigma)σ​=λmin​(Σ), program (5) maximizes f(S)=Tr⁡[Sxx−SxySyy−1Syx]f(S)=\operatorname{Tr}[S_{xx}-S_{xy}S_{yy}^{-1}S_{yx}]f(S)=Tr[Sxx​−Sxy​Syy−1​Syx​] over S∈S+dS\in\mathbb S^d_+S∈S+d​ (with Sxx∈S+nS_{xx}\in\mathbb S^n_+Sxx​∈S+n​, Syy∈S+mS_{yy}\in\mathbb S^m_+Syy​∈S+m​) subject to

Tr⁡[S+Σ−2(Σ1/2SΣ1/2)1/2]≤ρ2,S⪰σ‾Id.\operatorname{Tr}\bigl[S+\Sigma-2(\Sigma^{1/2}S\Sigma^{1/2})^{1/2}\bigr]\le\rho^2,\qquad S\succeq\underline\sigma I_d .Tr[S+Σ−2(Σ1/2SΣ1/2)1/2]≤ρ2,S⪰σ​Id​.

Formalization targets

Goal: Theorem 2.5 (p. 3)

  1. The optimal value of (2) equals the optimal value of (5).
  2. If S⋆S^\starS⋆ is optimal in (5), then
ψ⋆(y)=Sxy⋆(Syy⋆)−1(y−μy)+μx\psi^\star(y) = S^\star_{xy}(S^\star_{yy})^{-1}(y-\mu_y)+\mu_xψ⋆(y)=Sxy⋆​(Syy⋆​)−1(y−μy​)+μx​

is a distributionally robust MMSE estimator. 3. For the same S⋆S^\starS⋆, Q⋆=Nd(μ,S⋆)\mathbb Q^\star=\mathcal N_d(\mu,S^\star)Q⋆=Nd​(μ,S⋆) lies in P\mathcal PP and is a least favorable prior.

Existence and uniqueness of an optimal S⋆S^\starS⋆ are not part of the statement.

Milestones, in the order of the proof

  • Proposition 2.2 (Gelbrich formula): for Σ1,Σ2⪰0\Sigma_1,\Sigma_2\succeq 0Σ1​,Σ2​⪰0,
W2(Nd(μ1,Σ1),Nd(μ2,Σ2))=∥μ1−μ2∥2+Tr⁡[Σ1+Σ2−2(Σ21/2Σ1Σ21/2)1/2].W_2\bigl(\mathcal N_d(\mu_1,\Sigma_1),\mathcal N_d(\mu_2,\Sigma_2)\bigr)=\sqrt{\|\mu_1-\mu_2\|^2+\operatorname{Tr}\bigl[\Sigma_1+\Sigma_2-2(\Sigma_2^{1/2}\Sigma_1\Sigma_2^{1/2})^{1/2}\bigr]}.W2​(Nd​(μ1​,Σ1​),Nd​(μ2​,Σ2​))=∥μ1​−μ2​∥2+Tr[Σ1​+Σ2​−2(Σ21/2​Σ1​Σ21/2​)1/2]​.
  • Lemma A.1: the closed form of sup⁡S⪰0⟨D,S⟩−γTr⁡[S−2(Σ1/2SΣ1/2)1/2]\sup_{S\succeq0}\langle D,S\rangle-\gamma\operatorname{Tr}[S-2(\Sigma^{1/2}S\Sigma^{1/2})^{1/2}]supS⪰0​⟨D,S⟩−γTr[S−2(Σ1/2SΣ1/2)1/2] and its unique maximizer.
  • (A.1b): under a fixed normal distribution, affine estimators y↦Gy+gy\mapsto Gy+gy↦Gy+g achieve the minimum mean square error over L\mathcal LL.
  • Theorem 2.3 (minimax theorem): inf⁡ψ∈Lsup⁡Q∈PEQ[∥x−ψ(y)∥2]=sup⁡Q∈Pinf⁡ψ∈LEQ[∥x−ψ(y)∥2]\inf_{\psi\in\mathcal L}\sup_{\mathbb Q\in\mathcal P}\mathbb E^{\mathbb Q}[\|x-\psi(y)\|^2] = \sup_{\mathbb Q\in\mathcal P}\inf_{\psi\in\mathcal L}\mathbb E^{\mathbb Q}[\|x-\psi(y)\|^2]infψ∈L​supQ∈P​EQ[∥x−ψ(y)∥2]=supQ∈P​infψ∈L​EQ[∥x−ψ(y)∥2].
  • Lemma A.2: the maximizer of ⟨S,D⟩\langle S,D\rangle⟨S,D⟩ under the Wasserstein-type trace constraint, and S⋆⪰σ‾IdS^\star\succeq\underline\sigma I_dS⋆⪰σ​Id​.
  • (A.6): for S≻0S\succ0S≻0, min⁡G⟨[In−G−G⊤G⊤G],S⟩=f(S)\min_G\bigl\langle\begin{bmatrix}I_n&-G\\-G^\top&G^\top G\end{bmatrix},S\bigr\rangle=f(S)minG​⟨[In​−G⊤​−GG⊤G​],S⟩=f(S), uniquely at G⋆=SxySyy−1G^\star=S_{xy}S_{yy}^{-1}G⋆=Sxy​Syy−1​.

Significance

The result. Theorem 2.5 gives the robust estimator in closed form from one optimal covariance matrix, and identifies the least favorable distribution as a normal distribution with the nominal mean. Together with Theorem 2.3 it shows that the pair (ψ⋆,Q⋆)(\psi^\star,\mathbb Q^\star)(ψ⋆,Q⋆) is a saddle point: the robust estimator is the Bayesian estimator for the least favorable prior. For ρ=0\rho=0ρ=0 the program has the single feasible point Σ\SigmaΣ and the estimator reduces to the classical conditional mean, so the theorem contains the Kalman update as a special case. The finite program is what makes the robust filter of §4 computable; the paper's §3 (formalized in the companion mission of this series) solves it with a Frank–Wolfe method.

Formalizing it. The result is proved in the paper; nothing here is open. No part of it is machine-checked yet. The Gelbrich formula for normal distributions (Proposition 2.2) is cited from the literature and has no formal proof in Mathlib; neither does Lemma A.1, cited from earlier work of the same authors. A closely related statement for a Wasserstein ball over all distributions around an elliptical nominal (Kuhn et al. 2019, Theorem 25) is posed on the platform as WassersteinDRO.Shrinkage.distributionally_robust_mmse_estimator; it is a different theorem, since the ambiguity set there is convex and contains non-normal distributions.

Difficulty

The ambiguity set P\mathcal PP is not convex, because a mixture of normal distributions is generally not normal. The standard route to a minimax equality, Sion's theorem, needs convexity on nature's side and therefore does not apply to (2) directly; the obvious attempt to interchange the infimum and the supremum fails at this step. The space L\mathcal LL of measurable estimators is infinite-dimensional, and the Wasserstein constraint is an infimum over couplings, so a proof must pass to finite-dimensional descriptions on both sides (means and covariances for Q\mathbb QQ, affine maps for ψ\psiψ) and justify each passage. The matrix-analytic parts involve the nonlinear map S↦(Σ1/2SΣ1/2)1/2S\mapsto(\Sigma^{1/2}S\Sigma^{1/2})^{1/2}S↦(Σ1/2SΣ1/2)1/2, for which Mathlib has square roots but little calculus.

Formalization scope

  • Rd\mathbb R^dRd is EuclideanSpace ℝ (Fin n ⊕ Fin m); the Sum.inl coordinates are xxx, the Sum.inr coordinates are yyy, and the blocks of a matrix are toBlocks₁₁, toBlocks₁₂, toBlocks₂₁, toBlocks₂₂.
  • Nd(c,S)\mathcal N_d(c,S)Nd​(c,S) is Mathlib's multivariateGaussian c S with S.PosSemidef; only the nominal Σ\SigmaΣ is required to be positive definite.
  • W2W_2W2​ is the published coupling-based WassersteinDRO.Shrinkage.wassersteinDistance 2 in [0,∞][0,\infty][0,∞]. The ambiguity set is defined through it, not through the Gelbrich formula; the formula is Proposition 2.2, a milestone.
  • Expectations are lower Lebesgue integrals in [0,∞][0,\infty][0,∞], with no integrability guard. A non-integrable estimator therefore has infinite risk and cannot lower the infimum through a junk value.
  • The values of (2) and of nature's problem are in [0,∞][0,\infty][0,∞]; the value of (5) is the published sdpValue in EReal, and the two are compared through the coercion.
  • σ‾\underline\sigmaσ​ is passed as a real number that is the least eigenvalue of Σ\SigmaΣ.
  • "Equivalent" in Theorem 2.5 is read as the three claims above. A version that asserts only equality of values, or optimality of ψ⋆\psi^\starψ⋆ among affine estimators only, is weaker than the theorem and is not the goal.
  • Lemma A.2 carries the added hypothesis ρ>0\rho>0ρ>0, without which its algebraic equation has no admissible root.

A complete development needs the Gelbrich formula for (possibly degenerate) normal distributions, conditional means of jointly normal vectors, a minimax theorem for convex–concave functions on compact sets, Lagrangian duality for a convex semidefinite program, and calculus of the positive semidefinite square root. The first three are reusable well beyond this mission, and contributions of any of them as separate results are welcome.

Selected references

  • S. Shafieezadeh-Abadeh, V. A. Nguyen, D. Kuhn, P. Mohajerin Esfahani, Wasserstein Distributionally Robust Kalman Filtering, NeurIPS 2018; arXiv:1809.08830v3. https://arxiv.org/abs/1809.08830
  • M. Gelbrich, On a formula for the L² Wasserstein metric between measures on Euclidean and Hilbert spaces, Mathematische Nachrichten 147, 1990. https://doi.org/10.1002/mana.19901470121
  • V. A. Nguyen, D. Kuhn, P. Mohajerin Esfahani, Distributionally robust inverse covariance estimation, arXiv:1802.01981, 2018. https://arxiv.org/abs/1802.01981
  • D. Kuhn, P. Mohajerin Esfahani, V. A. Nguyen, S. Shafieezadeh-Abadeh, Wasserstein distributionally robust optimization: theory and applications in machine learning, INFORMS TutORials, 2019; arXiv:1908.08729. https://arxiv.org/abs/1908.08729
  • D. P. Bertsekas, Convex Optimization Theory, Athena Scientific, 2009.
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Wasserstein Distributionally Robust Kalman Filtering 2: The Bisection Algorithm Returns a Feasible, ε-Suboptimal Solution of the Frank–Wolfe Direction-Finding SubproblemResearch Paper

Motivation

A Kalman filter estimates the hidden state xxx of a linear system from observations yyy, assuming the joint law of z=(x,y)z = (x, y)z=(x,y) is a known Gaussian. When that law is only approximately known, Shafieezadeh-Abadeh, Nguyen, Kuhn and Mohajerin Esfahani (arXiv:1809.08830v3, NeurIPS 2018) replace it by the worst Gaussian within a 2-Wasserstein ball of radius ρ\rhoρ around a nominal Gaussian with covariance Σ\SigmaΣ. Their Theorem 2.5 shows that the resulting minimax estimation problem is equivalent to a finite nonlinear semidefinite program, program (5), whose objective is f(S)=Tr[Sxx−SxySyy−1Syx]f(S) = \mathrm{Tr}[S_{xx} - S_{xy}S_{yy}^{-1}S_{yx}]f(S)=Tr[Sxx​−Sxy​Syy−1​Syx​].

Each step of the robust filter solves program (5), so the filter is only practical if (5) can be solved quickly. The paper solves it with a Frank–Wolfe method (Algorithm 2), which avoids projections by replacing the objective with its linearization at the current iterate and maximizing that linear function over the original feasible set. That inner problem, the direction-finding subproblem, is again a nonlinear SDP. The paper's Algorithm 1 solves it by a one-dimensional bisection, and Theorem 3.2 states that this bisection returns a feasible solution whose objective value is within a prescribed tolerance ε\varepsilonε of the optimum. This mission formalizes that guarantee.

Setting

Fix dimensions n≥1n \ge 1n≥1 (state) and mmm (observation), d=n+md = n + md=n+m, and index coordinates so that the first nnn belong to xxx. Let S+d\mathbb{S}^d_+S+d​ and S++d\mathbb{S}^d_{++}S++d​ denote the positive semidefinite and positive definite d×dd\times dd×d matrices, and ⟨A,B⟩=Tr[A⊤B]\langle A, B\rangle = \mathrm{Tr}[A^\top B]⟨A,B⟩=Tr[A⊤B] the trace inner product. The data are a nominal covariance Σ∈S++d\Sigma \in \mathbb{S}^d_{++}Σ∈S++d​ with smallest eigenvalue σ‾=λmin⁡(Σ)\underline\sigma = \lambda_{\min}(\Sigma)σ​=λmin​(Σ), a radius ρ>0\rho > 0ρ>0, a tolerance ε>0\varepsilon > 0ε>0, and the current Frank–Wolfe iterate S∈S+dS \in \mathbb{S}^d_+S∈S+d​, written in blocks Sxx,Sxy,Syx,SyyS_{xx}, S_{xy}, S_{yx}, S_{yy}Sxx​,Sxy​,Syx​,Syy​.

The gradient matrix is

D=∇f(S)=[In,−G]⊤[In,−G],G=SxySyy−1,D = \nabla f(S) = [I_n, -G]^\top [I_n, -G], \qquad G = S_{xy}S_{yy}^{-1},D=∇f(S)=[In​,−G]⊤[In​,−G],G=Sxy​Syy−1​,

which is positive semidefinite and nonzero. The direction-finding subproblem (7b) is

max⁡L⪰σ‾Id ⟨L,D⟩s.t.Tr[L+Σ−2(Σ1/2LΣ1/2)1/2]≤ρ2;\max_{L \succeq \underline\sigma I_d}\ \langle L, D\rangle \quad \text{s.t.}\quad \mathrm{Tr}\Big[L + \Sigma - 2\big(\Sigma^{1/2} L \Sigma^{1/2}\big)^{1/2}\Big] \le \rho^2 ;L⪰σ​Id​max​ ⟨L,D⟩s.t.Tr[L+Σ−2(Σ1/2LΣ1/2)1/2]≤ρ2;

its constraint says that the Gaussian with covariance LLL lies within Wasserstein distance ρ\rhoρ of the nominal Gaussian, and F\mathcal FF denotes its feasible set. For γ\gammaγ with γId≻D\gamma I_d \succ DγId​≻D the paper defines

h(γ)=ρ2−⟨Σ,(Id−γ(γId−D)−1)2⟩,L(γ)=γ2(γId−D)−1Σ(γId−D)−1,h(\gamma) = \rho^2 - \big\langle \Sigma, (I_d - \gamma(\gamma I_d - D)^{-1})^2\big\rangle, \qquad L(\gamma) = \gamma^2 (\gamma I_d - D)^{-1}\Sigma(\gamma I_d - D)^{-1},h(γ)=ρ2−⟨Σ,(Id​−γ(γId​−D)−1)2⟩,L(γ)=γ2(γId​−D)−1Σ(γId​−D)−1, Δ(γ)=γ(ρ2−Tr[Σ])−⟨L(γ),D⟩+γ2⟨(γId−D)−1,Σ⟩.\Delta(\gamma) = \gamma(\rho^2 - \mathrm{Tr}[\Sigma]) - \langle L(\gamma), D\rangle + \gamma^2\langle(\gamma I_d - D)^{-1}, \Sigma\rangle .Δ(γ)=γ(ρ2−Tr[Σ])−⟨L(γ),D⟩+γ2⟨(γId​−D)−1,Σ⟩.

Algorithm 1 takes the largest eigenvalue λ1\lambda_1λ1​ of DDD and a unit eigenvector v1v_1v1​, and starts from the bracket [LB,UB]=[γmin⁡,γmax⁡][LB, UB] = [\gamma_{\min}, \gamma_{\max}][LB,UB]=[γmin​,γmax​] with

γmin⁡=λ1(1+v1⊤Σv1/ρ),γmax⁡=λ1(1+Tr[Σ]/ρ).\gamma_{\min} = \lambda_1\big(1 + \sqrt{v_1^\top\Sigma v_1}/\rho\big), \qquad \gamma_{\max} = \lambda_1\big(1 + \sqrt{\mathrm{Tr}[\Sigma]}/\rho\big).γmin​=λ1​(1+v1⊤​Σv1​​/ρ),γmax​=λ1​(1+Tr[Σ]​/ρ).

Each pass sets γ=(UB+LB)/2\gamma = (UB + LB)/2γ=(UB+LB)/2 and L=L(γ)L = L(\gamma)L=L(γ), replaces LBLBLB by γ\gammaγ if h(γ)<0h(\gamma) < 0h(γ)<0 and UBUBUB by γ\gammaγ otherwise, and the loop exits with output LLL as soon as h(γ)>0h(\gamma) > 0h(γ)>0 and Δ(γ)<ε\Delta(\gamma) < \varepsilonΔ(γ)<ε.

Formalization targets

Goal: Theorem 3.2

For all inputs as above:

  1. Soundness. At every pass kkk whose exit test holds, with trial point γk\gamma_kγk​,
L(γk)∈Fand⟨L′,D⟩≤⟨L(γk),D⟩+ε  for all L′∈F.L(\gamma_k) \in \mathcal F \quad\text{and}\quad \langle L', D\rangle \le \langle L(\gamma_k), D\rangle + \varepsilon \ \text{ for all } L' \in \mathcal F .L(γk​)∈Fand⟨L′,D⟩≤⟨L(γk​),D⟩+ε  for all L′∈F.
  1. Termination. If hhh has no zero at a dyadic point γmin⁡+j(γmax⁡−γmin⁡)/2k\gamma_{\min} + j(\gamma_{\max} - \gamma_{\min})/2^kγmin​+j(γmax​−γmin​)/2k, 0<j≤2k0 < j \le 2^k0<j≤2k, then some pass satisfies the exit test.

The termination hypothesis is a correction: the printed theorem asserts that the algorithm outputs a solution, but if a trial point (or γmax⁡\gamma_{\max}γmax​) coincides exactly with the root of hhh, the upper end of the bracket becomes that root, all later trial points have h<0h < 0h<0, and the loop never exits. Soundness holds unconditionally. The normalization ∥v1∥=1\|v_1\| = 1∥v1​∥=1 is also added; the page says only "an eigenvector", and γmin⁡\gamma_{\min}γmin​ is not a lower bound for a rescaled one.

Milestones (App. A.3, p. 13, in the order the proof uses them)

  • L(γ)∈FL(\gamma) \in \mathcal FL(γ)∈F whenever γId≻D\gamma I_d \succ DγId​≻D and h(γ)>0h(\gamma) > 0h(γ)>0.
  • Weak duality: ⟨L′,D⟩≤γ(ρ2−Tr[Σ])+γ2⟨(γId−D)−1,Σ⟩\langle L', D\rangle \le \gamma(\rho^2 - \mathrm{Tr}[\Sigma]) + \gamma^2\langle (\gamma I_d - D)^{-1}, \Sigma\rangle⟨L′,D⟩≤γ(ρ2−Tr[Σ])+γ2⟨(γId​−D)−1,Σ⟩ for all L′∈FL' \in \mathcal FL′∈F and γ≥0\gamma \ge 0γ≥0 with γId≻D\gamma I_d \succ DγId​≻D.
  • The suboptimality display: if h(γ⋆)=0h(\gamma^\star) = 0h(γ⋆)=0 then ⟨L(γ⋆)−L(γ),D⟩≤γ(ρ2−Tr[Σ])+γ2⟨(γId−D)−1,Σ⟩−⟨L(γ),D⟩\langle L(\gamma^\star) - L(\gamma), D\rangle \le \gamma(\rho^2 - \mathrm{Tr}[\Sigma]) + \gamma^2\langle(\gamma I_d - D)^{-1}, \Sigma\rangle - \langle L(\gamma), D\rangle⟨L(γ⋆)−L(γ),D⟩≤γ(ρ2−Tr[Σ])+γ2⟨(γId​−D)−1,Σ⟩−⟨L(γ),D⟩.
  • Lemma A.3: every root γ⋆\gamma^\starγ⋆ of hhh with γ⋆Id≻D\gamma^\star I_d \succ Dγ⋆Id​≻D lies in [γmin⁡,γmax⁡][\gamma_{\min}, \gamma_{\max}][γmin​,γmax​].

Significance

Theorem 3.2 is what makes Algorithm 1 a valid linear-maximization oracle for the Frank–Wolfe method on program (5): the convergence analysis of Frank–Wolfe with inexact oracles requires exactly a feasible point whose linearized objective is within a known tolerance of the optimum. It also shows that the inner problem, a semidefinite program with a matrix square root in the constraint, reduces to a scalar root-finding problem once DDD is diagonalized, which is why the robust filter runs at a cost comparable to an eigendecomposition per step.

The result is proved in the paper; no machine-checked version exists. The formal version adds what the paper leaves implicit: the termination edge case, the normalization of v1v_1v1​, and the requirement D≠0D \ne 0D=0 (here n≥1n \ge 1n≥1). The milestone statements on Lagrangian weak duality for the Wasserstein–Gaussian trust region and on the location of the multiplier are reusable for other covariance-robust programs of the same shape, such as Wasserstein shrinkage estimation.

Difficulty

The algorithm is elementary; the analysis is not. Feasibility of L(γ)L(\gamma)L(γ) requires computing the matrix square root (Σ1/2L(γ)Σ1/2)1/2(\Sigma^{1/2}L(\gamma)\Sigma^{1/2})^{1/2}(Σ1/2L(γ)Σ1/2)1/2 in closed form and comparing eigenvalues of non-commuting products to obtain L(γ)⪰σ‾IdL(\gamma) \succeq \underline\sigma I_dL(γ)⪰σ​Id​. The dual bound is a semidefinite Lagrangian duality statement over a constraint involving a matrix square root, whose inner supremum (Lemma A.1, cited from earlier work) must be evaluated in closed form. Termination needs monotonicity and continuity of hhh on (λ1,∞)(\lambda_1, \infty)(λ1​,∞) and a careful account of which side of the root each bisection update lands on; the obvious invariant "the root stays strictly inside the bracket" fails precisely in the excluded dyadic case.

Formalization scope

Matrices are indexed by Fin n ⊕ Fin m with blocks toBlocks₁₁ (xxxxxx), toBlocks₁₂ (xyxyxy), toBlocks₂₂ (yyyyyy). The feasible set F\mathcal FF is the published definition WassersteinDRO.Shrinkage.sdpFeasibleSet ρ Σ σ, which uses the published positive semidefinite square root psdSqrt. Mathlib's matrix inverse returns 000 on singular matrices; every statement involving hhh, LLL or Δ\DeltaΔ assumes γId≻D\gamma I_d \succ DγId​≻D or works at trial points that lie above λ1\lambda_1λ1​, and Syy−1S_{yy}^{-1}Syy−1​ is allowed to be this junk value because the theorem admits any S∈S+dS \in \mathbb{S}^d_+S∈S+d​ (then D=diag(In,0)D = \mathrm{diag}(I_n, 0)D=diag(In​,0)). The constants σ‾\underline\sigmaσ​ and λ1\lambda_1λ1​ are passed as reals together with hypotheses that pin them to the smallest eigenvalue of Σ\SigmaΣ and the largest eigenvalue of DDD. The algorithm is modelled as its sequence of passes, not as a function returning an output, so a non-terminating run cannot silently produce a value.

A formalization that defines the output with a fuel bound or a default value, that states only optimality of L(γ⋆)L(\gamma^\star)L(γ⋆) at the exact root, or that assumes termination as a hypothesis would not be Theorem 3.2; ε\varepsilonε-suboptimality is stated against every feasible point, not against a supremum that could be a junk value.

A complete development needs the spectral theorem for symmetric matrices, uniqueness of the positive semidefinite square root, and semidefinite Lagrangian duality; Mathlib supplies the first two. Proofs of the milestones are welcome contributions.

Selected references

  • S. Shafieezadeh-Abadeh, V. A. Nguyen, D. Kuhn, P. Mohajerin Esfahani, Wasserstein Distributionally Robust Kalman Filtering, NeurIPS 2018; arXiv:1809.08830v3. https://arxiv.org/abs/1809.08830
  • V. A. Nguyen, D. Kuhn, P. Mohajerin Esfahani, Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator, Optimization Online, 2018; arXiv:1805.07194 (reference [18] of the paper; source of Lemma A.1). https://arxiv.org/abs/1805.07194
  • M. Jaggi, Revisiting Frank–Wolfe: Projection-Free Sparse Convex Optimization, ICML 2013 (reference [13]; Frank–Wolfe with approximate oracles). https://proceedings.mlr.press/v28/jaggi13.html
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Convex OptimizationDynamical SystemsOptimization·Captain: mikedeng1

Fast Convergence of Inertial Dynamics and Algorithms with Asymptotic Vanishing Viscosity 1: For α > 3 Every Trajectory of ẍ + (α/t)ẋ + ∇Φ(x) = 0 Converges Weakly to a Minimizer of ΦResearch Paper

Motivation

Nesterov's accelerated gradient method and its proximal variant FISTA minimize a smooth convex function with a value gap of order O(1/k2)O(1/k^2)O(1/k2) after kkk iterations, against O(1/k)O(1/k)O(1/k) for plain gradient descent. They are among the most widely used first-order methods in large-scale optimization, signal processing and machine learning. Su, Boyd and Candès (JMLR 2016) observed that, for α=3\alpha = 3α=3, the second-order differential equation

x¨(t)+αt x˙(t)+∇Φ(x(t))=0(1)\ddot x(t) + \frac{\alpha}{t}\,\dot x(t) + \nabla\Phi(x(t)) = 0 \tag{1}x¨(t)+tα​x˙(t)+∇Φ(x(t))=0(1)

is a continuous-time limit of Nesterov's scheme, and proved Φ(x(t))−min⁡Φ=O(1/t2)\Phi(x(t)) - \min\Phi = O(1/t^2)Φ(x(t))−minΦ=O(1/t2) for α≥3\alpha \ge 3α≥3. A rate on the values says nothing about the trajectory itself: whether x(t)x(t)x(t) (or the iterates xkx_kxk​) converges at all was a long-standing question. The paper of Attouch, Chbani, Peypouquet and Redont answers it for α>3\alpha > 3α>3: every trajectory of (1) converges weakly to a minimizer of Φ\PhiΦ.

Timeline:

  • 1983 — Nesterov introduces the accelerated gradient method with an O(1/k2)O(1/k^2)O(1/k2) rate on the values.
  • 2000 — Alvarez proves weak convergence of the trajectories of the heavy ball with friction, x¨+γx˙+∇Φ(x)=0\ddot x + \gamma\dot x + \nabla\Phi(x) = 0x¨+γx˙+∇Φ(x)=0, whose damping is constant (SIAM J. Control Optim. 38).
  • 2009 — Beck and Teboulle extend it to composite problems (FISTA), still with a rate on the values only.
  • 2014–2016 — Su, Boyd, Candès identify (1) as the continuous model of Nesterov's method and prove the O(1/t2)O(1/t^2)O(1/t2) rate for α≥3\alpha\ge3α≥3.
  • 2015 — Attouch, Chbani, Peypouquet, Redont prove weak convergence of the trajectories of (1) for α>3\alpha > 3α>3 (this mission), and of the iterates of the corresponding algorithms; Chambolle and Dossal obtain the discrete result independently. The case α=3\alpha = 3α=3 remains open.

Setting

Let H\mathcal HH be a real Hilbert space with inner product ⟨⋅,⋅⟩\langle\cdot,\cdot\rangle⟨⋅,⋅⟩ and norm ∥⋅∥\|\cdot\|∥⋅∥, and let Φ:H→R\Phi:\mathcal H\to\mathbb RΦ:H→R be a continuously differentiable convex function with gradient ∇Φ\nabla\Phi∇Φ. Fix a damping parameter α>0\alpha>0α>0 and an initial time t0>0t_0>0t0​>0. A solution of (1) on [t0,+∞[[t_0,+\infty[[t0​,+∞[ is a pair of maps x,vx, vx,v from the time axis to H\mathcal HH such that, at every t≥t0t\ge t_0t≥t0​, xxx has derivative v(t)v(t)v(t) and vvv has derivative −αtv(t)−∇Φ(x(t))-\frac{\alpha}{t}v(t) - \nabla\Phi(x(t))−tα​v(t)−∇Φ(x(t)). So v=x˙v=\dot xv=x˙, and x¨\ddot xx¨ satisfies (1). The damping coefficient α/t\alpha/tα/t is singular at 000 and vanishes as t→+∞t\to+\inftyt→+∞; this is why the time origin is positive.

Three functions of time measure the progress of a solution:

  • the global energy W(t)=12∥x˙(t)∥2+Φ(x(t))W(t) = \frac12\|\dot x(t)\|^2 + \Phi(x(t))W(t)=21​∥x˙(t)∥2+Φ(x(t));
  • the anchored distance hz(t)=12∥x(t)−z∥2h_z(t) = \frac12\|x(t)-z\|^2hz​(t)=21​∥x(t)−z∥2 to a point z∈Hz\in\mathcal Hz∈H;
  • when Φ\PhiΦ has a minimizer x∗x^*x∗, the anchored energies, for λ,ξ≥0\lambda,\xi\ge0λ,ξ≥0,
Eλ,ξ(t)=t2(Φ(x(t))−min⁡Φ)+12∥λ(x(t)−x∗)+tx˙(t)∥2+ξ2∥x(t)−x∗∥2.\mathcal E_{\lambda,\xi}(t) = t^2\big(\Phi(x(t))-\min\Phi\big) + \tfrac12\|\lambda(x(t)-x^*) + t\dot x(t)\|^2 + \tfrac{\xi}{2}\|x(t)-x^*\|^2 .Eλ,ξ​(t)=t2(Φ(x(t))−minΦ)+21​∥λ(x(t)−x∗)+tx˙(t)∥2+2ξ​∥x(t)−x∗∥2.

x(t)x(t)x(t) converges weakly to xˉ\bar xxˉ as t→+∞t\to+\inftyt→+∞ if ⟨x(t),y⟩→⟨xˉ,y⟩\langle x(t),y\rangle\to\langle\bar x,y\rangle⟨x(t),y⟩→⟨xˉ,y⟩ for every y∈Hy\in\mathcal Hy∈H. A weak limit point of x(t)x(t)x(t) is the weak limit of a sequence x(sn)x(s_n)x(sn​) with sn→+∞s_n\to+\inftysn​→+∞. argmin⁡Φ\operatorname{argmin}\PhiargminΦ is the set of minimizers of Φ\PhiΦ.

Formalization targets

Goal: Theorem 2.16

If argmin⁡Φ≠∅\operatorname{argmin}\Phi\ne\emptysetargminΦ=∅ and α>3\alpha>3α>3, then for every solution xxx of (1) there is xˉ∈argmin⁡Φ\bar x\in\operatorname{argmin}\Phixˉ∈argminΦ with

x(t)⇀xˉ(t→+∞).x(t)\rightharpoonup\bar x\qquad(t\to+\infty).x(t)⇀xˉ(t→+∞).

The goal asserts only the qualitative conclusion; no rate or constant enters it.

Milestones

  1. Lemma 2.1 (energy dissipation): W˙(t)=−αt∥x˙(t)∥2\dot W(t) = -\frac{\alpha}{t}\|\dot x(t)\|^2W˙(t)=−tα​∥x˙(t)∥2 for t>t0t>t_0t>t0​; WWW is nonincreasing with a limit in R∪{−∞}\mathbb R\cup\{-\infty\}R∪{−∞}, finite when Φ\PhiΦ is bounded below.
  2. (7): h¨z(t)+αth˙z(t)+Φ(x(t))−Φ(z)≤∥x˙(t)∥2\ddot h_z(t) + \frac{\alpha}{t}\dot h_z(t) + \Phi(x(t)) - \Phi(z) \le \|\dot x(t)\|^2h¨z​(t)+tα​h˙z​(t)+Φ(x(t))−Φ(z)≤∥x˙(t)∥2.
  3. Lemma 2.2: ∫t0t1s(W(s)−Φ(z)) ds≤C−1th˙z(t)−32αW(t)\int_{t_0}^t \frac1s(W(s)-\Phi(z))\,ds \le C - \frac1t\dot h_z(t) - \frac{3}{2\alpha}W(t)∫t0​t​s1​(W(s)−Φ(z))ds≤C−t1​h˙z​(t)−2α3​W(t) with an explicit constant CCC.
  4. Theorem 2.3 i), ii) (minimizing property, every α>0\alpha>0α>0): lim⁡W(t)=lim⁡Φ(x(t))=inf⁡Φ∈R∪{−∞}\lim W(t) = \lim \Phi(x(t)) = \inf\Phi\in\mathbb R\cup\{-\infty\}limW(t)=limΦ(x(t))=infΦ∈R∪{−∞}, and every weak limit point of x(t)x(t)x(t) is a minimizer.
  5. Remark 2.6: the Lyapunov inequality for ddtEλ,ξ\frac{d}{dt}\mathcal E_{\lambda,\xi}dtd​Eλ,ξ​, and monotonicity of Eλ,λ(α−λ−1)\mathcal E_{\lambda,\lambda(\alpha-\lambda-1)}Eλ,λ(α−λ−1)​ for α≥3\alpha\ge3α≥3, 2≤λ≤α−12\le\lambda\le\alpha-12≤λ≤α−1.
  6. Theorem 2.14 ii) with (13), (15): for α>3\alpha>3α>3, ∥x(t)−x∗∥2≤E2,2(α−3)(t0)/(α−3)\|x(t)-x^*\|^2 \le \mathcal E_{2,2(\alpha-3)}(t_0)/(\alpha-3)∥x(t)−x∗∥2≤E2,2(α−3)​(t0​)/(α−3) and ∫t0∞t∥x˙(t)∥2 dt≤E2,2(α−3)(t0)/(α−3)\int_{t_0}^{\infty} t\|\dot x(t)\|^2\,dt \le \mathcal E_{2,2(\alpha-3)}(t_0)/(\alpha-3)∫t0​∞​t∥x˙(t)∥2dt≤E2,2(α−3)​(t0​)/(α−3).
  7. Lemma A.4: if tw¨+αw˙≤gt\ddot w + \alpha\dot w \le gtw¨+αw˙≤g a.e. with α>1\alpha>1α>1, g≥0g\ge0g≥0 integrable and www bounded below, then [w˙]+[\dot w]_+[w˙]+​ is integrable and lim⁡w(t)\lim w(t)limw(t) exists.
  8. Lemma A.2 (Opial's lemma, continuous form).

Significance

The result. Theorem 2.16 is the first proof that the trajectories of the continuous Nesterov model converge, not only their values. The paper's discrete analysis parallels the continuous one and yields weak convergence of the iterates of Nesterov-type forward–backward algorithms for α>3\alpha>3α>3 (Theorem 5.3, a companion mission). The minimizing property (Theorem 2.3) holds for every α>0\alpha>0α>0, with no minimizer assumed, and shows that a bounded trajectory exists only if Φ\PhiΦ has a minimizer. Strong convergence results under further geometric assumptions (Theorems 3.1–3.3) build directly on Theorem 2.16.

Formalizing it. All statements are proved on paper. None of them, nor the differential equation (1), is formalized on Prove2Me or in Mathlib; the discrete analogue (weak convergence of the unperturbed accelerated forward–backward iterates) exists on the platform as a separate result. A formal development provides: a reusable encoding of solutions of a non-autonomous second-order ODE in a Hilbert space; Lyapunov arguments with one-sided derivatives on a half-line; a continuous-time Opial lemma, which is the standard tool for weak convergence of evolution equations and of semigroups; and the scalar differential-inequality lemma A.4.

Difficulty

Lyapunov functions such as WWW or Eα−1,0\mathcal E_{\alpha-1,0}Eα−1,0​ give rates on the values but not convergence of x(t)x(t)x(t): a decreasing energy says nothing about where the trajectory goes, and the minimizer set may be a continuum. The natural first idea, showing that ∥x(t)−x∗∥\|x(t)-x^*\|∥x(t)−x∗∥ is eventually monotone as for the gradient flow, fails: the inertial term makes hx∗h_{x^*}hx∗​ oscillate, and (7) controls only th¨+αh˙t\ddot h + \alpha\dot hth¨+αh˙, a second-order quantity. Making it usable requires integrability of t∥x˙(t)∥2t\|\dot x(t)\|^2t∥x˙(t)∥2 on [t0,+∞[[t_0,+\infty[[t0​,+∞[, which is not available from the Su–Boyd–Candès energy and holds only for α>3\alpha>3α>3; the borderline α=3\alpha=3α=3 is open. In infinite dimension, convergence is genuinely weak: bounded sets are not compact, so the argument must identify weak limit points through weak lower semicontinuity of Φ\PhiΦ and conclude through Opial's lemma.

Formalization scope

  • Space. H\mathcal HH is a real inner product space that is complete (InnerProductSpace ℝ H, CompleteSpace H); no finite dimension is assumed, since in finite dimension weak and norm convergence coincide.
  • Potential. Φ:H→R\Phi:\mathcal H\to\mathbb RΦ:H→R with ConvexOn ℝ Set.univ Φ and ContDiff ℝ 1 Φ; ∇Φ\nabla\Phi∇Φ is Mathlib's gradient. Minimizers are points zzz with Φ(z)≤Φ(y)\Phi(z)\le\Phi(y)Φ(z)≤Φ(y) for all yyy; min⁡Φ\min\PhiminΦ is written Φ(x∗)\Phi(x^*)Φ(x∗) for such a point.
  • Solutions. (1) is the first-order system x˙=v\dot x=vx˙=v, v˙=−αtv−∇Φ(x)\dot v=-\frac{\alpha}{t}v-\nabla\Phi(x)v˙=−tα​v−∇Φ(x), with derivatives within [t0,+∞[[t_0,+\infty[[t0​,+∞[ at every t≥t0t\ge t_0t≥t0​ and t0>0t_0>0t0​>0. Existence of solutions is not assumed or proved: every statement concerns a given solution, as in the paper.
  • Limits and integrals. Limits in R∪{−∞}\mathbb R\cup\{-\infty\}R∪{−∞} are taken in EReal, inf⁡Φ\inf\PhiinfΦ as an EReal infimum. Improper integrals ∫t0+∞\int_{t_0}^{+\infty}∫t0​+∞​ carry an explicit integrability conjunct; integrals over [t0,t][t_0,t][t0​,t] are interval integrals of continuous functions.
  • Explicit constants. The constant CCC of Lemma 2.2 and the bounds E2,2(α−3)(t0)/(α−3)\mathcal E_{2,2(\alpha-3)}(t_0)/(\alpha-3)E2,2(α−3)​(t0​)/(α−3) of (13), (15) are stated exactly as the paper computes them.
  • Trivializations ruled out. Weak convergence is not replaced by norm convergence, the derivative relations between xxx, vvv and their energies are part of the hypotheses or conclusions (never a free deriv), and the solution predicate is non-vacuous: the constant trajectory for Φ=0\Phi=0Φ=0 and the explicit solution x(t)=t2x(t)=t^2x(t)=t2 of Example 2.5 satisfy it.
  • Citation. Theorem numbers and pages refer to the Optimization Online preprint 5179 (October 2015), titled "…asymptotic vanishing damping", not to the Mathematical Programming typesetting.

Contributions of reusable infrastructure are welcome, in particular a continuous-time Opial lemma, weak lower semicontinuity of continuous convex functions on a Hilbert space along weakly convergent sequences, and integration-by-parts lemmas for one-sided derivatives on a half-line.

Selected references

  • H. Attouch, Z. Chbani, J. Peypouquet, P. Redont, Fast convergence of inertial dynamics and algorithms with asymptotic vanishing damping, Optimization Online preprint 5179, 2015; published as …vanishing viscosity, Mathematical Programming 168 (2018) 123–175. https://optimization-online.org/wp-content/uploads/2015/10/5179.pdf, https://doi.org/10.1007/s10107-016-0992-8
  • 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 (2016) 1–43. https://jmlr.org/papers/v17/15-084.html
  • F. Alvarez, On the minimizing property of a second order dissipative system in Hilbert spaces, SIAM Journal on Control and Optimization 38 (2000) 1102–1119. https://doi.org/10.1137/S0363012998335802
  • Z. Opial, Weak convergence of the sequence of successive approximations for nonexpansive mappings, Bulletin of the AMS 73 (1967) 591–597. https://doi.org/10.1090/S0002-9904-1967-11761-0
  • A. Beck, M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences 2 (2009) 183–202. https://doi.org/10.1137/080716542
  • A. Chambolle, Ch. Dossal, On the convergence of the iterates of the "fast iterative shrinkage/thresholding algorithm", Journal of Optimization Theory and Applications 166 (2015) 968–982. https://doi.org/10.1007/s10957-015-0746-4
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