Rudin PMA IX: Functions of Several VariablesTextbook
Motivation
Chapter 9 of Walter Rudin's Principles of Mathematical Analysis (3rd edition, McGraw-Hill,
1976) develops the differential calculus of mappings f:Rn→Rm. The definition of the derivative changes character: it is no longer a number but
a linear transformationf′(x), the one that approximates the increment
of f to first order. Once that is in place, the chapter proves the two theorems that
make nonlinear analysis possible: the inverse function theorem (Theorem 9.24), which says
that a continuously differentiable map with invertible derivative at a point is locally
invertible with a continuously differentiable inverse, and the implicit function theorem
(9.28), which solves f(x,y)=0 locally for x in terms
of y.
The message of both is that a nonlinear map behaves locally like its linearization, provided
that linearization is invertible and varies continuously.
This mission is the ninth in a series formalizing Rudin Chapters 1–11; it uses the
completeness and compactness results of Missions II and IV and the mean value estimates of
Mission V, and it prepares the change-of-variables machinery used in Mission X.
Setting
L(Rn,Rm) is the space of linear maps with the operator norm
∥A∥=sup∣x∣≤1∣Ax∣. A map f defined on an open E⊆Rn
is differentiable atx with derivative A∈L(Rn,Rm) if
h→0lim∣h∣∣f(x+h)−f(x)−Ah∣=0,
and f∈C′(E) — a C′-mapping — if it is differentiable on E and
x↦f′(x) is continuous. The partial derivativeDjfi is the derivative of t↦fi(x+tej) at t=0. A map
φ of a metric space into itself is a contraction if
d(φ(x),φ(y))≤cd(x,y) for some c<1.
Formalization targets
Goal — inverse function theorem (Theorem 9.24)
Let f be a C′-mapping of an open E⊆Rn into Rn and
suppose f′(a) is invertible at some a∈E. Then there are open
sets U∋a and V∋f(a) such that
f∣U is injective,f(U)=V,g=(f∣U)−1∈C′(V).
Milestones
invertible operators form an open set; inversion is continuous(9.8)(g∘f)′(x)=g′(f(x))f′(x)(9.15)differentiability gives all partial derivatives(9.17)∥f′∥≤M on a convex E⇒∣f(b)−f(a)∣≤M∣b−a∣(9.19)f∈C′(E)⟺the Djfi exist and are continuous(9.21)a contraction of a complete metric space has a unique fixed point(9.23)implicit function theorem(9.28)D21f continuous at (a,b)⇒D12f(a,b)=D21f(a,b)(9.41)differentiation under the integral sign(9.42)
Significance
The inverse function theorem is the local classification statement of differential calculus: it
says that the only local obstruction to invertibility is degeneracy of the derivative, and it is
the mechanism behind coordinate changes, the rank theorem (9.32), and the change-of-variables
formula for integrals in Chapter 10. The implicit function theorem is its standard reformulation
and is what makes level sets of smooth maps into manifolds. Theorem 9.21 is the practical
criterion for the C′ hypothesis, since it reduces it to continuity of finitely many partial
derivatives; Theorem 9.41 shows that the symmetry of second derivatives, though intuitive,
requires a hypothesis; Theorem 9.19 is the several-variable substitute for the mean value
theorem, whose equality form already failed in Chapter 5.
Mathlib has the Fréchet derivative, the inverse and implicit function theorems for Banach
spaces, the Banach fixed-point theorem, and symmetry of second derivatives. This mission states
the Rudin versions concretely in Rn — with the explicit open sets U and V and
the inverse mapping produced as data, rather than through a bundled local homeomorphism — and
so provides a bridge between the book's formulations and the library's.
Difficulty
The inverse function theorem is the first theorem in the book whose proof combines several
chapters at once: the contraction principle (9.23) gives local surjectivity by solving
f(x)=y as a fixed point of
x↦x+A−1(y−f(x)); openness of the
set of invertible operators (9.8) keeps the derivative invertible near a; the mean
value inequality (9.19) controls the error; and the continuity of inversion gives the C′
regularity of g. The delicate point is that all estimates must hold uniformly on a
neighbourhood chosen in advance, so the order in which the neighbourhoods are shrunk matters.
For Theorem 9.41 the trap is the hypothesis: continuity of D21f at the single point
(a,b) is assumed, not continuity of both mixed partials on a neighbourhood; the conclusion is
existence of D12f at that point, and it genuinely fails without some such hypothesis.
Formalization scope
Conventions fixed by this mission:
Euclidean spaces are EuclideanSpace ℝ (Fin n); linear maps are →L[ℝ] (continuous linear
maps), which in finite dimension is the same as Rudin's L(Rn,Rm), with
the operator norm.
Derivatives are HasFDerivAt, and the C′ condition is ContDiffOn ℝ 1.
Partial derivatives are stated as HasDerivAt of the line restriction
t ↦ f (x + t • eⱼ) at t = 0, avoiding any coordinate-projection bookkeeping;
eⱼ = EuclideanSpace.single j 1.
Invertibility of a derivative is Function.Bijective, which for a continuous linear map
between finite-dimensional spaces is equivalent to the existence of a continuous linear
inverse.
In 9.8 the inverse operator is supplied as a function inv constrained on the invertible
operators, so that continuity of inversion can be stated without bundling.
Theorem 9.41 uses explicitly supplied partial derivative functions D1f, D2f, D21f, which
is how Rudin states the hypotheses, and the conclusion asserts existence of D₁₂f at the
point as a HasDerivAt statement.
Theorem 9.42 is stated for the Riemann–Stieltjes integral of Mission VI, matching Rudin's
hypotheses α increasing and φ(·,t) ∈ ℛ(α).
Selected references
Walter Rudin, Principles of Mathematical Analysis, 3rd edition, McGraw-Hill, 1976,
Chapter 9 (pp. 204–243).
A Generalization of Brouwer's Fixed Point Theorem: An Upper Semi-Continuous Map with Nonempty Closed Convex Values on a Bounded Closed Convex Set in Euclidean Space Has a Fixed PointResearch Paper
Motivation
Brouwer's fixed point theorem says that a continuous map of a closed simplex into itself has a fixed point. Many existence questions in game theory and mathematical economics produce instead a point-to-set mapping: to each point x it assigns a whole set Φ(x) of admissible responses, for instance the set of best replies of a player to the others' strategies. Such a set need not be a single point, and no continuous single-valued selection need exist. Kakutani's 1941 note, A generalization of Brouwer's fixed point theorem, gives the fixed point theorem for this setting, and shows on the same three pages that it implies two results of J. von Neumann: an intersection theorem for two closed subsets of a product of convex sets, and the minimax theorem.
Timeline, as the paper records it:
1912–1929: Brouwer's theorem; the paper cites the proof of Knaster, Kuratowski and Mazurkiewicz (Fund. Math. 14, 1929).
1928: von Neumann proves the minimax theorem for matrix games (Math. Ann. 100).
1937: von Neumann proves the intersection theorem (Kakutani's Theorem 2) in his paper on an economic equilibrium system, "by using a notion of integral in Euclidean spaces".
1941: Kakutani proves Theorem 1 (simplex), the Corollary (bounded closed convex sets), and derives Theorems 2 and 3 from it.
1950: Nash's existence proof for equilibrium points of n-person games applies Kakutani's theorem (PNAS 36).
Setting
Let E=Rm with its Euclidean norm, and let S⊆E. Kakutani writes R(S) for the family of all closed convex subsets of S; as printed, it contains the empty set. A point-to-set mapping of S into R(S) assigns to each x∈S a closed convex set Φ(x)⊆S.
Φ is upper semi-continuous (in the paper's sense) when
xn∈S,xn→x0∈S,yn∈Φ(xn),yn→y0⟹y0∈Φ(x0).
This is a sequential closed-graph condition. The paper remarks that for a closed S it is equivalent to the graph {(x,y):x∈S,y∈Φ(x)} being closed in S×S.
An r-dimensional closed simplex is the convex hull of r+1 affinely independent points of E. A fixed point of Φ is a point x0∈S with x0∈Φ(x0).
Formalization targets
Goal: the Corollary (p. 458)
Let S⊆Rm be nonempty, bounded, closed and convex, and let Φ be upper semi-continuous on S with every value Φ(x), x∈S, a nonempty closed convex subset of S. Then
∃x0∈S:x0∈Φ(x0).
Milestones
Brouwer's fixed point theorem (§1, p. 457) — a published, proved platform theorem, referenced.
Approximation step of the proof of Theorem 1 (pp. 457–458): for every ε>0 there are a point x of the simplex, points x0,…,xr within ε of x, selections yi∈Φ(xi) and weights λ∈Δr with x=∑iλixi=∑iλiyi.
Limit step of the proof of Theorem 1 (p. 458): such data for every ε>0, on a compact S, force a fixed point.
Theorem 1 (p. 457): the fixed point statement for an r-dimensional closed simplex.
Enclosing simplex (proof of the Corollary, p. 458): every bounded subset of Rm lies in an m-dimensional closed simplex S′.
Retraction (proof of the Corollary, p. 458): a continuous map of S′ into S fixing S.
Composition (proof of the Corollary, p. 458): x↦Φ(ψ(x)) is upper semi-continuous on S′ with values in R(S)⊆R(S′).
Closed-graph equivalence (§1, p. 457), for closed S and Φ(x)⊆S.
Theorem 2 (p. 458, von Neumann's intersection theorem): if K⊆Rm, L⊆Rn are bounded closed convex, K=∅, and U,V⊆K×L are closed with all sections Ux0⊆L, Vy0⊆K nonempty, closed and convex, then U∩V=∅.
Milestones 8 and 9 stand off the goal's proof path: the paper proves Theorem 2 from the Corollary, using milestone 8 to check upper semi-continuity.
Significance
The Corollary is the form in which Kakutani's theorem is used: existence of equilibrium points in n-person games (Nash), of competitive equilibria (Arrow–Debreu), and of solutions of generalized games and variational inequalities all reduce to a fixed point of a point-to-set mapping with nonempty closed convex values on a compact convex set. Theorem 2 in turn gives the minimax theorem (the paper's Theorem 3) in a few lines.
The results are classical and their proofs are known; what is missing is a machine-checked version. Mathlib does not contain Kakutani's theorem, and on Prove2Me the theorem is not posed. Brouwer's theorem is proved on the platform (AGT.brouwer_fixed_point) and enters as a reference milestone. Theorem 3 is already proved on the platform as a special case of Sion's minimax theorem (FamousTheorems.sion_minimax_theorem, Mathlib's Sion.minimax) and is therefore not posed here. A related open platform statement, Debreu's 1952 fixed point lemma for a contractible polyhedron with contractible values and a closed graph (SocialEquilibrium.Existence.fixed_point_of_contractible_polyhedron), generalizes Theorem 1 on polyhedra but not the Corollary, since a bounded closed convex set need not be a polyhedron. Once proved, the goal is the standard entry point for formalized equilibrium existence results.
Difficulty
Brouwer's theorem needs a continuous single-valued map, and a point-to-set mapping with closed convex values need not admit a continuous selection: the mapping on [−1,1] equal to {1} for x<0, [−1,1] at 0 and {−1} for x>0 is upper semi-continuous and has none. So the obvious route, select and apply Brouwer, fails; any proof must pass through approximations and a limit in which the graph condition and the convexity of the limiting value both enter. The approximation step needs a family of triangulations of the simplex with mesh tending to zero, which Mathlib does not provide. The transfer from a simplex to a general convex set needs a continuous retraction, which uses convexity and closedness of S in Euclidean space.
Formalization scope
The ambient space is EuclideanSpace ℝ (Fin m), m=0 included. A point-to-set mapping is a total function Φ : E → Set E; only its values on S are constrained.
IsClosedConvexSubset S A is the paper's A∈R(S) (A⊆S, closed, convex) and admits A=∅ as printed. Nonemptiness of the values (and of S, and of K in Theorem 2) is a separate, labelled hypothesis: with Φ≡∅, or S=∅, the printed hypotheses hold and there is no fixed point, while the paper's proof picks "an arbitrary point yn from Φ(xn)".
IsUpperSemicontinuous S Φ is the paper's sequential condition, with sequences in S and limit x0∈S. It is not Berge's open-neighbourhood upper hemicontinuity.
Explicit readings of loose phrases: "the n-th barycentric simplicial subdivision" becomes "for every ε>0" with the selected vertices within ε of the point (no subdivision is built into the statement); "it is clear that … converges" becomes compactness of S in the limit step; "take a closed simplex S′ which contains S" becomes an m-dimensional affinely independent hull; "a continuous retracting point-to-point mapping" becomes ContinuousOn ψ S', MapsTo ψ S' S and ψ x = x on S; "clearly an upper semi-continuous point-to-set mapping of S′ into R(S)⊆R(S′)" is stated as three conclusions; "closed subset of S×S" is closedness in E×E (the same for closed S); U⋅V is U∩V; "K×L in Rm+n" is the product Rm×Rn.
A trivializing formalization would allow empty values, an empty domain, a degenerate simplex, or approximation data for a single coarse ε; each is excluded by the statements as written.
Infrastructure a complete development needs: barycentric subdivisions or another construction of approximate fixed points; nearest-point projection onto closed convex sets in Euclidean space; transport of the Corollary to Rm×Rn for Theorem 2. The two definitions are generic and reusable for any later set-valued existence result. Proofs by any faithful route are welcome, including a route through Brouwer's theorem and a continuous approximate selection.
B. Knaster, C. Kuratowski, S. Mazurkiewicz, Ein Beweis des Fixpunktsatzes für n-dimensionale Simplexe, Fund. Math. 14 (1929), 132–137. https://doi.org/10.4064/fm-14-1-132-137
J. von Neumann, Über ein ökonomisches Gleichungssystem und eine Verallgemeinerung des Brouwerschen Fixpunktsatzes, Ergebnisse eines Math. Kolloquiums 8 (1937), 73–83.
Ordinary Differential Equations and Dynamical Systems VII: Attracting Sets and Liouville's TheoremTextbook
Motivation
Planar flows are tame: the Poincaré–Bendixson theorem says that a bounded forward orbit in the plane tends to a fixed point or a periodic orbit. From dimension three on this fails, and the long-time behavior of a flow has to be described through sets rather than single orbits. Chapter 8 of Gerald Teschl's graduate text Ordinary Differential Equations and Dynamical Systems (AMS GSM 140, 2012; author's preliminary version at mat.univie.ac.at) develops two tools for this. The first is the attracting set produced by a trapping region, the device used to show that the Lorenz equation (Lorenz 1963) has an attractor. The second is volume: Liouville's formula for volumes measures how a flow expands or contracts phase space, which shows that the Lorenz attractor has Lebesgue measure zero, and in the special case of a Hamiltonian system gives Liouville's theorem, that the flow preserves volume. Liouville's theorem, with Poincaré's recurrence theorem as its companion, underlies Hamiltonian mechanics and equilibrium statistical mechanics.
This mission states those results for the local flow of a C1 vector field on an open subset of Rn, as in the book.
Setting
Let M⊆Rn be open and f:M→Rn of class C1. An integral curve of x˙=f(x) is a differentiable φ on an open interval with φ˙(t)=f(φ(t)). For each x∈M there is a unique maximal one through x at time 0, defined on the open interval Ix∋0. Its value at time t is Φ(t,x), the flow. The flow is local: Ix may be bounded.
For X⊆M, the ω+-limit setω+(X) is the set of y∈M for which there are tk→∞ and xk∈X with Φ(tk,xk)→y. The stable and unstable sets of Λ are W±(Λ)={x∈M:d(Φ(t,x),Λ)→0 as t→±∞}, where d is the distance to a set. A set is invariant if it contains the full orbit of each of its points. An invariant Λ is attracting if W+(Λ) is a neighborhood of Λ. A trapping region is an open connected E with compact closure E⊆M such that Φ(t,E)⊂E for all t>0.
The divergence of f is divf=∑i∂fi/∂xi=tr(df). On phase space Rn×Rn∋(p,q), a Hamilton function H∈C2 defines Hamilton's equationsq˙=∂H/∂p, p˙=−∂H/∂q, whose right-hand side is the Hamiltonian vector fieldXH. The Lorenz equation is x˙=−σ(x−y), y˙=rx−y−xz, z˙=xy−bz with σ,r,b>0. Volume ∣⋅∣ is Lebesgue measure.
Formalization targets
Goal: Liouville's theorem (Theorem 8.10)
For H∈C2(Ω) on an open Ω⊆Rn×Rn, with Φ the flow of XH on Ω, every measurable U⊆Ω and every time t at which all points of U are alive satisfy
∣Φ(t,U)∣=∣U∣.
Milestones
Lemma 8.8 (Liouville's formula for volumes). Take f∈C1(Rn), U bounded and open, and t0 with every point of U alive at t0. Write V(t)=∣Φ(t,U)∣. Then V(t0)<∞ and
V˙(t0)=∫Φ(t0,U)divf(x)dx.
Theorem 8.11 (Poincaré). Let Φ be a volume preserving bijection of a bounded region D. Then every neighborhood U⊆D contains an x with Φk(x)∈U for some k≥1.
Lemma 8.5. For a trapping region E, Λ=ω+(E)=⋂t≥0Φ(t,E) is nonempty, invariant, compact, connected and attracting.
Lemma 8.6. For a trapping region E, W−(x)⊆ω+(E) for every x∈ω+(E).
Lemma 8.7. For the Lorenz equation with r≤1, the origin is the only fixed point, and every solution exists for all t≥0 and tends to the origin.
Significance
The result itself. Liouville's theorem is the basic structural fact of Hamiltonian dynamics. It rules out asymptotically stable equilibria and attractors of positive measure for Hamiltonian systems. It makes Lebesgue measure invariant, which is the starting point of ergodic theory for mechanical systems. Combined with Theorem 8.11, it gives recurrence for any Hamiltonian motion confined to a bounded energy region. Lemma 8.8 is the quantitative version for arbitrary flows: for the Lorenz equation it gives V(t)=Ve−(1+σ+b)t, so the attractor from Lemma 8.5 has measure zero. Lemmas 8.5 and 8.6 produce attractors and show that they contain the unstable manifolds of their points.
Formalizing it. All of these results are classical and proved in the book. Mathlib has the change-of-variables formula, Poincaré recurrence for conservative measure-preserving maps (MeasureTheory.Conservative), and an ω-limit set for global flows (omegaLimit). It has no flow of a nonlinear ODE with its differentiable dependence on initial conditions, no volume formula along such a flow, and no Hamiltonian formalism. The mission asks for those pieces in the book's local-flow setting, with its hypotheses made explicit.
Difficulty
Lemma 8.8 and Theorem 8.10 need the flow map x↦Φ(t,x) to be a C1 diffeomorphism of the set of points alive at time t onto its image. They also need its Jacobian to satisfy the first variational equation Π˙=df(Φt(x))Π, whose determinant obeys Liouville's formula for linear systems. None of this is available for a general C1 vector field in Mathlib, which provides local existence and uniqueness of integral curves but not differentiability of the flow in the initial condition. Differentiating V(t) under the integral also needs uniform control on a compact set of initial points. That is why Lemma 8.8 asks the closure of U, not just U, to be alive at t0. Without that, Φ(t0,U) can have infinite volume (x˙=x2, U=(0,1), t0=1).
Lemmas 8.5 and 8.6 are point-set topology. The obstacle there is the local flow: every statement about Φ(t,⋅) must first establish that the points involved are alive at time t. The book avoids this by assuming completeness at the start of the chapter. For Lemma 8.7, the Liapunov function L=rx2+σy2+σz2 from the text has L˙≤0, not L˙<0, so convergence to the origin requires the Krasovskii–LaSalle argument, not a plain Liapunov one.
Formalization scope
Rn is EuclideanSpace ℝ (Fin n) and phase space is EuclideanSpace ℝ (Fin n) × EuclideanSpace ℝ (Fin n) with points (p,q), momenta first. volume on it is Lebesgue measure on R2n. The flow is a pair (I,Φ) satisfying IsMaximalFlow, which makes t↦Φ(t,x) the unique maximal integral curve on Ix. Nothing assumes Ix=R. The chapter's standing completeness assumption is replaced by explicit aliveness hypotheses, and these are implied by completeness. The explicit readings are:
Theorem 8.10: "Hamiltonian flow" means H∈C2 on an open Ω. "Volume" means Lebesgue measure of any measurable U⊆Ω that is alive at time t.
Lemma 8.8: f∈C1 on Rn. U is alive at t0. The conclusion also states V(t0)<∞ and integrability of divf on Φ(t0,U), and V is the real-valued volume.
Theorem 8.11: "region" means a measurable bounded set. "Volume preserving" means that images of measurable subsets of D are measurable and have the same measure. "Any neighborhood U" means a neighborhood of some point, hence nonempty. n∈N means k≥1.
Lemma 8.5: a trapping region has E⊆M and E forward complete, and "connected" includes nonempty. "Invariant" is two-sided.
Lemma 8.7: "all solutions converge" includes existence for all t≥0. The flow is quantified universally.
A trivializing formalization is excluded. The goal is not stated for a complete or globally Lipschitz flow, not for sets of measure zero or bounded open sets only, and not with divXH=0 assumed. It must be proved for the flow of XH built from H itself.
A complete development needs differentiability of the flow in the initial condition and the first variational equation. It also needs Liouville's formula detΠ(t)=exp∫0ttrA for linear systems, and change of variables for the flow map. Each is reusable beyond this mission; contributions of any of them as separate lemmas are welcome.
Notes on Partial Differential Equations III: Weak Derivatives and the Gagliardo–Nirenberg–Sobolev InequalityTextbook
Motivation
Almost every existence theory for elliptic, parabolic and hyperbolic partial differential equations works in Sobolev spaces: function spaces defined through weak derivatives and Lp integrability rather than classical differentiability. Energy methods produce solutions whose derivatives are known only to be square-integrable. Sobolev embedding theorems are what turn that information back into integrability or continuity of the solution itself, and they are used at every step of the regularity theory.
This mission covers Chapter 3, §3.1–3.8 of J. K. Hunter's graduate lecture notes Notes on Partial Differential Equations (UC Davis, revised 6/18/2014): weak derivatives, the spaces Wk,p, approximation by test functions, and the two embedding regimes p<n and p>n.
Timeline. S. L. Sobolev proved the embedding for p>1 in 1938 by potential-theoretic methods. E. Gagliardo (1958) and L. Nirenberg (1959) independently gave the elementary proof, including p=1, that the notes follow. The inequality behind it, for products of functions of n−1 variables, is also known as the Loomis–Whitney inequality (1949). C. B. Morrey (1940) proved the Hölder estimate for p>n. The sharp constants were found by Federer–Fleming and Maz'ya (1960, p=1, equivalent to the isoperimetric inequality) and by G. Talenti (1976, 1<p<n).
Setting
Throughout, Rn carries Lebesgue measure and the Euclidean norm ∣x∣, and Ω⊆Rn is open. A test functionφ∈Cc∞(Ω) is infinitely differentiable, and its support is a compact subset of Ω. For a multi-index α∈N0n with ∣α∣=∑iαi, ∂α=∂1α1⋯∂nαn.
A locally integrable f has weak derivative∂αf=g∈Lloc1(Ω) if
∫Ωgφdx=(−1)∣α∣∫Ωf∂αφdxfor all φ∈Cc∞(Ω).
The Sobolev spaceWk,p(Ω), for k∈N and 1≤p≤∞, consists of the f∈Lloc1(Ω) whose weak derivatives ∂αf, ∣α∣≤k, exist and lie in Lp(Ω). It is normed by ∥f∥Wk,p=(∑∣α∣≤k∫Ω∣∂αf∣pdx)1/p, with the maximum of the suprema when p=∞.
For 1≤p<n the Sobolev conjugate is p∗=np/(n−p), so that 1/p∗=1/p−1/n. For f∈Cc∞(Rn), Df is the gradient, ∣Df∣ its Euclidean length, and ∥Df∥p=(∫∣Df∣pdx)1/p. The Hölder seminorm is [u]α=supx=y∣u(x)−u(y)∣/∣x−y∣α.
Formalization targets
Goal: the Gagliardo–Nirenberg–Sobolev inequality (Theorem 3.28)
For n≥2, 1≤p<n and every f∈Cc∞(Rn),
∥f∥p∗≤2(n−p)p(n−1)∥Df∥p.
Milestones
Lemma 3.26: if g∈L1(R) has compact support and ∫g=0, then ∫−∞xgdt≤21∫∣g∣dt.
Theorem 3.27, the product inequality (3.10): for nonnegative gi∈Cc∞(Rn−1), ∫Rn∏i=1ngi(xi′)dx≤∏i=1n∥gi∥n−1, where xi′ is x with the ith coordinate omitted.
Theorem 3.24: Cc∞(Rn) is dense in Wk,p(Rn) for 1≤p<∞.
Theorem 3.31: W1,p(Rn)↪Lq(Rn) for 1≤p<n and p≤q≤p∗, with ∥f∥q≤C(n,p,q)∥f∥W1,p.
Theorem 3.36 (Morrey): for n<p<∞ and α=1−n/p there is C=C(n,p) with [f]α≤C∥Df∥p and sup∣f∣≤C∥f∥W1,p for all f∈Cc∞(Rn).
The goal's constant is explicit, and it is not the sharp one. A later sharp constant (Talenti's) would strengthen the goal but would not invalidate it.
Significance
The result. Theorem 3.28 is the quantitative core of the embedding W1,p↪Lp∗. Density (Theorem 3.24) extends it to all of W1,p(Rn), giving Theorem 3.31. From there, extension operators and Rellich–Kondrachov compactness follow, and with them the existence and regularity of weak solutions to elliptic equations (Chapters 4–6 of the same notes). Morrey's inequality is the complementary statement for p>n and yields continuity of Sobolev functions. Without these estimates, a weak solution obtained by the Lax–Milgram or Galerkin method carries no pointwise or integrability information.
Formalizing it. Mathlib contains a Gagliardo–Nirenberg–Sobolev inequality for C1 compactly supported functions with a non-explicit constant (MeasureTheory.eLpNorm_le_eLpNorm_fderiv_of_eq, restated on the platform as FamousTheorems.gagliardo_nirenberg_sobolev and included here as a reference item). Mathlib has no weak derivatives in the integral form used here, no Wk,p(Ω) spaces on open sets, no density theorem for them, no W1,p↪Lq embedding, and no Morrey inequality. This mission asks for the explicit constant, the Loomis–Whitney-type product inequality in the form the proof uses, and the first results on Sobolev spaces built from integral-form weak derivatives.
Difficulty
The explicit constant cannot be read off Mathlib's existing inequality, whose constant is defined through its own proof. It has to come from the one-dimensional bound (Lemma 3.26) combined with the product inequality (3.10). Theorem 3.27 is an induction on dimension in which Hölder's inequality is applied on sections Rn−2 of Rn−1 and then integrated over the remaining coordinate. Expressing "x with the ith coordinate omitted" and Fubini across those splittings of Rn is the bulk of the measure-theoretic bookkeeping. For p>1 the argument has to be applied to ∣f∣s, which is only C1.
Theorems 3.24 and 3.31 need weak derivatives to commute with mollification and cutoff. They also need the weak derivative of a limit to be identified, and completeness of Lp∗. None of this is available for the integral-form definition. Morrey's inequality needs averages over balls and polar-coordinate integration.
Formalization scope
Rn is EuclideanSpace ℝ (Fin n) with volume. Coordinates are 0-based (i : Fin n), and multi-indices are Fin n → ℕ. ∣Df∣ is ‖gradient f x‖, which equals the operator norm of fderiv ℝ f x. Lp norms are eLpNorm in [0,∞], with real exponents coerced by ENNReal.ofReal.
C∞ is ContDiff ℝ ((⊤ : ℕ∞) : WithTop ℕ∞). ContDiff ℝ ⊤ means analytic in current Mathlib, and using it for test functions would leave only the zero function, making every function weakly differentiable. A test function on Ω has HasCompactSupport φ and tsupport φ ⊆ Ω, so the test class is all of Cc∞(Ω). A smaller class would trivialize weak differentiability, and this formalization rules that out.
Weak derivatives (Definitions 3.1, 3.2) are predicates on real functions on Rn, of which only the values on Ω matter. MemW k p Ω f is membership in Wk,p(Ω). sobolevNorm is the norm of Definition 3.23, valued in [0,∞] and built from a chosen weak derivative (unique a.e.). For p=∞ it uses the essential supremum.
Constant of Theorem 3.28. The printed constant (3.11), C(n,p)=2npn−pn−1, is false for the Euclidean ∣Df∣. At p=1 it equals 2n1, below the sharp constant nαn1/n1 quoted in the notes on the following page. The goal states nC(n,p)=2(n−p)p(n−1), which is what the proof's intermediate estimate ∥f∥p∗≤2s(∏i∥∂if∥p)1/n gives.
Theorem 3.27 writes n=m+1 with m≥1 and states the left side as a lower Lebesgue integral of the nonnegative product. In Theorem 3.36 one constant serves (3.12) and (3.13), and the supremum bound is stated pointwise. The p=∞ clause is outside the stated range and is omitted. Every existential constant is quantified after the dimension and exponents and before the function.
Reusable beyond this mission: the weak-derivative and Wk,p definitions (intended for the later chapters on compactness, elliptic and parabolic equations), and the product inequality. Proofs of any milestone are welcome, including ones through Mathlib's existing Sobolev-inequality machinery if they reach the explicit constant.
Newton's Law of Universal Gravitation: the Shell TheoremTextbook
Motivation
Newton's law of universal gravitation states that any two point masses attract each other with a force proportional to the product of their masses and inversely proportional to the square of their distance. It is the first quantitative law of gravity (Newton, Principia, 1687) and it remains the working model of gravity in celestial mechanics, geophysics and engineering whenever relativistic effects are negligible.
The law is stated for point masses, while planets, moons and apples are extended bodies. Newton delayed publication for about twenty years because he could not prove that the Earth attracts external bodies as if all its mass were concentrated at its centre. The result that closes this gap is the shell theorem: a spherically symmetric distribution of mass exerts, on every external point, the same gravitational attraction as a point mass at its centre carrying the total mass; and mass located at radii larger than that of the test point exerts no net force at all.
Timeline (as described in the source):
c. 1666: Newton applies the inverse-square law to the orbit of the Moon, assuming — without proof — that the Earth acts as a point mass.
1687: Principia published, including the proof of the shell theorem (Book I, Proposition 75, Theorem 35).
1798: Cavendish experiment, the first laboratory test of gravitation between masses.
Setting
Space is E3=R3 with the Euclidean norm. The gravitational constant is G=6.67430×10−11.
Point force. A body of mass m1 at r1 exerts on a body of mass m2 at r2 the force
Point field and potential. A point mass M at c produces the field g(x)=−∥x−c∥3GM(x−c) and the potential V(x)=−∥x−c∥GM.
Mass distributions. An extended body is a (finite, Borel) measure μ on E3; μ(A) is the mass contained in the region A. Its gravitational field is the superposition of the point fields of its elements,
gμ(x)=∫E3−∥x−y∥3G(x−y)dμ(y).
Spherical symmetry.μ is spherically symmetric (about the origin) if μ is invariant under every linear isometry of E3 (every rotation and reflection fixing 0).
Model bodies. The uniform shellσM,a of mass M and radius a (normalised surface measure on the sphere ∥y∥=a) and the uniform solid ballβM,R of mass M and radius R (normalised Lebesgue measure on ∥y∥<R).
Formalization targets
Goal: shell theorem, exterior part
If μ is a finite, spherically symmetric mass distribution carried by the closed ball ∥y∥≤R, then for every x with ∥x∥>R,
gμ(x)=−∥x∥3Gμ(E3)x.
Milestones
Vector form: F12=−F21, and ∥F21∥=Gm1m2/∥r2−r1∥2.
Field form: F21=m2g(r2) where g is the field of m1 at r1; the field is conservative, g=−∇V.
Shell theorem, interior part: mass at radii larger than ∥x∥ exerts no net field at x; more precisely, the field at x equals that of the mass enclosed in the ball of radius ∥x∥, concentrated at the centre.
Hollow sphere: g=0 inside, g= point field of M outside.
Uniform solid sphere: g(x)=−R3GMx inside, point field of M for ∥x∥≥R.
Significance
The shell theorem is what licenses treating the Sun, planets and moons as point masses in the n-body problem and in Kepler orbit computations, and it gives the linear growth of the gravitational field inside a uniform planet. The interior statement ("no net force inside a hollow shell") has an exact analogue in electrostatics (Coulomb's law is also inverse-square).
The results are classical and fully proved on paper. This mission asks for machine-checked proofs, together with the reusable infrastructure they require: vector-valued integrals against rotation-invariant measures, the surface measure of the sphere, and the Newtonian kernel x/∥x∥3.
Difficulty
Newton's argument (or the modern one via Gauss's law) is geometric: slice the shell into rings, or apply the divergence theorem to a sphere. Neither is directly available: Mathlib has no divergence theorem on spheres in the required form, and the ring-slicing computation requires explicit spherical coordinates for a surface measure that is defined abstractly. Using symmetry alone shows the field is radial, but its magnitude requires an actual computation of a singular integral (for the interior of a solid ball the integrand is unbounded).
Formalization scope
Space is EuclideanSpace ℝ (Fin 3). Mass distributions are Measures; the field is a Bochner integral. Because a non-integrable Bochner integral is defined to be 0, statements where integrability is not automatic either carry an explicit integrability hypothesis or are placed where the integrand is bounded (test point strictly outside the support).
Spherical symmetry is invariance under all linear isometries (Space ≃ₗᵢ[ℝ] Space), centred at the origin.
Masses of model bodies are nonnegative reals (NNReal); radii are assumed strictly positive.
The uniform shell uses Mathlib's Measure.toSphere (surface measure on the unit sphere) pushed forward by scaling.
Units are not modelled; G is the real number 6.67430×10−11.
Contributions welcome: a general "radial field" lemma from spherical symmetry, the computation of the field of a uniform shell, and the disintegration of a spherically symmetric measure into shells.
I. Newton, The Principia: Mathematical Principles of Natural Philosophy, transl. I. B. Cohen and A. Whitman, University of California Press, 1999 — Book I, Proposition 75, Theorem 35.
Notes on Partial Differential Equations IV: Extension, Trace and the Rellich–Kondrachov TheoremTextbook
Motivation
Existence theory for elliptic, parabolic and nonlinear PDEs mostly runs through one argument: build approximate solutions, bound them in a Sobolev norm, extract a convergent subsequence, and pass to the limit. The extraction step needs a compact embedding: a bound on derivatives must buy convergence of the functions themselves in a weaker norm. On a bounded domain this is the Rellich–Kondrachov theorem. It is what makes the resolvent of the Dirichlet Laplacian compact, and so gives the Fredholm alternative and the discrete spectrum of elliptic operators on bounded domains. The companion topics of the same section, extension and trace, are how Sobolev functions are moved between a domain and all of space, and how they are given boundary values.
This mission follows Chapter 3 (§3.9–3.10) and Theorem 1.15 of J. K. Hunter's Notes on Partial Differential Equations (UC Davis lecture notes, revised 6/18/2014).
Timeline. Rellich proved the compactness of H01↪L2 on bounded domains in 1930. Kondrachov extended it to W1,p and exponents below the Sobolev exponent in 1945. The Lp compactness criterion used in the notes goes back to Kolmogorov (1931) and M. Riesz (1933); a modern account with a history is Hanche-Olsen and Holden 2010. Gagliardo characterized the traces of W1,p in 1957.
Setting
Throughout, Rn carries Lebesgue measure, and functions are real-valued. For an open set Ω⊆Rn, a test functionϕ∈Cc∞(Ω) is smooth with compact support inside Ω. A locally integrable g is the weak derivative∂αf of a locally integrable f if ∫Ωgϕ=(−1)∣α∣∫Ωf∂αϕ for every test function ϕ. Here α∈N0n is a multi-index of order ∣α∣=∑iαi.
For k∈N and 1≤p≤∞, the Sobolev spaceWk,p(Ω) consists of the f whose weak derivatives ∂αf, ∣α∣≤k, exist and lie in Lp(Ω). For p<∞ its norm is
∥f∥Wk,p(Ω)=(∣α∣≤k∑∫Ω∣∂αf∣pdx)1/p,
and for p=∞ it is the maximum of the essential suprema. The space W0k,p(Ω) is the closure of Cc∞(Ω) in this norm. For 1≤p<n the Sobolev conjugate is p∗=np/(n−p).
The upper half-space is R+n={x=(x′,xn):xn>0}, with closure R+n and boundary ∂R+n={(x′,0)}≅Rn−1. The space Cc∞(R+n) consists of the functions that are smooth up to the boundary and vanish outside a compact set. Unlike Cc∞(R+n), they need not vanish on ∂R+n.
A subset of a metric space is precompact if its closure is compact. C0(Rn) is the space of continuous functions vanishing at infinity, with the sup norm.
In Lean the weak derivative is HasWeakDeriv (with weakDeriv and weakGradNorm) in the series' shared namespace HunterPDE.Shared; the other objects are MemW k p Ω f, sobolevNorm k p Ω f, MemW0 k p Ω f, sobolevConjugate, upperHalfSpace m, IsSmoothCompactClosedHalfSpace and boundaryPoint, in the namespace HunterPDE.Compactness.
Formalization targets
Goal: Rellich–Kondrachov (Theorem 3.45)
Let Ω be a bounded open set in Rn, 1≤p<n and 1≤q<p∗. If F⊆W01,p(Ω) satisfies ∥f∥W1,p(Ω)≤M for all f∈F, then
{f1Ω:f∈F}is precompact in Lq(Rn).
Milestones
Theorem 1.15 (Kolmogorov–Riesz). For 1≤p<∞, a set F⊆Lp(Rn) is precompact if and only if it is bounded, tight (supf∥f∥Lp(∣x∣>R)→0 as R→∞), and Lp-equicontinuous:
∀ε>0∃δ>0:∣h∣<δ⟹∥f(⋅+h)−f∥Lp(Rn)<ε∀f∈F.
Theorem 3.41 (extension). There is a bounded linear E:W1,p(R+n)→W1,p(Rn) with Ef=f a.e. on R+n and ∥Ef∥W1,p(Rn)≤C(n,p)∥f∥W1,p(R+n).
Theorem 3.42 (density).Cc∞(R+n) is dense in Wk,p(R+n) for 1≤p<∞.
Theorem 3.44 (trace). For 1≤p<∞ there is a bounded linear T:W1,p(R+n)→Lp(Rn−1) with (Tf)(x′)=f(x′,0) on Cc∞(R+n) and ∥Tf∥Lp≤C(p)∥f∥W1,p(R+n), whose kernel is exactly W01,p(R+n).
Theorem 3.48 (p>n). For Ω bounded and n<p<∞, a family with suppf⋐Ω and ∥Df∥Lp≤C is precompact in C0(Rn).
The goal is stated qualitatively (precompactness), with no constants, so it does not depend on any particular form of the Sobolev inequality.
Significance
The result itself. Rellich–Kondrachov is the source of compactness in the L2 theory of elliptic equations. With it, the solution operator of a coercive elliptic problem on a bounded domain is compact. That yields the Fredholm alternative, the discreteness of the Dirichlet spectrum, and existence results for semilinear problems via compactness and fixed-point arguments. The trace theorem makes the boundary condition "u=0 on ∂Ω" meaningful for Sobolev functions, and identifies W01,p as the space in which Dirichlet problems are posed. The extension theorem transfers whole-space results to domains. Theorem 3.48 gives the uniform-norm counterpart above the critical exponent.
Formalizing it. All results here are classical and proved. The notes prove 3.45, 3.48 and the forward direction of 3.44. They describe 3.41 and 3.42 in a sentence each, and cite 1.15 without proof. Mathlib has the Lebesgue spaces Lp, Arzelà–Ascoli for bounded continuous functions, and mollification. It has no weak derivatives, no Sobolev spaces on domains, no trace, no extension operators and no Kolmogorov–Riesz criterion. A complete development would supply the first machine-checked versions of these in the notes' form, and a formal W01,p compactness theorem usable by the elliptic chapters of the same series.
Difficulty
The obvious route to the goal is: approximate by test functions, extend by zero, and verify the three Kolmogorov–Riesz conditions. The first two follow from the Sobolev inequality and the boundedness of Ω. The hard parts are elsewhere. First, the Kolmogorov–Riesz criterion is not in Mathlib, and its "if" direction needs a mollification and total-boundedness argument in Lp(Rn). Second, the Lq-equicontinuity for q above p needs an interpolation between an L1 translation estimate and the critical Sobolev embedding into Lp∗, so the Gagliardo–Nirenberg–Sobolev inequality must be available for W01,p(Ω), not only for smooth compactly supported functions. Third, weak derivatives are defined by integration against test functions, and every density step has to be carried through that definition. The exponent restriction q<p∗ is sharp (Example 3.47 in the notes), so no argument that ignores it can succeed.
Formalization scope
Rn is EuclideanSpace ℝ (Fin n) with volume. Open sets are Ω : Set _ with IsOpen Ω, and bounded means Bornology.IsBounded Ω. Coordinates are 0-based (i : Fin n), and multi-indices are α : Fin n → ℕ.
Functions are ℝⁿ → ℝ, and only their values on the relevant set matter. weakDeriv is a chosen representative, used only where a weak derivative is assumed to exist. sobolevNorm takes values in ℝ≥0∞.
W01,p(Ω) is the norm closure of Cc∞(Ω), not "zero trace". "Bounded in W01,p(Ω)" is a uniform bound on sobolevNorm 1 p Ω.
In the goal, F is placed in Lq(Rn) by extension by zero (Ω.indicator f). The conclusion asserts both that each extension lies in Lq and that the set of their classes in Mathlib's Lp ℝ q volume has compact closure. The hypothesis q<p∗ rules out q=∞.
Half-space results use dimension n=m+1 and the last coordinate Fin.last m. The boundary is Rm via x′↦(x′,0). Extension and trace operators are maps on functions that are linear up to a.e. equality on W1,p(R+n).
Conventions read off the page: in Theorem 3.44 the trace constant is quantified before the dimension ("depending only on p"), and its final clause is taken with k=1 (the printed "W0k,p" cannot mean k≥2, as the notes remark after the proof). Theorem 3.42 is stated for 1≤p<∞, and Theorem 3.41 for 1≤p≤∞. Theorem 3.48 assumes n≥1.
A trivializing reading is ruled out: the goal's conclusion is compactness of the closure in Lq(Rn) of the zero extensions themselves, not of some other representative. The norm bound is on the genuine weak derivatives, which the hypotheses guarantee exist, not on a junk value.
Reusable infrastructure: weak derivatives and Sobolev spaces on open sets, the Kolmogorov–Riesz criterion, and the half-space extension and trace operators. Contributions of any of these as standalone lemmas are welcome.
Notes on Partial Differential Equations V: Weak Solutions of Elliptic PDEs and the Fredholm AlternativeTextbook
Motivation
Second-order elliptic equations in divergence form, such as −∇⋅(A∇u)+b⋅∇u+cu=f, model steady diffusion, electrostatics in inhomogeneous media and linear elasticity. They are also the stationary part of every parabolic and hyperbolic problem studied later in the same notes. When the coefficients are only bounded and measurable, classical solutions need not exist. The modern theory replaces them by weak solutions in the Sobolev space H01(Ω) and settles existence, uniqueness and solvability by Hilbert-space methods. This mission formalizes that theory as presented in Chapter 4 of J. K. Hunter's Notes on Partial Differential Equations (UC Davis lecture notes, revised 6/18/2014). The chapter follows the standard development of Evans, Partial Differential Equations, Chapter 6, and of Gilbarg–Trudinger, Chapter 8.
Timeline. Riesz (1907) and Fréchet represented bounded functionals on Hilbert spaces. Fredholm (1903) proved the alternative for integral equations of the second kind, and Riesz (1918) extended it to compact operators. Lax and Milgram (1954) removed the symmetry assumption from the Riesz approach, and Gårding (1953) proved the coercivity estimate for elliptic forms. Together these give the existence and Fredholm theory for the Dirichlet problem stated here.
Setting
Let Ω⊆Rn be open. A test function is a smooth ϕ with compact support in Ω. The space H01(Ω) is the closure of the test functions in the norm ∥u∥H012=∫Ω(u2+∣Du∣2)dx; each element carries a function u and a weak gradient Du, both square-integrable. H−1(Ω) is the space of bounded linear functionals on H01(Ω), and f∈L2(Ω) acts on it by ⟨f,ϕ⟩=∫Ωfϕdx.
Given coefficients aij,bi,c∈L∞(Ω) with aij=aji, the operator is
Lu=−i,j=1∑n∂i(aij∂ju)+i=1∑n∂i(biu)+cu.
It is uniformly elliptic if ∑i,jaij(x)ξiξj≥θ∣ξ∣2 for some θ>0, almost every x and every ξ. Its bilinear form on H01(Ω) is
a(u,v)=∫Ω(i,j∑aij∂iu∂jv−i∑biu∂iv+cuv)dx,
and the formal adjoint L∗u=−∑∂i(aij∂ju)−∑bi∂iu+cu has the form a∗(u,v)=a(v,u). For μ∈R, u is a weak solution of Lu+μu=f with u=0 on ∂Ω if u∈H01(Ω) and a(u,ϕ)+μ∫Ωuϕ=⟨f,ϕ⟩ for all ϕ∈H01(Ω). The resolventK=(L+μI)−1 maps f∈L2(Ω) to that solution.
A bounded operator T on a Hilbert space is Fredholm if its kernel is finite-dimensional and its range is closed with finite-dimensional orthogonal complement. Its index is dimkerT−dim(ranT)⊥.
Formalization targets
Goal: the Fredholm alternative (Theorem 4.24)
For Ω bounded, L uniformly elliptic and λ∈R, exactly one of the following holds:
(1)ker(L∗−λ)=0 and ∀f∈L2(Ω)∃!u∈H01(Ω):Lu−λu=f;(2)0<dimker(L−λ)=dimker(L∗−λ)<∞,Lu−λu=f solvable⟺f⊥L2ker(L∗−λ),
with non-unique solutions in case (2). Every kernel and equation is meant in the weak sense on H01(Ω).
Milestones
Poincaré inequality (Theorem 4.9): ∫Ωu2≤C∫Ω∣Du∣2 on H01(Ω) when Ω lies between two parallel hyperplanes. Unique solvability of −Δu=f∈H−1(Ω) (Theorem 4.11).
The Lax–Milgram theorem (Theorem 4.20).
Energy estimates (Theorem 4.21): C1∥u∥H012≤a(u,u)+γ∥u∥L22 and ∣a(u,v)∣≤C2∥u∥H01∥v∥H01, with the explicit γ=θ−c0 if b=0 and γ=2θ1∑∥bi∥L∞2+2θ−c0 otherwise.
Unique weak solvability of Lu+μu=f for μ≥γ (Theorem 4.22). The resolvent K is bounded on L2(Ω) with adjoint (L∗+μI)−1, and it is compact when Ω is bounded (Theorem 4.23).
The abstract Fredholm alternative for Fredholm operators of index zero (Theorem 4.50).
The spectral theorem for symmetric L (Theorem 4.25): eigenvalues λ1≤λ2≤⋯→∞ of finite multiplicity, with an orthonormal eigenbasis of L2(Ω).
Significance
The Fredholm alternative is the complete linear solvability theory of the Dirichlet problem for general elliptic operators on bounded domains. Uniqueness for the homogeneous problem is equivalent to existence for all data. When uniqueness fails, the obstruction is a finite set of orthogonality conditions given by the adjoint problem. This feeds directly into the spectral theory of Theorem 4.25, into eigenfunction expansions, and into the existence theory for parabolic and hyperbolic equations (Galerkin approximation in later chapters). Nonlinear elliptic problems are routinely reduced to it through linearization and the implicit function theorem.
Mathlib has Lax–Milgram for continuous coercive forms, compact operators, the Riesz representation theorem and the spectral theorem for compact self-adjoint operators. It does not have Sobolev spaces with zero boundary values as used here, elliptic bilinear forms, Fredholm operators or their index. As far as a search of the platform shows, none of these results has a machine-checked statement for elliptic operators with L∞ coefficients. The mission would give formal statements, and eventually proofs, of textbook results that are fully proved on paper.
Difficulty
The abstract functional analysis is close to the library. The difficulty lies in connecting it to the PDE objects. Gårding's inequality needs pointwise uniform ellipticity turned into an integral bound on the weak gradient, with the non-symmetric first-order term controlled explicitly enough to reach the stated γ. Compactness of the resolvent needs the Rellich–Kondrachov theorem: H01(Ω) embeds compactly in L2(Ω) for bounded Ω, which is not in Mathlib in this form. The goal then relates the weak problem on H01(Ω) to operator statements on L2(Ω) in both directions, including the solution spaces and the adjoint's kernel. A proof that argues only on L2(Ω), without tracking that the solutions lie in H01(Ω) and satisfy the weak identity for every H01 test function, does not reach the stated result.
Formalization scope
Rn is EuclideanSpace ℝ (Fin n) with Lebesgue measure. Coordinates are 0-based.
H01(Ω) (H10 n Ω) is modelled by jets. It is the closure, in L2(Ω;R×Rn), of the pairs (ϕ,∇ϕ) for smooth compactly supported ϕ with support in Ω. Its norm is exactly the standard H1 norm, and it is a Hilbert space. H−1(Ω) is StrongDual ℝ (H10 n Ω).
Coefficients are bundled in Coeffs n. Admissible is L∞(Ω) membership plus a.e. symmetry. Uniform ellipticity carries θ>0.
Weak solutions are tested against every ϕ∈H01(Ω), not only test functions. f∈L2(Ω) is an element of Lp ℝ 2 on Ω.
The printed display (4.21) of the adjoint form has +∑bi(∂iu)v. That contradicts both a∗(u,v)=a(v,u) and the stated L∗. The formalization uses a∗(u,v)=a(v,u).
Theorem 4.22's printed equation "Lu+μf=0" is read as Lu+μu=f, as in Definition 4.19 and the proof.
Theorem 4.25's printed λ1<λ2 is stated as λ1≤λ2, since the strict inequality fails for disconnected Ω. Ω=∅ and n≥1 are assumed there.
In Theorem 4.21 the explicit γ is asserted for every a.e. lower bound c0 of c, which is equivalent to the book's c0=infc.
The Fredholm index is an integer computed from Module.finrank and is only used for Fredholm operators.
Solution spaces are subspaces of H01(Ω). Their finite-dimensionality is asserted separately from the equality of dimensions, so a finrank equality between two infinite-dimensional spaces (both 0 in Lean) cannot stand in for the book's claim.
A resolvent defined by an arbitrary choice with junk value 0 is only used for μ≥γ, where the weak solution exists and is unique. The theorem asserts that this function is a continuous linear map, so it cannot be satisfied vacuously.
Needed infrastructure, reusable beyond this mission: the jet model of H01 and its Poincaré inequality, Gårding-type estimates, a Rellich–Kondrachov compactness theorem for H01(Ω)↪L2(Ω), and Fredholm operators with index on Hilbert spaces. Contributions of any of these as separate lemmas are welcome.
L. C. Evans, Partial Differential Equations, 2nd ed., AMS Graduate Studies in Mathematics 19, 2010, Chapter 6. https://doi.org/10.1090/gsm/019
P. D. Lax and A. N. Milgram, "Parabolic equations", Contributions to the Theory of Partial Differential Equations, Annals of Mathematics Studies 33, 1954, 167–190. https://doi.org/10.1515/9781400882182-010
L. Gårding, "Dirichlet's problem for linear elliptic partial differential equations", Mathematica Scandinavica 1 (1953), 55–72. https://doi.org/10.7146/math.scand.a-10364
D. Gilbarg and N. S. Trudinger, Elliptic Partial Differential Equations of Second Order, Springer Classics in Mathematics, 2001, Chapter 8. https://doi.org/10.1007/978-3-642-61798-0
Robust Mean-Covariance Solutions for Stochastic Optimization II: An Inverse S-Shaped Derivative with Finite Limits Gives the Two-Point Support PropertyResearch Paper
Motivation
In stochastic optimization the law of a random return r is rarely known exactly, while its mean and variance can be estimated. A robust mean-covariance decision maker therefore evaluates a utility u by its worst case
U=inf{Eν[u(r)]:ν a law on R with mean μ and variance σ2}.
Popescu (Operations Research 55(1), 2007) shows that for large classes of utilities this infinite-dimensional problem collapses to a one-dimensional one. The paper projects multivariate problems to a single dimension (the subject of the first mission of this series) and then asks for which u the univariate worst case sits on laws with two support points. This mission formalizes the answer the paper gives for utilities whose marginal utility u′ is decreasing and changes curvature once, with finite limits: log-logistic utilities C+log1+e−ax1 used in statistics and classification, and catenary-type utilities C−bcosh(ax), each plus a concave quadratic.
The result has a bounded-support precursor: Birge and Dulá (Annals of Operations Research 30, 1991, Theorem 5.1, as cited on p. 102 of Popescu 2007) proved an analogous two-point statement for functions on a bounded interval. Popescu's Proposition 5 is the unbounded version on the whole real line.
Setting
Let u:R→R.
The family Q collects the coefficient triples of quadratics lying below u:
Q={(A,B,C)∣q(y)=Ay2+By+C≤u(y)∀y∈R}.
A two-point law with support {a,b}, a<b, puts mass p∈(0,1) on a and 1−p on b. It has mean μ and variance σ2 when pa+(1−p)b=μ and p(a−μ)2+(1−p)(b−μ)2=σ2.
Two-point support property (Definition 1). u has it with respect to (μ,σ2) if some quadratic q with coefficients in Q meets u at two points a<b, i.e. q(a)=u(a), q(b)=u(b), and a two-point law with support {a,b}, mean μ and variance σ2 exists. u has the two-point support property if this holds for every μ∈R and every σ>0.
Shapes (Definition 2). f:R→R is convex-concave if for some x0 it is convex on (−∞,x0) and concave on (x0,∞); concave-convex if −f is convex-concave; S-shaped if increasing and convex-concave; inverse S-shaped if −f is S-shaped. So an inverse S-shaped f is decreasing, concave on (−∞,x0) and convex on (x0,∞).
Lemma 1's quadratic. For a<b and slopes qa,qb, set
A zero of g (p. 110). There is a<μ with g(a)=0; with b=μ+σ2/(μ−a) one has a<b, (b−μ)(μ−a)=σ2 and b−au(b)−u(a)=2u′(a)+u′(b).
Significance
The result. Through the paper's Proposition 4, two-point support turns the worst-case expected utility over all laws with mean μ and variance σ2 into a minimization over a single parameter p∈(0,1) of pu(μ+(1−p)/pσ)+(1−p)u(μ−p/(1−p)σ). Combined with the projection property of the paper's Section 2, this gives tractable robust counterparts of multivariate stochastic programs whose objective depends on a linear combination x′R of random returns. Proposition 5 is the paper's sufficient condition that places a concrete class of utilities in this regime; it is also closed under adding any quadratic.
Formalizing it. The result is proved on paper; no machine-checked version is known. The formalization produces a checked characterization of two-point support (Lemma 1), a reusable encoding of convex-concave and S-shaped functions, and a proof of Proposition 5. The printed final step of the paper's proof is incomplete (see Difficulty), so a complete formal proof requires an argument the paper does not spell out.
Difficulty
Conditions (a) and (b) of Lemma 1 come from a sign change of g on (−∞,μ): the two limits in milestone 4 need a l'Hôpital-type argument and the continuity of a monotone derivative. The central difficulty is condition (c): showing that the quadratic built from a pair (a,b) lies below u on the whole line. The obvious argument takes any zero a of g and counts the intersections of the linear q′ with the inverse S-shaped u′. This fails when u′ is affine on an interval: a zero of g can then produce a quadratic that coincides with u on [a,b] but crosses above u just left of a. A proof must therefore choose the zero of g, or the pair (a,b), with care, and the curvature hypotheses are not strict.
Formalization scope
Functions are ℝ → ℝ; u′ is deriv u, and the goal assumes Differentiable ℝ u. Finite limits are Tendsto (deriv u) atBot (𝓝 l) and Tendsto (deriv u) atTop (𝓝 l) for some real l; the one-sided limit at μ is along 𝓝[<] μ.
"Increasing" in Definition 2 is strict (StrictMono); the paper writes "nondecreasing" for the weak notion. Convexity and concavity are ConvexOn/ConcaveOn on the open half-lines Set.Iio x₀, Set.Ioi x₀, as printed.
A two-point law is encoded by its mass p∈(0,1) on a, with a<b; this is equivalent to a probability measure on R with support {a,b}, mean μ and variance σ2.
"Intersects it at two points a,b" is read as q(a)=u(a) and q(b)=u(b) for some a<b (at least two contact points). This is the reading under which Lemma 1 is an equivalence.
The two-point support property quantifies over σ>0: no two-point law has variance 0, so including σ=0 would make the property false for every u.
The two-point support property is defined from Definition 1 (supporting quadratic plus feasible law), not from Lemma 1's conditions, so Lemma 1 is not a tautology. Every division by b−a, z−y or μ−y occurs under a<b or y<μ.
Useful infrastructure: l'Hôpital's rule at infinity (Analysis/Calculus/LHopital), Darboux's theorem for derivatives, the intermediate value theorem, and one-sided limits of monotone functions. The shape definitions of Definition 2 are reusable for S-shaped value functions elsewhere (prospect theory, sigmoidal utilities). Proofs of the milestones, of the goal, and alternative arguments for condition (c) are all welcome. Related platform work: Wasserstein Distributionally Robust Optimization II.
Selected references
I. Popescu, Robust Mean-Covariance Solutions for Stochastic Optimization, Operations Research 55(1):98–112, 2007. https://doi.org/10.1287/opre.1060.0353
J. R. Birge, J. H. Dulá, Bounding separable recourse functions with limited distribution information, Annals of Operations Research 30:277–298, 1991 (cited in Popescu 2007, reference list)
In Witten's cubic open string field theory (Witten 1986) the interaction glues half-strings together, and in the usual center-of-mass variables the action contains infinitely many time derivatives. Such a theory has no evident initial value formulation, which obstructs a Hamiltonian treatment and a direct discussion of causality.
Erler and Gross (2004) show that after a unitary change of basis in which the string field depends on the lightcone component of the string midpointx+(π/2) (and on transverse center-of-mass coordinates), the cubic vertex contains no derivatives with respect to lightcone time. This reduces to five linear relations among the Neumann coefficients of the three-string vertex, the midpoint identities (A.6)–(A.10) of the paper. One of them, (A.8), is a closed numerical identity: a specific infinite series built from Neumann coefficients equals −ln1627, where ln1627=V00 is the zero-mode Neumann coefficient. This mission formalizes that identity and the chain of intermediate results the paper uses to prove it (Appendix B), together with a divergence statement from Section 2 explaining why other midpoint bases fail.
For real z the left side is cos(32arctanz)+isin(32arctanz), so A2n is the 2n-th Taylor coefficient of cos(32arctanz) at 0 (e.g. A2=−92). The even Neumann vector and the vector β are
In Lean these are ErlerGross.neumannA, ErlerGross.neumannMEven (with neumannMEven n=m2n) and ErlerGross.betaVec. Two auxiliary objects appear in the proof: the (B.3) terms
bn=2n−12−2n−321−2n−341(n≥1),
(ErlerGross.b3Term) and the κ-basis integrand
g(κ)=1+2cosh2πκ1−cosh2πκ⋅2κsinh2πκ1
(ErlerGross.kappaIntegrand), which arises from the continuous spectrum κ∈R of the Neumann matrices (Rastelli–Sen–Zwiebach 2001).
Formalization targets
Goal — midpoint identity (A.8)
ln1627+n≥1∑3m2nβ2n=0,
formalized as: the series ∑n≥13m2nβ2n converges absolutely (Lean HasSum) to −ln1627.
Milestones (Appendix B and Section 2)
κ-basis representation: g is integrable and ∑n≥13m2nβ2n=2∫Rg.
Residue evaluation: ∫Rg=∑n≥1bn.
Eq. (B.3): ∑n≥13m2nβ2n=2∑n≥1bn.
Auxiliary series: ∑n≥1n(2n+1)1=2−2ln2 and ∑n≥1n(9n2−1)1=23(ln3−1).
Closed form: ∑n≥1bn=−21ln1627.
Integral formula: ∫0∞coshbxcoshaxdx=2bπcos2bπa1 for complex a,b with ∣ℜa∣<ℜb.
Section 2: for any real regulator with ω2n→0, ∑n≥12n((−1)n−ω2n)2=+∞.
Significance
In the paper, (A.8) is one of the relations which, together with (A.6), (A.7), (A.9), (A.10), removes lightcone-time derivatives from the cubic vertex. This is what gives the theory an initial value formulation in lightcone time and makes the Hamiltonian BRST quantization of Section 4 possible. Unlike the other four identities, which are relations between distributions and are verified in the paper's spectral basis, (A.8) is an equality of real numbers. It can therefore be checked completely in Lean.
A complete formalization would produce a machine-checked proof of a Neumann-coefficient identity. It would also add reusable material: Taylor coefficients of exp(32iarctanz), the cosh/cosh integral, and digamma-type evaluations of rational series.
Difficulty
The goal defines m2n through Taylor coefficients, which have no closed form. The paper does not sum the series in the mode basis. It moves to the continuous κ basis of the Neumann spectrum and closes a contour. That step rests on eigenvector expansions v2n(κ) and the principal-value distribution β(κ), neither of which is in Mathlib. A direct route has to connect the coefficients A2n to the integral ∫g, or to the series ∑bn, some other way. The terms of ∑bn decay like n−2. The terms (−1)nA2n/n of the goal series decay only like n−5/3, so the goal series converges slowly.
Formalization scope
All quantities are real. Ak is given as f(k)(0)/k! for f=cos(32arctan⋅) (even k) or sin(32arctan⋅) (odd k), using Mathlib's iteratedDeriv.
Series are indexed from n=1 by shifting the Lean index n↦n+1. HasSum is unconditional (equivalently absolute) convergence. Every statement asserts convergence, so no statement can hold only because Lean assigns 0 to a divergent tsum.
g(0)=0 by Lean's convention x/0=0. This single point does not affect the integral.
Source misprints corrected: the paper's first auxiliary formula prints ∑n(2n+1)1=2−ln2. The true value is 2−2ln2≈0.6137, and that value is formalized. The formula for m2n is printed with left side m2m.
In the Section 2 statement the dimension factor D>0 is omitted, and only divergence to +∞ is asserted, not the logarithmic rate.
Not formalized: the identities (A.6), (A.7), (A.9), (A.10), eq. (B.2), and the eigenvectors vn(κ). Contributions that build this spectral machinery are welcome.
Selected references
T. G. Erler, D. J. Gross, Locality, Causality, and an Initial Value Formulation for Open String Field Theory, 2004. https://arxiv.org/abs/hep-th/0406199
Métodos Numéricos (Freitas) VII: EDOs, Método de Picard e Erro Global de EulerTextbook
Motivation
An initial value problem y′=f(x,y), y(x0)=y0, is the standard way a law of motion, a growth model or a circuit equation is written down, and only a small minority of such problems can be integrated in closed form. Single-step methods advance the solution by small increments: Euler's method takes the tangent line at the current point, and the higher-order Runge-Kutta schemes refine the same idea. What justifies them is a pair of theorems: that the problem has a unique solution at all, and that the computed polygon approaches that solution as the step size decreases.
This mission is the seventh in a series formalizing the lecture notes Métodos Numéricos by Sergio Roberto de Freitas (UFMS, 2000). It covers Chapter 9, Métodos Numéricos para EDO's, up to the convergence of Euler's method.
Setting
Consider the initial value problem
y′=f(x,y),qquady(x0)=y0,
with f defined on a rectangle D=[x0−a,x0+a]times[y0−b,y0+b].
Picard's method replaces the differential equation by the integral equation y(x)=y0+intx0xf(t,y(t)),dt and iterates it:
With M=maxD∣f∣, N=maxD∣partialf/partialy∣ and h=mina,b/M, the source states the bound ∣varphi(x)−yk(x)∣lefracMNk−1k!hk on [x0−h,x0+h], where varphi is the exact solution.
Euler's method with step h produces yi+1=yi+hf(xi,yi), xi=x0+ih. Its local error is O(h2) per step and its global error O(h).
Target
The goal theorem is the global error statement: for an initial value problem whose exact solution varphi is twice continuously differentiable on [x0,X] and whose right-hand side is Lipschitz in y there, there is a constant C such that for every step size hin(0,1] and every grid point x0+ihleX,
∣yi−varphi(x0+ih)∣leCh.
This is the precise form of the source's assertion E=O(h).
The milestones are the results the chapter states on the way: local existence and uniqueness for the initial value problem under a Lipschitz condition in y (Teorema 9.4.1), the Picard error bound, the convergence of the Picard iterates to the solution, and the one-step Taylor identity behind Euler's method.
Significance
The global error bound is what makes Euler's method a method rather than a heuristic: it converts an arbitrary choice of step size into a guaranteed accuracy, and it exhibits the characteristic loss of one order between the local error O(h2) and the global error O(h) that recurs for every single-step scheme. The existence and uniqueness theorem is the prerequisite for all of it — without uniqueness there is no well-defined object for the numerical solution to approximate — and the Picard iteration is both the constructive proof of that theorem and a method in its own right.
Difficulty
The global error proof has to handle the interaction of two error sources: the local truncation error at each step and the amplification of previous errors by the Lipschitz constant, which gives the discrete Gronwall recursion ei+1le(1+hL)ei+Ch2. Carrying that recursion to the closed-form bound, and doing it uniformly in h, is the core of the mission. The Picard bound requires the iterates to remain inside the rectangle so that the hypotheses on f continue to apply — that is what the choice h=mina,b/M is for — and an induction on k with the factorial denominator.
Formalization scope
The unknown is a real function of a real variable and f is a function of two real variables. A solution is a function varphi satisfying varphi(x0)=y0 and having derivative f(x,varphi(x)) at every point of the relevant interval. The Lipschitz condition in the second variable is stated explicitly, ∣f(x,u)−f(x,v)∣leL∣u−v∣, in place of the source's hypothesis that partialf/partialy is bounded; for the global-error goal it is assumed for all real u,v and all x in the interval, and in the Picard milestones only inside the rectangle. Uniqueness is stated as agreement of any two solutions on the interval, not as uniqueness of a function on all of mathbbR, since a solution is unconstrained outside the interval. Euler's iterates and the Picard iterates are explicit recursive definitions; the Picard integral is the interval integral, which is total, so no integrability side condition appears in the definition. The constant C in the goal is asserted to exist and to be positive, uniformly over step sizes in (0,1]; the restriction hle1 merely normalizes the range of steps considered.
Selected references
S. R. Freitas, Métodos Numéricos, Departamento de Computação e Estatística, Universidade Federal de Mato Grosso do Sul, 2000. Chapter 9, Métodos Numéricos para EDO's, pp. 183–213. (Course notes supplied with this mission.)
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Rudin PMA VII: Sequences and Series of FunctionsTextbook
Motivation
Chapter 7 of Walter Rudin's Principles of Mathematical Analysis (3rd edition, McGraw-Hill,
1976) asks when a limit of functions inherits the properties of its members. Pointwise
convergence preserves almost nothing: Rudin's opening examples give continuous fn with
discontinuous limit, and sequences where limn∫fn=∫limnfn. Uniform
convergence is the hypothesis that repairs this, and the chapter's second half asks the
converse question — which functions arise as uniform limits from a given family — answered by
the Stone–Weierstrass theorem (Theorem 7.32): an algebra of continuous real functions on a
compact set that separates points and vanishes nowhere is uniformly dense in all continuous
functions there.
The classical Weierstrass approximation theorem (7.26) — polynomials are dense in C[a,b] — is
the special case that made the general theorem worth proving, and is used in Chapter 8 for
Fourier series and in Chapter 11 for the density of continuous functions in L2.
This mission is the seventh in a series formalizing Rudin Chapters 1–11; it uses the compactness
results of Mission II, the continuity results of Mission IV and the Riemann–Stieltjes integral
of Mission VI.
Setting
A sequence fnconverges uniformly to f on E if for every ε>0 there is
N with ∣fn(x)−f(x)∣≤ε for all n≥N and all x∈E — the same N for
every point. A family F is equicontinuous on E if a single δ serves all
its members in the definition of uniform continuity; it is pointwise bounded if each orbit
{f(x):f∈F} is bounded, and uniformly bounded if one bound works for all
f and all x.
A set A of real functions is an algebra if it is closed under addition,
multiplication and multiplication by real scalars; it separates points on K if for
x=y in K some f∈A has f(x)=f(y); it vanishes at no point of K
if for each x∈K some f∈A has f(x)=0. The uniform closure of
A on K is the set of uniform limits on K of sequences from A.
Formalization targets
Goal — Stone–Weierstrass (Theorem 7.32)
Let K be compact and let A be an algebra of real continuous functions on K which
separates points on K and vanishes at no point of K. Then
Aunif⊇C(K,R):
every continuous real function on K is a uniform limit on K of members of A.
Milestones
uniform convergence⟺uniform Cauchy criterion(7.8)∣fn∣≤Mn on E,∑Mn<∞⇒∑fn converges uniformly(7.10)t→xlimnlimfn(t)=nlimt→xlimfn(t) under uniform convergence(7.11)a uniform limit of continuous functions is continuous(7.12)fn∈R(α),fn→f uniformly⇒f∈R(α),∫fndα→∫fdα(7.16)fn′→h uniformly,fn(x0) convergent⇒fn→g uniformly,g′=h(7.17)there is a continuous nowhere differentiable f:R→R(7.18)pointwise bounded+equicontinuous on compact⇒uniformly bounded, convergent subsequence(7.24,7.25)polynomials are uniformly dense in C[a,b](7.26)
Significance
Uniform convergence is the standard hypothesis under which limits commute with continuity,
integration and (with an extra condition) differentiation, and Theorems 7.11, 7.12, 7.16 and
7.17 are used throughout the rest of the book; Chapter 8 in particular builds the exponential,
trigonometric and Gamma functions as uniform limits and differentiates them term by term on the
strength of 7.17. Theorem 7.18 shows how weak pointwise differentiability is as a consequence of
continuity: a uniform limit of piecewise-linear functions can fail to be differentiable
anywhere. Arzelà–Ascoli is the compactness criterion for families of functions, and it is the
standard route to existence theorems for differential and integral equations.
Stone–Weierstrass is the structural theorem of the chapter: it replaces the combinatorial
Bernstein-polynomial proof of Weierstrass's theorem with a statement about algebras of
functions, applicable to trigonometric polynomials, polynomials in several variables, and
Lipschitz algebras alike.
Mathlib contains a Stone–Weierstrass theorem for subalgebras of C(X, ℝ) on compact Hausdorff
spaces, and a version of Arzelà–Ascoli. This mission states the results in Rudin's terms — plain
sets of functions on a compact subset K of a metric space, uniform closure defined by
sequences — so that they can be used together with the Riemann–Stieltjes integral built in
Mission VI, which is not part of the library.
Difficulty
Stone–Weierstrass is the one theorem in this mission whose proof is genuinely structural: from
the algebra one first produces ∣f∣ as a uniform limit of polynomials in f (which needs the
polynomial approximation of t on [0,1] and so cannot be circular with Theorem 7.26),
then maxima and minima of pairs, then functions matching prescribed values at two points, and
only then the local-to-global patching over a finite subcover. Each step is short; keeping the
uniform closure a lattice and an algebra simultaneously is the bookkeeping burden.
Two hypotheses are easy to lose and both are necessary: an algebra that vanishes at a point
cannot approximate functions that do not, and one that fails to separate two points cannot
approximate functions that distinguish them.
Formalization scope
Conventions fixed by this mission:
Uniform convergence is Mathlib's TendstoUniformlyOn … atTop; complex-valued sequences are
used where Rudin allows complex values.
Algebras, separation, non-vanishing and uniform closure are the predicates
Rudin.IsFunctionAlgebra, Rudin.SeparatesPointsOn, Rudin.VanishesAtNoPointOn,
Rudin.UniformClosureOn, defined for sets of functions X → ℝ and a compact subset K.
The goal's conclusion is membership in the uniform closure, i.e. the existence of an
approximating sequence from the algebra.
Equicontinuity and the two boundedness notions are Rudin.EquicontinuousOn,
Rudin.PointwiseBoundedOn, Rudin.UniformlyBoundedOn, stated with explicit ε and
δ as in Definitions 7.19 and 7.22.
Theorem 7.16 is stated for the Riemann–Stieltjes integral of Mission VI, not for a Mathlib
integral, so the two missions compose.
Theorem 7.26 is stated for complex-valued f and polynomials with complex coefficients
evaluated at real points, as in Rudin.
Selected references
Walter Rudin, Principles of Mathematical Analysis, 3rd edition, McGraw-Hill, 1976,
Chapter 7 (pp. 143–171).
M. H. Stone, The generalized Weierstrass approximation theorem, Mathematics Magazine 21
(1948), 167–184 and 237–254. https://doi.org/10.2307/3029750
Yukawa Potential: Fourier Transform, Screening and Scattering IdentitiesTextbook
Motivation
The Yukawa potential (or screened Coulomb potential)
VYukawa(r)=−g2re−αmr
was introduced by Hideki Yukawa in 1935 to model the short-range nuclear force as the exchange of a massive particle, later identified with the pion. The same profile reappears as the Debye–Hückel screened interaction in plasmas and electrolytes, as the static Green's function of the massive Klein–Gordon (screened Poisson) operator, and as the Newtonian correction in massive-graviton models. The mathematical facts behind these uses are classical, short, and stated in every textbook treatment — which makes them a natural, self-contained target for formalization: each one is an explicit identity of real analysis in three dimensions.
Timeline. Heisenberg (1932–33) proposed a short-range exchange interaction between nucleons; Fermi (1934) introduced exchange of light particles in beta decay; Yukawa (1935, Proc. Phys.-Math. Soc. Japan17, 48) combined the two ideas, derived the potential above, and predicted a mediating particle of mass about 200 electron masses; the pion was observed in 1947.
Setting
Throughout, g∈R is the coupling (amplitude) constant, α>0 a scaling constant, m≥0 the mass of the exchanged particle and r>0 the radial distance; 1/(αm) is the range. The potential is the function
V(r)=−g2re−αmr.
Points of space are x∈R3 with Euclidean norm ∣x∣, and the potential of a point source at the origin is x↦V(∣x∣). The LaplacianΔ is the usual sum of unmixed second partial derivatives on R3. For a uniform, infinitesimally thin spherical shell of radius R with total scaling constant G, the potential felt by a point of scaling constant g located at x is
Vshell(x)=Gg4π1∫S2∣x−Ru∣e−αm∣x−Ru∣dσ(u),
the average over the unit sphere S2 (uniform surface measure σ) of the pair interaction.
Formalization targets
Goal — Fourier transform of the Yukawa potential
For αm>0 and every k∈R3,
∫R3e−ik⋅xV(∣x∣)d3x=−g2∣k∣2+(αm)24π.
This is the identity the article's sections Fourier transform and Feynman amplitude rest on: the momentum-space Yukawa potential is the massive propagator 4π/(k2+(αm)2) times −g2.
Milestones (in the order of the article)
Negativity and monotonicity — for g=0, V<0 on (0,∞) and V is strictly increasing there.
Massless limit — for m=0, V(r)=−g2⋅r1 (the Coulomb potential).
Screened Poisson equation — ΔV(∣x∣)=(αm)2V(∣x∣) for x=0.
Inverse Fourier representation — for x=0, V(∣x∣)=limΛ→∞(2π)3−g2∫∣k∣<Λeik⋅x∣k∣2+(αm)24πd3k.
Born amplitude — ℏ2q−2μ∫0∞rV(r)sin(qr)dr=ℏ2[(αm)2+q2]2μg2 for q>0.
Momentum transfer — ∣p−p′∣=2psin(θ/2) when ∣p∣=∣p′∣=p and θ is the angle between them.
Total cross section — ℏ44μ2g4∫0π[(αm)2+4p2sin2(θ/2)]22πsinθdθ=ℏ44μ2g4(αm)2[(αm)2+4p2]4π.
Shell, exterior — for ∣x∣>R, Vshell(x)=Gg∣x∣e−αm∣x∣αmRsinh(αmR).
Shell, interior — for 0<∣x∣<R, Vshell(x)=GgRe−αmRαm∣x∣sinh(αm∣x∣).
Significance
The results. The Fourier identity is what makes the Yukawa potential the non-relativistic limit of single-meson exchange; milestone 3 identifies it as the Green's function of the screened Poisson operator −Δ+(αm)2; milestones 5–7 give the Born-approximation scattering amplitude and the closed-form total cross section quoted in quantum mechanics textbooks (e.g. Griffiths); milestones 8–9 are the Yukawa analogue of Newton's shell theorem, whose failure to reduce to a point source of the same strength is the reason massive-graviton models violate the weak equivalence principle.
Formalizing them. All statements are classical and proved on paper. The mission produces machine-checked versions together with reusable infrastructure: three-dimensional Fourier integrals of radial functions, Laplacians of radial functions, spherical averages with respect to the surface measure, and conditionally convergent oscillatory integrals.
Difficulty
The individual computations are short on paper but each crosses a gap in the library. The goal requires reducing a three-dimensional Lebesgue integral of a radial function to a one-dimensional integral (polar coordinates in R3), then evaluating ∫0∞e−arsin(kr)dr. The inverse transform is not absolutely convergent — 1/(∣k∣2+a2) is not integrable on R3 — so the naive Lebesgue statement is false (it would assign the value 0); only the ball-truncated limit is meaningful, and its evaluation needs the improper integral ∫0∞k2+a2ksin(kr)dk=2πe−ar. The shell theorem needs the surface measure on S2 and the Archimedes-type change of variables u↦∣x−Ru∣.
Formalization scope
All quantities are real numbers; space is EuclideanSpace ℝ (Fin 3) with Lebesgue measure. The potential is a single definition YukawaPotential.yukawaPotential g α m r; at r=0 Lean's division convention gives the junk value 0, so every statement either restricts to r>0 / x=0 or integrates (where a single point is null). The physical conventions α>0, m≥0 (and m>0 where the article divides by αm) are explicit hypotheses. The Fourier transform uses the convention V^(k)=∫e−ik⋅xV, V=(2π)−3∫eik⋅xV^; the inverse is stated as a limit over balls. The shell potential averages over the unit sphere with Mathlib's Measure.toSphere (uniform surface measure); since it is an average, its normalisation is irrelevant. The Laplacian is Mathlib's Laplacian.laplacian on inner-product spaces. Physical constants μ,ℏ in milestones 5 and 7 are arbitrary reals: the identities hold without sign assumptions on them.
Out of scope: the historical mass estimate of the pion, the heuristic wave-equation derivation of the photon/pion dispersion relations, the perturbative asymptotic expansions of Schrödinger eigenvalues and Regge trajectories (stated in the source only up to unspecified O(K−7) and "⋯" terms), and the m→0 limits of the shell formulas.
Contributions welcome: polar-coordinate integration for radial functions on R3, spherical-average lemmas, and the improper integrals above as standalone lemmas.
Computational Optimal Transport X: For the Cost c = d a Function Is a c-Transform if and only if It Is 1-Lipschitz, and Then f^c = −fTextbook
Motivation
Optimal transport compares two probability distributions by the least cost of moving one onto the other. When the ground cost is itself a distance, c(x,y)=d(x,y), the resulting quantity is the 1-Wasserstein distanceW1, also called the earth mover's distance. It is the transport distance most used in practice for comparing histograms, images and point clouds, and it is the setting of Chapter 6 of G. Peyré and M. Cuturi, Computational Optimal Transport (FnT ML 2019).
For general costs the dual problem has two potentials (f,g) coupled by the constraint f(x)+g(y)≤c(x,y). For a metric cost the dual collapses to a single potential constrained to be 1-Lipschitz. This is the Kantorovich–Rubinstein formula (Kantorovich and Rubinstein, 1958, cited by the book on p. 473), the basis of dual computations of W1: network-flow algorithms, the discrete linear programs of §6.1, and Wasserstein GANs, whose critics are 1-Lipschitz networks (Arjovsky, Chintala and Bottou, 2017). Proposition 6.1 of the book is the step that makes the collapse happen. It describes exactly which functions arise as c-transforms when c=d.
Setting
Let (X,d) be a metric space and take Y=X with ground costc(x,y)=d(x,y). For a real function f on X its c-transform (the book's (5.1)) is
fc(y)=x∈Xinfd(x,y)−f(x),y∈X.
The infimum can be −∞ when the family is unbounded below. A function of the form gc is called c-concave. The Lipschitz constant of f is
Lip(f)=sup{d(x,y)∣f(x)−f(y)∣:(x,y)∈X2,x=y},
and f is 1-Lipschitz when Lip(f)≤1, i.e. ∣f(x)−f(y)∣≤d(x,y) for all x,y.
For the discrete companion, fix points z1,…,zN∈X and probability histograms a,b∈ΣN (nonnegative entries summing to one). Let Dk,ℓ=d(zk,zℓ). The couplingsU(a,b) are the nonnegative N×N matrices with row sums a and column sums b. The optimal transport cost is LD(a,b)=minP∈U(a,b)⟨D,P⟩ with ⟨D,P⟩=∑k,ℓDk,ℓPk,ℓ.
Formalization targets
Goal: Proposition 6.1 (p. 472)
For every real function f on X:
(∃g∈C(X):f=gc)⟺Lip(f)≤1,andLip(f)≤1⟹fc=−f.
Milestones (from the book's proof, pp. 472–473)
If f=gc is real valued, then ∣f(x)−f(y)∣≤d(x,y) for all x,y.
If Lip(f)≤1 and g=−f, then gc(y)=infx[d(x,y)+f(x)]=f(y) for every y.
If Lip(f)≤1, then fc(y)=infx[d(x,y)−f(x)]=−f(y) for every y.
with the minimum on the left and the maximum on the right both attained.
The mission also references the measure-theoretic Kantorovich–Rubinstein theorem already proved on the platform (WassersteinDRO.Duality.kantorovich_rubinstein). It is the book's (6.1) on a normed space.
Significance
The result. Proposition 6.1 identifies the c-concave functions for a metric cost with the 1-Lipschitz functions, and shows that the c-transform of such a function is −f. Starting from the dual problem in a single potential, the book then replaces (g,gc) by (gc,−gc). This yields (6.1), W1(α,β)=maxLip(f)≤1∫fd(α−β). Consequently W1 is a norm on measures of zero total mass. In the discrete case the dual is the finite program (6.2), whose constraints only involve pairs of support points. The min-cost-flow formulations and the Lipschitz-critic methods built on W1 all start from this characterization.
Formalizing it. The proposition and (6.2) are classical, with complete proofs in the book. The mission produces machine-checked versions in a form that later chapters of this series can reuse. (6.2) is the discrete, linear-programming form of Kantorovich–Rubinstein duality, and the referenced platform theorem does not imply it without further work. To the best of this mission's search of the platform, neither Proposition 6.1 nor (6.2) has been formalized there. Villani's double-conjugate theorem (ψc)c=ψ for c-concave ψ is published, but it is a different statement.
Difficulty
Proposition 6.1 is short; its content is the characterization, not a long argument. One point needs care: an infimum over an arbitrary metric space can be −∞. The statement "f=gc" therefore carries the information that the infimum is finite at every point. An encoding in which the infimum of an unbounded family silently becomes 0 would make the forward direction false, or decide it by a default value. The discrete companion (6.2) is a genuine duality theorem. It needs both the attainment of the transport minimum and a strong-duality argument specialised to a metric cost matrix. Ordinary LP duality yields two potentials, and the reduction to a single 1-Lipschitz potential is exactly Proposition 6.1 on the finite set {z1,…,zN}.
Formalization scope
X is an arbitrary Lean MetricSpace; no compactness, completeness or separability is assumed, since the book's §6.1 assumes only that d is a distance.
The c-transform is the published MongeKantorovichYao.cConjugate, applied to the cost (x,y)↦d(x,y). Its infimum is taken in EReal over all x∈X, so it never takes a default value. "f=gc" is an equality in EReal with f real valued.
Lip(f)≤1 is Mathlib's LipschitzWith 1 f, which is equivalent to the book's supremum formula.
In the goal, g ranges over continuous real functions (the book's C(X)). The book's f∈C(X) is not assumed: both sides of the equivalence force f to be 1-Lipschitz, hence continuous. Milestone 1 allows any real g, which is more general than the book's statement.
No Nonempty assumption: for empty X every claim holds trivially, and for nonempty X the infimum ranges over a nonempty family.
In (6.2), indices are Fin N. The two measures are written on common points zk, which need not be distinct, and W1(α,β) is the discrete cost LD(a,b) of (2.11).
The statement is not trivialised by its encoding: a sorry-free check shows that fc equals −∞, not a junk value, on an unbounded-below example. The hypotheses of (6.2) are satisfiable, with ab⊤∈U(a,b).
Contributions welcome: proofs of the milestones and goal, and a proof of (6.2), via finite LP duality and the milestones or via the referenced measure-theoretic theorem.
L. V. Kantorovich and G. S. Rubinstein, On a space of totally additive functions, Vestn. Leningrad Universitet 13:52–59, 1958 (Russian; no stable online link located, cited after the book's bibliography).
The entropy-rate dimension formula for self-similar measures on the lineResearch Paper
Motivation
A self-similar measure on the line is the natural probability measure on the attractor of finitely many contracting similarities φi(x)=rix+ti, chosen with probabilities pi. Self-similar measures are the basic test case for fractal dimension theory; Bernoulli convolutions, the laws of ∑j±λj, are the classical example. When the pieces φi(K) are well separated, the dimension is given by the entropy-to-Lyapunov formula H(p)/χ. When they overlap, dimension can drop, and the question is exactly how much. If two compositions of the same length coincide as maps (an exact overlap), information is genuinely lost; the entropy-rate dimension conjecture, formulated in Varjú's survey, predicts that this is the only mechanism: the dimension equals min{1,hRW/χ}, where hRW is the entropy rate of the random walk on composed maps.
This mission asks for a formal proof of that formula, as stated in an OpenAI preprint dated September 24, 2026 (source). The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open.
Background
1946–1981 — Moran (Proc. Camb. Phil. Soc. 1946) and Hutchinson (Indiana 1981) give the similarity-dimension formula under separation and the invariant-measure construction.
1939–1996 — Erdős proves singularity of Bernoulli convolutions at reciprocal Pisot parameters (AJM 1939); Garsia introduces the entropy rate (Pacific J. Math. 1963); Solomyak proves a.e. absolute continuity (Annals 1995).
2009 — Feng and Hu prove exact dimensionality of self-similar measures without separation (CPAM 2009).
2014 — Hochman shows a dimension drop forces superexponential concentration of cylinders (Annals 2014).
2019 — Breuillard–Varjú (Ann. Probab. 2019) and Varjú's full dimension for transcendental parameters (Annals 2019).
2021 — Baker and Bárány–Käenmäki construct systems without exact overlaps whose cylinders approach superexponentially (Adv. Math. 2021, Adv. Math. 2021).
2022–2025 — Rapaport proves the no-exact-overlap formula for algebraic ratios (Ann. Sci. ENS 2022); Rapaport–Varjú treat homogeneous three-map systems (Duke 2024); Feng–Feng treat algebraic translations (J. LMS 2025).
2026 — Varjú's survey states the entropy-rate conjecture (Conjecture 3, arXiv:2509.22042).
September 2026 — The OpenAI preprint claims the conjecture in full (Theorem 1.1, p. 2).
Setting
Let Λ be a finite nonempty alphabet and φi(x)=rix+ti with 0<∣ri∣<1, ti∈R; different symbols may give the same map, and ratios may be negative and unequal. Let p=(pi) be a probability vector with all pi>0. The self-similar measureμ is the Borel probability measure with
μ=i∈Λ∑pi(φi)∗μ.
For a word w=i1⋯in let φw=φi1∘⋯∘φin, and let Gn be the random affine map φw when the letters are independent with law p. With logarithms in base 2,
hRW=n≥1infnH(Gn),χ=−i∑pilog∣ri∣>0,
where H is Shannon entropy of the distribution of the mapGn (coinciding words are merged). The lower Hausdorff dimension of μ is dimHμ=inf{dimHE:EBorel,μ(E)>0}. The system has no exact overlaps if distinct words of the same length give distinct maps.
Formalization targets
Milestone: Corollary 6.3 (p. 20) — dimension of the attractor
If there are no exact overlaps, the attractor K={limnφω1∘⋯∘φωn(0)} is compact and nonempty, the equation ∑i∣ri∣s=1 has a unique solution s∗≥0, and dimHK=min{1,s∗}.
Milestone: Corollary 6.4 (p. 20) — homogeneous systems
If ri=λ∈(0,1) for all i and there are no exact overlaps, then dimHμ=min{1,H(p)/log(1/λ)}.
Goal: Theorem 1.1 (p. 2)
For every such family and every strictly positive probability vector p,
dimHμ=min{1,χhRW}.
No separation assumption is made; exact overlaps and repeated generators are allowed.
Significance
The result itself. It removes every arithmetic and separation hypothesis from the dimension theory of self-similar measures on the line: the only way dimension can drop below min{1,H(p)/χ} is through exact overlaps, and then the drop is exactly measured by the map entropy. Without exact overlaps it gives the classical formula (Corollary 6.2, p. 19), and hence the dimension of every self-similar set without exact overlaps (Corollary 6.3), which is the exact-overlaps conjecture for sets. It asserts nothing about absolute continuity.
Formalizing it. Hausdorff dimension and self-similar measures exist only partially in Mathlib, and the proof needs a quantitative entropy calculus for finite laws at multiple scales. A formal proof would certify the general theorem together with the consequences that previously required separate arithmetic hypotheses. The only external input is the Feng–Hu exact-dimensionality theorem.
Difficulty
Hochman's method yields the formula under exponential separation of cylinders, but Baker and Bárány–Käenmäki showed that without exact overlaps cylinders can still come superexponentially close, so no lower bound on the separation is available, and exact overlaps add further collisions. The proof must therefore detect entropy hidden at arbitrarily fine scales with no control on the smallest positive distance. The preprint does this with a finite-law pair estimate independent of minimal separation (Lemma 3.2, p. 9), conditioning on block types to recover the map-entropy rate when addresses collide (Lemma 4.1, p. 13), and disjoint windows of entropy gain whose depths are chosen adaptively (Section 5).
Formalization scope
A System ι over a nonempty Fintype ι carries ratio, offset, weight with 0<∣ri∣<1, pi>0, ∑pi=1. SelfSimilar S μ is the equation μ=∑ipi(φi)∗μ for a probability measure μ on R.
wordAffine composes maps as φi∘φw and records (slope, translation); mapMass merges words with equal complete maps; walkEntropy n is base-2 Shannon entropy of Gn; entropyRate is the sInf over n≥1 of H(Gn)/n; lyapunov is base-2.
lowerHausdorffDimension μ is an iInf in ℝ≥0∞ of dimH E over measurable E with μ(E)>0; the goal compares it to ENNReal.ofReal (min 1 (h/χ)).
NoExactOverlaps is injectivity of wordAffine on all words; words of different lengths always differ in slope modulus, so this matches the same-length condition of the source. The homogeneous milestone allows a single map, a degenerate case where both sides are 0.
The attractor milestone uses its own coding map codingPoint and word functions, equivalent to the definitions above.
Projection moments, positive cap domination, and Riesz estimates on the sphereResearch Paper
Motivation
A linear projection can concentrate a measure even when the measure has no atoms; how much depends on how much mass sits near the affine subspaces that the projection collapses. Quantitative versions of this principle go back to the potential-theoretic projection method of Kaufman and Mattila, which relates ball-growth bounds ν(B(x,r))≤Krβ to the behaviour of projections. This preprint proves explicit, dimension-dependent versions for random exact projections and applies them to a learning question: how many noiseless Gaussian measurements ⟨xt,s⟩ does a learner with M bits of memory need to recover a unit vector s to angular accuracy ϵ?
The answer proved here is that M=o(d2) bits force T≥cdlog(1/ϵ) measurements, matching the real-valued randomized Kaczmarz scale, and more generally T≥cdlog(1/ϵ)/(1+M/d2) for every M.
Timeline
1975. Mattila relates Hausdorff dimension, orthogonal projections and ball-growth measures through inverse-distance energy averaging.
1984. Drury's affine-plane change of variables for k-plane transforms contains the simplex-volume Jacobian used in projection calculations.
2014–2016. Shamir's finite-message framework includes bounded-memory online learning; Steinhardt and Duchi (2015) prove memory-dependent minimax rates for sparse regression; Steinhardt, Valiant and Wager (2016) relate memory, communication and statistical queries; Raz (2016) proves the quadratic-memory versus exponential-sample separation for parity learning.
2019. Sharan, Sidford and Valiant prove an Ω(dlogr) sample bound for Gaussian regression with small uniform noise, d2/4 bits and Euclidean accuracy d−r, using high-moment expansions and successive orthogonalization. Dagan, Kur and Shamir prove quadratic-space bounds for two other linear-prediction tasks.
2026. The OpenAI preprint Projection moments, positive cap domination, and Riesz estimates on the sphere (dated September 27, 2026) gives three independent proofs of the Ω(dlog(1/ϵ)) bound for exact observations and o(d2) memory. It has not been peer reviewed; the Lean goal is open on this platform.
Setting
Let σ be uniform probability on Sd−1⊂Rd. A finite measure ν on Rd satisfies an all-ball mass bound with constants K,β if ν(B(x,r))≤Krβ for every ball. For an orthonormal k-frame P, P#ν is the image measure on Rk and g(P,⋅) its density. The Riesz functional of h:Sd−1→[0,1] is V(h)=supz∫h(s)∥s−z∥−d/2dσ(s).
In the finite-state regression experiment, the signal S∼σ; at each step a row xt∼N(0,Id) arrives with its exact label ⟨xt,S⟩. A learner keeps one of 2M states, may use arbitrary measurable randomized transitions of the current state and pair, stops by a deterministic horizon T, and outputs a unit vector from its terminal state, stopping index and fresh randomness. Success means arccos⟨S^,S⟩≤ϵ.
In Lean (OAI.ProjectionMoments, OAI.NoiselessRegression), learners are FiniteKernelLearner d M T (Markov-kernel transitions on completed observations, states Fin (2^M)), mixed over a seed space Ξ with probability ρ; seededSuccess is uniform-prior success; AllBallMass ν K β, coordinateDensity, frameLaw, capMeasure, rieszPotential encode the geometric objects.
Formalization targets
Goal: OAI.ProjectionMoments.main
A conjunction of the paper's results, including:
Theorem 2.4 (finite-memory precision bound). There is an absolute c>0 such that for every M(d)=o(d2), eventually in d, uniform-sphere success at least 2/3 at accuracy 0<ϵ≤1/10 forces
T≥cdlog(1/ϵ),
also under an every-signal guarantee.
Corollary 2.5, in the weakened form that M≤Ad2 gives T≥cAdlog(1/ϵ) (the paper proves T≥cdlog(1/ϵ)/(1+M/d2)).
Theorem 3.3 (integrated orthonormal-projection moment). If ν is finite, supported in B(z,R), with ν(B(x,r))≤Krβ, and k+q≤d, 0<β≤d, β−k−(q−2)≥1, then
Theorem 3.5, Corollary 3.4 and Proposition 3.7 (normalized and all-radii moments, one-block propagation), Proposition 4.1 (positive domination by countable sums of cap measures), Lemma 5.3 (projection density at deterministic offsets), and the explicit success bounds of Propositions 3.8, 4.4 and 5.4 for arbitrary M.
A bridge showing the kernel learner model agrees with the seeded deterministic learner model.
Significance
The result. The projection moment estimates are general statements about finite measures with ball-growth control and apply beyond learning. In the learning application they give three proofs, with different stopping and accuracy costs, that o(d2) bits cannot beat the dlog(1/ϵ) sample scale for exact Gaussian measurements, plus an explicit trade-off for all M.
Formalizing it. The results are in an unrefereed preprint; there is no machine-checked proof. The conjunct FixedQuadraticMemory coincides with part of the goal of the companion mission on Memory and precision in noiseless Gaussian regression, so progress here is shared.
Difficulty
Integrated density norms do not determine density values on a matrix-dependent graph, so pointwise statements need an explicitly chosen density version. The high moments of projected densities require controlling collisions of q independent points under projection, which leads to inverse affine heights whose integrability must be extracted from the ball-growth hypothesis at all scales. In the learning application, conditioning on an exact label produces a singular posterior, so arguments that track a posterior density fail.
Formalization scope
Space is EuclideanSpace ℝ (Fin d); frames are orthonormal families Fin k → E drawn by frameLaw; densities are ℝ≥0∞-valued with explicit measurable versions.
Parameters are natural-number floors (d/4, d/8, d/16); block counts appear as (T+k-1)/k.
Learners: FiniteKernelLearner with CompletedRules almost surely in the seed; success probabilities are ℝ≥0∞.
The goal has no trivial reading: each conjunct quantifies over all measures or learners satisfying the stated hypotheses, and the hypotheses are satisfiable.
Needed infrastructure: Stiefel and Haar measures, Gaussian polar decomposition of matrices, Gram–Schmidt Jacobians, spherical caps, Riesz potentials. Contributions proving individual conjuncts (for example Theorem 3.3) are welcome.
P. Mattila, Hausdorff Dimension, Orthogonal Projections and Intersections with Planes, Ann. Acad. Sci. Fenn. Ser. A I Math. 1 (1975). https://doi.org/10.5186/aasfm.1975.0110
S. W. Drury, Generalizations of Riesz Potentials and Lp Estimates for Certain k-Plane Transforms, Illinois J. Math. 28 (1984).
The maximal triangular Hilbert transform at the symmetric pointResearch Paper
Motivation: the triangular Hilbert transform
The triangular Hilbert transform is a bilinear singular integral on the plane that couples translations in two different coordinate directions:
B(F,G)(x,y)=p.v.∫RF(x+t,y)G(x,y+t)tdt.
Paired with a third function, it becomes a trilinear form whose three inputs depend on the three pairs of variables of (a,b,c). This "triangular" or entangled structure defeats the time–frequency methods that handle the one-dimensional bilinear Hilbert transform. Boundedness of this operator is a model problem for multilinear singular integrals with simplex structure, and is closely tied to norm convergence and variation of ergodic averages for two commuting transformations. Thiele listed the symmetric case (L3×L3×L3) as Problem 13 in the 2017 collection Some problems in harmonic analysis.
Timeline
2010 — Demeter and Thiele study the two-dimensional bilinear Hilbert transform and highlight the flat triangular operator (Amer. J. Math.).
2012 — Kovač proves boundedness of the twisted paraproduct, a bipartite relative of the triangular form, by energy telescoping (Rev. Mat. Iberoam.).
2015 — Kovač, Thiele and Zorin-Kranich prove bounds for a Walsh (dyadic) model with one structurally restricted input (Forum Math. Sigma).
2016–2017 — Tao proves sublogarithmic cancellation for multilinear Hilbert transforms (Collect. Math.); Zorin-Kranich extends it to simplex Hilbert transforms (Math. Res. Lett.); Thiele's problem appears in Grafakos et al. (arXiv:1701.06637).
2019 — Durcik, Kovač, Škreb and Thiele obtain square-root cancellation across finitely many smooth scales (Ergodic Theory Dynam. Systems); Durcik, Kovač and Thiele prove power-type cancellation with hard truncations, still growing like log(R/ε) at the symmetric point (J. Anal. Math.).
2021 — Durcik and Roos bound averages over directions (Proc. AMS); Christ, Durcik and Roos treat the curved variant along (t,t2) (Adv. Math.).
2026 — An OpenAI preprint, The maximal triangular Hilbert transform at the symmetric point (OpenAI Math Release, September 24, 2026), claims the scale-free L3×L3→L3/2 bound for the maximal truncated operator. It has not been peer reviewed, and its proof is not formally verified.
Setting
For complex functions F,G on R2 and 0<ε<R<∞, the hard truncation is
Bε,R(F,G)(x,y)=∫ε<∣t∣<RF(x+t,y)G(x,y+t)tdt,
and the maximal truncation is
B∗(F,G)(x,y)=0<ε<R<∞supBε,R(F,G)(x,y).
For F,G∈L3(R2) the finite truncations converge absolutely outside a common null set, and B∗ is measurable (Lemma 7.1 of the source).
Formalization targets
Goal: the maximal triangular Hilbert transform is bounded at the symmetric point (Theorem 1.1)
There is an absolute constant C such that, for all complex F,G∈L3(R2),
∥B∗(F,G)∥L3/2(R2)≤C∥F∥L3(R2)∥G∥L3(R2).
The Lean goal also includes the measure-theoretic preliminaries of Lemma 7.1: almost every point is a point where all finite truncations converge absolutely, and B∗ is almost-everywhere measurable. The goal statement is published on the platform with status Open.
Significance
The result itself. A uniform bound for the maximal operator controls a truncation chosen independently at each point, which is stronger than a uniform bound on each Bε,R. It yields existence of the joint principal value B(F,G) almost everywhere and in L3/2 (Theorem 7.2). By duality and a determinant-one change of variables, it gives endpoint-uniform bounds for the symmetric trilinear triangular form ∭G0(a,b)G1(b,c)G2(c,a)a+b+cdadbdc, which resolves Thiele's Problem 13 positively. Earlier bounds all grew with the number of scales.
Formalizing it. The estimate is a clean statement in Mathlib's Lp and lower-integral language. Its proof combines matrix trace inequalities, Gaussian heat-flow energies and discretization limits, all of which would have to be built. The finite-dimensional matrix inequalities (Sections 2–3 of the source) are self-contained and reusable for other entangled multilinear forms.
Difficulty
Time–frequency analysis, which handles the one-dimensional bilinear Hilbert transform, does not adapt to the triangular form because its three inputs are entangled: each depends on a different pair of variables, so no single frequency-localization serves all three. Energy and telescoping methods (Kovač; Durcik) work for bipartite forms, but the triangular cycle is the documented obstruction. Previous cancellation estimates lose a factor depending on the number of scales log(R/ε). The maximal version adds a further difficulty: the truncation endpoints vary from point to point, so the energy argument must tolerate entries that switch on and off at independently chosen scales, without losses depending on the number of scales or on matrix dimensions.
Formalization scope
Functions are ℝ × ℝ → ℂ with Lebesgue measure; hypotheses are MemLp F 3 and MemLp G 3 (which include a.e. strong measurability).
truncation F G E z is the Bochner integral of F(x+t,y)G(x,y+t)/t over {ε<∣t∣<R}, with endpoints packaged as Endpoints (0<ε<R).
GoodPoint requires integrability on the annuli 1/(n+2)<∣t∣<n+2 for all n, hence on every finite annulus. maximal is the ℝ≥0∞-valued supremum over all endpoint pairs at good points, and 0 elsewhere. The goal asserts that almost every point is good, so this junk value cannot help.
The norm is (∫−B∗3/2)2/3 in ℝ≥0∞, compared with eLpNorm F 3 * eLpNorm G 3. The constant is an existential ℝ≥0, so it is finite and independent of F,G.
Needed infrastructure: discretization of L3 functions, finite-dimensional trace inequalities for tr((R∗R)3/2), Gaussian heat-flow monotonicity, one-dimensional Hardy–Littlewood maximal bounds, and limits of smooth truncations. Contributions on the matrix inequalities or the jump-control lemma are welcome.
V. Kovač, C. Thiele and P. Zorin-Kranich, Dyadic triangular Hilbert transform of two general functions and one not too general function, Forum Math. Sigma (2015). https://doi.org/10.1017/fms.2015.25
L. Grafakos, D. Oliveira e Silva, M. Pramanik, A. Seeger and B. Stovall, Some problems in harmonic analysis (2017), Section 9, Problem 13. https://arxiv.org/abs/1701.06637v1
P. Durcik, V. Kovač, K. A. Škreb and C. Thiele, Norm-variation of ergodic averages with respect to two commuting transformations, Ergodic Theory Dynam. Systems (2019). https://doi.org/10.1017/etds.2017.48
The G/GI/N Queue in the Halfin–Whitt Regime 1: The Regulator Equation z(t) = x(t) + ∫₀ᵗ (z(t−s)+a)⁺ dB(s) Has a Unique Càdlàg Solution, Lipschitz on Bounded Intervals and Measurable in xResearch Paper
Motivation
The G/GI/N queue has N identical servers, a general arrival process, and independent service times with a common distribution F. In the Halfin–Whitt regime the number of servers and the offered load grow together, so that the fraction of delayed customers stays strictly between 0 and 1. This is the standard asymptotic model of large call centers and service systems (Halfin and Whitt, Operations Research 29, 1981, doi:10.1287/opre.29.3.567). For exponential service times the diffusion limit has been known since 1981; for general service distributions the queue length is no longer Markov, and its limit requires a different device.
Reed (Annals of Applied Probability 19, 2009, arXiv:0912.2837) supplied that device. The number in system Q satisfies a nonlinear convolution equation, (2.8) on p. 9 of the paper, in which the only nonlinearity is the term ∫0t(Q(t−s)−N)+dF(s). Section 3 of the paper isolates that equation as a deterministic regulator map: a map that takes an input path to the unique solution of the equation. Its fluid limit (Theorem 4.1) and diffusion limit (Theorem 5.1) are both obtained by applying this map, through the continuous mapping theorem, to the limits of simpler free processes. The regulator map plays here the role that the one-sided reflection map plays in conventional heavy-traffic theory.
Timeline:
1981. Halfin and Whitt: the diffusion limit for exponential service times.
2000. Puhalskii and Reiman: the GI/PH/N case, with phase-type service (MR1778580).
2004. Jelenković, Mandelbaum and Momčilović: deterministic service times (MR2074672).
2009. Reed: general service times, via the regulator map of Proposition 3.1. This mission covers that proposition.
Setting
A path is a function x:[0,∞)→R. It is càdlàg if it is right continuous everywhere and has a finite left limit at every t>0. D[0,∞) is the set of càdlàg paths. For T≥0 the supremum metric on [0,T] is ∥x∥T=sup0≤t≤T∣x(t)∣.
Let B be a cumulative distribution function on R, that is, B(t)=μ((−∞,t]) for a probability measure μ, and let a∈R. Write y+=max(y,0). For an input x∈D[0,∞), the regulator equation (3.1) asks for a path z∈D[0,∞) with
z(t)=x(t)+∫[0,t](z(t−s)+a)+dB(s),t≥0.
The integral runs over the closed interval [0,t], so an atom of B at 0 counts. The regulator mapφBa sends x to the solution z. The paper applies it with B=F, the service-time distribution: a=−1 for the fluid-scaled queue and a=0 for the diffusion-scaled one.
The proof works with the operator ΨBa(u)(t)=∫[0,t](u(t−s)+a)+dB(s) and the successive approximationsu0=0, un+1=x+ΨBa(un) of (A.3).
D[0,∞) carries the Kolmogorov σ-algebra, the one generated by the evaluations x↦x(t), t≥0. In the Lean development the objects are IsCadlag, supNorm, Dpath, SolvesRegulator, psi and picard.
Formalization targets
Goal: Proposition 3.1
Assume μ({0})<1. Then there is a map φBa:D[0,∞)→D[0,∞) with the following properties:
φBa(x) solves (3.1) for every x;
every càdlàg solution of (3.1) agrees with φBa(x) on [0,∞);
for each T≥0 there is a constant CT with
∥φBa(x1)−φBa(x2)∥T≤CT∥x1−x2∥Tfor all x1,x2∈D[0,∞);
φBa is measurable for the Kolmogorov σ-algebra.
The constant CT is not fixed. The proof gives (1−ε)−⌈T/δ⌉, but the goal asserts only its existence.
Milestones, in the order of the paper's proof
Point mass at c>0, (A.1)–(A.2). Equation (3.1) is the recursion z=x on [0,c) and z(t)=x(t)+(z(t−c)+a)+ for t≥c. Its solution is unique, and ∥z1−z2∥t≤k∥x1−x2∥t for (k−1)c≤t<kc.
Uniqueness. Assume B(y+δ)−B(y)<ε<1 for all y≥0 and μ([0,δ])<ε. Then two càdlàg solutions agree on [0,∞).
Lipschitz continuity. Under the same assumption, ∥z2−z1∥kδ≤(1−ε)−k∥x2−x1∥kδ for every k≥1.
Existence. Under the same assumption, the successive approximations converge uniformly on every [0,T] to a càdlàg solution.
Measurability of ΨBa.ΨBa maps càdlàg paths to càdlàg paths, and u↦ΨBa(u)(t) is measurable for each t≥0.
Example 1. For unit deterministic service times, the fluid equation (4.7) has the unique solution Qˉ(t)=1+t−⌊t⌋.
Proof of Corollary 5.1. Take equilibrium initial conditions. The input of (4.7) is then identically 1, and Qˉ≡1 is the unique solution.
Significance
Proposition 3.1 is the deterministic core of the paper. Uniqueness makes the queue length a well-defined function of its free process. Lipschitz continuity in the topology of uniform convergence on compacts carries convergence of the inputs over to convergence of the outputs. Measurability is needed because the inputs are random: it makes the output a random element of D[0,∞), so that the continuous mapping theorem applies. Without the proposition neither Theorem 4.1 nor Theorem 5.1 can be stated in the form the paper gives. Example 1 and Corollary 5.1 are the two explicit solutions the paper extracts from it: a periodic, discontinuous sawtooth, and a constant.
The proposition is proved in the paper's Appendix (pp. 32–36). As far as the platform's records show, none of it has been formalized. The mission produces a machine-checked version with every convention fixed. It also corrects the statement: as printed, the proposition fails when B is the unit mass at 0, because the equation then reads z(t)=x(t)+(z(t)+a)+. With a=0 and x≡1 this has no solution. The goal therefore carries the hypothesis μ({0})<1. That hypothesis holds for every service distribution of mean 1 on [0,∞), which is the only case the paper uses. It also covers the mixed cases that fall between the two cases of the printed proof, such as an atom at 0 together with mass elsewhere.
Theorems 4.1 and 5.1 themselves, which are weak convergence in D[0,∞) with the Skorohod J1 topology, are not part of this mission.
Difficulty
The equation is a Volterra equation with a nonlinear but 1-Lipschitz integrand. The first idea is to treat it as a contraction in ∥⋅∥T. That fails: the kernel B has total mass 1, so on [0,T] the operator ΨBa is only nonexpansive, not contractive. The contraction has to be recovered locally, from windows of length δ on which B puts mass less than 1. This works only if such windows exist, and they do not exist when B is a point mass at 0.
Atoms are the second difficulty. With atoms, t↦ΨBa(u)(t) is càdlàg but not continuous, and the closed-interval convention matters at every step. The third is measurability. The map u↦∫(u(t−s)+a)+dB(s) is not continuous in u for the Skorohod topology when B has atoms, so the paper splits B into its continuous and discrete parts.
Formalization scope
Paths. A path is ℝ → ℝ, and only its values on [0,∞) are constrained or read. Every equation is asserted for t≥0, and every uniqueness claim is equality on [0,∞) (Set.EqOn … (Set.Ici 0)). D[0,∞) is the structure Dpath of càdlàg paths.
Integrals.B is a probability measure μ on R. ∫0t⋯dB(s) is the integral over the closed interval Set.Icc 0 t, so it includes an atom at 0, and mass on (−∞,0) never enters.
Explicit readings of the paper's phrases.
"Unique solution in D[0,∞)" means unique among càdlàg paths, up to equality on [0,∞).
"Lipschitz continuous in the topology of uniform convergence over bounded intervals" means ∀T∃CT∀x1,x2. The constant is chosen before the inputs.
"Measurable with respect to the Borel σ-field generated by the Skorohod J1 topology" means measurable for the Kolmogorov σ-algebra generated by the evaluations at times t≥0. The paper uses this identification on p. 35, and it is Theorem 16.6 of Billingsley (1999). J1 is not defined.
"Uniform convergence" in the existence proof means ∥un−u⋆∥T→0 for every T.
Added hypothesis in the goal: μ({0})<1 (see Significance).
Window conditions in milestones 2–4. These are the paper's B(y+δ)−B(y)<ε for y≥0, plus μ([0,δ])<ε for the first window, which the proof uses as "B(δ)<ε".
Junk values. The real supremum ∥⋅∥T and the Bochner integral take junk values (zero) on unbounded or non-integrable data. Every statement applies them only to càdlàg paths, which are bounded and measurable on [0,T]. This is also why solutions are required to be càdlàg: a solution taken among arbitrary functions could exploit the zero value of a non-integrable integral. Likewise, the goal cannot be satisfied by a φ whose values solve (3.1) only vacuously, and cannot be satisfied by dropping the càdlàg requirement on the output.
Printed slips corrected in the Lean and disclosed in the items:
(A.2)'s step "k<t≤(k+1)c" means kc≤t<(k+1)c;
(A.3) "for n≥1" starts at n=0;
(4.18) has Qˉ(t) on the right where (4.7) gives Qˉ(t−1);
Corollary 5.1 cites "Theorem 5.1 of Section 4", meaning Theorem 4.1.
Infrastructure. A complete development needs the following, all reusable beyond this mission:
càdlàg paths on a half-line and their boundedness on compacts;
Lebesgue–Stieltjes convolution integrals of càdlàg paths against a probability measure;
the Kolmogorov σ-algebra on path space.
The related platform objects are of other shapes:
ChenWhitt93.Reflection.Basic (IsCadlagOn, vector-valued paths on [0,T]) and EthierKurtz_HasCadlagPaths (processes indexed by R≥0) define càdlàg paths in other settings.
BellmanDP.Inventory.renewal_equation_exists_unique is a linear renewal equation with a density kernel of mass below 1.
Contributions are welcome on any milestone, in any order. The windowed estimates (milestones 2–4) and the measurability step (milestone 5) are independent of each other.
S. Halfin, W. Whitt, Heavy-traffic limits for queues with many exponential servers, Operations Research 29(3), 1981, 567–588. doi:10.1287/opre.29.3.567
A. A. Puhalskii, M. I. Reiman, The multiclass GI/PH/N queue in the Halfin–Whitt regime, Advances in Applied Probability 32, 2000, 564–595. MR1778580
P. Jelenković, A. Mandelbaum, P. Momčilović, Heavy traffic limits for queues with many deterministic servers, Queueing Systems 47, 2004, 53–69. MR2074672
P. Billingsley, Convergence of Probability Measures, 2nd ed., Wiley, 1999, Chapter 16. doi:10.1002/9780470316962
Distribution Function Inequalities for Martingales 4: For Convex Φ of Moderate Growth, EΦ(f*) and EΦ(S(f)) Are Comparable for Every MartingaleResearch Paper
Motivation
Martingale inequalities compare what a fluctuating process does along a path with how large its successive increments are. Such estimates are used in probability and harmonic analysis because a martingale can represent information revealed step by step, while its square function measures accumulated fluctuation. In his 1973 Wald Memorial Lectures, Burkholder developed distribution function inequalities that cover many choices of size measure at once, extending the power norm comparisons treated earlier in the paper. Burkholder, 1973
The target of this mission is Theorem 15.1 of that paper: a two sided comparison for every convex function of moderate growth. The paper attributes the decomposition of a martingale into small and large jumps to Davis, and cites a convex comparison lemma introduced in earlier work and subsequently treated by Garsia and Neveu. These attributions matter to the scope of the mission: the decomposition and lemma are separate, named milestones, while Burkholder's printed two sided inequality is the goal. Burkholder, 1973, §§14–16
Setting
Work on a probability space (Ω,A,P) with an increasing sequence of sub sigma fields A0,A1,…. A real martingale f=(f1,f2,…) is observed from time 1 onward. Following the paper, put f0=0 in the formulas for its differences: d1=f1 and dk=fk−fk−1 for k≥2. This convention differs from the index zero value required for a Lean martingale, which is discussed below.
The square functionS(f) and maximal functionf∗ are pathwise quantities:
S(f)=(k=1∑∞dk2)1/2,f∗=k≥1sup∣fk∣.
Either quantity can be infinite. Their finite versions stop at n: Sn(f) sums the first n squared differences, and fn∗ takes the maximum through time n. For a positive exponent p, the paper writes ∥f∥p=supn≥1(E∣fn∣p)1/p. The notation EΦ(Y) means the expectation of a nonnegative transform of a nonnegative random variable Y.
A function of moderate growth is a nondecreasing continuous Φ:[0,∞]→[0,∞] with Φ(0)=0 and
Φ(2x)≤c(6.1)Φ(x)(x>0)
for some nonnegative growth constant c(6.1). The target adds convexity on finite nonnegative arguments. The examples Φ(x)=x and Φ(x)=xp for p≥1 satisfy these conditions. The paper sets aside Φ≡0 as trivial; the Lean statements allow it because every displayed comparison still holds.
Formalization targets
The two sided convex comparison
The goal is precisely the form printed as Theorem 15.1:
C1EΦ(S(f))≤EΦ(f∗)≤C2EΦ(S(f)),C1,C2>0.
The constants depend only on c(6.1), and are uniform over the probability space, filtration, martingale, and particular choice of Φ with that growth bound. The statement includes both sides; the paper writes a proof for the right side and says the left side follows by the same pattern. No integrability or boundedness is imposed on f or on the two expectations. Burkholder, 1973, Theorem 15.1, pp. 33–34
Supporting statements
The milestones record the paper's good λ lemma, Doob's cited maximal estimate, the predictable jump inequalities of §12, the quasi subadditivity of Φ, Davis's decomposition and its jump bounds, and the convexity lemma of §16. The latter compares a sum of nonnegative random variables with the sum of their conditional expectations. Its two Neveu statements are retained as cited intermediate targets. These results identify distinct mathematical interfaces that a solver can formalize and reuse; the milestone text is transcribed from the indicated pages of the article.
Significance
The theorem lets one replace the expected convex transform of a path's maximum by the expected transform of its quadratic fluctuation, up to constants controlled by a single growth parameter. Choosing Φ(x)=x recovers the L1 comparison displayed after Davis's decomposition; choosing a convex power gives the p≥1 power cases. The assertion is useful even when one or both expectations are infinite, because it gives a uniform relation across the whole class of admissible martingales and functions. Burkholder, 1973, §§14–15
The result is proved in the paper. This mission asks for a machine checked Lean proof of the known theorem and of the selected statements that support it. The definitions for square and maximal functions, moderate growth, and nonnegative conditional projections can be reused in other martingale comparisons. A previously posed platform theorem about Robbins–Monro noise sums has the shape of one power function instance, but its process and time window differ; it does not supply this general result.
Difficulty
The size of a single martingale increment can dominate both a maximum and a square function. A direct comparison of the two pathwise expressions therefore gives no uniform expectation bound for arbitrary martingales and convex Φ. The §12 estimates require predictable control of jumps, while the target has no such hypothesis. Davis's decomposition exposes the small and large jump contributions as different objects, and the §16 convexity lemma is needed to compare the conditional terms arising from large jumps. Convexity is essential for that lemma and for Theorem 15.1; the §12 good λ statements themselves assume only moderate growth. Burkholder, 1973, §§12, 14–16
Formalization scope
Lean uses Mathlib's Martingale f ℱ P for a process indexed by natural numbers. The article begins at 1 and uses f0=0 only in its difference formulas. The definitions of dk, S(f), f∗, and ∥f∥p therefore read positive indices and insert zero in the article's formulas; Lean's actual f(0) is the compatible conditional expectation extension needed by Mathlib. Davis's partial sums also begin at 1, and the decomposition theorem specifies the index zero extensions for its two martingales.
The square function, maximal function, Lp norm, and all nonnegative expectations take values in [0,∞]. This retains unbounded sample paths and nonintegrable expectations. For a nonnegative variable zk, E(zk∣Ak−1) is the extended conditional expectation condLExp, so a nonintegrable zk cannot silently become zero. Davis's signed yk is integrable, so its real conditional expectation uses Mathlib's ordinary conditional expectation. Predictable bounds such as ∣dk∣≤wk and the resulting claims are stated almost everywhere at each positive index.
The Lean predicate for moderate growth states the doubling law at zero and infinity as well as positive finite x; zero is automatic from Φ(0)=0, and the endpoint at infinity follows by continuity in the nontrivial case. Convexity is read from finite real arguments. The zero function is included. A valid formalization must retain both inequalities in the goal and choose its constants before the martingale and Φ; choosing constants after the data would make the goal much weaker.
The development needs finite and infinite square function sums, maximal functions, stopping and tail events, Mathlib martingales, conditional expectations, and convexity on the nonnegative reals. Contributions proving or improving any of the named milestones are welcome, including independent proofs of the cited Doob and Neveu statements. No extra boundedness or integrability assumption should be inserted into the goal to simplify a proof.
Selected references
D. L. Burkholder, Distribution Function Inequalities for Martingales, The Annals of Probability 1(1), 1973, pp. 19–42. DOI.
Quick Approximation to Matrices and Applications II: Every Square-Integrable f on [0,1]² Is a Sum of at Most 1/ε² Cut Functions plus an Error of Cut Norm at Most ε‖f‖₂Research Paper
Motivation
A large matrix, or a large graph, can be approximated in a weak but useful sense by a short sum of very simple pieces. Frieze and Kannan (Combinatorica 19 (1999) 175–220) proved that every real matrix is, up to an error small in the cut norm, a sum of at most 1/ϵ2cut matrices (constant on a combinatorial rectangle, zero elsewhere). This "weak regularity lemma" gives polynomial-time approximation schemes for dense instances of Max-Cut and other problems, with a number of pieces that is polynomial in 1/ϵ, in contrast to the tower-type bounds of Szemerédi's regularity lemma.
Section 8 of the paper states the same decomposition for functions on the unit square, where the rows and columns of the matrix are replaced by the continuum [0,1]. This continuous form became the weak regularity lemma for graphons (symmetric measurable functions [0,1]2→[0,1]) in the theory of dense graph limits developed by Lovász, Szegedy, Borgs, Chayes, Sós and Vesztergombi in the 2000s (Lovász–Szegedy 2006; Lovász, Large Networks and Graph Limits, 2012), where it is a standard step in proving compactness of the graphon space under the cut distance.
Setting
Let [0,1]2 carry Lebesgue measure dxdy. Let f:[0,1]2→R be Lebesgue measurable with
∥f∥22=∫[0,1]2f(x,y)2dxdy<∞.
For measurable S,T⊆[0,1] the rectangle integral is
f(S,T)=∫S×Tf(x,y)dxdy,
and the cut norm is ∥f∥C=supS,T∣f(S,T)∣, the supremum over measurable S,T. The measure of S is written ∣S∣.
A cut functionCUT(S,T,d), for measurable S,T⊆[0,1] and a real d, is the function equal to d on S×T and to 0 elsewhere. Given cut functions f1,…,fs, the error after t of them is
wt=f−(f1+⋯+ft),w0=f.
In the Lean development these are rectInt f S T, l2Norm f, cutFun S T d and cutFunSum Ss Ts d in the namespace FriezeKannan.Continuum, on the type Sq = unitInterval × unitInterval.
Formalization targets
Goal: Theorem 12 (p. 217)
For every measurable f with ∥f∥2<∞ and every ϵ>0 there exist cut functions f1,…,fs with
s≤ϵ21and∥ws∥C≤ϵ∥f∥2.(63)
The number of cut functions depends on ϵ alone, not on f.
Milestones (the proof on p. 217)
Drop identity. For square-integrable w, measurable S,T with ∣S∣∣T∣>0 and d=w(S,T)/(∣S∣∣T∣),
Drop bound. If ∣w(S,T)∣>ϵ∥f∥2, the same step lowers ∥w∥22 by at least ϵ2∥f∥22.
Inductive step. If ∥wt∥22≤(1−ϵ2t)∥f∥22, then either (63) holds with s=t, or one more cut function gives ∥wt+1∥22≤(1−ϵ2(t+1))∥f∥22.
Significance
The result. Theorem 12 says that the space of square-integrable kernels is, in the cut norm, uniformly approximable by step functions of bounded complexity: 1/ϵ2 rectangles suffice, whatever f is. Refining the rectangles into a common partition gives a partition of [0,1] into at most 41/ϵ2 parts on which f is close to a step function in cut norm, which is the weak regularity lemma for graphons. It is the ingredient behind compactness of graphon space, the approximation of graph parameters by sampling, and the equivalence of cut distance and left convergence in dense graph limit theory.
Formalizing it. The theorem is proved in the paper and in textbooks; this mission produces a machine-checked version. As far as the platform's catalog shows, no cut-norm decomposition of functions on [0,1]2 is formalized there, and Mathlib has no graphons or cut norm. The definitions here (rectangle integrals, cut functions, the cut norm as a bound over measurable rectangles) are a reusable starting point for graph-limit results.
Difficulty
The paper's computations are short; the difficulty of a complete formalization is in the measure theory: the error wt must be shown square integrable at every stage (cut functions with measurable sets are bounded and measurable); integrability of wt and wt2 over S×T follows from square integrability on a probability space; the product measure of S×T is ∣S∣∣T∣; and the case ∣S∣∣T∣=0 must be excluded from the hypothesis ∣wt(S,T)∣>ϵ∥f∥2 rather than assumed. A naive attempt that bounds the cut norm by an L2 approximation through step functions gives no bound on s in terms of ϵ alone.
Formalization scope
[0,1] is Mathlib's unitInterval with its Lebesgue measure, and [0,1]2 is the product type unitInterval × unitInterval with the product measure. Sets S,T are subsets of unitInterval, so no side condition S⊆[0,1] appears.
"Lebesgue measurable with ∥f∥2<∞" is MemLp f 2, whose almost-everywhere strong measurability is measurability for the completed Lebesgue measure; no Borel Measurable f hypothesis is added. l2Norm f is the square root of ∫f2; it is the true L2 norm under MemLp f 2, which every theorem about it assumes for the function whose norm is being lowered.
The cut norm is not defined as a supremum. A bound ∥w∥C≤c is written ∣w(S,T)∣≤c for all measurable S,T, which is the definition unfolded and avoids a real supremum of a possibly unbounded family. Every cut function and every test rectangle has measurable sides.
∣S∣ is (volume S).toReal, finite on [0,1].
Families of cut functions are indexed by Fin s; s=0 is the empty sum.
Added hypotheses, all disclosed in the items: ϵ>0 in every theorem (the paper's implicit range of the error parameter, §2.3); ∣S∣∣T∣>0 in the drop identity (implicit on the page, where d divides by ∣S∣∣T∣).
The width bound s≤1/ϵ2 is part of the goal. Without it the statement would follow from density of rectangle step functions in L2 and would not be Theorem 12.
Contributions welcome: proofs of the three milestones and of the goal, and general lemmas on set integrals over measurable rectangles of unitInterval × unitInterval and on MemLp of finite sums of indicator functions, which are reusable beyond this mission.
Minimization Methods for Non-Differentiable Functions I: The Subdifferential of a Nonnegative Combination of Convex FunctionsTextbook
Motivation
Many optimization problems in operations research have objectives that are convex but not differentiable: the maximum of finitely many linear or smooth functions, the value function of a Lagrangian dual, the cost of a two-stage linear program as a function of the first-stage decision. Gradient methods do not apply to these directly. N. Z. Shor's Minimization Methods for Non-Differentiable Functions (Springer Series in Computational Mathematics 3, 1985; translated by K. C. Kiwiel and A. Ruszczyński from the 1979 Russian edition) develops the algorithms that replace the gradient by a subgradient, and its first chapter sets up the calculus of subgradients these algorithms rely on.
This mission is the first of a series formalizing the book. It covers §1.2 (convex functions and the concept of subgradient) and §1.3 (rules for computing subgradients), printed pages 7–16. Every later mission in the series (the subgradient method, space dilation, the ellipsoid method, decomposition) assumes that a subgradient of the objective can be computed, and §1.3 is where the book explains how: by combining subgradients of simpler pieces.
Setting
Write En for n-dimensional Euclidean space with inner product (x,y) and norm ∥x∥. A function f with domain a convex set M⊆En is convex if its epigraph {(u,x):u≥f(x),x∈M} is convex, equivalently (1−α)f(x1)+αf(x2)≥f((1−α)x1+αx2) for x1,x2∈M and α∈[0,1].
Let x0 be an interior point of M. A vector g is a subgradient (or generalized gradient) of f at x0 if
f(x)−f(x0)≥(g,x−x0)for all x∈M.(1.3)
The set of all subgradients is the subdifferential, written G(x0) or Gf(x0). For f differentiable at x0 it is the single gradient; for f(x)=∣x∣ on E1 it is [−1,1] at x0=0.
The one-sided directional derivative of f at x0 in direction η is
fη′(x0)=t→0+limtf(x0+tη)−f(x0).
A direction η=0 is a direction of steepest descent at x0 if min∥ξ∥=1fξ′(x0)=fη′(x0)/∥η∥.
Formalization targets
Goal: Theorem 1.12 (p. 13), the subdifferential of a nonnegative combination
For convex f1,…,fk on En and a1,…,ak≥0, the function f=∑i=1kaifi is convex and, at every x0,
Gf(x0)={i=1∑kaigi:gi∈Gfi(x0),i=1,…,k}.
Both inclusions are part of the goal.
Milestones
Theorem 1.7 (p. 9): at an interior point x0 of the domain, G(x0) is nonempty, bounded, convex and closed.
Theorem 1.8 (p. 9): at an interior point, fη′(x0) exists for every η and fη′(x0)=maxg∈G(x0)(g,η), with the maximum attained.
Corollary (p. 12): an interior point x0 minimizes f on M if and only if 0∈G(x0).
Theorem 1.11 (p. 12): if 0∈/G(x0) and g0 is the element of G(x0) nearest the origin, then −g0 is a direction of steepest descent.
Theorem 1.9 (p. 11): f is convex on En if and only if fη′(x) exists everywhere and t↦fη′(x+tη) is nondecreasing for all x,η.
Theorem 1.10 (p. 11): a twice continuously differentiable f is convex if and only if its Hessian is positive semidefinite everywhere.
Theorem 1.13 (p. 14): for convex f1,…,fm, the function φ=maxifi is convex and Gfi(x0)⊆Gφ(x0) for every index i active at x0.
Significance
The result. Theorem 1.12 is the finite-dimensional, finite-valued case of the Moreau–Rockafellar sum rule. With Theorem 1.13 it is the book's recipe for computing subgradients of functions assembled from simple pieces by nonnegative combinations and pointwise maxima, the two operations that produce most nonsmooth convex objectives in practice (Lagrangian duals, penalty functions, piecewise-linear costs). Theorem 1.8 identifies the directional derivative with the support function of the subdifferential; the Corollary and Theorem 1.11 give the optimality condition and the steepest-descent direction that every descent method for nonsmooth convex functions starts from.
Formalizing it. These results are classical and have been proved many times in textbooks (Rockafellar, Convex Analysis, 1970, §23). Mathlib at the pinned revision has convexity, separation theorems and Carathéodory's theorem, but no subdifferential of a convex function on En and no max formula. The mission builds that layer: a subdifferential with the book's inequality (1.3), a one-sided directional derivative defined as a right-hand limit, and the calculus rules above. The platform has a Clarke-gradient analogue of Theorem 1.8 for locally Lipschitz functions and a Banach-space sum rule for f+21∥⋅∥2; neither states the convex, finite-dimensional results here.
Difficulty
The inclusion "⊇" in Theorem 1.12 is immediate from (1.3). The inclusion "⊆" is the content: a subgradient of the sum is a global object, and nothing in (1.3) splits it into subgradients of the pieces. Adding the inequalities of the pieces only produces vectors of the right form; it does not show every subgradient of f arises this way. Any argument has to use the finite dimension and the interior-point setting, which is where Theorems 1.7 and 1.8 (existence of subgradients, compactness of G(x0), existence of one-sided derivatives) come in.
Theorem 1.8 in turn needs the existence of a finite right-hand limit of the difference quotient, which requires both monotonicity of the quotient and a lower bound, and the existence of a subgradient attaining the maximum, which is a separation statement. Theorem 1.9's "if" direction must rebuild convexity from one-sided derivative information alone, with no differentiability assumption.
Formalization scope
En is EuclideanSpace ℝ (Fin n) and (x,y) is inner ℝ x y. A function is f : EuclideanSpace ℝ (Fin n) → ℝ; the domain M is a set, convexity on it is ConvexOn ℝ M f (which includes convexity of M), and interior points are x₀ ∈ interior M. Theorems 1.9, 1.10, 1.12 and 1.13 are stated on all of En (Set.univ), as the book proves them.
The subdifferential subdifferential M f x₀ is the set of g with f(x)−f(x0)≥(g,x−x0) for all x∈M. It is defined for any x0; the interior-point assumption is a hypothesis of each theorem that needs it.
HasOneSidedDirDeriv f x₀ η d is the right-hand limit Tendsto … (𝓝[>] 0) (𝓝 d), not Mathlib's two-sided lineDeriv. Maxima and minima are stated with IsGreatest/IsLeast (attained), never with sSup/sInf.
Theorem 1.12 is an equality of sets over indices Fin k; k=0 and zero coefficients are allowed, as in the book. Theorem 1.13's maximum is Finset.sup' over Fin m with m≥1, and it asserts only the inclusion the book states.
Theorem 1.11 is stated as the book's proof establishes it: the steepest-descent direction is minus the minimal-norm subgradient. The printed statement names the minimal-norm subgradient itself, along which the directional derivative is positive.
A formalization that states only "⊇" in Theorem 1.12, or only that each ∑aigi is a subgradient, is the easy half and does not count as the goal.
Theorems 1.1–1.6 (supporting hyperplane, separation, representation by extremal points, the convexity inequality, continuity on the interior) are Mathlib-level (geometric_hahn_banach_*, Carathéodory and Krein–Milman, ConvexOn.continuousOn_interior) and are not restated.
Contributions welcome: proofs of any milestone, a reusable lemma that the difference quotient of a convex function is monotone in t, and a general max formula; these are reusable in the later missions of the series, which define subgradients the same way.
Selected references
N. Z. Shor, Minimization Methods for Non-Differentiable Functions, Springer Series in Computational Mathematics 3, Springer, 1985, §§1.2–1.3, pp. 7–16. https://doi.org/10.1007/978-3-642-82118-9
R. T. Rockafellar, Convex Analysis, Princeton University Press, 1970, §23 (subgradients) and Theorem 23.8 (sum rule). https://doi.org/10.1515/9781400873173
J.-J. Moreau, "Fonctionnelles sous-différentiables", Comptes Rendus de l'Académie des Sciences 257 (1963), 4117–4119.
Mathematical Methods in Quantum Mechanics IX: The Free Schrödinger OperatorTextbook
Motivation
The free Schrödinger operatorH0=−Δ is the Hamiltonian of a quantum particle, or of N non-interacting particles, moving in Rn without external forces. Every Schrödinger operator H=−Δ+V studied in quantum mechanics is a perturbation of it, and each standard tool of the theory refers back to H0: the Kato–Rellich theorem asks that V be relatively bounded with respect to H0, Weyl's theorem compares essential spectra with that of H0, and scattering theory compares the dynamics e−itH with the free dynamics e−itH0. Knowing exactly what H0 is (its domain, its self-adjointness, its spectrum and its time evolution) is therefore the base case of the spectral theory of Schrödinger operators.
All of this is obtained from one tool, the Fourier transform, which turns differentiation into multiplication. This mission follows Chapter 7 of G. Teschl, Mathematical Methods in Quantum Mechanics (AMS GSM 99, 2009), doi:10.1090/gsm/099. The chapter first develops the Fourier transform on the Schwartz space, on L1 and on L2, and then applies it to H0. The same material appears in Reed–Simon (Vol. II, Sec. IX.1 and IX.7).
Setting
Write Rn for Euclidean space with px=∑jpjxj and x2=∣x∣2. For an integrable f:Rn→C the Fourier transform and its inverse are
The Schwartz spaceS(Rn) consists of the smooth functions all of whose derivatives decay faster than any polynomial. L1(Rn) and L2(Rn) are the usual Lebesgue spaces, and C∞(Rn) denotes the continuous functions vanishing at infinity. The convolution of f,g∈L1 is (f∗g)(x)=∫f(y)g(x−y)dny.
The Sobolev space is H2(Rn)={ψ∈L2∣p2ψ^(p)∈L2}. The free Schrödinger operator is
H0ψ=−Δψ=F−1(p2ψ^(p)),D(H0)=H2(Rn),
in units with ℏ=1 and mass m=1/2. Its time evolution is e−itH0ψ=F−1(e−itp2ψ^). For a linear operator A with domain D(A), the resolvent setρ(A) is the set of z∈C for which A−z maps D(A) bijectively onto the Hilbert space with a bounded inverse RA(z), and the spectrum is σ(A)=C∖ρ(A). For self-adjoint A and a vector ψ, the spectral measureμψ is the finite Borel measure on R with ⟨ψ,RA(z)ψ⟩=∫(λ−z)−1dμψ(λ) for all z∈/R. The absolutely continuous, singular continuous and pure point spectra σac,σsc,σpp are the spectra of A restricted to the vectors whose spectral measures are of the respective type. A subspace D⊆D(A) is a core if the closure of A∣D is A.
Formalization targets
Goal: Theorem 7.8
For n≥1, H0 is self-adjoint and
σ(H0)=σac(H0)=[0,∞),σsc(H0)=σpp(H0)=∅.
In the Lean statement the spectral-type part is the equivalent assertion that every spectral measure μψ of H0 is absolutely continuous with respect to Lebesgue measure.
Milestones
Gaussians (Lemma 7.3). For Rez>0, e−zx2/2∈S(Rn) and F(e−zx2/2)(p)=z−n/2e−p2/(2z), with zn/2=(z)n for the principal root.
Inversion on S (Theorem 7.4).F is a bijection of S(Rn) with inverse g↦gˇ, and F2f(x)=f(−x), so F4=I.
Plancherel (Theorem 7.5).F extends to a unitary operator on L2(Rn) with σ(F)={1,−1,i,−i}.
Riemann–Lebesgue (Lemma 7.6).F:L1→C∞ is injective and ∥f^∥∞≤(2π)−n/2∥f∥1.
Convolution (Lemma 7.7).f∗g∈L1, ∥f∗g∥1≤∥f∥1∥g∥1 and (f∗g)∧=(2π)n/2f^g^.
Core (Lemma 7.9).Cc∞(Rn) is a core for H0.
Asymptotics (Lemma 7.10). For ψ∈L2, e−itH0ψ(x)−(2it)−n/2eix2/(4t)ψ^(x/(2t))→0 in L2 as ∣t∣→∞.
Significance
The result. Theorem 7.8 says that a free quantum particle has no bound states and that its energy spectrum is purely absolutely continuous and fills [0,∞). Combined with the RAGE theorem this gives the escape of every free wave packet from bounded regions, and Lemma 7.10 makes the escape quantitative: at large times the position distribution is the momentum distribution rescaled by 2t. The self-adjointness of H0 on H2(Rn) and the core Cc∞(Rn) are the inputs to the Kato–Rellich treatment of atomic Hamiltonians, and σess(H0)=[0,∞) is the reference spectrum of Weyl's theorem and of the HVZ theorem.
Formalizing it. The results are classical. Mathlib contains the Fourier transform on L1, Schwartz space and L2 in the normalization e−2πi⟨x,ξ⟩ (including Fourier inversion, Riemann–Lebesgue and the L2 isometry), and the Prove2Me corpus holds several of these as FamousTheorems in that normalization. What is missing is Teschl's normalization with its constants, the spectrum of the L2 Fourier transform, the operator H0 as an unbounded self-adjoint operator, its resolvent spectrum and spectral type, and the free dynamics. This mission produces these objects in a form the later chapters (Kato–Rellich for atoms, Weyl, HVZ, scattering) can build on.
Difficulty
The Fourier-analytic milestones are close to Mathlib but not in it: every constant has to be transported from the 2π-in-the-exponent convention to Teschl's (2π)−n/2 convention, including the branch of zn/2 in Lemma 7.3 and the phase (2it)−n/2 in Lemma 7.10. The lower bound σ(F)⊇{1,−1,i,−i} in Theorem 7.5 needs an eigenfunction for each fourth root of unity, which the book defers to the Hermite functions of Section 8.3.
For the goal, self-adjointness and σ(H0)=[0,∞) require working with the resolvent spectrum of an unbounded LinearPMap, which Mathlib does not provide, and transporting self-adjointness and resolvents through the unitary Fourier transform. The spectral-type statement requires identifying the spectral measure μψ of an unbounded operator from its resolvent alone, without a spectral theorem in the library, and showing that it has a density for every ψ∈L2, not only for nice ψ. Lemma 7.9 is a statement about the graph norm of −Δ, which is not controlled by the L2 norm alone.
Formalization scope
Rn is EuclideanSpace ℝ (Fin n), so that px and x2 are the Euclidean ones, and L2(Rn) is Lp ℂ 2 volume. fourier and fourierInv are Teschl's integrals (7.3) and (7.8) with the prefactor (2π)−n/2. The L2 transform ψ^ (fourierL2) is Mathlib's unitary L2 transform rescaled by ψ^(p)=(2π)−n/2(FMψ)(p/2π), and Theorem 7.5 asserts that it is the unitary extension of (7.3). H0 (freeHamiltonian) and e−itH0 (timeEvolution) are defined as Fourier conjugates of the maximal multiplication operator by the symbol of −Δ and of multiplication by e−itp2, written with Mathlib's L2 transform, where the symbol is 4π2∣ξ∣2. These are the same operators as Teschl's, and the domain is exactly H2(Rn). Unbounded operators are Mathlib LinearPMaps, self-adjointness is Mathlib's IsSelfAdjoint, and the resolvent and spectrum are the bijective-with-bounded-inverse notions of Teschl, p. 73, not Mathlib's Banach-algebra spectrum, which is used only for the bounded operator F in Theorem 7.5. Statements that need it carry n≥1, which the book leaves implicit.
No projection-valued measure or functional calculus is taken as data. The spectral types in (7.25) are expressed through the spectral measures μψ, and these are pinned down intrinsically by their Borel transforms ⟨ψ,RH0(z)ψ⟩.
A trivializing formalization is ruled out: σ(H0) is the resolvent spectrum of the operator, not the essential range of p2, so σ(H0)=[0,∞) is not a computation on a multiplication symbol. The absolutely continuous measure in the goal must reproduce the actual resolvent matrix elements, so it cannot be chosen freely, and e−itH0 is not defined by its integral kernel (7.30).
A complete development needs change of normalization lemmas for the Fourier transform, the Hermite eigenfunctions of F, unitary equivalence of LinearPMaps and its effect on adjoints and resolvents, self-adjointness of maximal real multiplication operators, polar coordinates on Rn, and graph-norm approximation by cut-offs. The normalization bridge and the multiplication-operator results are reusable across the series. Contributions of either are welcome.
Selected references
G. Teschl, Mathematical Methods in Quantum Mechanics, With Applications to Schrödinger Operators, Graduate Studies in Mathematics 99, AMS, 2009, Chapter 7. https://doi.org/10.1090/gsm/099
M. Reed and B. Simon, Methods of Modern Mathematical Physics II: Fourier Analysis, Self-Adjointness, Academic Press, 1975, Sections IX.1 and IX.7.
E. M. Stein and G. Weiss, Introduction to Fourier Analysis on Euclidean Spaces, Princeton University Press, 1971, Chapter I. https://doi.org/10.1515/9781400883899
Mathematical Methods in Quantum Mechanics VI: Relatively Bounded Perturbations and the Kato–Rellich TheoremTextbook
Why relatively bounded perturbations matter
A quantum-mechanical Hamiltonian is usually a sum H=H0+V of a kinetic energy H0 (for a free particle, H0=−Δ in units with ℏ=1, m=1/2) and a potential energy V. Observables must be self-adjoint operators, not merely symmetric ones: only then does the spectral theorem apply and the time evolution e−itH exist as a unitary group. Self-adjointness of H0 is usually a direct computation. For the sum it is not, because H0 and V are unbounded, and the sum of two self-adjoint unbounded operators need not be self-adjoint or even densely defined.
The Kato–Rellich theorem settles this when V is small compared with H0 in a precise sense. The result goes back to Rellich's perturbation theory of spectral decompositions (see the notes to Section V.4 of Kato's monograph), and Kato used it in 1951 (Trans. AMS 70, 195–211) to prove that atomic Hamiltonians with Coulomb interactions are self-adjoint. It is the standard first tool in the mathematical theory of Schrödinger operators. This mission formalizes Section 6.1 of G. Teschl, Mathematical Methods in Quantum Mechanics (AMS GSM 99, 2009): the notion of relative boundedness, its characterizations, the theorem itself, and the second resolvent formula.
Setting
Let H be a complex Hilbert space with inner product ⟨⋅,⋅⟩, conjugate-linear in the first slot. A linear operatorA is a linear map A:D(A)→H defined on a subspace D(A)⊆H, its domain. The sum A+B is defined on D(A)∩D(B). A is symmetric if D(A) is dense and ⟨φ,Aψ⟩=⟨Aφ,ψ⟩ for φ,ψ∈D(A). It is self-adjoint if it equals its adjoint A∗, domains included, and essentially self-adjoint if its closure A (the operator whose graph is the closure of the graph of A) is self-adjoint. A is bounded from below byγ∈R if ⟨ψ,Aψ⟩≥γ∥ψ∥2 for all ψ∈D(A).
The resolvent setρ(A) is the set of z∈C for which A−z:D(A)→H is a bijection with bounded inverse RA(z)=(A−z)−1, the resolvent.
An operator B is A bounded if D(A)⊆D(B) and there are constants a,b≥0 with
∥Bψ∥≤a∥Aψ∥+b∥ψ∥,ψ∈D(A).(6.1)
The A-bound of B is the infimum of the admissible a. If B is A bounded, the product BRA(z) is defined on all of H for z∈ρ(A).
Formalization targets
Goal: the Kato–Rellich theorem (Theorem 6.4, self-adjoint case)
Let A be self-adjoint and B symmetric with A-bound less than one. Then
A+B with D(A+B)=D(A) is self-adjoint,
and if A is bounded from below by γ and (6.1) holds with constants a∈[0,1) and b≥0, then
A+B≥γ−max(a∣γ∣+b,1−ab).
The printed form of (6.3) has b/(a−1) where this statement has b/(1−a). The printed version is false: a two-dimensional example with γ=0, a≈0.93, b≈5.97 has lowest eigenvalue of A+B about −6.66<−b. The corrected constant is the one in Kato's monograph (Theorem V.4.11). The lower bound is stated for every admissible pair (a,b), not for the A-bound itself, which is an infimum and need not be attained.
Milestones
Lemma 6.1.A bounded operators form a linear space, and the A-bound of α1B1+α2B2 is at most ∣α1∣a1+∣α2∣a2.
Lemma 6.2. For A closed with nonempty resolvent set and B closable, the following are equivalent: B is A bounded; D(A)⊆D(B); BRA(z) is bounded for one z∈ρ(A); BRA(z) is bounded for all z∈ρ(A). The A-bound is at most infz∈ρ(A)∥BRA(z)∥.
Lemma 6.3. For A self-adjoint and BA bounded, the A-bound equals limλ→∞∥BRA(±iλ)∥, and also limλ→∞∥BRA(−λ)∥ when A is bounded from below.
Theorem 6.4, essentially self-adjoint case. For A essentially self-adjoint and B symmetric with A-bound less than one, A+B is essentially self-adjoint, D(A)⊆D(B) and A+B=A+B, with the same lower bound.
Lemma 6.5. For closed A, B with D(A)⊆D(B) and z∈ρ(A)∩ρ(A+B),
Kato–Rellich is the entry point to self-adjointness of Schrödinger operators. With the Sobolev estimates of later chapters it gives self-adjointness of −Δ+V on H2(R3) for V∈L2+L∞, which covers the hydrogen atom and, through the N-body extension, every atom and molecule with Coulomb interactions. The lower bound (6.3) says the perturbed Hamiltonian is still bounded from below, which is the mathematical form of stability of the ground state. The second resolvent formula is used throughout perturbation theory: to compare spectra (Weyl's theorem on essential spectra), to study resolvent convergence, and in scattering theory.
In a formal library, relative boundedness is the shared layer on which every later statement about Schrödinger operators rests. Mathlib has unbounded operators (LinearPMap), their adjoints and closures, but no relative boundedness, no resolvent of an unbounded operator, and no perturbation theorem for self-adjointness. The theorem is textbook material and has been proved many times; what does not yet exist is a machine-checked proof for unbounded operators in this generality.
Difficulty
Self-adjointness of A+B is a statement about the adjoint (A+B)∗, whose domain is defined implicitly. The inclusion A+B⊆(A+B)∗ follows from symmetry, but the reverse inclusion does not come from any manipulation on D(A). It requires knowing that certain ranges are all of H, and that range information is not visible from the inequality (6.1) alone. Relating the A-bound, an infimum over constants in an inequality on D(A), to norms of the bounded operators BRA(z) is where the self-adjointness of A enters. For the essentially self-adjoint case, the domains of three different closures have to be compared. For the lower bound, the constant depends on a and b in two regimes, and the printed version of the formula gets one regime wrong.
Formalization scope
Hilbert space.{H : Type*} [NormedAddCommGroup H] [InnerProductSpace ℂ H] [CompleteSpace H]; separability is not assumed and not needed.
Operators are LinearPMaps H →ₗ.[ℂ] H; A+B is Mathlib's sum on the intersection of the domains; closures are LinearPMap.closure; self-adjointness is Mathlib's IsSelfAdjoint; symmetric operators include density of the domain.
Relative bounds.IsRelativelyBoundedWith A B a b contains the domain inclusion D(A)⊆D(B) and a,b≥0. The A-bound relativeBound A B is valued in [0,∞] and equals ∞ exactly when B is not A bounded, so "A-bound less than one" cannot hold vacuously.
Resolvents.ρ(A) and RA(z) are defined for arbitrary operators as in Teschl (2.66): a bounded, everywhere defined two-sided inverse of A−z. Mathlib's Banach-algebra spectrum is not used. resolvent A z is 0 off ρ(A), and statements use it only at points of ρ(A) or eventually along rays that lie in ρ(A).
Norms of possibly unbounded operators (opNorm) are valued in [0,∞]; "BRA(z) is bounded" means everywhere defined with finite norm.
Additions to the printed statements. Lemma 6.2 carries ρ(A)=∅, without which its "for one z" clause is unsatisfiable. Lemma 6.1's "less than" is stated as "at most". (6.3) uses b/(1−a).
Ruling out trivialization. The hypotheses of the goal are only: A self-adjoint, B symmetric, and A-bound less than one. No hypothesis that A+B is closed, that Ran(A+B±i)=H, or that ±i∈ρ(A+B) appears, since any of these would assume the conclusion.
A complete development needs basic facts about resolvents of self-adjoint operators (±iλ∈ρ(A) and the norm estimates for RA), the closed graph theorem for closable operators, and Neumann series. All of these are reusable in later missions of this series: Weyl's theorem, the free Schrödinger operator, atomic Hamiltonians. Contributions that build these as general LinearPMap lemmas are welcome.
Selected references
G. Teschl, Mathematical Methods in Quantum Mechanics, With Applications to Schrödinger Operators, AMS Graduate Studies in Mathematics 99, 2009, Section 6.1. https://doi.org/10.1090/gsm/099
M. Reed and B. Simon, Methods of Modern Mathematical Physics II: Fourier Analysis, Self-Adjointness, Academic Press, 1975, Theorem X.12. ISBN 978-0-12-585002-5
Notes on Partial Differential Equations VI: Interior Regularity of Weak Solutions via Difference QuotientsTextbook
Motivation
A second-order elliptic equation in divergence form, Lu=−∑i,j∂i(aij∂ju)=f, is usually solved in a weak sense: the Lax–Milgram theorem or the Fredholm alternative produces a function u∈H1(Ω), which has only first-order weak derivatives, and the equation holds after one integration by parts against every test function. The phrase "solution of a PDE" then covers objects for which the second derivatives appearing in L need not exist. Regularity theory closes this gap: under smoothness of the coefficients and the data, weak solutions have exactly as many derivatives as the equation suggests. It is used throughout the calculus of variations, spectral theory and nonlinear PDE.
The interior H2 estimate for divergence-form operators with C1 coefficients goes back to the difference-quotient technique of L. Nirenberg (1955) and is standard in textbooks such as Evans, Partial Differential Equations, §6.3, and Gilbarg–Trudinger, Chapter 8. This mission follows the treatment in J. K. Hunter's lecture notes, §4.11 and Appendix 4.C.
Setting
Points of Rn are written x=(x1,…,xn), and ei is the ith standard unit vector. Let Ω⊆Rn be an open set. A set Ω′ is compactly contained in Ω, written Ω′⋐Ω, if Ω′ is nonempty and open, its closure Ω′ is compact, and Ω′⊂Ω.
A test function is an infinitely differentiable ϕ with compact support inside Ω (the space Cc∞(Ω)). A function g is the weak derivative∂αf of a locally integrable f (multi-index α) if ∫Ωgϕdx=(−1)∣α∣∫Ωf∂αϕdx for all test functions ϕ. The Sobolev spaceHk(Ω)=Wk,2(Ω) consists of the functions whose weak derivatives of order at most k exist and lie in L2(Ω), with norm ∥u∥Hk(Ω)=(∑∣α∣≤k∥∂αu∥L2(Ω)2)1/2; more generally Wk,p(Ω) uses Lp. The space H01(Ω) is the closure of Cc∞(Ω) in H1(Ω).
The coefficients are functions aij, 1≤i,j≤n, which are bounded (aij∈L∞(Ω)), symmetric (aij=aji) and uniformly elliptic: for some θ>0,
i,j=1∑naij(x)ξiξj≥θ∣ξ∣2for a.e. x∈Ω and all ξ∈Rn.
Given f∈L2(Ω), a function u∈H1(Ω) is a weak solution of Lu=f if
a(u,v):=i,j=1∑n∫Ωaij∂iu∂jvdx=∫Ωfvdxfor all v∈H01(Ω).
No boundary condition is imposed on u.
For h=0 the ith difference quotient of u:Rn→R is Dihu(x)=(u(x+hei)−u(x))/h, and Dhu=(D1hu,…,Dnhu) with pointwise length ∣Dhu∣=(∑i(Dihu)2)1/2.
Formalization targets
Goal: interior H2 regularity (Theorem 4.27)
If moreover aij∈C1(Ω), then for every Ω′⋐Ω there is a constant C, depending only on n, Ω′, Ω and the coefficients, such that every weak solution u of Lu=f with f∈L2(Ω) satisfies u∈H2(Ω′) and
∥u∥H2(Ω′)≤C(∥f∥L2(Ω)+∥u∥L2(Ω)).
Milestones: difference quotients (Appendix 4.C)
Proposition 4.52 (1): if u and its weak derivative ∂iu are locally integrable on Rn, then ∂iDjhu=Djh∂iu.
Proposition 4.52 (2): for u∈Lp(Rn), v∈Lp′(Rn), 1≤p≤∞, ∫(Dihu)vdx=−∫u(Di−hv)dx.
Theorem 4.53 (1): with d=dist(Ω′,∂Ω), 1≤p<∞ and 0<∣h∣<d, ∥Dihu∥Lp(Ω′)≤∥∂iu∥Lp(Ω) for each i.
Theorem 4.53 (2): if u∈Lp(Ω), 1<p<∞, and ∥Dhu∥Lp(Ω′)≤C for all 0<∣h∣<d/2, then u∈W1,p(Ω′) and ∥Du∥Lp(Ω′)≤C.
Stronger targets
Theorem 4.28: if aij∈Ck+1(Ω) and f∈Hk(Ω), then u∈Hk+2(Ω′) with ∥u∥Hk+2(Ω′)≤C(∥f∥Hk(Ω)+∥u∥L2(Ω)), C depending only on n,k,Ω′,Ω and the coefficients.
Corollary 4.29: if aij,f∈C∞(Ω), then u agrees a.e. in Ω with a C∞(Ω) function.
Significance
The goal theorem shows that weak solutions of divergence-form equations with C1 coefficients are strong solutions: their second weak derivatives are square integrable on compact subsets, so the equation Lu=f holds pointwise almost everywhere. The iterated version (Theorem 4.28) and its corollary turn the existence theory for weak solutions into existence of smooth solutions whenever the coefficients and data are smooth; in combination with Sobolev embedding this is how one proves that eigenfunctions of the Dirichlet Laplacian on an open set are smooth in its interior.
All of these results are classical and fully proved in the literature. To the best of the platform's records, none is formalized: Mathlib has Lebesgue spaces, Fréchet derivatives and Hilbert-space tools, but neither weak derivatives nor Sobolev spaces on open sets, and there is no elliptic regularity result in any proof assistant known to the maintainers. A complete development would therefore also deliver reusable infrastructure: weak derivatives, Wk,p(Ω) and its norm, difference quotients and their calculus, and cut-off functions subordinate to Ω′⋐Ω.
Difficulty
The formal computation behind the estimate — testing the equation against −∂k(η2∂ku) for a cut-off η and using ellipticity — is illegitimate for a weak solution, because the test function involves second derivatives of u that are not yet known to exist. Replacing derivatives by difference quotients makes every step meaningful but requires a separate theory: commuting difference quotients with weak derivatives, a discrete product rule and integration by parts, and a compactness argument in Lp (reflexivity) that turns an h-uniform bound into a weak derivative. The error terms involve difference quotients of the coefficients, which is where aij∈C1 enters, and the final estimate has to be closed with a Cauchy inequality and a second localization to replace ∥u∥H1 by ∥u∥L2.
Formalization scope
Space is Rn = EuclideanSpace ℝ (Fin n) with Lebesgue measure volume; coordinates are 0-based (i : Fin n), so the book's e1,…,en are EuclideanSpace.single 0 1, …. Functions are real-valued on all of Rn and only their values on the relevant open set matter. Weak derivatives (HasWeakDeriv, weakDeriv), Wk,p(Ω) (MemW), its norm (sobolevNorm, valued in ℝ≥0∞) and W0k,p(Ω) (MemW0) are defined in the mission; weakDeriv is a chosen representative and is only used under hypotheses guaranteeing existence. Exponents are p : ℝ≥0∞; conjugate exponents use ENNReal.HolderConjugate. Ck(Ω) and C∞(Ω) are ContDiffOn ℝ k and ContDiffOn ℝ ∞ on Ω (smooth, not analytic); "u∈C∞(Ω)" for a Sobolev function means a.e. equality on Ω with such a function.
Conventions committed to:
The standing assumptions of §4.6 on the operator — aij∈L∞(Ω), aij=aji, uniform ellipticity — are explicit hypotheses of Theorems 4.27–4.29, and f∈L2(Ω) (the setting (4.34)) is a hypothesis of Corollary 4.29.
Theorem 4.27 assumes aij∈C1(Ω) as printed, not C1(Ω).
Constants: ∃ C : ℝ is quantified after Ω, the coefficients, the ellipticity constant and Ω′ (and k), and before f and u.
dist(Ω′,∂Ω) in Theorem 4.53 is modelled by any d>0 with B(x,d)⊆Ω for all x∈Ω′; the largest such d is the book's distance (and +∞ when Ω=Rn), so this is the book's condition and avoids Lean's convention that the distance to the empty set is 0.
Two corrections of the printed statements: Proposition 4.52 (2) is stated with Di−hv on the right, as the book's proof derives; Theorem 4.53 (1) is stated componentwise, ∥Dihu∥Lp(Ω′)≤∥∂iu∥Lp(Ω), which is what its proof establishes and what the proof of Theorem 4.27 uses (the printed vector form with Euclidean lengths fails for large p, e.g. for u=max(x1,x2)).
The theorems are not trivialized by junk values: the difference quotient is only used at h=0, the weak formulation's Bochner integrals are integrable under the stated L∞ and L2 hypotheses, and the conclusion u∈H2(Ω′) asserts existence of all weak derivatives of order at most two, not merely a bound on a norm that could be computed from junk representatives.
Contributions welcome: a library of weak derivatives and Sobolev spaces on open subsets of Rn compatible with these definitions, cut-off functions for Ω′⋐Ω (Mathlib's ContDiffBump and exists_smooth_tsupport_subset are natural starting points), and Banach–Alaoglu for Lp, 1<p<∞.
L. C. Evans, Partial Differential Equations, 2nd ed., Graduate Studies in Mathematics 19, American Mathematical Society, 2010, §5.8.2 and §6.3. https://doi.org/10.1090/gsm/019
D. Gilbarg and N. S. Trudinger, Elliptic Partial Differential Equations of Second Order, Classics in Mathematics, Springer, 2001, §7.11 and §8.3. https://doi.org/10.1007/978-3-642-61798-0
L. Nirenberg, Remarks on strongly elliptic partial differential equations, Communications on Pure and Applied Mathematics 8 (1955), 649–675. https://doi.org/10.1002/cpa.3160080414
Ordinary Differential Equations and Dynamical Systems IV: Regular Sturm–Liouville ProblemsTextbook
Why regular Sturm–Liouville problems
Separation of variables reduces the heat equation, the wave equation and the Schrödinger equation on an interval to an eigenvalue problem for a second-order ordinary differential operator with boundary conditions at both ends. Whether the method works depends on one fact: the eigenfunctions of that operator must be complete, so that every reasonable initial profile can be expanded in them. Chapter 5 of Gerald Teschl's Ordinary Differential Equations and Dynamical Systems (author's preliminary version of AMS GSM 140, 2012) proves this for regular Sturm–Liouville problems. It does so without Lebesgue integration or Hilbert-space completeness: it works in the space of continuous functions with a weighted scalar product, and it develops the necessary spectral theory of compact symmetric operators from scratch.
A short history. Sturm and Liouville (1836–1837) studied the zeros and the eigenvalues of such equations and the expansion of functions in eigenfunctions. Hilbert and Schmidt (1904–1907) put the expansion on a rigorous footing through their theory of integral equations with symmetric kernels, from which the abstract spectral theorem for compact symmetric operators emerged.
Setting
Let a<b and let p,q,r be real functions on [a,b] with
for fixed real α,β (the separated boundary conditions; α=0 is Dirichlet, α=π/2 Neumann). The underlying space is H0=C([a,b],C) with the weighted scalar product
⟨f,g⟩=∫abf(x)∗g(x)r(x)dx
and norm ∥f∥=⟨f,f⟩. H0 is an inner product space but it is not complete. A number z∈C is an eigenvalue of L if Lf=zf for some f∈D(L) that does not vanish identically; f is an eigenfunction. An orthonormal basis of H0 is an orthonormal sequence (un) such that ∥f−∑n<N⟨un,f⟩un∥→0 for every f∈H0.
For the abstract part, H0 is any complex inner product space (again not assumed complete). A linear operator A on a dense subspace is symmetric if ⟨g,Af⟩=⟨Ag,f⟩; an everywhere defined A is compact if (Afn) has a convergent subsequence in H0 whenever (fn) is bounded.
For z∈C let ua(z,⋅), ub(z,⋅) solve Lu=zu with ua(z,a)=sinα, p(a)ua′(z,a)=cosα, ub(z,b)=sinβ, p(b)ub′(z,b)=cosβ. Their Wronskian is W(z)=ubpua′−pub′ua, which does not depend on the point where it is evaluated. When W(z)=0, the resolventRL(z) is the integral operator with the Green functionG(z,x,t)=W(z)−1ub(z,max(x,t))ua(z,min(x,t)) and weight r.
Formalization targets
Goal: eigenfunction expansion (Theorem 5.11)
There are real numbers En and real-valued eigenfunctions un∈D(L), Lun=Enun, such that:
the En are pairwise distinct and are all the eigenvalues of L, and every eigenfunction for En is a multiple of un (the eigenvalues are simple);
∣En∣→∞ (the eigenvalues accumulate only at ∞);
⟨um,un⟩=δmn, and for every f∈H0
f=n=0∑∞⟨un,f⟩unin H0,(5.70)
for f∈D(L) the series (5.70) converges uniformly on [a,b].
Milestones
Theorem 5.4. Eigenvalues of a symmetric operator are real, and eigenvectors for different eigenvalues are orthogonal.
Theorem 5.5. A compact symmetric operator on a nonzero inner product space has an eigenvalue α0 with ∣α0∣=∥A∥=sup∥f∥=1∥Af∥.
Theorem 5.6 (spectral theorem). A compact symmetric operator has real eigenvalues αj with orthonormal eigenvectors uj, infinitely many when H0 is infinite dimensional and then αj→0, and f=∑j⟨uj,f⟩uj for every f∈Ran(A); if Ran(A) is dense the uj are an orthonormal basis.
Lemma 5.9.W(z) is an entire function of z whose zeros are exactly the eigenvalues of L.
Lemma 5.10. For W(z)=0, RL(z) is compact on H0, and symmetric for real z.
Lemma 5.12 (Rayleigh–Ritz). The eigenvalues can be ordered E0<E1<⋯; E0=min⟨f,Lf⟩=minQ(f) over normalized f∈D(L), attained exactly at the eigenfunctions for E0; and min[a,b]q≤E0 when 0≤α≤π/2≤β≤π. Here Q is the quadratic form ∫ab(p∣f′∣2+q∣f∣2)dx plus the boundary terms cot(α)∣f(a)∣2−cot(β)∣f(b)∣2 (dropped when the angle is 0).
Significance
Theorem 5.11 is the statement that makes eigenfunction expansions legitimate: Fourier sine and cosine series are its special case p=r=1, q=0, and it covers any positive weight and any smooth positive stiffness with separated boundary conditions. Solutions of the heat and wave equations on an interval with variable coefficients are built from it. The uniform convergence for f∈D(L) is the version needed to differentiate or evaluate such series pointwise. Lemma 5.12 provides the variational characterisation of the lowest eigenvalue, the starting point for comparison theorems and eigenvalue estimates.
On the formal side, Mathlib has the spectral theorem for self-adjoint operators on finite-dimensional spaces, compact operators on normed spaces, and L2 spaces. It has no Sturm–Liouville theory, no Green functions for boundary value problems, and no spectral theorem for compact symmetric operators on incomplete inner product spaces. The platform has finite-dimensional Rayleigh-quotient results and compactness of L2 convolution operators, but nothing on this setting. The results here are classical and proved; what is missing is a machine-checked proof in the book's elementary setting of continuous functions.
Difficulty
The operator L is unbounded on H0 (Problem 5.16), so the obvious first idea, applying a spectral theorem for compact or bounded symmetric operators to L itself, does not work. Completeness of the eigenfunctions is also subtle because H0 is not complete. Hilbert-space arguments that produce limits in a completion, or that go through L2, do not by themselves give an expansion of a continuous function in continuous eigenfunctions with convergence in H0, still less uniform convergence. Simplicity of the eigenvalues depends on the separated boundary conditions and fails for periodic ones. On the formal side, one needs analytic dependence of solutions of a linear ODE on a complex parameter, one-sided derivatives at the endpoints, and interval integrals of piecewise-defined kernels.
Formalization scope
Spaces. The abstract results use a type with [NormedAddCommGroup E] [InnerProductSpace ℂ E] and noCompleteSpace. Symmetry on a dense domain is IsSymmetricOp; for everywhere defined operators it is Mathlib's LinearMap.IsSymmetric. Compactness is IsCompactOp: subsequences converge in H0 itself. H0=C([a,b],C) is represented by functions R→C continuous on Set.Icc a b, with SLInner the weighted scalar product (5.52) as an interval integral. It is notL2 and not a HilbertBasis.
Operator and domain.SLOp, SLDomain, IsSLEigenfunction implement (5.53)–(5.55) with derivWithin on Set.Icc a b (one-sided at the endpoints) and ContDiffOn ℝ 2. RegularSL is (5.45), including a<b. α,β are arbitrary reals.
Quantifier readings. "Countable, discrete, accumulating only at ∞" is an injective sequence En exhausting the eigenvalues with ∣En∣→∞. "Simple" is: every eigenfunction for En equals cun on [a,b]. "Can be chosen real-valued" is an existential choice of un:R→R. ∥A∥ in Theorem 5.5 is expressed by IsLUB of {∥Af∥:∥f∥=1}, and Theorem 5.5 assumes H0={0}. In Theorem 5.6, N∈N0∪{∞} is ℕ∞: N=∞ in infinite dimension and N=dimH0 otherwise. The minima of Lemma 5.12 are IsLeast (attained), and "equality iff f=u0" reads as "iff f is an eigenfunction for E0", i.e. f=cu0 with ∣c∣=1. minq≤E0 is ∃x∈[a,b],q(x)≤E0. Norm convergence is Re⟨f−SN,f−SN⟩→0.
Ruling out trivial readings. The resolvent is only considered with W(z)=0 (Lean's 0−1=0 would make it the zero operator). Scalar products are only applied to functions continuous on [a,b], so no integral takes a junk value. Eigenfunctions must be nonzero on [a,b]. The expansion must hold in the weighted norm for every continuous f, which an orthonormal family that is merely orthonormal would not satisfy.
A complete development needs linear ODEs with a complex parameter and their analytic dependence on it, Green functions, an Arzelà–Ascoli argument in C([a,b]), and the spectral theorem for compact symmetric operators on an inner product space. The definitions RegularSL, SLOp, SLDomain, SLInner and SLWronskian are reusable for the oscillation and comparison theory (Theorems 5.17–5.25) in the rest of the chapter. Contributions connecting IsCompactOp to Mathlib's IsCompactOperator on the completion are welcome.
J. C. F. Sturm, Mémoire sur les équations différentielles linéaires du second ordre, Journal de Mathématiques Pures et Appliquées 1 (1836), 106–186. http://www.numdam.org/item/JMPA_1836_1_1__106_0/
J. Liouville, Mémoire sur le développement des fonctions ou parties de fonctions en séries dont les divers termes sont assujettis à satisfaire à une même équation différentielle du second ordre, Journal de Mathématiques Pures et Appliquées 1 (1836), 253–265. http://www.numdam.org/item/JMPA_1836_1_1__253_0/
E. Schmidt, Zur Theorie der linearen und nichtlinearen Integralgleichungen. I, Mathematische Annalen 63 (1907), 433–476. https://doi.org/10.1007/BF01449770
Ordinary Differential Equations and Dynamical Systems II: Linear Systems and Floquet TheoryTextbook
Motivation
Linear systems of ordinary differential equations x˙=A(t)x are the first case of the general theory in which solutions can be described structurally rather than merely shown to exist. They arise directly (circuits, mechanical vibrations, Hill's equation for the stability of periodic motions, the Mathieu equation for parametric resonance) and indirectly, as the linearization of a nonlinear system along an equilibrium or a periodic orbit. In the second role they decide stability: the linearized stability theorems of later chapters, the Poincaré map of a periodic orbit and the stable-manifold theory all reduce questions about nonlinear flows to questions about linear systems with constant or periodic coefficients.
For periodic coefficients the central structural result is due to Gaston Floquet (Sur les équations différentielles linéaires à coefficients périodiques, Annales scientifiques de l'École Normale Supérieure 12 (1883), 47–88, doi:10.24033/asens.220), preceded by G. W. Hill's 1877 work on the lunar perigee, which studied the same equation in the scalar second-order case. Liouville's formula for the Wronski determinant goes back to Abel (1829) and Liouville (1838). This mission follows Chapter 3 of G. Teschl, Ordinary Differential Equations and Dynamical Systems (AMS Graduate Studies in Mathematics 140, 2012), in the author's preliminary version.
Setting
Fix n∈N, an intervalI⊆R and a continuous matrix function A:I→Rn×n. A solution of the linear first-order system
x˙(t)=A(t)x(t)(3.79)
on I is a function x with values in Rn that at every t∈I has derivative A(t)x(t) (one-sided at an endpoint of I). In Lean this is TeschlODE.Linear.IsSolution A I x.
The principal matrix solutionΠ(t,t0) is, for each t0∈I, the matrix function that solves the matrix initial value problem
dtdΠ(t,t0)=A(t)Π(t,t0),Π(t0,t0)=I.(3.83)
Its columns are the solutions of (3.79) starting at the canonical basis vectors, and every solution is x(t)=Π(t,t0)x(t0). In Lean the property is TeschlODE.Linear.IsPrincipalMatrixSolution A I Φ (the Lean name of Π is Φ). The Wronski determinant of n solutions φ1,…,φn is W(t)=det(φ1(t),…,φn(t)).
A system is periodic if I=R and A(t+T)=A(t) for all t, for some T>0 (3.117). Its monodromy matrix is M(t0)=Π(t0+T,t0) (3.119). A logarithm of a square matrix M is any matrix B with exp(B)=M (3.200), where exp is the matrix exponential.
Formalization targets
Goal: Floquet's theorem (Theorem 3.15)
Let A∈C(R,Rn×n) be T-periodic with T>0 and Π its principal matrix solution. For every t0∈R there exist Q(t0)∈Cn×n and P(⋅,t0):R→Cn×n with
Lemma 3.34 — a complex matrix has a logarithm iff its determinant is nonzero; a real matrix whose real eigenvalues are all positive has a real logarithm; the square of an invertible real matrix has a real logarithm.
Theorem 3.12 (variation of constants) — the solution of x˙=A(t)x+g(t), x(t0)=x0, is x(t)=Π(t,t0)x0+∫t0tΠ(t,s)g(s)ds.
Milestone 6 is not needed for the goal; it completes the linear theory of Section 3.4 on the same definitions and is the input to the perturbation results of Section 3.7.
Significance
Floquet's theorem reduces a periodic linear system to one with constant coefficients by a periodic change of variables. Its consequences in the book are the real version with doubled period (Corollary 3.16), the stability criterion in terms of Floquet multipliers (the eigenvalues of M(t0), Corollary 3.17), the reduction y=P−1x to y˙=Qy (Corollary 3.18), and the stability analysis of Hill's equation. Later in the book, the stability of a periodic orbit of a nonlinear system is read off from the Floquet multipliers of its linearization, via the Poincaré map.
All results of this mission have been proved for more than a century. What the mission adds is a machine-checked development: Mathlib has the matrix exponential (NormedSpace.exp, with Matrix.exp_add_of_commute), Picard–Lindelöf and Gronwall-type uniqueness for ODEs, but no principal matrix solution, no Liouville formula, no matrix logarithm and no Floquet theory. The two platform nodes named after Floquet's and Liouville's theorems are retired placeholders whose statement is True; no faithful formalization exists on the platform.
Difficulty
The obvious guess for the solution, exp(∫t0tA(s)ds)x0, is wrong as soon as the values A(t) do not commute, so neither Floquet's theorem nor the variation-of-constants formula can be obtained from the constant-coefficient theory by substitution. Floquet's theorem as stated is equivalent to the existence of a logarithm of the monodromy matrix: once M(t0)=exp(TQ) is available, the periodicity of P follows from Lemma 3.14. The existence of a matrix logarithm for every invertible complex matrix is the main missing piece of infrastructure; the logarithm is not unique and not continuous in the matrix, and a real invertible matrix need not have a real logarithm (a negative eigenvalue with a single Jordan block prevents it), which is why Q(t0) is complex. Liouville's formula, which gives detM(t0)=exp(∫0TtrA)=0, needs the derivative of a determinant along a matrix solution.
Formalization scope
Vectors are Fin n → ℝ, matrices Matrix (Fin n) (Fin n) ℝ; A(t)x is Matrix.mulVec. The goal and Lemma 3.34 use Matrix (Fin n) (Fin n) ℂ for P, Q and the complex logarithm; Π(t,t0) is compared with Pexp(⋅) after coercing its entries to C.
An interval is a set I with Set.OrdConnected I; continuity is ContinuousOn A I. Derivatives are HasDerivWithinAt … I t at every t∈I (one-sided at endpoints in I); the matrix derivative in (3.83) is taken entrywise. In Section 3.6 (Lemma 3.14 and the goal) I=R and A is Continuous.
The principal matrix solution enters as a hypothesis IsPrincipalMatrixSolution A Set.univ Φ. Theorem 3.10 shows such a Φ exists and Theorem 3.9 shows it is unique on I×I, so this hypothesis names the principal matrix solution, not an arbitrary function.
Periodicity is 0 < T and ∀ t, A (t + T) = A t. The periodicity of P is required with the same T and for all t, together with P(t0,t0)=I. Without the periodicity of P the goal would be trivial (Q=0, P=Π); with it, the goal carries the full content of the theorem.
The matrix exponential is Mathlib's NormedSpace.exp. "Logarithm" means any B with NormedSpace.exp B = M; no branch is fixed. In the third claim of Lemma 3.34 the hypothesis detA=0 is explicit: the book states it in the paragraph containing the lemma, and without it the claim fails at A=0.
"The solutions form an n-dimensional vector space" is expressed through restrictions to I: closure under linear combinations, n solutions linearly independent on I, and every solution a combination of them on I.
Needed infrastructure, reusable beyond this mission: existence and uniqueness for linear systems on arbitrary intervals, the principal matrix solution and its cocycle property Π(t,t1)Π(t1,t0)=Π(t,t0), the derivative of det along a matrix solution, and the matrix logarithm (via the Jordan form or via the holomorphic functional calculus). Contributions of any of these as standalone lemmas are welcome.
G. Floquet, Sur les équations différentielles linéaires à coefficients périodiques, Annales scientifiques de l'École Normale Supérieure 12 (1883), 47–88. doi:10.24033/asens.220
W. J. Culver, On the existence and uniqueness of the real logarithm of a matrix, Proceedings of the AMS 17 (1966), 1146–1151. doi:10.1090/S0002-9939-1966-0202740-6