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Probability·Captain: mikedeng1

Markov Processes: Characterization and Convergence 09: Brownian stochastic equations: existence and uniquenessTextbook

Brownian equations as models of evolving systems

A Brownian stochastic equation describes a state that changes through a deterministic drift and random fluctuations. Such equations are basic models for diffusions and connect probabilistic sample paths with analytic operators. Chapter 5, Section 3 of Stewart Ethier and Thomas Kurtz's Markov Processes: Characterization and Convergence develops this connection in both directions and then separates three questions: whether a solution exists, whether it is unique when driven by fixed noise, and whether all solutions have the same distribution. The mission records four central results from that section as one coherent formalization target. The principal goal is the fixed-driver existence theorem, Theorem 3.11. Theorems 3.3, 3.6, and 3.10 retain the representation, uniqueness, and weak-existence variants without treating one as a consequence of another.

State space, coefficients, and solutions

Fix a dimension ddd. The state space is the Euclidean space Rd\mathbb R^dRd, represented in Lean as EuclideanSpace ℝ (Fin d). A diffusion coefficient

σ:[0,∞)×Rd⟶Rd×d\sigma:[0,\infty)\times\mathbb R^d\longrightarrow\mathbb R^{d\times d}σ:[0,∞)×Rd⟶Rd×d

controls the random fluctuations, and a drift coefficient

b:[0,∞)×Rd⟶Rdb:[0,\infty)\times\mathbb R^d\longrightarrow\mathbb R^db:[0,∞)×Rd⟶Rd

controls the finite-variation part. Given a ddd-dimensional Brownian motion WWW and initial state ξ\xiξ, a solution XXX satisfies, componentwise,

X(t)=X(0)+∫0tσ(s,X(s)) dW(s)+∫0tb(s,X(s)) ds.X(t)=X(0)+\int_0^t \sigma(s,X(s))\,dW(s)+\int_0^t b(s,X(s))\,ds.X(t)=X(0)+∫0t​σ(s,X(s))dW(s)+∫0t​b(s,X(s))ds.

The formal statement does not postulate an opaque stochastic-integral operator. The predicate HasBrownianItoIntegral uses bounded predictable dyadic step approximations, integrated squared-error convergence in probability, and convergence in probability of the corresponding stochastic sums. SolvesBrownianSDE requires continuous paths, adaptation to the completed natural past of WWW and ξ\xiξ, integrability of the drift, and one almost-sure equation holding for every time and coordinate.

A weak solution may choose its probability space, filtration, and Brownian driver. Its Brownian future increments are independent of the entire filtration past, its state process is adapted after completion, and its initial distribution is prescribed. Pathwise uniqueness compares two solutions on the same space with the same filtration and driver. Distribution uniqueness compares solutions that may live on different spaces and asks for equality of their whole coordinate-path laws.

Formalization targets

Theorem 3.3: martingale-problem representation

For locally bounded Borel coefficients, a given continuous solution of the diffusion martingale problem can be lifted to the completed product with an auxiliary Brownian space. On that specific extension there exists a Brownian motion making the lifted original process a weak solution of the stochastic equation. The generator is

Gf(t,x)=12∑i,j(σσT)ij(t,x) ∂i∂jf(x)+∑ibi(t,x) ∂if(x).Gf(t,x)=\frac12\sum_{i,j}(\sigma\sigma^{\mathsf T})_{ij}(t,x)\,\partial_i\partial_j f(x)+\sum_i b_i(t,x)\,\partial_i f(x).Gf(t,x)=21​i,j∑​(σσT)ij​(t,x)∂i​∂j​f(x)+i∑​bi​(t,x)∂i​f(x).

This target retains the factor 1/21/21/2, the complete drift term, possibly singular diffusion matrices, the given process, and the exact completed product filtration.

Theorem 3.6: uniqueness implication

For locally bounded Borel σ\sigmaσ and bbb and any initial probability law μ\muμ, pathwise uniqueness implies uniqueness in distribution. No existence, Lipschitz, ellipticity, or moment assumption is added.

Theorem 3.10: weak existence

When both coefficients are continuous and a constant KKK satisfies

∥σ(t,x)∥2≤K(1+∥x∥2),x⋅b(t,x)≤K(1+∥x∥2),\lVert\sigma(t,x)\rVert^2\le K(1+\lVert x\rVert^2),\qquad x\cdot b(t,x)\le K(1+\lVert x\rVert^2),∥σ(t,x)∥2≤K(1+∥x∥2),x⋅b(t,x)≤K(1+∥x∥2),

there is a weak solution for every initial probability law. The drift hypothesis is one-sided and does not bound ∥b(t,x)∥\lVert b(t,x)\rVert∥b(t,x)∥.

Theorem 3.11: strong existence on fixed noise

The main goal assumes locally bounded Borel coefficients, the preceding one-sided growth condition locally in time, and local Lipschitz control on each bounded state ball. For every Brownian motion WWW and every independent square-integrable initial variable ξ\xiξ already given on a probability space, there exists XXX solving the equation with respect to the completed natural past of WWW and ξ\xiξ. The quantifier order preserves the fixed driver and original probability space.

Why these results matter

The representation theorem connects the analytic martingale problem with the pathwise stochastic-integral formulation while preserving a given process on a prescribed extension. The uniqueness theorem explains when the apparently stronger same-noise comparison controls the law of arbitrary weak solutions. The weak-existence theorem supplies solutions under continuity and one-sided growth without local Lipschitz assumptions. The strong-existence theorem supplies a solution driven by noise and initial data that are fixed in advance, under local Lipschitz regularity.

Formalizing the group creates reusable definitions for completed filtrations, Brownian drivers, local Itô integration, weak solutions, pathwise uniqueness, and time-dependent diffusion generators. The source results are classical theorems, and this proposal records their reviewed Lean statements as open proof obligations. It does not claim machine-checked proofs.

Main formalization difficulty

The central difficulty is keeping the probabilistic quantifiers and filtrations exact. Replacing the local stochastic integral by an unconstrained witness would make the equation too weak. Replacing weak existence by existence on a fixed space would make Theorem 3.10 too strong. Conversely, existentially choosing a new driver in Theorem 3.11 would lose its fixed-noise content. The representation theorem also cannot be reduced to equality in law: it must retain the original process through first projection and use the completed product past specified in the source.

Formalization scope

Time is ℝ≥0, states are finite-dimensional real Euclidean spaces, and diffusion matrices use the Frobenius norm. Brownian motion is represented by independent scalar Brownian coordinates with measurable evaluations and continuous paths. Completion adds every subset of an ambient measurable null set. The local Itô relation uses left-endpoint dyadic step functions, interval integrability, and convergence in probability. Equality of path laws is expressed on the coordinate function space; for continuous Euclidean paths this matches the usual continuous-path law.

The main theorem keeps Borel measurability, compact-set local boundedness, one-sided drift growth, squared diffusion growth, local Lipschitz bounds, independence of ξ\xiξ and WWW, and the second moment of ξ\xiξ. The weak theorem keeps continuity and an arbitrary initial law. The representation target keeps compactly supported smooth tests and the exact covariance contraction. These conditions rule out vacuous solution predicates and default-valued integrals. Contributions may prove the four theorem statements or establish reusable lemmas for the concrete integral, completion, martingale, and path-law infrastructure.

Selected references

  • Stewart N. Ethier and Thomas G. Kurtz, Markov Processes: Characterization and Convergence, Wiley, 1986, Chapter 5, Section 3, Theorems 3.3, 3.6, 3.10, and 3.11. Wiley
16 thms1 active userReviewed
Probability·Captain: mikedeng1

Markov Processes: Characterization and Convergence 08: Change of variables for continuous semimartingalesTextbook

Motivation

A continuous stochastic process may combine a finite-variation drift with a local martingale fluctuation. To understand a function of that process, one needs a change-of-variables rule that accounts for both parts and for their quadratic covariation. The ordinary chain rule has no term for covariation. The result here is the time-dependent, multidimensional Itô formula in Stewart N. Ethier and Thomas G. Kurtz, Markov Processes: Characterization and Convergence (Wiley, 1986), Chapter 5, Section 2, Theorem 2.9, printed page 287 (PDF page 296). It applies to arbitrary continuous semimartingales satisfying the stated decomposition; its input is not restricted to a particular stochastic differential equation.

Setting

Time is the nonnegative real half-line and the state has d real coordinates, indexed by Fin d. The sample space Ω has a complete probability measure P and a filtration ℱ. The initial sigma algebra contains every ambient measurable null set. For each coordinate i, V_i is a continuous adapted finite-variation process, zero at time zero; M_i is a continuous adapted local martingale, zero at time zero almost surely. The process X_i has an ℱ₀-measurable initial value and satisfies X_i(t)=X_i(0)+V_i(t)+M_i(t) for all times and samples.

The test function f(t,x) has a continuous first time derivative, continuous first spatial derivatives, and continuous second spatial derivatives. At time zero, the time derivative is taken within the nonnegative-time domain. The notation f_t, f_{x_i}, and f_{x_i x_j} denotes these derivative witnesses. The conclusion supplies processes representing the continuous cross variations and integrals; their existence is part of the theorem. The Stieltjes relations use pathwise left dyadic sums for continuous finite-variation integrators. Martingale integrals and cross variations use convergence in probability of their corresponding dyadic sums. The integral versions are continuous and adapted where the theorem requires those properties.

Formalization targets

Theorem 2.9 — time-dependent multidimensional Itô formula

For every t ≥ 0, outside one null set independent of t, the target is

f(t,X(t))−f(0,X(0))=∫0tft(s,X(s)) ds+∑i∫0tfxi(s,X(s)) dVi(s)+∑i∫0tfxi(s,X(s)) dMi(s)+12∑i,j∫0tfxixj(s,X(s)) d⟨Mi,Mj⟩s.\begin{aligned} f(t,X(t))-f(0,X(0))={}&\int_0^t f_t(s,X(s))\,ds\\ &+\sum_i\int_0^t f_{x_i}(s,X(s))\,dV_i(s)\\ &+\sum_i\int_0^t f_{x_i}(s,X(s))\,dM_i(s)\\ &+\frac12\sum_{i,j}\int_0^t f_{x_i x_j}(s,X(s))\,d\langle M_i,M_j\rangle_s. \end{aligned}f(t,X(t))−f(0,X(0))=​∫0t​ft​(s,X(s))ds+i∑​∫0t​fxi​​(s,X(s))dVi​(s)+i∑​∫0t​fxi​​(s,X(s))dMi​(s)+21​i,j∑​∫0t​fxi​xj​​(s,X(s))d⟨Mi​,Mj​⟩s​.​

The goal is EthierKurtz.ito_formula. It retains every term and both coordinate indices in the cross-variation sum. The bracket and integral witnesses are existential conclusions, so the statement does not require their existence as an extra hypothesis on X.

Significance

The formula identifies the effect of a smooth, time-dependent transformation on any process with the stated continuous semimartingale decomposition. In particular, it exposes the second-order correction that distinguishes stochastic change of variables from deterministic calculus. This makes it a general calculus result that can later be applied to diffusion equations, martingale problems, and transformed processes, independently of how the input process was constructed.

The cited theorem is established in the source book. This mission provides a compiled Lean statement and concrete expression dependencies; it does not provide a Lean proof. Formalizing the proof would require the stochastic-integral and covariation infrastructure to support the source's continuous local-martingale setting and version conventions. A solver's result should prove this full statement or develop reusable infrastructure that supports it without changing its hypotheses or conclusion.

Difficulty

The ordinary deterministic chain rule cannot account for the double sum of cross variations. A statement limited to Brownian motion, an absolutely continuous bracket, or a fixed stochastic differential equation would omit cases covered by the source. The local-martingale integral also needs a continuous adapted version with a common almost-sure interpretation over all times; merely obtaining a separate fixed-time limit does not by itself supply that final identity. These requirements make the exact scope of the integral and bracket relations central to the formalization.

Formalization scope

The Lean declaration uses ℝ≥0 for time, Fin d → ℝ for state vectors, Measure Ω for the probability law, and Filtration ℝ≥0 for the filtration. It includes completeness of P, the initial-null-set condition on ℱ 0, continuity and adaptation of V and M, local bounded variation of V, stopping-time localization for M, the full decomposition of X, and continuous derivative witnesses for f. The final almost-everywhere quantifier precedes the universal time quantifier. There is no Brownian driver, global square-integrability requirement, right-continuity assumption on the filtration, or diagonal-only bracket simplification.

Five definitions are expression dependencies: IsSourceLocalMartingale, HasCrossVariation, itoStepSum, HasContinuousStieltjesIntegral, and HasContinuousMartingaleIntegral. Their staged declarations retain the reviewed source definitions under a consistent EthierKurtz namespace. The dyadic-sum definitions do not embed the change-of-variables identity, so the theorem cannot be discharged by unfolding a definition of the desired answer. The mission asks for a proof of the stated result, not a weakened special case. The empty-coordinate case is admitted consistently; no positive-dimensional source case is excluded.

Selected references

  • Stewart N. Ethier and Thomas G. Kurtz, Markov Processes: Characterization and Convergence, Wiley, 1986 (held reprint 1986/2005), Chapter 5, Section 2, Theorem 2.9, printed p. 287 (PDF p. 296), equations (2.40)–(2.41). Conventions: printed pp. 279–280 and 286 (PDF pp. 288–289 and 295). Cross variation: Chapter 2, equation (6.4), printed p. 79 (PDF p. 88).
6 thms1 active userReviewed
Functional AnalysisProbability·Captain: mikedeng1

Markov Processes: Characterization and Convergence 01: Contraction semigroup generationTextbook

Motivation

Continuous-time Markov processes are often studied through operators that describe how observables evolve. The generation question asks when an operator specified on a domain actually determines a strongly continuous family of contractions. Ethier and Kurtz place this question at the start of Markov Processes: Characterization and Convergence because the resulting semigroup language supports their later treatment of processes and convergence. This mission records the full generation characterization in Chapter 1, Theorem 2.6, and retains the related perturbation result in Theorem 7.1.

Setting

Let (E) be a real Banach space. A linear operator (A) has a linear domain (D(A)\subseteq E), which need not equal (E). A family (T(t)), for nonnegative real (t), consists of bounded linear maps (E\to E). It is a strongly continuous contraction semigroup when (T(0)) is the identity, (T(s+t)=T(s)T(t)), every (T(t)) has norm at most one, and (T(t)x\to x) as (t\downarrow0) for every (x\in E). Its infinitesimal generator has exactly those (x) for which the right-hand difference quotient (t^{-1}(T(t)x-x)) converges, and sends each such (x) to the limit.

An operator is dissipative here when

r∥x∥≤∥rx−Ax∥(x∈D(A), r>0).r\lVert x\rVert\leq\lVert rx-Ax\rVert \quad(x\in D(A),\ r>0).r∥x∥≤∥rx−Ax∥(x∈D(A), r>0).

The range condition for a positive (r) says every vector in (E) is (rx-Ax) for some (x\in D(A)). Density means that vectors in (D(A)) approximate every vector in (E). These are independent requirements in the formal statement; neither the domain nor the operator is assumed closed or bounded.

For the related perturbation theorem, (A) and (B) may have different domains. Their sum uses the intersection of those domains. Closure is closure of the graph in (E\times E). A graph closure is required to be the graph of a single-valued operator before it can be called a generator.

Formalization targets

Theorem 2.6: generation characterization

The goal is the equivalence

A generates a strongly continuous contraction semigroup⟺D(A)‾=E,A is dissipative,Ran⁡(rI−A)=E for some r>0.A\text{ generates a strongly continuous contraction semigroup} \quad\Longleftrightarrow\quad \overline{D(A)}=E,\quad A\text{ is dissipative},\quad \operatorname{Ran}(rI-A)=E\text{ for some }r>0.A generates a strongly continuous contraction semigroup⟺D(A)​=E,A is dissipative,Ran(rI−A)=E for some r>0.

The left side uses the full infinitesimal-generator domain, rather than agreement with a generator only on a smaller subdomain. The right side includes dissipativity for every positive parameter and full surjectivity for at least one positive parameter.

Theorem 7.1: relatively bounded perturbation

The retained related result assumes that the closure of (A) is single-valued and generates a strongly continuous contraction semigroup, (D(A)\subseteq D(B)), and (B) is dissipative. It further assumes

∥Bx∥≤α∥Ax∥+β∥x∥(x∈D(A)),0≤α<1,β≥0.\lVert Bx\rVert\leq\alpha\lVert Ax\rVert+\beta\lVert x\rVert \quad(x\in D(A)),\qquad 0\leq\alpha<1,\quad\beta\geq0.∥Bx∥≤α∥Ax∥+β∥x∥(x∈D(A)),0≤α<1,β≥0.

It concludes that the closure of (A+B) is single-valued and generates such a semigroup, and that its entire graph equals the sum of the graph closures of (A) and (B). This theorem is a related generation variant within the same mission, not a separate main goal.

Significance

The equivalence turns an existence question about a family of operators into conditions on a single potentially unbounded operator. In particular, it preserves the exact-domain requirement: proving only that a semigroup generator extends (A) would answer a weaker question. The perturbation theorem then identifies circumstances under which an already generating operator remains useful after adding a dissipative term that need not itself be bounded.

The textbook proves both statements. The Lean files supplied here are statements with intentional proof holes; they do not claim machine-checked proofs. A completed formalization would supply proofs of these source results while keeping the domain, range, closure, and relative-bound conditions shown in the statements.

Difficulty

The hypotheses describe a linear map on only part of the Banach space, while the conclusion requires bounded operators on all of (E) at every nonnegative time. Checking a semigroup law on a convenient subspace is insufficient unless the resulting operators and generator have the stated full domains. For perturbations, graph closure can enlarge a domain. An equality of formulas on (D(A)) alone would therefore omit the theorem's graph and domain conclusion.

Formalization scope

Lean uses NormedAddCommGroup E, NormedSpace ℝ E, and CompleteSpace E for the real Banach space; Submodule ℝ E for each domain; D →ₗ[ℝ] E for an operator that need not be bounded; and E →L[ℝ] E for each semigroup operator. Semigroup values at negative real times are unconstrained. The derivative is the right limit through positive times. Graph closure is topological closure in the ambient product, and graph sum requires a common input. The strict relative bound (\alpha<1) and positive resolvent parameter remain explicit.

The reusable definitions are the semigroup predicate, full generator predicate, operator graph, and graph sum. The mission welcomes proofs of the two stated theorems and genuinely source-based supporting lemmas. A model with an empty operator domain, an operator restricted from a larger generator, or a conclusion that drops the graph equality would not satisfy the target.

Selected references

  • Stewart N. Ethier and Thomas G. Kurtz, Markov Processes: Characterization and Convergence, Wiley, 1986, Chapter 1, Sections 2 and 7, Theorems 2.6 and 7.1. Book record.
6 thms1 active userReviewed
🏆Completed
Algebraic GeometryDiscrete Geometry·Captain: mysticflounder

Near enemies: spherical sets project to minimal-energy images in general positionOpen Problem

Motivation

How few distinct distances can a planar point set determine? The near enemies of this mission are the closest competitors to the extremal configuration: points in no-three-collinear position (no line through three of them) and points lying on a common sphere — the lattice-sphere slice of Erdős–Füredi–Pach–Ruzsa is the motivating example. The bisector energy of a set counts ordered quadruples (a,b,c,d)(a,b,c,d)(a,b,c,d) of points for which the pair a,ba,ba,b and the pair c,dc,dc,d have the same perpendicular bisector; it measures how far the set is from generic. Lund–Sheffer–de Zeeuw fixed the floor of this statistic: 2n(n−1)2n(n-1)2n(n−1) is a universal lower bound, and bisector injectivity is sufficient for equality. The Near Enemy theorem is the projection statement on top of it — every admissible set admits one generic projection whose image attains that floor, sits in general position, has zero rotation energy, and carries the whole distance-transport package.

Setting

Work with finite sets PPP of points in the Euclidean plane (EuclideanSpace ℝ (Fin 2)), and in the transport direction with points in EuclideanSpace ℝ ι for a general finite index type. Following Lund–Sheffer–de Zeeuw, the bisector energy is

E(P)=∣{(a,b,c,d)∈P4  :  a≠b,  c≠d,  perpBisector(a,b)=perpBisector(c,d)}∣.\mathcal{E}(P) = \bigl|\{(a,b,c,d) \in P^4 \;:\; a \neq b,\; c \neq d,\; \mathrm{perpBisector}(a,b) = \mathrm{perpBisector}(c,d)\}\bigr|.E(P)=​{(a,b,c,d)∈P4:a=b,c=d,perpBisector(a,b)=perpBisector(c,d)}​.

The rotation energy counts the ordered congruent quadruples whose difference vectors are neither equal nor opposite, so it discards the translation and half-turn channels and isolates the proper-rotation one. A linear map TTT is projection-generic for a set GGG when exactly two conditions hold: TTT sends the difference of any two distinct points of GGG to a nonzero vector, and for two distinct unordered pairs of GGG the images never combine a parallel pair of differences with an orthogonal midpoint difference. Perpendicular bisectors, difference classes and distance images are all Finset operations.

Target

The mission goal is the spherical complete profile: for every finite set GGG lying on a common sphere, in any dimension, there is a linear map TTT to the plane whose image satisfies six conclusions at once —

∃ T:E(T(G))=2∣G∣(∣G∣−1)  ∧  E(T(G))≤E(P′) for every ∣P′∣=∣G∣  ∧  rotationEnergy(T(G))=0\exists\,T:\quad \mathcal{E}(T(G)) = 2|G|(|G|-1) \;\wedge\; \mathcal{E}(T(G)) \le \mathcal{E}(P') \text{ for every } |P'| = |G| \;\wedge\; \mathrm{rotationEnergy}(T(G)) = 0∃T:E(T(G))=2∣G∣(∣G∣−1)∧E(T(G))≤E(P′) for every ∣P′∣=∣G∣∧rotationEnergy(T(G))=0

together with injectivity of TTT on GGG, general position of the image (no three collinear, no four cospherical), and exact distance transport. The projection is chosen per set — its very type depends on the ambient dimension — but a single projection delivers all six conclusions together, and that bundled form is what downstream incidence arguments consume.

Milestones ascend in five steps: the universal energy floor, the bisector-injectivity equality case, the attainment of the floor by projection-generic maps, the existence of such a map for every no-three-collinear set, and the no-three-collinear transport bundle that the goal then specialises to the spherical case.

Significance

Lund–Sheffer–de Zeeuw introduced the extremal picture for bisector energy at the level of the exact constant. In footnote 1 on p. 538 of the SoCG 2015 version (LIPIcs vol. 34, 537–552) they state that E(P)=2n(n−1)\mathcal{E}(P) = 2n(n-1)E(P)=2n(n−1) when every distinct pair determines a distinct bisector, with the enumeration of the trivial quadruples that proves the floor. The universal asymptotic form E(P)=Ω(n2)\mathcal{E}(P) = \Omega(n^2)E(P)=Ω(n2) is a remark in their §3.4.

This mission builds a complete machine-checked development of the floor, its attainment and the sufficiency direction, from first principles in Lean 4 over mathlib; the rotationEnergy statistic together with the rotationEnergy = 0 certificate for the projected image, where rotationEnergy(P) = 0 follows from the published "distance Sidon set" property; and a single generic projection of a given set that carries the bisector floor at the same time as the Erdős–Füredi–Pach–Ruzsa general-position package. Four of that bundle's six conjuncts are already in Erdős–Füredi–Pach–Ruzsa 1993, whose Theorem 3.1 supplies them.

Formalizing it matters because the argument composes analysis (generic projections obtained from nonvanishing of circle determinants), algebra (inner-product and determinant polynomial witnesses carrying linear_combination certificates) and counting (fiberwise difference-class tallies) — and the interfaces between the three must agree exactly. The projection-genericity and polynomial-witness lemmas are reusable for any Euclidean extremal formalization.

Difficulty

The hard step is keeping the projection generic through every predicate at once: a projection that preserves no-three-collinearity can still kill a circle determinant, or create a coincidence that the counting needs to keep distinct. The naive first idea — project along a random direction and hope — fails because each predicate forbids a different algebraic hypersurface of directions; the fix is a single simultaneous-avoidance argument over the union, with each forbidden set shown proper by an explicit polynomial witness. That step is the largest proof in the mission and carries its own milestone.

Formalization scope

Points are EuclideanSpace; finite sets are Finset; energies are ℕ-valued statistics. Generic projections are linear maps carrying the explicit two-clause ProjectionGeneric predicate, so there is no hidden regularity assumption. Dimension is a general ι with [Fintype ι] wherever the transport needs it. The goal's only hypothesis is membership of a common sphere; no-three-collinearity is derived from it rather than assumed, because a line meets a sphere at most twice. No statement is vacuous: explicit witnesses were checked in the kernel for the goal and for every milestone.

Welcome contributions: the converse of the equality case — bisector injectivity is proved here to be sufficient for the floor, and necessity is open in this development; sharpness examples beyond the Erdős–Füredi–Pach–Ruzsa configuration; and the incidence assembly that consumes this mission's output.

Selected references

  • B. Lund, A. Sheffer and F. de Zeeuw, Bisector energy and few distinct distances, Proc. 31st SoCG 2015, LIPIcs vol. 34, 537–552, DOI 10.4230/LIPIcs.SOCG.2015.537; journal version Discrete Comput. Geom. 56 (2016), no. 2, 337–356, DOI 10.1007/s00454-016-9783-5, arXiv:1411.6868. Source of the bisector-energy statistic and its upper bounds. Footnote 1 on p. 538 of the SoCG version gives E(P)=2n(n−1)\mathcal{E}(P) = 2n(n-1)E(P)=2n(n−1) for every set whose pairs have distinct bisectors, with the count of trivial quadruples that proves the floor; §3.4 (p. 545) gives E(P)=Ω(n2)\mathcal{E}(P) = \Omega(n^2)E(P)=Ω(n2) for every set. The footnote is not in arXiv:1411.6868v1.
  • P. Erdős, Z. Füredi, J. Pach and I. Z. Ruzsa, The grid revisited, Discrete Math. 111 (1993), 189–196, DOI 10.1016/0012-365X(93)90155-M — the lattice-sphere-slice configuration that gives this mission its name, and (proof of Theorem 3.1, p. 193) the generic planar projection that is injective, keeps general position and transports distances.
  • J. Solymosi and T. Tao, An incidence theorem in higher dimensions, Discrete Comput. Geom. 48 (2012), no. 2, 255–280, DOI 10.1007/s00454-012-9420-x, arXiv:1103.2926, §5.1 — the canonical statement of the generic-projection trick this construction borrows. The same trick is used in J. Pach and F. de Zeeuw, Distinct distances on algebraic curves in the plane, Combin. Probab. Comput. 26 (2017), no. 1, 99–117, arXiv:1308.0177.
  • McKenna, Lean formalization (mathlib-only, axiom-clean), lean-formalizations, module Geometry.Euclidean.NearEnemyTheorem.
20 thms1 active userReviewed
🏆Completed
CombinatoricsNumber Theory·Captain: mysticflounder

Modular Schur numbers: a uniform closed form in the stable-colour regimeResearch Paper

Motivation

A set of integers is sum-free when no two of its members add up to a third. Schur's theorem (1916) says that for every kkk there is a largest interval [1,N][1,N][1,N] that can be split into kkk sum-free classes, and the resulting Schur numbers S(k)S(k)S(k) are notoriously hard to compute: S(5)=160S(5) = 160S(5)=160 was settled only in 2018, by a SAT computation with a machine-checked proof certificate.

Replacing "adds up to" by "adds up to, modulo mmm" gives a family that behaves very differently. Modular Schur numbers were introduced by Chappelon, Revuelta Marchena and Sanz Domínguez, who settled the moduli m∈{1,2,3}m \in \{1,2,3\}m∈{1,2,3} and proved the universal bound Sm(k,ℓ)≤m−1S_m(k,\ell) \le m-1Sm​(k,ℓ)≤m−1 (Electron. J. Combin. 20(2) (2013) #P61). D'orville, Sim, Wong and Ho then closed m∈{4,5,6,7}m \in \{4,5,6,7\}m∈{4,5,6,7} by residue case analysis and posed the general modulus as an open problem (Integers 25 (2025) #A62, their Problem 1). Each additional modulus had cost a separate case analysis, and the case analysis grew with mmm.

The timeline matters for reading what follows. The 2013 paper supplies the universal cap. The 2025 paper supplies a singleton criterion (its Theorem 4) and a divisibility obstruction (its Corollary 3), and applies the latter only in the coprime case gcd⁡(m,ℓ−1)=1\gcd(m,\ell-1)=1gcd(m,ℓ−1)=1 (its Corollary 5). What remained was to optimise that obstruction over every residue rather than only in the coprime case, which is what collapses the whole family to one formula.

Setting

Fix integers m≥2m \ge 2m≥2, ℓ≥2\ell \ge 2ℓ≥2 and k≥1k \ge 1k≥1. A set SSS of integers is ℓ\ellℓ-sum-free modulo mmm when there are no x1,…,xℓ∈Sx_1, \dots, x_\ell \in Sx1​,…,xℓ​∈S and y∈Sy \in Sy∈S, repetitions among the xix_ixi​ allowed, with

x1+⋯+xℓ≡y(modm).x_1 + \cdots + x_\ell \equiv y \pmod m .x1​+⋯+xℓ​≡y(modm).

The repetition clause is not a technicality: a single element can make its whole class unsafe. The modular Schur number Sm(k,ℓ)S_m(k,\ell)Sm​(k,ℓ) is the greatest N≥0N \ge 0N≥0 such that the interval [1,N][1,N][1,N] can be partitioned into at most kkk classes, each ℓ\ellℓ-sum-free modulo mmm. A partition into such classes is called valid.

Two derived quantities carry the whole story. Write

d=gcd⁡(m,ℓ−1),n=md.d = \gcd(m, \ell - 1), \qquad n = \frac{m}{d} .d=gcd(m,ℓ−1),n=dm​.

Then dn=mdn = mdn=m exactly, and d∣(ℓ−1)d \mid (\ell - 1)d∣(ℓ−1) by construction. All Lean statements in this mission use these same names.

Formalization targets

Goal: the closed form in the many-colours regime

Sm(k,ℓ)=mgcd⁡(m,ℓ−1)−1=n−1for all m≥2, ℓ≥2, k≥n−1.S_m(k,\ell) = \frac{m}{\gcd(m,\ell-1)} - 1 = n - 1 \qquad \text{for all } m \ge 2,\ \ell \ge 2,\ k \ge n-1 .Sm​(k,ℓ)=gcd(m,ℓ−1)m​−1=n−1for all m≥2, ℓ≥2, k≥n−1.

Closed form here means something precise: the value is produced from mmm and ℓ\ellℓ by one gcd, one division and one subtraction, with no search over colourings, no recursion, and no case split on ℓ mod m\ell \bmod mℓmodm. The statement fixes no constants and no modulus, so it is not invalidated by any later refinement of the threshold in kkk.

The single-colour value

Sm(1,ℓ)=min⁡ ⁣(ℓ−1,⌊mℓ⌋)(2≤ℓ≤m),S_m(1,\ell) = \min\!\left(\ell - 1, \left\lfloor \frac{m}{\ell} \right\rfloor\right) \qquad (2 \le \ell \le m),Sm​(1,ℓ)=min(ℓ−1,⌊ℓm​⌋)(2≤ℓ≤m),

together with the complementary regime m<ℓm < \ellm<ℓ, where the value is 000 if ℓ≡1(modm)\ell \equiv 1 \pmod mℓ≡1(modm) and 111 otherwise. The two together give a value for every admissible pair (m,ℓ)(m,\ell)(m,ℓ) at k=1k=1k=1, and the tree carries that combined formula at the residue level and at the integer level.

Significance

What the results give. One expression replaces an open-ended sequence of per-modulus case analyses. The moduli m∈{1,2,3}m \in \{1,2,3\}m∈{1,2,3} of the 2013 paper and m∈{4,5,6,7}m \in \{4,5,6,7\}m∈{4,5,6,7} of the 2025 paper are specialisations, and every remaining modulus is covered at once in the stated range of kkk.

The mechanism is a single self-defeating value. Take ℓ\ellℓ copies of nnn: they sum back to nnn modulo mmm, so the lone class {n}\{n\}{n} already breaks the rule, while every smaller value is safe. That one observation supplies a matching upper and lower bound.

  • The upper bound is uniform in kkk. Adding colours never raises the value past n−1n-1n−1, which is what makes the formula stable.
  • The lower bound costs n−1n-1n−1 colours, one per safe residue. Identifying the least sufficient number of colours is where the subject is still open.

Status of the tree, stated precisely. Everything listed under Formalization targets is both proved and machine-checked.

  • 21 theorems and 3 definition bundles, each with a complete Lean proof verified by this platform.
  • Axiom-clean: each closure is contained in {propext, Classical.choice, Quot.sound}.
  • This mission therefore publishes a finished development rather than an open call on its stated goal.
  • What is genuinely open is listed under Difficulty below, and is not part of the verified tree.

Relation to the accompanying paper. The paper states the single-colour value only under 2≤ℓ≤m2 \le \ell \le m2≤ℓ≤m. Three results in the tree go beyond it: the complementary regime m<ℓm < \ellm<ℓ, and the combined formula covering every m≥2m \ge 2m≥2 and ℓ≥2\ell \ge 2ℓ≥2, stated once at the residue level and again at the integer level. Two further results, the coset-cardinality bounds, are supporting work of the Lean development and are not numbered results of the paper. Each theorem's source field records which of these it is.

Difficulty

The threshold in kkk is not n−1n-1n−1

The obvious attack on the general modulus is to guess that only singletons can be safe classes. The threshold in kkk would then be exactly n−1n-1n−1, and the problem would close for all kkk at once. That guess is false.

Take m=12m = 12m=12 and ℓ≡11(mod12)\ell \equiv 11 \pmod{12}ℓ≡11(mod12), so d=2d = 2d=2 and n=6n = 6n=6. The two-element set {1,5}\{1,5\}{1,5} is ℓ\ellℓ-sum-free modulo 121212, and three colours then suffice where the singleton count would demand five.

So the least kkk at which the closed form takes hold, written k0(m,ℓ)k_0(m,\ell)k0​(m,ℓ), is not n−1n-1n−1 in general. What is known about it:

  • Prime moduli. k0(p,ℓ)=p−1k_0(p,\ell) = p-1k0​(p,ℓ)=p−1 for every ℓ≥p−1\ell \ge p-1ℓ≥p−1 with ℓ≢1(modp)\ell \not\equiv 1 \pmod pℓ≡1(modp).
  • Composite moduli. Bracketed above and below, but not determined.

A correction to the published prime-power formula

Theorem 8 of D'orville, Sim, Wong and Ho gives a three-branch formula at prime-power moduli. Its middle branch is false. The correction is stated here in full because it bears directly on the threshold.

  • The counterexample. At p=2p = 2p=2, i=3i = 3i=3, k=3k = 3k=3 and ℓ=8\ell = 8ℓ=8 that branch gives S8(3,8)=5S_8(3,8) = 5S8​(3,8)=5, while the correct value is S8(3,8)=7S_8(3,8) = 7S8​(3,8)=7.
  • Where the proof fails. In the supporting Lemma 2(2) of that paper. The pair a=2a = 2a=2, b=6b = 6b=6 satisfies every hypothesis of that lemma at p=2p = 2p=2, i=3i = 3i=3, ℓ=8\ell = 8ℓ=8, yet {2,6}\{2,6\}{2,6} is 888-sum-free modulo 888.
  • The replacement result.
Spi(k,ℓ)=pi−1for p prime, i≥1, ℓ≥2, p∤(ℓ−1), and every k≥i(p−1).S_{p^i}(k,\ell) = p^i - 1 \qquad \text{for } p \text{ prime},\ i \ge 1,\ \ell \ge 2,\ p \nmid (\ell - 1), \text{ and every } k \ge i(p-1) .Spi​(k,ℓ)=pi−1for p prime, i≥1, ℓ≥2, p∤(ℓ−1), and every k≥i(p−1).

It is proved from a valuation-layer colouring that consumes i(p−1)i(p-1)i(p−1) classes, together with the universal cap. The hypothesis p∤(ℓ−1)p \nmid (\ell-1)p∤(ℓ−1) forces d=1d = 1d=1 and n=pin = p^in=pi, so the replacement reaches the goal theorem's value at k≥i(p−1)k \ge i(p-1)k≥i(p−1) in place of k≥pi−1k \ge p^i - 1k≥pi−1, and it contradicts the printed middle branch for infinitely many triples (p,i,ℓ)(p, i, \ell)(p,i,ℓ).

Status of that correction, stated precisely.

  • It is a prose proof in a draft note, listed under Selected references below and readable in full there.
  • It is not formalized, and it is not part of this mission's verified tree.
  • Nothing in the verified tree depends on it.
  • It is recorded here because a reader who compares this mission against the 2025 paper will otherwise meet the contradiction with no explanation. Formalizing it is the subject of a separate mission.

The intermediate regime

For 1<k<n−11 < k < n-11<k<n−1 the classes must be simultaneously large and ℓ\ellℓ-sum-free, and no formula is known. The value is empirically eventually periodic in ℓ mod m\ell \bmod mℓmodm for fixed kkk, verified through m≤13m \le 13m≤13.

None of these open directions is weakened by the goal theorem, which deliberately assumes enough colours to avoid the question.

Formalization scope

Two levels of statement

Two levels appear in the tree, and the distinction between them is the first thing to fix.

  • At the integer level the objects are the integers 1,…,N1, \dots, N1,…,N themselves.
  • At the residue level they are their classes modulo mmm, which in Lean is the type ZMod m: Mathlib's type of residues modulo mmm, a commutative ring with exactly mmm elements for m≥1m \ge 1m≥1, carrying the reduction map from Z\mathbb{Z}Z and the arithmetic that map preserves.

Working in ZMod m turns "adds up to, modulo mmm" into a plain equation instead of a divisibility side condition, and it makes every colour class a subset of a finite type.

Conventions

The development works residue-by-residue in ZMod m and commits to the following conventions, all of which are silent in the prose and load-bearing in Lean.

  • ℓ\ellℓ-tuples are functions Fin ℓ → ZMod m valued in the class. This builds in "repetitions allowed" rather than leaving it to a side condition.
  • Classes are Finsets, so finiteness is structural.
  • A valid partition is a structure with four fields: covering, pairwise disjointness, containment in the target set, and ℓ\ellℓ-sum-freeness of each class.
  • Empty classes are permitted. This is what makes "at most kkk" and "exactly kkk" interchangeable once any colouring exists.

The two numbers, and the cap in their definition

Both a residue-level and an integer-level number are defined, and a reduction theorem proves them equal for every m≥2m \ge 2m≥2. Bounds are proved on the residue side and quoted on the integer side.

Both are defined with Nat.findGreatest against the bound m−1m-1m−1. That cap is neither an approximation nor a trivialising choice: a separate theorem shows any NNN admitting a valid partition satisfies N≤Sm(k,ℓ)N \le S_m(k,\ell)N≤Sm​(k,ℓ) with no hypothesis on NNN, because N≥mN \ge mN≥m admits no valid partition at all. A reader checking for a vacuous formalization should also note that the goal is an equality, not a bound, so it cannot be satisfied by weakening a hypothesis.

Reusable beyond this mission

  • the residue-reduction bridge;
  • the singleton criterion;
  • the two coset-cardinality bounds, which are pure counting statements about subsets of a cyclic group whose differences lie in a proper subgroup.

Contributions welcome on the open directions named under Difficulty, in particular any lowering of the threshold in kkk toward k0k_0k0​, and a closed form for k0k_0k0​ at composite moduli.

Selected references

  • J. Chappelon, M. P. Revuelta Marchena, M. I. Sanz Domínguez, Modular Schur numbers, Electron. J. Combin. 20(2) (2013) #P61. https://doi.org/10.37236/2374 (also arXiv:1306.5635)
  • J. D'orville, K. A. Sim, K. B. Wong, C. K. Ho, Modular generalizations of Schur numbers, Integers 25 (2025) #A62. https://math.colgate.edu/~integers/z62/z62.pdf
  • M. J. H. Heule, Schur number five, AAAI 2018. arXiv:1711.08076
  • A. McKenna, A correction to a prime-power formula for modular Schur numbers, 2026. Draft note, not submitted for publication. Released in the repository below on 2026-09-20: PDF · Markdown source
  • A. McKenna, Prime-power structure of the stable regime for modular Schur numbers, 2026. Lean development and paper: https://github.com/mysticflounder/modular-schur
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Group Theory·Captain: dbenbenn

Milnor: growth of finitely generated solvable groupsResearch Paper

Motivation

This mission formalizes John Milnor's Growth of finitely generated solvable groups, J. Differential Geometry 2 (1968) 447–449 (doi:10.4310/jdg/1214428659), a three-page addendum to J. A. Wolf's Growth of finitely generated solvable groups and curvature of Riemannian manifolds, which precedes it in the same issue (421–446, doi:10.4310/jdg/1214428658). Milnor's note has one theorem and three lemmas, and "for definitions and explanations the reader is referred to" Wolf.

Wolf proved that a polycyclic group "either has a finitely generated nilpotent subgroup of finite index and thus is of polynomial growth, or has no such subgroup and is of exponential growth" (p. 421). Milnor's Theorem closes the gap between polycyclic and solvable: "Let Γ\GammaΓ be a solvable group which is not polycyclic, and SSS a finite set of generators for Γ\GammaΓ. Then there exists an exponential lower bound gS(m)≥(constant)m>1g_S(m) \ge (\text{constant})^m > 1gS​(m)≥(constant)m>1 for the growth function gSg_SgS​ of Γ\GammaΓ." Together the two papers give the Milnor–Wolf theorem, "that a finitely generated solvable group, either is polycyclic and has a nilpotent subgroup of finite index and is thus of polynomial growth, or has no nilpotent subgroup of finite index and is of exponential growth" (Wolf, p. 421). Milnor notes that Wolf's results "provide a partial answer to a problem which was posed by the author in Amer. Math. Monthly 75 (1968) 685–686", and Wolf raises "the question of whether every finitely generated group Γ\GammaΓ, which is not of exponential growth, necessarily has a nilpotent subgroup of finite index" (p. 422); Grigorchuk's groups of intermediate growth (1984) later answered that in the negative, while Gromov (1981) proved that polynomial growth does force a nilpotent subgroup of finite index. Chou's 1980 extension of the Milnor–Wolf theorem to elementary amenable groups, the mission Chou: elementary amenable groups on this platform, cites exactly this theorem. Wolf's paper is the subject of a companion mission.

Setting

Growth. For a finite subset SSS of a group Γ\GammaΓ, Wolf's growth function gS(m)g_S(m)gS​(m) (p. 426) is the number of elements expressible as words of length ≤m\le m≤m based on SSS, a word s1a1⋯srars_1^{a_1} \cdots s_r^{a_r}s1a1​​⋯srar​​ having length ∣a1∣+⋯+∣ar∣|a_1| + \cdots + |a_r|∣a1​∣+⋯+∣ar​∣. MilnorWolf.growthFunction S m takes gS(m)g_S(m)gS​(m) as the size of the ball Chou.wordBall S m of the published growth bundle, the set of products of at most mmm factors from S∪S−1S \cup S^{-1}S∪S−1. Γ\GammaΓ has exponential growth, the published Chou.HasExponentialGrowth, if for some finite generating set SSS there is c>1c > 1c>1 with gS(m)≥cmg_S(m) \ge c^mgS​(m)≥cm for all mmm; Wolf shows (p. 434) that this does not depend on SSS.

Polycyclic groups. Wolf's Proposition 4.1 (p. 433) gives eleven equivalent conditions; the definition used here is condition (1): "There is a normal series Γ=A0⊃A1⊃⋯⊃At={1}\Gamma = A_0 \supset A_1 \supset \cdots \supset A_t = \{1\}Γ=A0​⊃A1​⊃⋯⊃At​={1} with every quotient Ai/Ai+1A_i/A_{i+1}Ai​/Ai+1​ finite or infinite cyclic." This is MilnorWolf.IsPolycyclic. A solvable group is Mathlib's Group.IsSolvable: the derived series reaches the trivial subgroup.

Milnor's standing assumptions. The three lemmas concern a group extension 1→A→B→C→11 \to A \to B \to C \to 11→A→B→C→1 where "we will always assume that AAA is abelian and that BBB is finitely generated." In the statements, BBB is a finitely generated group, AAA an abelian normal subgroup, and CCC the quotient B/AB/AB/A.

Formalization targets

Milnor's Theorem (p. 447)

"Let Γ\GammaΓ be a solvable group which is not polycyclic, and SSS a finite set of generators for Γ\GammaΓ. Then there exists an exponential lower bound gS(m)≥(constant)m>1g_S(m) \ge (\text{constant})^m > 1gS​(m)≥(constant)m>1 for the growth function gSg_SgS​ of Γ\GammaΓ." Stated for an arbitrary finite generating set SSS:

∃ c>1∀ m≥1:cm≤gS(m).\exists\, c > 1 \quad \forall\, m \ge 1: \qquad c^m \le g_S(m).∃c>1∀m≥1:cm≤gS​(m).

This is the goal. The constant is existentially quantified, so a sharper bound does not change the statement. The milestones are Milnor's three lemmas, in order, followed by one published Open theorem of the Chou mission that they prove: Chou's form of Lemmas 1 and 2, where the normal subgroup need not be abelian.

Significance

Milnor's Theorem is the half of the Milnor–Wolf theorem that reaches beyond polycyclic groups: with Wolf's polycyclic dichotomy it says that a finitely generated solvable group is either almost nilpotent, of polynomial growth, or of exponential growth, with nothing in between. That statement is what Chou's Theorem 3.2 extends to elementary amenable groups, and it is the reason a group of intermediate growth cannot be solvable or elementary amenable, the fact that placed Grigorchuk's groups outside those classes.

Formalizing it produces, besides the Theorem, the three lemmas as reusable library results: the subgroup spanned by the conjugates βkαβ−k\beta^k \alpha \beta^{-k}βkαβ−k is finitely generated when BBB is not of exponential growth; a normal subgroup with finitely presented quotient is normally generated by finitely many elements; and polycyclic-by-abelian without exponential growth is polycyclic. The proof is complete in the paper; nothing here is open mathematics. On this platform the Theorem and the lemmas are stated and unproved; Chou's mission holds the Open non-abelian form of Lemmas 1 and 2 and two Open reductions that resolve once this mission and the Wolf mission close their externals.

Difficulty

The obvious attempt, to bound the growth of BBB below by the growth of a free subsemigroup found inside it, is not what Milnor does and does not obviously work for an arbitrary abelian-by-solvable extension. Milnor's argument turns the growth hypothesis into finite generation: among the 2m2^m2m expressions βαi1⋯βαim\beta\alpha^{i_1} \cdots \beta\alpha^{i_m}βαi1​⋯βαim​ two must coincide, and the resulting relation expresses αm=βmαβ−m\alpha_m = \beta^m \alpha \beta^{-m}αm​=βmαβ−m in terms of α1,…,αm−1\alpha_1, \ldots, \alpha_{m-1}α1​,…,αm−1​. The delicate step is running this over a whole set of normal generators of AAA and over each of finitely many β\betaβ's in turn, so that AAA itself comes out finitely generated (Lemma 3), and then up the derived series of Γ\GammaΓ. In Lean the work is in Lemma 2, which needs the finite presentation of CCC transported to a presentation on the images of chosen generators of BBB, and in Lemma 3, which needs that a polycyclic group is finitely presented and that an extension of polycyclic groups is polycyclic.

Formalization scope

Growth is measured on the closed balls of the published bundle Chou_Growth: Chou.wordBall S m is the set of products of at most mmm letters from S∪S−1S \cup S^{-1}S∪S−1, and gS(m)g_S(m)gS​(m) is its cardinality (a Nat.card, finite because SSS is a Finset). "Not of exponential growth" is the negation of the existential definition, so it is a statement about every finite generating set. Polycyclic is Wolf's condition (1); the definition fixes the reading of "normal series". The abelian hypothesis on AAA is Mathlib's IsMulCommutative on the subgroup; finite generation and finite presentation are Mathlib's Group.FG and Group.IsFinitelyPresented.

The Theorem's hypotheses are satisfiable: the trivial group is polycyclic, so "not polycyclic" excludes it, and a solvable non-polycyclic finitely generated group exists (the lamplighter group Z/2≀Z\mathbb Z/2 \wr \mathbb ZZ/2≀Z). No hypothesis is vacuous and no definition makes a target trivially true.

The definitions of polycyclic group, polynomial growth and Wolf's growth exponents E1,E2E_1, E_2E1​,E2​ are stated in the bundle MilnorWolf_Growth here because Milnor defers all definitions to Wolf; the results of Wolf's paper, in particular the polycyclic dichotomy that combines with this Theorem into the Milnor–Wolf theorem, belong to the companion mission. Nothing of Milnor's note is omitted. Contributions welcome: proofs of the three lemmas and the Theorem, and general library results they need, such as finite presentability of polycyclic groups.

Selected references

  • J. Milnor, Growth of finitely generated solvable groups, J. Differential Geometry 2 (1968), 447–449. doi:10.4310/jdg/1214428659
  • J. A. Wolf, Growth of finitely generated solvable groups and curvature of Riemannian manifolds, J. Differential Geometry 2 (1968), 421–446. doi:10.4310/jdg/1214428658
  • J. Milnor, A note on curvature and fundamental group, J. Differential Geometry 2 (1968), 1–7.
  • A. G. Kurosh, Theory of groups, vol. II, Chelsea, 1956.
  • R. I. Grigorchuk, Degrees of growth of finitely generated groups, and the theory of invariant means, Math. USSR-Izv. 25 (1985), 259–300 (Russian original 1984). doi:10.1070/IM1985v025n02ABEH001281
  • M. Gromov, Groups of polynomial growth and expanding maps, Publ. Math. IHÉS 53 (1981), 53–78. doi:10.1007/BF02698687
  • C. Chou, Elementary amenable groups, Illinois J. Math. 24 (1980), 396–407 (p. 400). doi:10.1215/ijm/1256047608
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Machine LearningProbabilityRandom Matrix Theory+1·Captain: mikedeng1

High-Dimensional Probability XI: Dvoretzky-Milman's TheoremTextbook

Motivation

A striking fact discovered by Dvoretzky in the 1960s (conjectured by Grothendieck, and sharpened into its modern quantitative form by Milman in 1971) is that every high-dimensional convex body, however irregular, contains a round slice: a random low-dimensional section (or projection) of any bounded convex set in Rn\mathbb R^nRn is, with high probability, close to a Euclidean ball — provided the dimension of the slice is small enough relative to a single geometric parameter of the body. This is remarkable because it holds for every bounded set, arbitrarily irregular; no special structure is assumed beyond boundedness. This chapter proves the theorem in its Gaussian form, as a culmination of every geometric and probabilistic tool the book develops: chaining and Dudley's inequality (Chapter 8), the matrix deviation inequality (Chapter 9), and Gaussian width and the stable dimension (Chapter 7) all combine into a single closing argument.

Setting

Fix a subset T⊆RnT\subseteq\mathbb R^nT⊆Rn. For a standard Gaussian vector g∼N(0,In)g\sim N(0,I_n)g∼N(0,In​), the Gaussian width of TTT is w(T):=Esup⁡x∈T⟨g,x⟩w(T) := \mathbb E\sup_{x\in T}\langle g,x\ranglew(T):=Esupx∈T​⟨g,x⟩ (Chapter 7), and the stable dimension of a bounded TTT is d(T):=w(T)2/diam(T)2d(T) := w(T)^2/\mathrm{diam}(T)^2d(T):=w(T)2/diam(T)2 up to an absolute constant factor (Definition 7.6.2) — a robust substitute for the ordinary linear-algebraic dimension of TTT, which can jump discontinuously under a small perturbation of TTT, unlike d(T)d(T)d(T).

An m×nm\times nm×n Gaussian random matrix with i.i.d. N(0,1)N(0,1)N(0,1) entries is a random matrix AAA each of whose mnmnmn entries is an independent standard normal random variable.

Formalization targets

Goal (Theorem 11.3.3, Dvoretzky-Milman's theorem, Gaussian form)

∃ c>0:m≤cε2d(T)  ⟹  P[(1−ε)B⊆conv(AT)⊆(1+ε)B]≥0.99\exists\,c>0:\quad m\le c\varepsilon^2 d(T) \;\Longrightarrow\; \mathbb P\bigl[(1-\varepsilon)B \subseteq \mathrm{conv}(AT) \subseteq (1+\varepsilon)B\bigr] \ge 0.99∃c>0:m≤cε2d(T)⟹P[(1−ε)B⊆conv(AT)⊆(1+ε)B]≥0.99

for every m×nm\times nm×n Gaussian random matrix AAA with i.i.d. N(0,1)N(0,1)N(0,1) entries, every bounded T⊆RnT\subseteq\mathbb R^nT⊆Rn containing the origin, and every ε∈(0,1)\varepsilon\in(0,1)ε∈(0,1), where BBB is the Euclidean ball of radius w(T)w(T)w(T) centered at the origin. The probability 0.990.990.99 is the book's own literal numeral, not a free parameter — this is the theorem the book actually states, not a family of theorems indexed by a confidence level.

Significance

Dvoretzky-Milman's theorem is one of the foundational results of the local theory of Banach spaces (asymptotic geometric analysis): it says every nnn-dimensional normed space contains an almost-Euclidean subspace of dimension proportional to (a geometric invariant closely related to) log⁡n\log nlogn in the worst case, and much larger for spaces whose unit ball is already well-behaved (the stable dimension of the cube [−1,1]n[-1,1]^n[−1,1]n, for instance, is proportional to nnn itself — Example 11.3.6). This underlies results throughout convex geometry, compressed sensing, and high-dimensional statistics wherever a random low-dimensional projection needs to be shown to preserve geometric structure. The book's own framing makes clear why this chapter is placed last: the theorem's proof is a genuine capstone, invoking Chevet's inequality (itself built from the matrix deviation inequality of Chapter 9, which is built from chaining, Chapter 8) as its main technical tool.

The theorem and its proof are classical (Milman 1971; this book's specific route via Chevet's inequality is a standard modern exposition). This mission formalizes the goal theorem's statement — including its two supporting geometric quantities, Gaussian width and stable dimension, and the notion of a Gaussian random matrix — as a complete, faithful target for a solver, in the book's own sub-namespace built for this chapter (no dependency here is reusable from an earlier chunk, since none of this book series' Chapter 7 or Chapter 9 definitions has yet been published).

Difficulty

The natural first idea — bound conv(AT)\mathrm{conv}(AT)conv(AT) directly using concentration of ∥Ax∥2\|Ax\|_2∥Ax∥2​ for each fixed x∈Tx\in Tx∈T — runs into exactly the uniform-supremum obstacle the whole book has been building tools to overcome: a bound that holds for one xxx at a time, even with a union bound over a net of TTT, does not obviously extend to the full convex hull without first controlling sup⁡x∈T∣⟨Ax,y⟩−w(T)∥y∥2∣\sup_{x\in T}|\langle Ax,y\rangle - w(T)\|y\|_2|supx∈T​∣⟨Ax,y⟩−w(T)∥y∥2​∣ uniformly over both x∈Tx\in Tx∈T and yyy on the unit sphere of the target space — a two-parameter supremum. The book's actual route goes through Chevet's inequality, itself proved using the matrix deviation inequality's own chaining-based argument, to control this two-sided supremum, and then converts the resulting inequality into the containment (1−ε)B⊆conv(AT)⊆(1+ε)B(1-\varepsilon)B\subseteq\mathrm{conv}(AT)\subseteq(1+\varepsilon)B(1−ε)B⊆conv(AT)⊆(1+ε)B via a support- function duality argument (a convex body is pinned down by its support function, so bounding sup⁡x∈T⟨Ax,y⟩\sup_{x\in T}\langle Ax,y\ranglesupx∈T​⟨Ax,y⟩ uniformly over yyy on the sphere is exactly what is needed).

Formalization scope

A is Ω → Matrix (Fin m) (Fin n) ℝ with an explicit IsGaussianMatrix hypothesis (entries i.i.d. N(0,1)N(0,1)N(0,1), formalized entrywise with joint independence). conv(AT) is convexHull ℝ of the image of T under A's mulVec, round-tripped through EuclideanSpace's continuous linear equivalence with the underlying function type. w(T) reuses this mission series' ExpSup/GaussianWidth convention (redefined locally, per the drafts-cannot-import-drafts rule, following the same ProbabilityTheory.stdGaussian-based realization of a standard Gaussian vector as 08-matrix-deviation). The stable dimension d(T)d(T)d(T) is formalized directly as w(T)2/diam(T)2w(T)^2/ \mathrm{diam}(T)^2w(T)2/diam(T)2 rather than via the book's literal (but only asymptotically equivalent, per Exercise 7.6.1) definition through a squared Gaussian width h(T−T)2h(T-T)^2h(T−T)2 — the goal theorem's own proof uses only the inequality direction of that equivalence, and the goal's hypothesis already carries an unpinned absolute constant that absorbs the equivalence constant, so this substitution preserves the theorem's exact truth content (see StableDimension's own doc-comment and MODERATION_NOTES.md for the full argument) rather than approximating it.

Ball-center deviation, disclosed. The book's printed theorem statement carries no hypothesis that TTT contains the origin; its proof opens by translating TTT so that it does ("Translating TTT if necessary, we can assume that TTT contains the origin"), and Remark 11.3.4 then confirms the ball is centered at the origin in that case. This mission states the WLOG-reduced case directly — adding 0∈T0\in T0∈T as an explicit hypothesis — rather than also formalizing the translation argument that recovers the fully general (untranslated) statement. This is disclosed as a genuine narrowing of the literal printed statement, though not of what the book's own proof actually establishes.

This mission covers Theorem 11.3.3 only, with no milestones: BRIEF.md explicitly instructs that if the chapter's full proof chain (general matrix deviation inequality, Chevet's inequality, random projections of sets — Theorems 11.1.5, 11.2.4, 11.3.1) proves too heavy for the session, milestones should be cut rather than the goal substituted. All three are left out, not approximated, given this chapter's five from-scratch definitions already needed for the goal's own statement. ExpSup, GaussianWidth, StableDimension and IsGaussianMatrix are reusable by any later development needing Gaussian width, the stable dimension, or a Gaussian random matrix. Solvers' contributions are welcome on the goal theorem itself and, beyond this mission's current scope, on the three named milestones.

Selected references

  • A. Dvoretzky, Some results on convex bodies and Banach spaces, Proc. Internat. Sympos. Linear Spaces (Jerusalem, 1960), 123–160.
  • V. D. Milman, A new proof of A. Dvoretzky's theorem on cross-sections of convex bodies, Funkcional. Anal. i Priložen. 5 (1971), 28–37.
  • R. Vershynin, High-Dimensional Probability: An Introduction with Applications in Data Science, Cambridge University Press, 2018, Chapter 11. https://doi.org/10.1017/9781108231596
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AlgebraAnalysisNumber Theory·Captain: lisamegawatts

Lindemann–Weierstrass I: Exponential IndependenceResearch Paper

Motivation

The exponential function turns addition into multiplication. When its inputs are algebraic numbers, that elementary identity meets a rigid arithmetic boundary: distinct algebraic exponents cannot produce an algebraic linear relation among their exponentials. This principle is the Lindemann–Weierstrass theorem, one of the central results of transcendence theory. Its familiar consequences include the transcendence of Euler's number eee and of π\piπ, and therefore the impossibility of squaring the circle with straightedge and compass.

The historical line runs from Hermite's 1873 proof that eee is transcendental, through Lindemann's 1882 proof that π\piπ is transcendental, to Weierstrass's general formulation in 1885. Modern algebraic presentations organize the theorem around conjugates, Galois symmetry, algebraic integers, and an auxiliary-polynomial estimate. The Lean development formalized here follows Yuyang Zhao's mathlib contribution PR #28013, whose mathematical reference is Jacobson's Basic Algebra I, §4.12, Theorem 4.22.

Setting

A complex number is algebraic if it is a root of a nonzero polynomial with rational, equivalently integer, coefficients. A complex number is transcendental if it is not algebraic. Write Q‾⊂C\overline{\mathbb Q}\subset\mathbb CQ​⊂C for the field of algebraic complex numbers and exp⁡(z)=ez\exp(z)=e^zexp(z)=ez for the complex exponential.

For a family (ui)i∈I(u_i)_{i\in I}(ui​)i∈I​ in Q‾\overline{\mathbb Q}Q​, injectivity means that distinct indices carry distinct exponents. A family (xi)(x_i)(xi​) is linearly independent over Q‾\overline{\mathbb Q}Q​ when every finite relation ∑iaixi=0\sum_i a_i x_i=0∑i​ai​xi​=0 with algebraic coefficients has all ai=0a_i=0ai​=0. It is algebraically independent over Q‾\overline{\mathbb Q}Q​ when no nonzero multivariate polynomial with algebraic coefficients vanishes on the family.

The strongest target uses natural-number linear independence of (ui)(u_i)(ui​): distinct finitely supported tuples of natural coefficients give distinct sums ∑iniui\sum_i n_i u_i∑i​ni​ui​. This is exactly the condition needed to distinguish the exponent attached to every monomial.

Formalization targets

Exponential linear independence

For every injective algebraic family (ui)(u_i)(ui​),

{eui:i∈I} is linearly independent over Q‾.\{e^{u_i}:i\in I\}\text{ is linearly independent over }\overline{\mathbb Q}.{eui​:i∈I} is linearly independent over Q​.

This includes the finite Lindemann–Weierstrass relation as its load-bearing finite core.

Hermite–Lindemann and classical constants

For every nonzero algebraic a∈Ca\in\mathbb Ca∈C,

ea is transcendental.e^a\text{ is transcendental}.ea is transcendental.

The same development records the transcendence of eee, the transcendence of π\piπ, and the transcendence of every nonzero principal logarithm of an algebraic complex number.

Integer winding consumer

Let α≠0\alpha\ne0α=0 be algebraic and let w:I→Zw:I\to\mathbb Zw:I→Z be injective. The proved Hermite–Lindemann theorem discharges the formerly conditional winding interface and gives

(eiαw(j))j∈I linearly independent over Q‾.\bigl(e^{i\alpha w(j)}\bigr)_{j\in I}\text{ linearly independent over }\overline{\mathbb Q}.(eiαw(j))j∈I​ linearly independent over Q​.

The integer labels are inputs to this arithmetic theorem. A separate topological or dynamical development is responsible for producing them as winding numbers.

Algebraic independence capstone

If (ui)(u_i)(ui​) is a natural-number-linearly-independent family in Q‾\overline{\mathbb Q}Q​, then

{eui:i∈I} is algebraically independent over Q‾.\{e^{u_i}:i\in I\}\text{ is algebraically independent over }\overline{\mathbb Q}.{eui​:i∈I} is algebraically independent over Q​.

This is the mission's capstone because it turns the linear theorem into a reusable multivariate interface: polynomial monomials become exponentials of distinct natural combinations.

Significance

The theorem separates two kinds of structure that otherwise coexist in the exponential map. The character law ex+y=exeye^{x+y}=e^xe^yex+y=exey supplies exact multiplicative relations, but the theorem rules out unintended linear relations over algebraic coefficients. For integer winding consumers, one algebraic nonzero generator aaa produces the two-sided phase family (ena)n∈Z(e^{na})_{n\in\mathbb Z}(ena)n∈Z​; after a Laurent-polynomial shift, the theorem makes distinct integer labels linearly independent over Q‾\overline{\mathbb Q}Q​. Winding supplies the discrete labels, while transcendence supplies arithmetic distinguishability.

The formalization contributes more than the named corollaries. It exposes a finite exponential-relation theorem, the algebraic orbit-sum reduction used by it, and general infinite-family interfaces. These components can be reused in later work on exponential polynomials, logarithms of algebraic numbers, and arithmetic representations of topological charges.

This mission formalizes a known theorem; it is not presented as an open mathematical problem. The private theorem graph is already machine-checked against Lean 4.30 and Mathlib revision c5ea00351c28e24afc9f0f84379aa41082b1188f. The mission records that proof as an independently inspectable dependency graph before any later upstream integration.

Difficulty

The analytic approximation alone is insufficient. It produces a small complex error, but smallness does not imply vanishing, and taking a field norm does not repair the gap because the other embeddings have no corresponding analytic bound. Likewise, a field automorphism of Q‾\overline{\mathbb Q}Q​ cannot be moved through the complex exponential as an algebraic operation.

The formal statement therefore requires both an analytic and an arithmetic layer. The arithmetic layer must replace a hypothetical algebraic relation by a Galois-stable relation with integer data and a genuinely nonzero integer contribution. The analytic layer must then make the absolute value of that integer strictly less than one. Managing conjugacy classes, root multisets, denominator clearing, finite supports, and the asymptotic prime choice in one kernel-checked chain is the central formalization difficulty.

Formalization scope

The development is pinned to Lean 4.30 and Mathlib revision c5ea00351c28e24afc9f0f84379aa41082b1188f. Algebraic complex numbers are represented by integralClosure ℚ ℂ; transcendence corollaries are stated with Transcendental ℤ, which is equivalent to the usual absence of a nonzero integer polynomial relation. The finite theorem uses Fintype; the general linear and algebraic independence theorems permit arbitrary universe-zero index types and reduce relations to finite support internally.

The auxiliary algebraic theorem is stated over an arbitrary algebraically closed field over Q\mathbb QQ and a multiplicative character on its additive group. The analytic consumer specializes this character to the complex exponential. Two small support modules provide quotient lifting for finitely supported functions and evaluation identities for symmetric multivariate polynomials.

The condition a≠0a\ne0a=0 in Hermite–Lindemann is load-bearing: e0=1e^0=1e0=1 is algebraic. Injectivity of the exponent family is load-bearing for linear independence: duplicate exponents duplicate vectors. The capstone's natural-number linear independence is not algebraic independence of the exponents and must not be silently strengthened or weakened.

The source is an attributed, compatibility-preserving port of the May 2026 Lean 4.30 snapshot of mathlib PR #28013. Platform packaging uses the conservative ASCII rename linearIndependent_exp_finite for the upstream private helper and phi for one Greek binder. The elaborated theorem types were compared against the upstream source; these are naming changes only.

Selected references

  • Yuyang Zhao, The Lindemann–Weierstrass theorem, mathlib4 PR #28013, 2022–2026. https://github.com/leanprover-community/mathlib4/pull/28013
  • Nathan Jacobson, Basic Algebra I, 2nd edition, W. H. Freeman, 1985, §4.12, Theorem 4.22.
  • Mathlib contributors, AnalyticalPart: the analytic estimate for Lindemann–Weierstrass. https://leanprover-community.github.io/mathlib4_docs/Mathlib/NumberTheory/Transcendental/Lindemann/AnalyticalPart.html
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Number Theory·Captain: lisamegawatts

Integer Winding Transcendence I: Exponential Phase IndependenceTextbook

Motivation

Integer winding is one of the simplest ways that continuous geometry produces discrete arithmetic. A loop in the circle has an integer winding number, while the complex exponential turns an additive parameter into a multiplicative phase. This mission asks what arithmetic information survives when those two constructions are combined. Its answer is a conditional but exact bridge: once Hermite--Lindemann supplies one transcendental phase, distinct integer winding labels produce a linearly independent family over the algebraic numbers.

The transcendence input is classical. Lindemann proved in 1882 that the exponential of a nonzero algebraic number is transcendental, and Weierstrass subsequently established the broader theorem now called Lindemann--Weierstrass. A modern statement appears as Theorem 1.1 of Javier Fresán's notes on the Hermite--Lindemann--Weierstrass theorem: exponentials of rationally linearly independent algebraic numbers are algebraically independent. The present mission deliberately does not formalize that analytic theorem. It isolates and formalizes the algebraic consumer that becomes available immediately after its one-variable consequence is supplied.

Setting

Let K⊆EK\subseteq EK⊆E be a field extension and let z∈Ez\in Ez∈E. For every integer nnn, the Laurent power znz^nzn is defined when z≠0z\ne0z=0. An element zzz is transcendental over KKK when no nonzero polynomial with coefficients in KKK vanishes at zzz. The first target proves that transcendence rules out every finite KKK-linear relation among the two-sided family

{zn:n∈Z}.\{z^n:n\in\mathbb Z\}.{zn:n∈Z}.

For a complex parameter β\betaβ, define the integer exponential character

χβ(n)=exp⁡(nβ),n∈Z.\chi_\beta(n)=\exp(n\beta),\qquad n\in\mathbb Z.χβ​(n)=exp(nβ),n∈Z.

It satisfies χβ(n)=exp⁡(β)n\chi_\beta(n)=\exp(\beta)^nχβ​(n)=exp(β)n and the character law χβ(m+n)=χβ(m)χβ(n)\chi_\beta(m+n)=\chi_\beta(m)\chi_\beta(n)χβ​(m+n)=χβ​(m)χβ​(n). The mission registers the Hermite--Lindemann assertion as an explicit proposition: for every nonzero complex number β\betaβ algebraic over Q\mathbb QQ, exp⁡(β)\exp(\beta)exp(β) is transcendental over Q\mathbb QQ.

Write Q‾\overline{\mathbb Q}Q​ for the subfield of complex numbers algebraic over Q\mathbb QQ. If α≠0\alpha\ne0α=0 is algebraic, then iαi\alphaiα is nonzero and algebraic. Hermite--Lindemann therefore makes z=exp⁡(iα)z=\exp(i\alpha)z=exp(iα) transcendental, first over Q\mathbb QQ and then over Q‾\overline{\mathbb Q}Q​. Integer phases are exactly the Laurent powers znz^nzn.

Formalization targets

Laurent-power independence

For every field extension E/KE/KE/K and every z∈Ez\in Ez∈E transcendental over KKK,

(zn)n∈Zis linearly independent over K.\bigl(z^n\bigr)_{n\in\mathbb Z} \quad\text{is linearly independent over }K.(zn)n∈Z​is linearly independent over K.

Integer exponential character

For every β∈C\beta\in\mathbb Cβ∈C and m,n∈Zm,n\in\mathbb Zm,n∈Z,

χβ(n)=exp⁡(β)n,χβ(m+n)=χβ(m)χβ(n),χβ(0)=1.\chi_\beta(n)=\exp(\beta)^n, \qquad \chi_\beta(m+n)=\chi_\beta(m)\chi_\beta(n), \qquad \chi_\beta(0)=1.χβ​(n)=exp(β)n,χβ​(m+n)=χβ​(m)χβ​(n),χβ​(0)=1.

Conditional all-integer phase independence

Assuming Hermite--Lindemann, if α∈C\alpha\in\mathbb Cα∈C is nonzero and algebraic over Q\mathbb QQ, then

(exp⁡(iαn))n∈Zis linearly independent over Q‾.\bigl(\exp(i\alpha n)\bigr)_{n\in\mathbb Z} \quad\text{is linearly independent over }\overline{\mathbb Q}.(exp(iαn))n∈Z​is linearly independent over Q​.

Winding-labelled capstone

For any injective integer label w:I→Zw:I\to\mathbb Zw:I→Z under the same hypotheses,

(exp⁡(iαw(j)))j∈Iis linearly independent over Q‾.\bigl(\exp(i\alpha w(j))\bigr)_{j\in I} \quad\text{is linearly independent over }\overline{\mathbb Q}.(exp(iαw(j)))j∈I​is linearly independent over Q​.

The label www may be supplied downstream by a winding-number construction, a self-linking number, or another independently proved integer invariant. This packet consumes the integer; it does not manufacture winding from continuous data.

Significance

The result separates topology from arithmetic cleanly. A geometric or dynamical development is responsible for producing an integer label and proving when labels are distinct. The present mission then turns that discrete distinction into a strong arithmetic conclusion about the corresponding complex phases. Because the Laurent-power theorem is stated over an arbitrary field extension, it is reusable outside circle topology and transcendence theory.

The formalization also records the exact limits of the conclusion. The phase with label zero is 111 and is not individually transcendental. Repeated winding labels force repeated vectors and therefore destroy linear independence. At zero coupling every phase collapses to 111. Finally, the character law supplies multiplicative relations, so the indexed phases are not being claimed algebraically independent as separate variables. The theorem is linear independence over Q‾\overline{\mathbb Q}Q​, not algebraic independence of an unconstrained family.

Difficulty

The main algebraic difficulty is the presence of negative exponents. Ordinary polynomial evaluation detects finite relations among nonnegative powers, but an integer-indexed relation is a Laurent polynomial. The formal statement must ensure that evaluation of Laurent polynomials at a nonzero transcendental element is injective. It must also transport transcendence from Q\mathbb QQ to the algebraic closure embedded in C\mathbb CC without replacing the registered field by an informal copy.

The transcendence theorem itself is a much larger analytic and algebraic-number-theoretic development. Treating it as an explicit hypothesis is therefore load-bearing: no unproved axiom or hidden instance may assert Hermite--Lindemann. Full Lindemann--Weierstrass is stronger than needed for this one-parameter family, since all exponents are integer multiples of a single algebraic generator.

Formalization scope

The mission targets Mathlib revision c5ea00351c28e24afc9f0f84379aa41082b1188f with Lean 4.30. Laurent polynomials are Mathlib's finitely supported integer-indexed monoid algebra. The algebraic numbers are represented by algebraicClosure ℚ ℂ, the subtype of complex numbers algebraic over the rationals. Linear independence is the ordinary Mathlib module-theoretic predicate.

The reusable core proves Laurent-power independence for arbitrary fields and arbitrary field extensions. The complex consumer uses Mathlib's complex exponential, the algebraicity of iii, and the algebraic-closure transcendence transfer. The mission includes explicit degenerate controls for zero coupling and duplicate labels. It does not prove Hermite--Lindemann, Lindemann--Weierstrass, transcendence of π\piπ, a topological winding theorem, or algebraic independence of the phase family.

Selected references

  • Javier Fresán, Gevrey Arithmetic and E-functions, Chapter 1, Theorem 1.1 (Hermite--Lindemann--Weierstrass), 2023. https://javier.fresan.perso.math.cnrs.fr/gevrey.pdf
  • Encyclopedia of Mathematics, Lindemann theorem. https://encyclopediaofmath.org/wiki/Lindemann_theorem
  • Mathlib, Mathlib.Algebra.Polynomial.Laurent, Laurent-polynomial definitions and evaluation. https://leanprover-community.github.io/mathlib4_docs/Mathlib/Algebra/Polynomial/Laurent.html
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Dynamical SystemsMathematical PhysicsTopology·Captain: lisamegawatts

Winding Dynamics I: Homotopy Conservation and Reset BalanceTextbook

Motivation

Phase winding is an integer attached to a circle-valued field on a closed spatial cycle. It distinguishes configurations that cannot be continuously deformed into one another while remaining circle-valued and spatially continuous. In oscillator and spin models this integer is often described informally as conserved by smooth evolution, while changes of winding are attributed to phase slips, vortices, singularities, or branch-cut crossings. The purpose of this mission is to turn that informal division into an exact Lean interface.

The continuum and finite-lattice settings must be separated. A jointly continuous field on a spatial circle really does provide a homotopy of circle maps, so its degree is invariant. A finite list of continuously moving vertex phases does not by itself determine a continuous field on the geometric realization of the lattice. Principal shortest-arc interpolation becomes ambiguous at antipodal bonds, and the corresponding discrete winding can jump even though every vertex phase remains continuous. The mission therefore treats winding as a first integral only on the regular sector and records every failure of regularity through an integer reset ledger.

This distinction is relevant to circle-valued reductions of the Kuramoto model, the finite XY model, and Lohe-type dynamics. Kuramoto's original synchronization model concerns coupled phase oscillators, while Lohe's non-Abelian extension replaces phases by group-valued variables. A model-specific conservation theorem is justified only after the dynamics has been connected to an actual circle-valued spatial loop or to the registered finite principal-branch interface.

Setting

A circle loop is a continuous map from a closed parameter interval to S1S^1S1 whose two endpoints agree. Its winding number is the integer obtained from the endpoint of a lift to the universal cover R→S1\mathbb R\to S^1R→S1. When the loop's basepoint moves during a deformation, the loop is normalized by the inverse of its value at the chosen spatial basepoint; this produces a based loop without changing its winding.

A continuous Circle-field segment is a jointly continuous map

U:[t0,t1]×S1⟶S1.U:[t_0,t_1]\times S^1\longrightarrow S^1.U:[t0​,t1​]×S1⟶S1.

Each time slice UtU_tUt​ is a spatial loop. Such a segment has no branch-cut convention: it is intrinsic topological data.

For a finite directed edge system (including a finite periodic lattice), a state assigns a real lift to every vertex. Each oriented edge receives an integer principal turn. A state is branch regular when no stored edge is antipodal. A coherent finite reset ledger stores successive principal-turn cochains TiT_iTi​ and defines the reset ki=Ti+1−Tik_i=T_{i+1}-T_iki​=Ti+1​−Ti​. For a certified closed integer cycle CCC, the pairing ⟨ki,C⟩\langle k_i,C\rangle⟨ki​,C⟩ is its registered winding jump.

A Kuramoto, XY, or Lohe consumer must supply the missing model-specific data. For a continuum consumer this is a jointly continuous circle-valued field. For a finite consumer it is a continuous vertex trajectory together with branch regularity away from registered events. A Lohe consumer additionally needs a continuous Circle readout or invariant Circle carrier; preservation of a rotor constraint alone does not provide that reduction.

Formalization targets

Continuous-field conservation

For every jointly continuous Circle-field segment, the two endpoint loops have equal winding:

wind⁡(Ut1)=wind⁡(Ut0).\operatorname{wind}(U_{t_1})=\operatorname{wind}(U_{t_0}).wind(Ut1​​)=wind(Ut0​​).

The statement must cover moving loop basepoints through explicit normalization. Winding is defined directly from Mathlib's exponential covering map as the floor of the zero-based lift endpoint divided by 2π2\pi2π.

Branch-regular finite conservation

For every finite directed principal-phase trajectory on a preconnected time domain that remains branch regular, every registered integer-chain winding is constant:

WC(t1)=WC(t0).W_C(t_1)=W_C(t_0).WC​(t1​)=WC​(t0​).

Continuity of the vertex phases alone is not a sufficient hypothesis and must not appear as a replacement for branch regularity or spatial interpolation.

Exact reset balance

For a finite coherent ledger with steps i=0,…,N−1i=0,\ldots,N-1i=0,…,N−1, endpoint winding change equals the sum of the reset periods:

WC(TN)−WC(T0)=∑i=0N−1⟨ki,C⟩.W_C(T_N)-W_C(T_0) =\sum_{i=0}^{N-1}\langle k_i,C\rangle.WC​(TN​)−WC​(T0​)=i=0∑N−1​⟨ki​,C⟩.

The conservation theorem is the empty-ledger or zero-period special case. The statement is an exact integer identity and does not assert an energy lower bound, vortex separation, or a thermodynamic-limit result.

Dynamics adapters

The generic dynamics adapter requires a jointly continuous ambient-state segment, a registered carrier containing it, closed spatial profiles, and a continuous readout from that carrier to the Circle. Kuramoto/XY or Lohe consumers must separately prove those hypotheses for their model. A second fence states that a global continuous readout from a simply connected carrier maps every loop to a nullhomotopic Circle loop; nonzero Lohe winding therefore requires a separately registered non-simply-connected carrier, such as a preserved U(1)U(1)U(1) orbit, or a different explicit interface.

Significance

The resulting theorem family makes precise the statement that winding obstructs unwinding. In the intrinsic continuum setting, winding cannot change while the field remains a continuous S1S^1S1-valued map. In the finite principal-branch setting, winding is piecewise constant and every change has an exact integer certificate. This separates a topological conservation law from the physical or analytic question of how much energy is needed to realize a certificate.

For formalization, the mission supplies a reusable boundary between topology and dynamics. A dynamics development can establish continuity and carrier preservation without reimplementing covering-space winding. A lattice development can consume the same integer through reset cochains without claiming that a vertex-only path is a homotopy of spatial loops. Later energy-barrier, vortex, and transport results can depend on the reset balance rather than on an informal conservation principle.

Difficulty

The principal difficulty is that several superficially similar notions of continuity have different consequences. Continuity in time of finitely many vertex phases is continuity into the configuration torus (S1)V(S^1)^V(S1)V, which is connected and does not preserve a principal-edge winding sector. Continuity of a map on time times the geometric spatial cycle is stronger. A formal statement that confuses them would make the desired theorem false.

There are two additional interface risks. First, the canonical Circle lift is based, whereas a physical phase field normally has a moving value at the chosen spatial origin. Second, the current Lohe development establishes algebraic identities and infinitesimal rotor preservation, not a global continuous flow in a selected Circle subgroup. These distinctions remain visible in the theorem hypotheses.

Formalization scope

The mission targets Lean 4.30 with Mathlib revision c5ea00351c28e24afc9f0f84379aa41082b1188f, matching the cited LeanProofs development. It reuses Mathlib's unit interval, continuous maps, path homotopies, Circle covering map, local constancy, and finite sums. The continuum statement concerns spatial S1S^1S1 only. The finite theorem is graph-generic: it uses finite oriented edges, integer edge cochains, certified closed integer cycles, coherent successive reset states, and branch regularity on a preconnected time domain.

The scope excludes ODE or PDE existence and uniqueness, preservation of a Circle carrier by a particular Lohe vector field, extraction of a coherent reset ledger from a physical event trajectory, arbitrary graph interpolation, accumulating reset times, thermodynamic limits, and energetic barriers. Those may be attached later through explicit interfaces. No theorem claims global winding conservation for an unrestricted finite vertex trajectory, and no theorem identifies group-valued Lohe motion with Circle motion without a declared continuous readout.

Selected references

  • Monumental Systems, CircleFundamentalGroupWindingV1, LeanProofs commit b656238b73d5f0f74515f6574a1dcb4e0216129f, 2026. https://github.com/MonumentalSystems/LeanProofs/blob/b656238b73d5f0f74515f6574a1dcb4e0216129f/LeanProofs/Rosetta/CircleFundamentalGroupWindingV1.lean#L81
  • Monumental Systems, FiniteTorusPrincipalResetEventV1, LeanProofs commit b656238b73d5f0f74515f6574a1dcb4e0216129f, 2026. https://github.com/MonumentalSystems/LeanProofs/blob/b656238b73d5f0f74515f6574a1dcb4e0216129f/LeanProofs/StatMech/FiniteTorusPrincipalResetEventV1.lean#L152-L167
  • Monumental Systems, CircleWindingTranslationHolonomyV1, LeanProofs commit b656238b73d5f0f74515f6574a1dcb4e0216129f, 2026. https://github.com/MonumentalSystems/LeanProofs/blob/b656238b73d5f0f74515f6574a1dcb4e0216129f/LeanProofs/Rosetta/CircleWindingTranslationHolonomyV1.lean#L107
  • Y. Kuramoto, “Self-entrainment of a population of coupled non-linear oscillators,” in International Symposium on Mathematical Problems in Theoretical Physics, Lecture Notes in Physics 39, 1975, pp. 420–422. https://doi.org/10.1007/BFb0013365
  • M. A. Lohe, “Non-Abelian Kuramoto models and synchronization,” Journal of Physics A: Mathematical and Theoretical 42 (2009), 395101. https://doi.org/10.1088/1751-8113/42/39/395101
  • A. Hatcher, Algebraic Topology, Chapter 1, Cambridge University Press, 2002. https://pi.math.cornell.edu/~hatcher/AT/ATch1.pdf
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Algebra·Captain: lisamegawatts

Grade-4 Cartan Mixing (Weinberg/Cabibbo correction)Open Problem

Formalize the corrected theory of flavor mixing angles in the su(3) Cartan sector of Cl(6,0), replacing the retired Killing-form/GUT normalization story. The mechanism: T3 and T8 commute, so mixing is carried not by their commutator but by the complete ordered products retained in grade 4. Milestone path: (M1) the grade-4 projection Pi4: Sym^2(A2) -> span{AB,AC,BC} is an isomorphism, with Pi4(e3^2) = -AB, Pi4(e3 e8) = (BC-AC)/sqrt 3, Pi4(e8^2) = (1/3)AB - (2/3)AC - (2/3)BC and tan(2 theta) = sqrt 3 (w-v)/(2u-v-w) for a retained grade-4 field G4 = u AB + v AC + w BC. (M2) the bridge: the primitive finite-T8 Cartan vector Phi = t e3 + e8 with t = sqrt 5 - 2 (the exact r = 16 closure) has grade-4 image exactly the rank-one family tensor phi phi^T; its traceless part is t[[-2,1],[1,2]], the Cabibbo family tensor up to one family-state sign, giving theta_C = arctan(sqrt 5 - 2) ~ 13.28 degrees; the grade-4 tensor has the same Sym2 structure as a left-handed Yukawa Gram operator M M^dagger. (M3, guarded goal) identify the r = 16 tensor with the relative left-family Yukawa tensor, closing the Sym2/Gram bridge. Foundational lemmas (A2 Cartan plane with [T3,T8] = 0, the complete 7-bracket su(3) table, grade-4 square residuals, grade-6 cubic channel) are landed in the LeanProofs repository and will be contributed as importable platform nodes ahead of the milestones. Recorded provenance: HAM memories #2848 (FullGradeCartanMixingTensorV1, 2026-09-14) and #3099 (CabibboGramSym2BridgeV1, 2026-09-17), proof DAG galaxy.proof-dag.v1 grade4-cartan-mixing.

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Dynamical SystemsMathematical PhysicsTopology·Captain: lisamegawatts

Winding Proto-Time I: Neutral Clock CoreTextbook

Motivation

A real lift of a circle-valued phase records both a principal representative and an integer sheet. This packet isolates the neutral arithmetic of that lifted reading before any physical interpretation as time, dynamics, causality, or a preferred vacuum.

Setting

A clock candidate is a function from an arbitrary event type to the real universal cover of the circle. Principalization separates each real value into a representative modulo 2π2\pi2π and an integer sheet. Real origin shifts, integral deck shifts, and orientation reversal are treated as distinct transformations.

Formalization targets

The intended packet covers principal-ledger reconstruction, the conditional strict order induced by an injective or monotone lift, affine-origin and deck freedom, orientation reversal, an adapter from separately established circle winding to an integer clock turn, and a scalar power-law integrability boundary.

The exact target statements remain subject to reconciliation with the immutable LeanProofs source before this private draft is submitted. In particular, no arithmetic quotient identity may be presented as the full Circle winding adapter, and no scalar integrability theorem may be interpreted as a PDE blow-up result.

Significance

This separates universal-cover bookkeeping from later consumers. A subsequent packet may connect the ledger to reset cochains, null-pair torsors, or dynamics only through explicit adapters.

Difficulty

The main risks are sign conventions at the principal cut, degeneracy on empty event types, confusion between the continuous real origin action and the integral deck action, and circular definitions that manufacture winding from the desired clock integer.

Formalization scope

The development targets Lean 4.30 and Mathlib revision c5ea00351c28e24afc9f0f84379aa41082b1188f. It makes no claim of monotone physical time, dynamical selection, causal order, global foliation, PDE existence, singularity formation, or a canonical origin or orientation.

Selected references

  • Monumental Systems, WindingProtoTimeV1Targets, LeanProofs commit b656238b73d5f0f74515f6574a1dcb4e0216129f, 2026. https://github.com/MonumentalSystems/LeanProofs/blob/b656238b73d5f0f74515f6574a1dcb4e0216129f/LeanProofs/Rosetta/WindingProtoTimeV1Targets.lean
  • Monumental Systems, CircleFundamentalGroupWindingV1, LeanProofs commit b656238b73d5f0f74515f6574a1dcb4e0216129f, 2026. https://github.com/MonumentalSystems/LeanProofs/blob/b656238b73d5f0f74515f6574a1dcb4e0216129f/LeanProofs/Rosetta/CircleFundamentalGroupWindingV1.lean
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Combinatorics·Captain: Yuxuan Xu

Magic Squares IV: The Special Classes of Order-Three Magic SquaresResearch Paper

Motivation

The first three missions in this programme settle the ordinary 3×33\times33×3 magic squares end to end: Mission I proved MacMahon's count M3(3e)=2e2+2e+1M_{3}(3e)=2e^{2}+2e+1M3​(3e)=2e2+2e+1, Mission II his semi-magic count H3(t)=3(t+34)+(t+22)H_{3}(t)=3\binom{t+3}{4}+\binom{t+2}{2}H3​(t)=3(4t+3​)+(2t+2​), and Mission III classified the normal squares (Lo Shu uniqueness). All three work with the plain magic condition.

This mission counts the two special classes that are singled out by requiring more than magicness, in the opposite directions one expects:

  • the panmagic (pandiagonal) squares, whose broken diagonals must also have the magic sum — a strengthening so strong that for order three the whole family collapses;
  • the symmetric magic squares, whose array must equal its transpose — a symmetry that only removes a few conditions and leaves a genuine family.

Writing P3(t)P_{3}(t)P3​(t) and S3(t)S_{3}(t)S3​(t) for the two counting functions, the goal is to determine both for every line sum ttt:

P3(t)={1,3∣t0,3∤t,S3(t)={2t3+1,3∣t0,3∤t.P_{3}(t)=\begin{cases}1,&3\mid t\\ 0,&3\nmid t\end{cases}, \qquad S_{3}(t)=\begin{cases}\dfrac{2t}{3}+1,&3\mid t\\[2mm] 0,&3\nmid t\end{cases}.P3​(t)={1,0,​3∣t3∤t​,S3​(t)=⎩⎨⎧​32t​+1,0,​3∣t3∤t​.

Setting

Everything is built on the vocabulary of MagicSquares (Mission I):

  • IsPanMagic — semi-magic, and every broken diagonal in both directions has the line sum, indices read modulo nnn;
  • IsSymmetric — Mij=MjiM_{ij}=M_{ji}Mij​=Mji​;
  • panMagicCount, symmetricMagicCount — the cardinalities of the two filtered finsets of arrays over Fin (t+1), which is lossless because every entry of a square of line sum ttt is at most ttt.

The new definition module MagicSquaresSpecial3 records the two explicit shapes that the proofs produce: constSquare3 e (the array all of whose entries are eee, read over the ambient Fin (3e+1)) and

symmMagic3(e,a)=(a2e−ae2e−aeaea2e−a),\mathrm{symmMagic3}(e,a)=\begin{pmatrix} a & 2e-a & e\\ 2e-a & e & a\\ e & a & 2e-a\end{pmatrix},symmMagic3(e,a)=​a2e−ae​2e−aea​ea2e−a​​,

together with the parameter set symmParamSet e ={0,…,2e}=\{0,\dots,2e\}={0,…,2e} and its cardinality symmParamCount e.

Formalization targets

Goal — the complete count

special_three_count: for every natural number ttt, the pair of equalities displayed above. The proof splits on 3∣t3\mid t3∣t and reduces to four child nodes.

The route

  1. Panmagic collapses to the constant square (pan_three_card). Writing the array as a,b,c;d,m,f;g,h,ia,b,c;d,m,f;g,h,ia,b,c;d,m,f;g,h,i, the twelve line equations form a linear system whose only nonnegative solution is a=b=⋯=i=ea=b=\dots=i=ea=b=⋯=i=e. So P3(3e)=1P_{3}(3e)=1P3​(3e)=1.
  2. Symmetry is classified by a corner (symmetric_magic_three_classify). Symmetry identifies three pairs of entries, leaving five free cells and five line equations; the anti-diagonal 2c+m=3e2c+m=3e2c+m=3e forces c=m=ec=m=ec=m=e, and the rows give M=symmMagic3(e,M00)M=\mathrm{symmMagic3}(e, M_{00})M=symmMagic3(e,M00​).
  3. A bijection onto an interval (symm_three_bij). Sending a symmetric magic square of line sum 3e3e3e to M00M_{00}M00​ is a bijection onto {0,1,…,2e}\{0,1,\dots,2e\}{0,1,…,2e}; hence S3(3e)=2e+1S_{3}(3e)=2e+1S3​(3e)=2e+1.
  4. The divisibility obstruction (pan_three_otherwise, symm_three_otherwise). Both classes consist of magic squares, and an order-three magic square has centre t/3t/3t/3 (center_of_order_three), so 3∤t3\nmid t3∤t forces both counts to vanish.

Significance

The results. The three order-three counts behave completely differently in the same parameter: MacMahon's M3M_{3}M3​ is quadratic, the symmetric count is linear, and the panmagic count is constant. That contrast is the point of the order-three study — order three is small enough to be completely understood, and the special classes show how differently the two natural strengthenings of the magic condition act. It is also exactly what is lost at order four, where no closed form is known for any of the three.

Formalizing them. The mathematical content is elementary, but the two classes require genuinely different proof techniques, which is what makes the mission worth formalizing:

  • For the panmagic case the six broken diagonals together with the rows and columns give a subtraction-free linear system over N\mathbb{N}N, so the uniqueness step is a single omega call. The only work is exposing the twelve equations, which requires reducing the index arithmetic i+ki+ki+k and rev(i)+k\mathrm{rev}(i)+krev(i)+k on Fin 3.
  • For the symmetric case the answer is a family, and the admissibility bound a≤2ea\le 2ea≤2e is a statement about truncated subtraction: the entry 2e−a2e-a2e−a is computed in N\mathbb{N}N, so the row identity a+(2e−a)+e=3ea+(2e-a)+e=3ea+(2e−a)+e=3e is satisfiable precisely for a≤2ea\le 2ea≤2e. Formalizing the bijection therefore needs an honest treatment of that truncation, where the panmagic case needs none.

Difficulty

Truncated subtraction, in the admissibility direction. The classification M = symmMagic3 e (M 0 0) is true for every MMM, without any bound on M00M_{00}M00​; the bound only appears when asking which members of the family are squares of line sum 3e3e3e. Keeping those two statements apart is what makes the bijection proof manageable: classification is a pure omega computation, while admissibility is a one-line argument that a+(2e−a)=2ea+(2e-a)=2ea+(2e−a)=2e forces a≤2ea\le 2ea≤2e.

Finite but not decidable. panMagicCount and symmetricMagicCount are cardinalities of filtered finsets over a function type, so the proofs cannot be decide or norm_num — the platform forbids native_decide in any case. Both counting theorems are therefore stated as Finset.card_bij / card_eq_one arguments over explicit bijections, not as finite evaluations.

Formalization scope

  • The in-scope statements are the two closed forms for all ttt, together with the classification of the symmetric family that the bijection is built on.
  • Parametrization follows MacMahon; the symmetric shape is the diagonal slice c=ec=ec=e of his two-parameter family, which is why the count drops from quadratic to linear.
  • Nothing here re-proves Mission I: the divisibility obstruction is inherited from the already-proved center_of_order_three.
  • Reusable beyond this mission: the order-three classification of symmetric magic squares, the observation that panmagic order-three squares are exactly the constant ones, and the technique of discharging twelve-index linear systems over Fin 3 with a single omega.

Selected references

  • P. A. MacMahon, Combinatory Analysis, Vol. II, Cambridge University Press, 1916.
  • M. Beck, T. Cohen, J. Cuomo and P. Gribelyuk, The number of "magic" squares, cubes and hypercubes, Amer. Math. Monthly 110 (2003), 707--717. https://arxiv.org/abs/math/0201013
  • W. S. Andrews, Magic Squares and Cubes, 2nd ed., Dover, 1960.
  • H. Behforooz, Symmetric and panmagic squares (survey of the symmetry properties of magic squares), and the standard pandiagonal literature.
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Number Theory·Captain: davidloeffler

Kriz–Nordentoft: Horizontal p-adic L-functionsResearch Paper

Motivation

Central values of twists of elliptic-curve and modular-form LLL-functions govern arithmetic questions ranging from Mordell--Weil ranks to the distribution of nonvanishing twists. Classical Iwasawa theory organizes twists whose conductors grow vertically through powers of one prime. Kriz and Nordentoft introduce a horizontal analogue in which the order of the character is fixed while its conductor acquires new prime factors. Their paper Horizontal p-adic L-functions constructs measures encoding these twists and proves a structure theorem that forces quantitative nonvanishing.

The mission formalizes the paper's common mechanism behind its nonvanishing theorems: Fourier theory of horizontal measures, norm-compatible modular-symbol theta elements, interpolation of central values, and propagation from nonzero measures to logarithmic-power lower bounds. The elliptic-curve statements in Theorems 1.1 and 1.2 are obtained in the paper by specializing this mechanism to weight-two newforms and adding the stated Galois-representation hypotheses.

Setting

Fix a prime ppp. The digit group is the compact product

Gp=∏n≥0Z/pZ.G_p=\prod_{n\ge 0}\mathbf Z/p\mathbf Z.Gp​=n≥0∏​Z/pZ.

A continuous character of GpG_pGp​ has finite image and factors through a finite product. For a complete algebraically closed nonarchimedean field KKK of residue characteristic ppp, a horizontal measure is represented in Lean by a continuous KKK-linear functional on the Banach space C(Gp,K)C(G_p,K)C(Gp​,K). Its Fourier transform is

ν^(χ)=∫Gpχ dν.\widehat\nu(\chi)=\int_{G_p}\chi\,d\nu.ν(χ)=∫Gp​​χdν.

The integral measures arising from the digit Iwasawa algebra have Fourier norms in a discrete geometric lattice. This is recorded explicitly by HasDiscreteFourierNorm; it is the norm-language counterpart of the discrete valuation hypothesis used in Corollary 2.9.

On the arithmetic side, modular symbols produce theta elements at finite squarefree conductors. Their projection maps do not initially form a compatible inverse system: an Euler factor appears each time a prime is removed. At an orderly prime this factor is a unit, so normalization produces a compatible system and hence a horizontal ppp-adic LLL-function. Evaluation at a character gives the modified central LLL-value of the corresponding twist.

Formalization targets

Finite-correction structure and interpolation

For every nonzero integral horizontal measure ν\nuν, there is a finite set MνM_\nuMν​ of characters and a constant c>0c>0c>0 such that

∣ν^(χχ0)∣p=c\lvert\widehat\nu(\chi\chi_0)\rvert_p=c∣ν(χχ0​)∣p​=c

for some χ0∈Mν\chi_0\in M_\nuχ0​∈Mν​, for every continuous character χ\chiχ. If ν\nuν interpolates modified central values, these corrected values are nonzero and have optimal ppp-adic size. This is Theorem 1.6, equivalently the digit-algebra case of Corollary 2.9, combined with Corollary 5.4.

Elliptic-curve specialization

For a modular elliptic curve over Q\mathbf QQ, the normalized theta system is assembled into the horizontal ppp-adic LLL-function of Definition 5.3 and shown to satisfy Corollary 5.4. The final milestones then specialize the structure and propagation theorems to prove Theorems 1.1 and 1.2 (assuming modularity), retaining the three alternative hypotheses of Theorem 1.1 and the jointly-good hypothesis for simultaneous nonvanishing in Theorem 1.2.

Quantitative propagation

If a fixed-order family has counting function bounded below by X/(log⁡X)1−αX/(\log X)^{1-\alpha}X/(logX)1−α and every character is related to a nonvanishing one by one of finitely many conductor-bounded corrections, the same lower bound holds for the nonvanishing subfamily. This isolates the formal content of Theorem 5.9 used in the applications of Section 5.4.

Significance

The structure theorem replaces one-variable Weierstrass preparation in an infinite-dimensional, non-noetherian Iwasawa algebra. It shows that the zero set of a nonzero horizontal measure is rigid enough that finitely many translations detect an optimal Fourier value everywhere. Through interpolation, this converts a single nonzero horizontal ppp-adic LLL-function into infinitely many nonzero complex central values with a quantitative lower bound.

A formal proof will add reusable infrastructure for nonarchimedean Fourier analysis on profinite products, finite group rings, inverse systems of theta elements, and character-counting asymptotics. None of these results currently has a machine-checked proof in Mathlib. The development is arranged so the analytic structure theorem and the modular-symbol construction can be attacked independently.

Difficulty

The central obstacle is that the horizontal Iwasawa algebra is neither noetherian nor reduced. A direct compactness or finite-generation argument on its spectrum is unavailable. At finite level, Fourier inversion introduces the group order into the valuation estimates; globally, one must control compatible finite quotients while keeping the exceptional correcting set finite. The arithmetic half has a separate normalization problem: raw theta elements satisfy norm relations only up to Euler factors, and interpolation must track imprimitive characters and the removed Euler factors exactly.

Formalization scope

The first version works with the exponent-ppp digit group GpG_pGp​, which is the setting of Theorem 1.6. Characters take values in the unit group of a complete algebraically closed ultrametric field KKK. Measures are continuous linear functionals on C(Gp,K)C(G_p,K)C(Gp​,K), and discreteness of integral Fourier values is an explicit hypothesis. The definition does not assume the finite-correction conclusion.

The modular-form layer is exposed through RawThetaSystem, ThetaSystem, and InterpolationDatum. Milestones must construct these data from genuine modular symbols and prove their norm and interpolation fields; merely postulating the desired nonvanishing values does not satisfy the goal. The quantitative statement uses an explicit eventual lower bound rather than asymptotic notation hidden behind an uninterpreted predicate.

Selected references

  • Daniel Kriz and Asbjørn Christian Nordentoft, Horizontal p-adic L-functions, arXiv:2310.20678v3, 2025. https://arxiv.org/abs/2310.20678
  • Yuri Manin, Periods of parabolic forms and p-adic Hecke series, Mathematics of the USSR-Sbornik 21 (1973). https://doi.org/10.1070/SM1973v021n03ABEH002016
  • Glenn Stevens, The cuspidal group and special values of L-functions, Transactions of the AMS 291 (1985). https://doi.org/10.1090/S0002-9947-1985-0797057-3

Current proof decomposition

The new definition node isolates the horizontal Iwasawa algebra and the interpolation interface. The first new milestone constructs a nonzero horizontal 222-adic LLL-function νE\nu_EνE​. The second milestone assumes such a pair (νE,νE≠0)(\nu_E,\nu_E\ne0)(νE​,νE​=0) and combines the horizontal-measure structure theorem, the Friedberg--Hoffstein quadratic seed, Corollary 5.10, and fixed-order character counting to obtain the lower bound in Theorem 1.1(1). The mission goal then follows immediately by applying the existence milestone and passing its witness to the implication milestone.

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Combinatorics·Captain: mysticflounder

Balog-Szemeredi-Gowers theorem over additive energyResearch Paper

Motivation

Additive combinatorics studies what arithmetic structure follows from statistical signals. The Balog–Szemerédi–Gowers theorem is its central regularity statement: a pair of finite sets with large additive energy (many additive quadruples) contains large subsets whose sumset is small. Balog and Szemerédi proved the first version in 1994 using the regularity lemma, which gave a tower-type dependence between the parameters; Gowers obtained a polynomial dependence in 1998. The theorem powers results across the field — from sum-product estimates to the structure of sets with small doubling — and its proof assembles three reusable machines: dependent random choice, the popular-sum graph, and Ruzsa calculus.

Setting

Work in an arbitrary abelian group GGG (Lean: AddCommGroup G). For finite X,Y⊆GX, Y \subseteq GX,Y⊆G, the additive energy E(X,Y)E(X,Y)E(X,Y) counts quadruples (x,x′,y,y′)(x,x',y,y')(x,x′,y,y′) with x+y=x′+y′x + y = x' + y'x+y=x′+y′; the trivial maximum is ∣X∣3|X|^3∣X∣3 when ∣X∣=∣Y∣|X| = |Y|∣X∣=∣Y∣. The sumset X+YX + YX+Y is {x+y}\{x + y\}{x+y}, and the difference set X−YX - YX−Y is defined pointwise. A set has small doubling when ∣X+X∣|X + X|∣X+X∣ is linear in ∣X∣|X|∣X∣. The Lean development uses Finset.addEnergy and Finset.addConvolution from Mathlib.

A bipartite graph here is an edge set EEE of type Finset (G × G) with E⊆A×sBE \subseteq A \times^s BE⊆A×sB, not a Mathlib SimpleGraph; solvers should state graph hypotheses that way. Given such an EEE, the partial sumset A+EBA +_E BA+E​B is {a+b:(a,b)∈E}\{a + b : (a,b) \in E\}{a+b:(a,b)∈E}, following Tao–Vu Definition 2.28.

Target

The mission goal is the two-set (equal-cardinality) form:

E(X,Y)≥η∣X∣3  ⟹  ∃X′⊆X,Y′⊆Y, ∣X′∣,∣Y′∣≥c∣X∣, ∣X′−Y′∣≤C∣X∣.E(X,Y) \ge \eta |X|^3 \implies \exists X' \subseteq X, Y' \subseteq Y,\ |X'|,|Y'| \ge c|X|,\ |X' - Y'| \le C|X|.E(X,Y)≥η∣X∣3⟹∃X′⊆X,Y′⊆Y, ∣X′∣,∣Y′∣≥c∣X∣, ∣X′−Y′∣≤C∣X∣.

This statement is assembled from the sources rather than quoted from them: Tao–Vu Lemma 2.30 supplies the energy-to-graph step, Fox–Sudakov §5.1 (the same theorem as Tao–Vu Theorem 2.29) supplies the graph-level bound, and Ruzsa calculus converts a sumset bound into the difference-set bound above. No cited work states this exact form, and the goal deliberately keeps ccc and CCC existential; the explicit-constant variant is proved separately in the mission with c=η/16c = \eta/16c=η/16.

The four milestones follow the sources' own numbering and are, in dependency order, Fox–Sudakov Lemma 5.1, Fox–Sudakov Lemma 5.2, the Fox–Sudakov §5.1 / Tao–Vu Theorem 2.29 graph bound with explicit constants, and Tao–Vu Lemma 2.30. The first three lie on the goal's proof path; the fourth is the reusable packaging of the energy-to-graph step.

Significance

The result converts a purely statistical hypothesis (many additive quadruples) into genuine algebraic structure (a large subset with a small difference set) with polynomial losses — the step that makes energy methods usable. It is a standard tool behind quantitative Freiman-type arguments.

Formalizing it matters because the constants are the content: the development tracks explicit constants through dependent random choice (graph level c=δ/8c = \delta/8c=δ/8 and C=213K3/δ5+212/δ5C = 2^{13}K^3/\delta^5 + 2^{12}/\delta^5C=213K3/δ5+212/δ5; energy level c0=η/16c_0 = \eta/16c0​=η/16 and C0=213(4/η)3/(η/2)5+212/(η/2)5C_0 = 2^{13}(4/\eta)^3/(\eta/2)^5 + 2^{12}/(\eta/2)^5C0​=213(4/η)3/(η/2)5+212/(η/2)5), which paper proofs often leave implicit. Fox–Sudakov state the application for sets of integers; the formalization is over an arbitrary AddCommGroup, with no further hypothesis on the group. Mathlib at the pinned revision (v4.33.1) contains no BSG statement, so this fills a genuine upstream gap.

Difficulty

The hard step is dependent random choice: sampling a random vertex subset of the popular-sum graph must simultaneously keep many vertices and keep the induced subgraph dense, and the two requirements fight each other. The naive first idea — take the densest neighborhood — loses control of the vertex count; the fix is a two-stage Markov-plus-payoff selection whose density analysis needs the exact path-count lower bound, not just an order estimate.

Two places where the formalization departs from Fox–Sudakov are recorded on the affected statements rather than hidden: the length-three path count admits degenerate paths (the source's a′≠aa' \ne aa′=a and b′≠bb' \ne bb′=b terms are dropped, which weakens the conclusion and is sound for the BSG use), and the density parameter is instantiated at a guaranteed lower bound rather than the exact edge density.

Formalization scope

Sets are Finset G in an AddCommGroup G with DecidableEq; energy is Finset.addEnergy; graphs are edge sets Finset (G × G), with the pointwise sumset and difference operations from open scoped Pointwise. Density hypotheses are stated with explicit real constants. The counting lemmas at the bottom of the development (sum_addConvolution_eq_card_product, path3_count_le_triple_rep_count, restricted_sumset_via_multiplicity) are unconditional; the statements that need them carry the nonemptiness, equal-cardinality and density hypotheses that exclude degenerate zero-energy configurations. Welcome contributions: the single-set polynomial Freiman–Ruzsa consequences, and non-abelian variants.

Selected references

  • A. Balog and E. Szemerédi, A statistical theorem of set addition, Combinatorica 14 (1994), 263–268.
  • W. T. Gowers, A new proof of Szemerédi's theorem for arithmetic progressions of length four, Geom. Funct. Anal. 8 (1998), 529–551.
  • J. Fox and B. Sudakov, Dependent random choice, Random Structures & Algorithms 38 (2011), 68–99 (Lemmas 5.1/5.2 and §5.1 BSG application).
  • T. Tao and V. Vu, Additive Combinatorics, Cambridge Univ. Press (2006), Definition 2.28 (partial sumsets), Theorem 2.29 (BSG, p. 79) and Lemma 2.30 (energy to partial sumset, p. 80).
  • C. Reiher and T. Schoen, Note on the theorem of Balog, Szemerédi, and Gowers, Combinatorica 44 (2024), no. 3, 691–698 (arXiv:2308.10245).
  • I. Ruzsa's inequalities via Mathlib's Finset.pluennecke_ruzsa_inequality_nsmul_add; see G. Petridis, New proofs of Plünnecke-type estimates for product sets in groups, Combinatorica 32 (2012), 721–733 (arXiv:1101.3507).
  • McKenna, Lean formalization (mathlib-only, axiom-clean), lean-formalizations, modules Combinatorics/Additive/BalogSzemerediGowers and BSGEnergyToGraph.
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Combinatorics·Captain: Yuxuan Xu

Magic Squares III: The Complete Classification of Order-Three Magic SquaresResearch Paper

Motivation

The first two missions in this programme counted order-three squares. Mission I proved MacMahon's magic count M3(3e)=2e2+2e+1M_{3}(3e)=2e^{2}+2e+1M3​(3e)=2e2+2e+1 and Mission II his semi-magic count H3(t)=3(t+34)+(t+22)H_{3}(t)=3\binom{t+3}{4}+\binom{t+2}{2}H3​(t)=3(4t+3​)+(2t+2​). What neither does is classify: counting tells you how many squares there are, but not what they look like.

This mission closes that gap for the most classical case of all. A normal magic square of order three is a 3×33\times33×3 array containing each of 1,2,…,91,2,\dots,91,2,…,9 exactly once, whose rows, columns and two main diagonals all sum to the magic constant 151515. The statement to be proved is the uniqueness of the Lo Shu square:

every normal magic square of order three is one of the eight images of (492357816)\begin{pmatrix}4&9&2\\3&5&7\\8&1&6\end{pmatrix}​438​951​276​​ under the symmetry group of the square.

In particular there are exactly 888 of them, and they form a single orbit under the dihedral group D4D_{4}D4​.

Setting

MacMahon's parametrization (already formalized in MagicSquaresParam3) writes every order-three magic square of line sum 3e3e3e as

mkMagic3(e,a,c)=(a3e−a−cce+c−aee+a−c2e−ca+c−e2e−a),\mathrm{mkMagic3}(e,a,c)= \begin{pmatrix} a & 3e-a-c & c\\ e+c-a & e & e+a-c\\ 2e-c & a+c-e & 2e-a \end{pmatrix},mkMagic3(e,a,c)=​ae+c−a2e−c​3e−a−cea+c−e​ce+a−c2e−a​​,

with (a,c)(a,c)(a,c) ranging over the finite admissible set paramSet e. For a normal square the magic constant is 151515, so e=5e=5e=5 and the centre entry is 555.

Normality (IsNormal) means every entry lies in [1,9][1,9][1,9] and the nine entries are pairwise distinct — equivalently, they are a permutation of 1,…,91,\dots,91,…,9.

Formalization targets

Goal — Lo Shu uniqueness

\\#\\{(a,c)\in \\mathrm{paramSet}\\ 5 : \\mathrm{mkMagic3}(5,a,c)\\ \\text{is normal}\\} = 8,

together with the identification of those eight parameter pairs. By the bijection magic_three_param_bij this is exactly the statement that there are eight normal magic squares of order three, i.e. that Lo Shu is unique up to the symmetry group of the square.

The route

  1. Normality bounds the parameters. If mkMagic3(5,a,c)\mathrm{mkMagic3}(5,a,c)mkMagic3(5,a,c) is normal then 1leale91\\le a\\le 91leale9 and 1lecle91\\le c\\le 91lecle9, because aaa and ccc are corner entries. This reduces the classification to a finite search over 818181 pairs.
  2. Classification (magic_three_normal_classify). Within that range, mkMagic3(5,a,c)\mathrm{mkMagic3}(5,a,c)mkMagic3(5,a,c) is normal exactly when (a,c)(a,c)(a,c) is one of
(2,4),(2,6),(4,2),(4,8),(6,2),(6,8),(8,4),(8,6).\\{(2,4),(2,6),(4,2),(4,8),(6,2),(6,8),(8,4),(8,6)\\}.(2,4),(2,6),(4,2),(4,8),(6,2),(6,8),(8,4),(8,6).

The eight surviving pairs are precisely those with a,ca,ca,c distinct corners of the Lo Shu square; the excluded ones are those with a+c=10a+c=10a+c=10, for which the (2,1)(2,1)(2,1) entry a+c−5a+c-5a+c−5 collides with the centre 555. 3. Converse (magic_three_normal_converse). Each of the eight pairs really does give a normal square.

Significance

The result itself. The uniqueness of Lo Shu is the oldest non-trivial classification in combinatorics — it is the order-three case of the classification problem for magic squares, and the reason n=3n=3n=3 is special: for n=4n=4n=4 there are 880880880 normal squares (up to symmetry) and for n≥5n\ge 5n≥5 no classification is known. Formalizing it shows that the counting machinery of Missions I and II can be turned around and used as a classification tool: the parametrization plus a finite verification give the complete list, not just the cardinality.

Formalizing it. The whole proof is a finite case check over 818181 parameter pairs, so the mathematical content is small and the formalization difficulty is concentrated in making the finiteness usable. Two things have to be arranged before automation can see the problem:

  • IsNormal is stated with a Function.Injective, which is not decidable as stated; it must first be rewritten into an explicit conjunction of entrywise bounds and pairwise inequalities over Fin 3.
  • The quantifiers over Fin 3 do not unfold by simp alone; one needs Fin.forall_fin_succ to expand them before norm_num can decide the 818181 resulting ground instances.

Difficulty

Finiteness must be manufactured. Nothing in IsNormal mentions a bound on aaa or ccc, so the first step is to derive 1≤a,c≤91\le a,c\le 91≤a,c≤9 from the entrywise bounds of normality. Skipping it leaves an infinite search that interval_cases cannot start.

Truncated subtraction. The parametrization is written over N\mathbb{N}N, so entries such as a+c−5a+c-5a+c−5 and 15−a−c15-a-c15−a−c truncate at zero. Every ground instance must be evaluated with the truncation in place — which is why the classification is carried out by evaluating the actual entries rather than by manipulating symbolic inequalities.

Formalization scope

  • Normal means: entries in [1,n2][1,n^{2}][1,n2] and pairwise distinct (IsNormal).
  • The classification is over MacMahon parameters, so it inherits the parametrization of MagicSquaresParam3 and the bijection of Mission I.
  • Trivializing formalizations are ruled out: the goal is not a declaration that some finite set has eight elements, but a derived classification — normality must be characterized by an explicit list of parameter pairs.
  • Reusable beyond this mission: the decidable reformulation of IsNormal for Fin 3 (and the Fin.forall_fin_succ technique for unfolding finite quantifiers), the list of the eight Lo Shu parameters, and the order-three classification itself.

Selected references

  • P. A. MacMahon, Combinatory Analysis, Vol. II, Cambridge University Press, 1916.
  • M. Beck, T. Cohen, J. Cuomo and P. Gribelyuk, The number of "magic" squares, cubes and hypercubes, Amer. Math. Monthly 110 (2003), 707--717. https://arxiv.org/abs/math/0201013
  • W. S. Andrews, Magic Squares and Cubes, 2nd ed., Dover, 1960 (the classical enumeration for n=4n=4n=4).
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Algebraic GeometryDiscrete Geometry·Captain: mysticflounder

Pach-de Zeeuw: finite Bezout bound for real plane curvesResearch Paper

Motivation

The distinct-distances problem asks how few distinct distances a finite planar point set can determine. Guth and Katz proved the near-optimal bound Ω(n/log⁡n)\Omega(n / \log n)Ω(n/logn) in 2015. Their argument passes through incidence geometry: distances become incidences between points and curves, and the Elekes–Sharir framework converts the problem into an incidence bound for lines in three-space. Pach and de Zeeuw showed the same pipeline works for points on a fixed algebraic curve, replacing line incidences with curve incidences. The algebraic prerequisite for that replacement — that two bounded-degree real plane curves with no shared component meet in finitely many points, with an explicit degree-dependent bound — is what this mission formalizes.

Setting

A real plane curve here is the real zero set of a nonzero bivariate polynomial p∈R[x,y]p \in \mathbb{R}[x, y]p∈R[x,y], written V(p)={(x,y):p(x,y)=0}V(p) = \{(x,y) : p(x,y) = 0\}V(p)={(x,y):p(x,y)=0}. Its total degree is the maximum i+ji+ji+j over monomials xiyjx^i y^jxiyj with nonzero coefficient. A curve is irreducible when its polynomial is irreducible.

The paper says two curves have a common component when their polynomials share a nonconstant factor. The Lean development uses a different, weaker hypothesis, NoCommonCurveComponent: no infinite irreducible real curve lies inside both sets. A shared factor whose real zero set is finite (for example x2+y2x^2 + y^2x2+y2) violates the paper's hypothesis but satisfies the Lean one, so the Lean theorem covers strictly more pairs of curves than the paper's statement.

Currying views ppp as a univariate polynomial in one coordinate whose coefficients are polynomials in the other. In the Lean code the polynomial variable is coordinate 000 and the coefficient (base) variable is coordinate 111; this description writes the base coordinate as xxx and the fiber coordinate as yyy, so a line {x=c}\{x = c\}{x=c} is called vertical. The resultant of two curried polynomials is a polynomial in xxx alone. The development uses one direction of its defining property: if the two specializations at xxx share a real root, the resultant vanishes at xxx. All Lean statements use MvPolynomial (Fin 2) ℝ for plane polynomials and EuclideanSpace ℝ (Fin 2) for points.

Target

The mission's goal is a finite-intersection bound in the spirit of Theorem 2.1 of Pach–de Zeeuw (Bézout's inequality), but it is not that theorem. It differs in both directions:

∀d1,d2, ∃C>0, ∀C1,C2 of total degree≤d1,d2 with no common infinite irreducible component:C1∩C2 is finite and ∣C1∩C2∣≤C.\forall d_1, d_2,\ \exists C > 0,\ \forall C_1, C_2 \text{ of total degree} \le d_1, d_2 \text{ with no common infinite irreducible component}:\quad C_1 \cap C_2 \text{ is finite and } |C_1 \cap C_2| \le C.∀d1​,d2​, ∃C>0, ∀C1​,C2​ of total degree≤d1​,d2​ with no common infinite irreducible component:C1​∩C2​ is finite and ∣C1​∩C2​∣≤C.
  • The conclusion is an existential degree-dependent constant. The proof's witness is C=(d1+d2+1)8+1C = (d_1+d_2+1)^8 + 1C=(d1​+d2​+1)8+1. The paper's sharp bound d1⋅d2d_1 \cdot d_2d1​⋅d2​ is not proved here.
  • The hypothesis is the weaker "no common infinite irreducible component" described above, so the statement is not a formal consequence of the paper's Theorem 2.1; the shared-finite-factor case is handled separately in the proof by a singular-point count.

The six milestones are the algebraic inputs: coefficient-root counting, the two resultant-nonvanishing criteria, the fiber bound, and the two mixed vertical/nonvertical pair bounds that carry the constant d1⋅d2d_1 \cdot d_2d1​⋅d2​. The nonvertical–nonvertical pair bound (primitive_nonvertical_pair_intersection_bound) carries the cruder constant ((d1+d2)2+1)⋅max⁡(d1,d2)((d_1+d_2)^2+1)\cdot\max(d_1,d_2)((d1​+d2​)2+1)⋅max(d1​,d2​). Above the milestones sit the irreducible-pair assembly with constant (d1+d2+1)4(d_1+d_2+1)^4(d1​+d2​+1)4 and the factorized assembly with constant (d1+d2+1)8(d_1+d_2+1)^8(d1​+d2​+1)8, from which the goal follows.

Significance

The result is the algebraic input to the Pach–de Zeeuw distinct-distances theorem for points on curves: without a uniform finite-intersection bound, the incidence count that drives the distance bound cannot even be stated. The formalization pins down every constant and every non-degeneracy hypothesis (non-verticality, no shared infinite component, coprimality) that the argument consumes.

The proof composes four toolkits — univariate root counting, Sylvester-matrix resultant degree bounds, normalized-factor decompositions, and a smooth implicit-function nonsingularity argument — whose interfaces must agree exactly. The resulting lemmas (resultant criteria, fiber bounds, partial-derivative degree bounds) are reusable for other real-algebraic incidence formalizations over MvPolynomial.

Difficulty

The central difficulty is elimination with explicit constants: the resultant converts a two-variable intersection problem into a one-variable root count, but every step (currying, specialization, factor-pair summation) must preserve a usable degree bound, and the degenerate configurations (vertical fibers, shared factors with finite real zero set, singular points) each need a separate finite bound.

The textbook route — Bézout's inequality over C\mathbb{C}C, then observing that real intersection points are complex ones — is not taken, for two reasons. Mathlib has no plane-curve Bézout theorem to invoke. And under the weaker Lean hypothesis the two polynomials may share an irreducible factor with finite real zero set, in which case the complex intersection is infinite and no complex count applies; that branch is closed by bounding the singular points of the shared factor instead.

Formalization scope

Points are EuclideanSpace ℝ (Fin 2); curves are MvPolynomial (Fin 2) ℝ zero sets; finiteness is Set.Finite with Set.ncard bounds. The development commits to total degree (not weighted degrees) and to the currying order that eliminates Lean coordinate 000; variable-style implicit degree bounds d1,d2d_1, d_2d1​,d2​ are explicit {d₁ d₂ : ℕ} binders on the platform. No statement is vacuous: every intersection bound carries the non-degeneracy hypothesis (non-associated irreducibles, non-divisibility, nonzero partials) that excludes the infinite-intersection cases. Contributions welcome: the sharp d1d2d_1 d_2d1​d2​ general bound (currently an existential constant), the paper's hypothesis form (no common factor at all), and the incidence assembly that consumes this mission's output.

Selected references

  • János Pach and Frank de Zeeuw, Distinct distances on algebraic curves in the plane, Combin. Probab. Comput. 26 (2017), no. 1, 99–117, arXiv:1308.0177, DOI 10.1017/S0963548316000225. Theorem 2.1 there cites C. G. Gibson, Elementary Geometry of Algebraic Curves, Lemma 14.4, for Bézout's inequality.
  • McKenna, Lean formalization of the algebraic preliminaries and Bézout bound, lean-formalizations, modules PachDeZeeuw.AlgebraicPrelim and PachDeZeeuw.Bezout (mathlib-only, axiom-clean).
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CombinatoricsOptimizationTheoretical Computer Science·Captain: moutei

Primal-Dual Online Algorithms III: Set-Cover Approximation via CertificatesTextbook

Motivation

Set cover is the standard worked example of the primal-dual method, and Chapter 2 of Buchbinder's thesis uses it that way: it is where the machinery of §2.1 is first turned on a concrete NP-hard problem. Two analyses appear. The greedy algorithm, analysed by dual fitting, buys the set with the best cost-per-newly-covered-element ratio and charges the price to the elements it covers; the resulting element prices form an infeasible dual that becomes feasible after scaling by HnH_nHn​. The primal-dual algorithm instead raises the price of an uncovered element until some set's constraint goes tight, buys that set, and repeats; the resulting dual is feasible, and each bought set is paid for by elements of frequency at most fff, giving an fff-approximation.

Both analyses have the same shape, and it is the shape that matters for the rest of the series: the algorithm never sees the optimum. It maintains a dual solution, and the approximation ratio falls out of comparing the primal it built against the dual it accumulated.

Setting

An instance consists of a finite type EEE of elements, a finite type SSS indexing available sets, an assignment s↦As⊆Es \mapsto A_s \subseteq Es↦As​⊆E, and a nonnegative cost c:S→Rc : S \to \mathbb{R}c:S→R. Every element is assumed to lie in at least one available set; the source leaves this implicit, and without it no cover exists and the approximation statements are vacuous. The covering LP and its packing dual are

(P)min⁡∑scsxs  s.t. ∑s:e∈Asxs ≥ 1  (∀e∈E),x≥0,(P)\quad \min \sum_{s} c_s x_s \ \text{ s.t. } \sum_{s : e \in A_s} x_s \ \ge\ 1 \ \ (\forall e \in E), \quad x \ge 0,(P)mins∑​cs​xs​  s.t. s:e∈As​∑​xs​ ≥ 1  (∀e∈E),x≥0, (D)max⁡∑eye  s.t. ∑e∈Asye ≤ cs  (∀s∈S),y≥0.(D)\quad \max \sum_{e} y_e \ \text{ s.t. } \sum_{e \in A_s} y_e \ \le\ c_s \ \ (\forall s \in S), \quad y \ge 0.(D)maxe∑​ye​  s.t. e∈As​∑​ye​ ≤ cs​  (∀s∈S),y≥0.

The frequency of an element is the number of sets containing it, and fff denotes the maximum frequency over all elements.

The two standing assumptions — nonnegative costs, and every element lying in some available set — are carried by a bundled SetCoverInstance, not passed as loose hypotheses. Every source-facing statement in the mission takes such an instance and reads those facts off its fields, so none of them can be instantiated at data violating either. The two indicator lemmas are the exceptions and are labelled as generalized assisting results: one has no cost function in scope at all, and the other's hypothesis that a given CCC covers is strictly stronger than coverability of the family.

Costs are permitted to be zero and the ground type is permitted to be empty. No Nonempty E hypothesis appears anywhere; when EEE is empty, f=0f = 0f=0 and the fff-approximation bound reads cost(C)≤0\mathrm{cost}(C) \le 0cost(C)≤0, which the certificate's tightness clause forces to be 0≤00 \le 00≤0 rather than anything false.

Formalization targets

The results are stated about certificates, not about executable algorithms. This is the central modelling decision of the mission and it is deliberate: the mathematical content of the source's proofs is entirely a statement about the invariants the output satisfies, and separating that from the question of whether a particular procedure produces such output keeps each half provable on its own.

A primal-dual certificate is a pair (C,y)(C, y)(C,y) where C⊆SC \subseteq SC⊆S covers EEE, yyy is dual-feasible, and every s∈Cs \in Cs∈C has a tight dual constraint, ∑e∈Asye=cs\sum_{e \in A_s} y_e = c_s∑e∈As​​ye​=cs​.

Goal — the primal-dual fff-approximation

For any primal-dual certificate (C,y)(C,y)(C,y) and any fractional cover xxx,

∑s∈Ccs ≤ f⋅∑s∈Scsxs.\sum_{s \in C} c_s \ \le\ f \cdot \sum_{s \in S} c_s x_s .s∈C∑​cs​ ≤ f⋅s∈S∑​cs​xs​.

Since this holds against every fractional cover, it holds in particular against an optimal one, so the cover CCC costs at most fff times the fractional optimum and a fortiori at most fff times the integral optimum.

The double-counting step

The one substantive step of the goal is split out as its own target: for a primal-dual certificate,

∑s∈Ccs ≤ f⋅∑e∈Eye.\sum_{s \in C} c_s \ \le\ f \cdot \sum_{e \in E} y_e .s∈C∑​cs​ ≤ f⋅e∈E∑​ye​.

Tightness rewrites the cover's cost as a double sum over chosen sets and their elements; exchanging the order groups it by element, each charged at most fff times. With this and weak duality, the goal is two lines.

The greedy bound

A greedy certificate at ratio ρ\rhoρ is a cover CCC and a nonnegative yyy with ∑s∈Ccs=∑eye\sum_{s \in C} c_s = \sum_{e} y_e∑s∈C​cs​=∑e​ye​ and ∑e∈Asye≤ρ cs\sum_{e \in A_s} y_e \le \rho\, c_s∑e∈As​​ye​≤ρcs​ for every sss. For such a certificate and any fractional cover xxx,

∑s∈Ccs ≤ ρ⋅∑scsxs.\sum_{s \in C} c_s \ \le\ \rho \cdot \sum_{s} c_s x_s .s∈C∑​cs​ ≤ ρ⋅s∑​cs​xs​.

Instantiating ρ=Hn\rho = H_nρ=Hn​ is what recovers the source's greedy guarantee; the harmonic bound itself is already in Mathlib.

Set-cover weak duality and LP attainment

Every dual packing is bounded by every fractional cover, ∑eye≤∑scsxs\sum_e y_e \le \sum_s c_s x_s∑e​ye​≤∑s​cs​xs​; the fractional optimum is at most the integral optimum; and both optima are attained, not merely bounded below. Attainment of the fractional optimum is a genuine linear-programming fact and is the hardest supporting item in the mission.

Significance

This is where the series first converts a dual-feasibility invariant into an approximation ratio on a concrete combinatorial problem, and the two certificate predicates are reused verbatim by the online covering missions later in the series. Set cover approximation has, as far as we can determine, no prior formalization in Mathlib or in any public Lean library: there is no set-cover problem statement, no greedy analysis, and no fff-approximation result to build on.

Difficulty

The two certificate bounds are finite-summation arguments of moderate length — the work is in a double-counting step that reindexes a sum over chosen sets into a sum over elements, weighted by frequency. Attainment of the fractional optimum is different in kind: it needs a compactness or vertex argument about the covering polytope and is the item most likely to need real work. Zero-cost sets are permitted throughout, so any later algorithm definition that divides by a cost must handle that case explicitly.

Formalization scope

Definitions cover §2.2 of the source, excluding §2.2.2 (randomized rounding), which is deferred to a separate mission because its expected-cost and failure-probability analysis is measure-theoretic and shares no infrastructure with the deterministic results.

Two theorems are not in this mission: that the greedy algorithm produces a greedy certificate, and that the primal-dual algorithm produces a primal-dual certificate. Those require defining the algorithms and proving termination and coverage, and are planned as a second wave. Until that wave lands, the source's Theorems 2.4 and 2.6 should not be described as fully formalized — what this mission establishes is the certificate-to-ratio half of each.

Selected references

  • Niv Buchbinder, Designing Competitive Online Algorithms via a Primal-Dual Approach, PhD thesis, Tel Aviv University, 2008, §2.2, pp. 10–14. https://www.tau.ac.il/~nivb/download/phd-thsis.pdf
  • Vijay V. Vazirani, Approximation Algorithms, Springer, 2001, Chapters 2 and 15 — the standard treatment of the greedy and primal-dual set-cover analyses.
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Combinatorics·Captain: Yuxuan Xu

Magic Squares II: MacMahon's Enumeration of Order-Three Semi-Magic SquaresResearch Paper

Motivation

This is the second mission in the magic-squares formalization programme, and it takes up the case the first one deliberately left open.

Counting semi-magic squares — arrays of nonnegative integers whose rows and columns all share a common line sum, with the diagonals unconstrained — is the "honest" version of the enumeration problem. For order three the magic count M3(t)M_{3}(t)M3​(t) (mission I) is only a quasi-polynomial: it vanishes unless 3∣t3\mid t3∣t and equals 2e2+2e+12e^{2}+2e+12e2+2e+1 on t=3et=3et=3e. The semi-magic count H3(t)H_{3}(t)H3​(t) has no such periodicity. MacMahon computed it in 1915:

H3(t)  =  3(t+34)+(t+22).H_{3}(t)\;=\;3\binom{t+3}{4}+\binom{t+2}{2}.H3​(t)=3(4t+3​)+(2t+2​).

It is an honest polynomial in ttt of degree 4=(3−1)24=(3-1)^{2}4=(3−1)2, and that degree is not an accident: Ehrhart and Stanley proved that for every order nnn the function Hn(t)H_{n}(t)Hn​(t) is a polynomial of degree (n−1)2(n-1)^{2}(n−1)2 satisfying the reciprocity law Hn(−n−t)=(−1)n−1Hn(t)H_{n}(-n-t)=(-1)^{n-1}H_{n}(t)Hn​(−n−t)=(−1)n−1Hn​(t). The order-three formula above is the smallest nontrivial instance of that theorem, and the only one small enough that every step of the derivation can still be exhibited explicitly.

So this mission is the natural companion to mission I: same objects, same platform vocabulary, but the counting step is genuinely harder — the parameter space is four-dimensional rather than two, and the parametrization is not injective until it is normalized.

Setting

Fix nnn and a line sum ttt. A square of order nnn is an n×nn\times nn×n array MMM of nonnegative integers.

  • MMM is semi-magic with line sum ttt if every row and every column sums to ttt. No condition is imposed on the two diagonals, and entries need not be distinct.
  • Hn(t)H_{n}(t)Hn​(t) is the number of such squares. Every entry is at most ttt, so Hn(t)H_{n}(t)Hn​(t) is the cardinality of a finite set.

For n=3n=3n=3 the whole family is governed by the six permutation matrices. Split them into the three even ones — the identity and the two 333-cycles — whose supports are the transversals

D={00,11,22},E={01,12,20},F={02,10,21},D=\{00,11,22\},\qquad E=\{01,12,20\},\qquad F=\{02,10,21\},D={00,11,22},E={01,12,20},F={02,10,21},

and the three odd ones — the transpositions — with supports

A={00,12,21},B={02,11,20},C={01,10,22}.A=\{00,12,21\},\qquad B=\{02,11,20\},\qquad C=\{01,10,22\}.A={00,12,21},B={02,11,20},C={01,10,22}.

Adding them with multiplicities u,v,wu,v,wu,v,w (even) and x,y,zx,y,zx,y,z (odd) gives

M=(u+xv+zw+yw+zu+yv+xv+yw+xu+z),M=\begin{pmatrix} u+x & v+z & w+y\\ w+z & u+y & v+x\\ v+y & w+x & u+z\end{pmatrix},M=​u+xw+zv+y​v+zu+yw+x​w+yv+xu+z​​,

whose six line sums all equal u+v+w+x+y+zu+v+w+x+y+zu+v+w+x+y+z; so this is a semi-magic square of line sum ttt whenever the multiplicities sum to ttt.

Formalization targets

Goal — MacMahon's semi-magic count

H3(t)  =  3(t+34)+(t+22)for every t≥0.H_{3}(t)\;=\;3\binom{t+3}{4}+\binom{t+2}{2}\qquad\text{for every }t\ge 0 .H3​(t)=3(4t+3​)+(2t+2​)for every t≥0.

This is the goal because it is the weakest statement that still pins down the answer: it asserts the shape of H3H_{3}H3​ without naming the parametrization, and it survives verbatim as the n=3n=3n=3 case of Stanley's theorem that HnH_{n}Hn​ is a polynomial of degree (n−1)2(n-1)^{2}(n−1)2.

The route

  1. Canonical decomposition (sm3_canonical). Every 3×33\times33×3 semi-magic square arises from the display above, and the representation becomes unique after normalizing: put u=min⁡Du=\min Du=minD, v=min⁡Ev=\min Ev=minE, w=min⁡Fw=\min Fw=minF, subtract the corresponding even permutation matrices, and the residual odd multiplicities satisfy min⁡(x,y,z)=0\min(x,y,z)=0min(x,y,z)=0. The normalization is necessary — without it the single relation
D+E+F=A+B+C  (=J)D+E+F=A+B+C\;(=J)D+E+F=A+B+C(=J)

identifies distinct 666-tuples — and it is exactly what makes the count a partition rather than an inclusion–exclusion. 2. Bijection (sm3_bij). The map from normalized coefficient vectors to semi-magic squares is a bijection, so H3(t)=sm3Count(t)H_{3}(t)=\mathrm{sm3Count}(t)H3​(t)=sm3Count(t). 3. Stars and bars (comps_card). The number of kkk-tuples of nonnegative integers summing to nnn is (n+k−1n)\binom{n+k-1}{n}(nn+k−1​); the case k=5k=5k=5 is what the count needs. 4. Evaluating the parameter count (sm3_params_card). Partitioning the normalized vectors according to the first zero among (x,y,z)(x,y,z)(x,y,z) writes sm3Count(t)\mathrm{sm3Count}(t)sm3Count(t) as

(t+44)+(t+34)+(t+24),\binom{t+4}{4}+\binom{t+3}{4}+\binom{t+2}{4},(4t+4​)+(4t+3​)+(4t+2​),

which collapses to 3(t+34)+(t+22)3\binom{t+3}{4}+\binom{t+2}{2}3(4t+3​)+(2t+2​) by two applications of Pascal's identity.

Significance

The result itself. H3H_{3}H3​ is the n=3n=3n=3 case of a theorem that launched a subject: Stanley's proof that Hn(t)H_{n}(t)Hn​(t) counts lattice points in the Birkhoff polytope t⋅Bnt\cdot B_{n}t⋅Bn​ makes HnH_{n}Hn​ an Ehrhart polynomial, and the order-three formula is the first nontrivial value of it. Beck, Cohen, Cuomo and Gribelyuk (Amer. Math. Monthly 110 (2003), 707--717) revisited exactly this computation on the way to their quasi-polynomial theorem for the magic counts, and Beck and Zaslavsky later pushed the same technique to the panmagic and symmetric refinements. Getting H3H_{3}H3​ machine-checked therefore validates the whole hierarchy at its base.

Formalizing it. Nothing here is open; the mathematics is a century old. What is missing is the formalized artifact, and the difficulty is concentrated in two places that are formalization difficulties rather than mathematical ones.

First, surjectivity of the permutation-matrix parametrization. The usual proof quotes Birkhoff–von Neumann, which in turn needs Hall's marriage theorem. For order three one can instead do it by hand: subtract the three even transversal minima and show that the residual satisfies M01=M10M_{01}=M_{10}M01​=M10​. That last step is a six-case argument in linear arithmetic — if b=M01>c=M10b=M_{01}>c=M_{10}b=M01​>c=M10​ then each of the three ways for the transversal EEE to have minimum zero forces c≥bc\ge bc≥b — and it is precisely the kind of step that is invisible on paper and must be made explicit in a proof assistant.

Second, the counting step. The parameter set is a filtered finset of functions Fin 6 → Fin (t+1), while the formula is stated with binomial coefficients over N\mathbb{N}N. Connecting them requires stars-and-bars, proved from scratch (by induction on the number of parts plus the hockey-stick identity), because the available library results count sub-multisets rather than compositions. And the final collapse to MacMahon's form is a chain of Pascal identities that must be applied in the right order to stay inside N\mathbb{N}N, where subtraction is truncated.

Difficulty

Two traps deserve to be named.

Uniqueness needs the normalization. The representation by six multiplicities is not injective: J=D+E+F=A+B+CJ=D+E+F=A+B+CJ=D+E+F=A+B+C. Any formalization that counts 666-tuples directly will overcount, and the correction is not a subtraction but a choice of canonical representative. Deciding "first zero among (x,y,z)(x,y,z)(x,y,z)" is what turns the count into a genuine partition.

Truncated subtraction. The decomposition is expressed over N\mathbb{N}N, so every identity — in particular the recovery of the multiplicities from a square — must be stated with the admissibility inequalities as explicit hypotheses. A truncated subtraction is only correct because normalization forbids the truncation, and that side condition has to be discharged rather than assumed.

Formalization scope

  • Squares are indexed by Fin n; semiMagicCount n t is the cardinality of a finset of arrays over Fin (t+1) — lossless, since every entry is at most ttt.
  • The parametrization and its normalization are defined over N\mathbb{N}N with truncated subtraction where necessary.
  • Trivializing formalizations are ruled out. The goal is not a statement about a hardcoded small ttt, nor about a finset declared to have the right cardinality: the count must be derived, by an explicit bijection followed by an explicit evaluation of a finite sum.
  • Reusable beyond this mission: the canonical decomposition of 3×33\times33×3 semi-magic squares (equivalently, the toric description of the order-three Birkhoff polytope with its single relation), the stars-and-bars lemma for compositions into any number of parts, and the order-three counts themselves.

Selected references

  • P. A. MacMahon, Combinatory Analysis, Vol. II, Cambridge University Press, 1916 (the H3H_3H3​ formula dates to his 1915 work).
  • M. Beck, T. Cohen, J. Cuomo and P. Gribelyuk, The number of "magic" squares, cubes and hypercubes, Amer. Math. Monthly 110 (2003), 707--717. https://arxiv.org/abs/math/0201013
  • M. Beck and T. Zaslavsky, Six little squares and how their numbers grow, J. Combin. Theory Ser. A 113 (2006). https://arxiv.org/abs/math/0502370
  • R. P. Stanley, Enumerative Combinatorics, Vol. I, 2nd ed., Cambridge University Press, 2012 (Ehrhart theory and reciprocity for HnH_nHn​).
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Combinatorics·Captain: Yuxuan Xu

Magic Squares I: MacMahon's Enumeration of Order-Three Magic SquaresResearch Paper

Motivation

Counting magic squares — arrays of nonnegative integers whose rows, columns and two main diagonals all share a common line sum — is one of the oldest problems in enumerative combinatorics, and the testing ground on which the general theory was built. MacMahon computed the order-three count in 1915 by hand; sixty years later Stanley, and then Beck, Cohen, Cuomo and Gribelyuk (Amer. Math. Monthly 110 (2003), 707--717), showed that for general order nnn the counting functions are quasi-polynomials in the line sum, by identifying them with Ehrhart quasi-polynomials of rational polytopes. The order-three case is the oldest nontrivial instance of that theory and the one where every step can still be checked by hand.

The subject therefore has a curious status: the enumerative answer for n=3n=3n=3 has been known for over a century, and the structural facts behind it (a 3×33\times33×3 magic square is determined by two corner entries; opposite cells sum to twice the centre) are folklore — but none of it has a machine-checked proof. This mission formalizes the classical derivation end to end.

Setting

Fix an order nnn and a type α\alphaα of entries. A square of order nnn is an n×nn\times nn×n array MMM with entries in α\alphaα; its row sums, column sums, and the two diagonal sums (main and anti-diagonal) are the sums of the entries along those lines.

  • MMM is semi-magic with line sum sss if every row and every column sums to sss.
  • MMM is magic with line sum sss if in addition both main diagonals sum to sss.
  • MMM is panmagic (pandiagonal) if every broken diagonal, in both directions, also sums to sss.

No distinctness of entries is required. Let Hn(t)H_n(t)Hn​(t) denote the number of semi-magic and Mn(t)M_n(t)Mn​(t) the number of magic squares of order nnn with nonnegative integer entries and line sum ttt. Every entry of such a square is at most ttt, so these are finite counts.

For n=3n=3n=3 the whole family is parametrized. If MMM has line sum 3e3e3e then the centre cell equals eee, and writing a=M00a=M_{00}a=M00​ and c=M02c=M_{02}c=M02​ the eight line identities force

M=(a3e−a−cce+c−aee+a−c2e−ca+c−e2e−a).M=\begin{pmatrix} a & 3e-a-c & c\\ e+c-a & e & e+a-c\\ 2e-c & a+c-e & 2e-a \end{pmatrix}.M=​ae+c−a2e−c​3e−a−cea+c−e​ce+a−c2e−a​​.

All nine entries are nonnegative exactly when

e≤a+c≤3e,a≤e+c,c≤e+a,e\le a+c\le 3e,\qquad a\le e+c,\qquad c\le e+a,e≤a+c≤3e,a≤e+c,c≤e+a,

and substituting p=a−ep=a-ep=a−e, q=c−eq=c-eq=c−e turns these into ∣p∣+∣q∣≤e|p|+|q|\le e∣p∣+∣q∣≤e: the ℓ1\ell_1ℓ1​ ball of radius eee in Z2\mathbb{Z}^2Z2.

Formalization targets

Goal — MacMahon's count

M3(3e)  =  2e2+2e+1,M_{3}(3e)\;=\;2e^{2}+2e+1 ,M3​(3e)=2e2+2e+1,

together with the companion vanishing M3(t)=0M_3(t)=0M3​(t)=0 when 3∤t3\nmid t3∤t. This is the count of 3×33\times33×3 magic squares of line sum 3e3e3e with nonnegative integer entries (entries need not be distinct). It is the goal because it is the weakest stable statement: it asserts only the shape of the answer, not the intermediate parametrization, and it survives verbatim as the n=3n=3n=3 case of the general quasi-polynomial theorem.

Stronger — the parametrization itself

That the map M↦(M00,M02)M\mapsto(M_{00},M_{02})M↦(M00​,M02​) is a bijection from the 3×33\times33×3 magic squares of line sum 3e3e3e onto the admissible parameter pairs, and that the latter are counted by the ℓ1\ell_1ℓ1​-ball cardinality. This is the route the mission actually takes; the count is its corollary.

Further — semi-magic counts

H3(t)H_3(t)H3​(t), the analogous count for semi-magic squares, is a genuinely different and harder quasi-polynomial. It is listed as a stretch target, not a milestone.

Significance

The result itself. MacMahon's formula is the base case of the Ehrhart-theory reading of magic-square enumeration; Beck--Cohen--Cuomo--Gribelyuk's quasi-polynomial theorem for general nnn degenerates to it at n=3n=3n=3, so it is the sanity check any generalization must pass. The parametrization behind it is what makes the "how many" question finite-dimensional at all: it reduces a search over t9t^9t9 arrays to a count of lattice points in a two-dimensional ball. Downstream, the same parametrization governs the classification of normal 3×33\times33×3 magic squares (the Lo Shu square and its symmetries) and the associativity identity Mij+M2−i,2−j=2M11M_{ij}+M_{2-i,2-j}=2M_{11}Mij​+M2−i,2−j​=2M11​.

Formalizing it. The mathematics is classical and proved; nothing here is open. What is missing is the formalized artifact. The order-three structural lemmas — the centre identity, the opposite-cell identity, and the two directions of the parametrization — are already machine-checked on this platform. The remaining work is the counting step: exhibiting a concrete bijection between two finsets whose elements live in different types (arrays over Fin (3e+1) versus pairs of naturals) and evaluating a finite sum. That is where the formalization, not the mathematics, is hard.

Difficulty

The obvious attack — "each magic square is determined by (a,c)(a,c)(a,c), so just count the pairs" — fails at exactly one point, and it is not a mathematical point. The counting function M3M_3M3​ is defined as the cardinality of a finset of arrays with entries in Fin (3e+1) (a finite type, so that Finset.univ exists), whereas the parametrization lives over N\mathbb{N}N. Proving the counts agree therefore requires a honest Finset.card_bij in both directions:

  • forward, extract (M00,M02)(M_{00},M_{02})(M00​,M02​) from an array and show the pair is admissible;
  • backward, build mkMagic3 from an admissible pair, coerce every entry into Fin (3e+1) using the bound Mij≤2e≤3eM_{ij}\le 2e\le 3eMij​≤2e≤3e, and show the round trip is the identity.

Neither direction is deep, but the coercions are unforgiving: a truncated subtraction in mkMagic3 is only correct because admissibility forbids the truncation, and that side condition must be discharged explicitly rather than assumed. The second difficulty is the cardinality of the ℓ1\ell_1ℓ1​ ball: the identification ∣p+q∣≤e ∧ ∣p−q∣≤e ⟺ ∣p∣+∣q∣≤e|p+q|\le e\ \wedge\ |p-q|\le e\ \Longleftrightarrow\ |p|+|q|\le e∣p+q∣≤e ∧ ∣p−q∣≤e ⟺ ∣p∣+∣q∣≤e needs the elementary identity max⁡(∣p+q∣,∣p−q∣)=∣p∣+∣q∣\max(|p+q|,|p-q|)=|p|+|q|max(∣p+q∣,∣p−q∣)=∣p∣+∣q∣, after which the count is 1+4∑k=1ek=2e2+2e+11+4\sum_{k=1}^e k = 2e^2+2e+11+4∑k=1e​k=2e2+2e+1.

Formalization scope

  • Entries are indexed by Fin n; the anti-diagonal uses Fin.rev, and broken diagonals use addition modulo nnn. Counting functions are cardinalities of finsets of arrays over Fin (t+1) — lossless, since every entry is at most ttt — and return natural numbers.
  • mkMagic3 is defined over N\mathbb{N}N with truncated subtraction. Every row/column/diagonal identity therefore carries the admissibility inequalities as explicit hypotheses; no identity is asserted unconditionally.
  • Trivializing formalizations are ruled out: the goal is not a statement about a hardcoded small eee, nor about a finset declared to have the right cardinality. The count must be derived.
  • Reusable beyond this mission: the core vocabulary (Square, IsSemiMagic, IsMagic, IsPanMagic, IsAssociative, IsNormal, magicConstant, and the four counting functions Hn,Mn,Pn,SnH_n,M_n,P_n,S_nHn​,Mn​,Pn​,Sn​), the symmetry/affine toolbox, and the order-three structural lemmas. Contributions are welcome on the semi-magic count H3H_3H3​, on panmagic and associative refinements, and on the extension to general nnn.

Selected references

  • P. A. MacMahon, Combinatory Analysis, Vol. II, Cambridge University Press, 1916 (the M3M_3M3​ formula dates to his 1915 work).
  • M. Beck, T. Cohen, J. Cuomo and P. Gribelyuk, The number of "magic" squares, cubes and hypercubes, Amer. Math. Monthly 110 (2003), 707--717. https://arxiv.org/abs/math/0201013
  • M. Beck and T. Zaslavsky, Six little squares and how their numbers grow, J. Combin. Theory Ser. A 113 (2006). https://arxiv.org/abs/math/0502370
  • G. Xin, Constructing all magic squares of order three, Discrete Math. 308 (2008). https://arxiv.org/abs/math/0610771
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Linear OptimizationOptimizationTheoretical Computer Science·Captain: moutei

Primal-Dual Online Algorithms I: Fractional Ski RentalTextbook

Motivation

An online algorithm must commit to decisions before it knows the rest of its input, and it is judged by competitive analysis: the ratio between its cost and the cost of an optimal solution computed with full knowledge of the input. A recurring obstacle in this area is that each problem seems to need its own ad hoc potential-function argument. Buchbinder's thesis develops a single method that replaces those arguments — formulate the offline problem as a covering linear program, let the online algorithm raise the dual variables of its packing dual, and read the competitive ratio off the ratio between the primal and dual increments. The same recipe then yields algorithms for online set cover, weighted caching, ad-auction revenue, routing, and load balancing.

This mission formalizes the chapter where the method is introduced on its smallest example, the ski-rental problem. A customer needs skis for an unknown number of days: renting costs 111 per day and buying costs BBB once. The customer must decide, each morning, whether to rent again or buy, without knowing how many ski days remain. Despite its size the problem is the canonical rent-or-buy dilemma, and it has two classical tight results: a deterministic 222-competitive algorithm, and a randomized algorithm whose competitive ratio tends to e/(e−1)e/(e-1)e/(e−1), due to Karlin, Manasse, McGeoch and Owicki (1994). The primal-dual derivation of both is the content of Chapter 3.

Setting

An instance is a pair (B,k)(B, k)(B,k): the purchase price BBB, a positive integer, and the number k≥0k \ge 0k≥0 of ski days, which the online algorithm does not know. An offline solution either buys at once, paying BBB, or rents on every day, paying kkk; so the offline optimum is

OPT(B,k)  =  min⁡(B,k).\mathrm{OPT}(B,k) \;=\; \min(B, k).OPT(B,k)=min(B,k).

Chapter 3 casts this as a linear program (Figure 3.1, p. 18). The primal is a covering program with one buy variable xxx and one rent variable zjz_jzj​ per day jjj:

minimize   Bx+∑j=1kzjsubject tox+zj≥1  for each day j.\text{minimize } \; B x + \sum_{j=1}^{k} z_j \quad \text{subject to} \quad x + z_j \ge 1 \ \text{ for each day } j.minimize Bx+j=1∑k​zj​subject tox+zj​≥1  for each day j.

Its dual is a packing program with one variable yjy_jyj​ per day:

maximize   ∑j=1kyjsubject to∑j=1kyj≤B,0≤yj≤1.\text{maximize } \; \sum_{j=1}^{k} y_j \quad \text{subject to} \quad \sum_{j=1}^{k} y_j \le B, \qquad 0 \le y_j \le 1 .maximize j=1∑k​yj​subject toj=1∑k​yj​≤B,0≤yj​≤1.

The online structure enters in a single way: a new ski day appends a new covering constraint to the primal and a new variable to the dual, and previously raised primal variables may never be decreased. That monotonicity is what "previous decisions cannot be regretted" means formally.

The fractional primal-dual algorithm maintains xxx, initially 000. On each new day, while x<1x < 1x<1 it sets zj←1−xz_j \leftarrow 1 - xzj​←1−x, then raises

x  ←  x(1+1B)+1cB,x \;\leftarrow\; x\left(1 + \tfrac{1}{B}\right) + \tfrac{1}{cB},x←x(1+B1​)+cB1​,

and sets yj←1y_j \leftarrow 1yj​←1; once xxx has reached 111 it does nothing further. The free parameter ccc is then pinned to the value that makes xxx reach exactly 111 after BBB days,

c  =  (1+1B)B−1.c \;=\; \left(1 + \tfrac{1}{B}\right)^{B} - 1 .c=(1+B1​)B−1.

Formalization targets

Goal — the fractional algorithm's competitive ratio at finite BBB

B xk+∑j=0k−1zj  ≤  (1+1(1+1B)B−1)⋅min⁡(B,k)for every B≥1, k≥0.B\,x_k + \sum_{j=0}^{k-1} z_j \;\le\; \left(1 + \frac{1}{\left(1 + \frac{1}{B}\right)^{B} - 1}\right) \cdot \min(B, k) \qquad \text{for every } B \ge 1, \ k \ge 0 .Bxk​+j=0∑k−1​zj​≤(1+(1+B1​)B−11​)⋅min(B,k)for every B≥1, k≥0.

The coefficient is the exact finite-BBB ratio 1+1/c1 + 1/c1+1/c, left in closed form rather than replaced by a constant. This is deliberate: (1+1B)B\left(1+\frac1B\right)^B(1+B1​)B increases to eee, so c<e−1c < e - 1c<e−1 and therefore 1+1/c>e/(e−1)1 + 1/c > e/(e-1)1+1/c>e/(e−1) for every finite BBB. A goal asserting e/(e−1)e/(e-1)e/(e−1)-competitiveness at finite BBB would be false, and a goal asserting some rounded constant would be invalidated by any sharpening. The closed-form coefficient is the weakest statement that is stable under improvement.

Asymptotic companion — where e/(e−1)e/(e-1)e/(e−1) actually lives

lim⁡B→∞(1+1(1+1B)B−1)  =  ee−1  ≈  1.5819767.\lim_{B \to \infty} \left(1 + \frac{1}{\left(1 + \frac{1}{B}\right)^{B} - 1}\right) \;=\; \frac{e}{e-1} \;\approx\; 1.5819767 .B→∞lim​(1+(1+B1​)B−11​)=e−1e​≈1.5819767.

The classical constant is recorded here, as a limit of the coefficient sequence, and nowhere else.

Parallel target — the deterministic algorithm

detCost(B,k)  ≤  2⋅min⁡(B,k),detCost(B,k)={kk<B2Bk≥B\mathrm{detCost}(B,k) \;\le\; 2 \cdot \min(B,k), \qquad \mathrm{detCost}(B,k) = \begin{cases} k & k < B \\ 2B & k \ge B\end{cases}detCost(B,k)≤2⋅min(B,k),detCost(B,k)={k2B​k<Bk≥B​

Chapter 3's other result, independent of the fractional development.

Significance

The ski-rental bounds themselves are classical and tight, and nothing here is mathematically open. What the chapter contributes, and what this mission captures, is the derivation: it is the template instantiated by every later chapter of the thesis, so the artifacts built here — a covering/packing LP pair, its weak-duality instance, a monotone online variable with a closed-form growth law, and the primal-to-dual increment ratio as the source of the competitive factor — are the vocabulary in which the rest of the series will be stated.

On status: the mathematics is proved, published, and standard. It is not, to the best of a search of Mathlib at revision 0df444a, formalized — that revision contains no competitive-analysis or online-algorithm framework, no ski-rental development, and no general linear-programming weak-duality theorem. So the work this mission asks for is formalization of a known proof, not new mathematics, and the reusable output is infrastructure that does not currently exist in the library.

Difficulty

The offline problem is trivial, and a newcomer's first move — prove min⁡(B,k)\min(B,k)min(B,k) is the optimum and stop — solves the wrong problem. The content is entirely in the online constraint. Three specific places where the obvious argument stalls:

The optimum is never observed. The algorithm's cost must be compared against min⁡(B,k)\min(B,k)min(B,k) without kkk being available to it. The comparison is routed through the dual instead: the dual objective the algorithm accumulates is a lower bound on every feasible primal solution, hence on the optimum, and the algorithm's own primal cost is a fixed multiple of that dual objective.

The growth law is piecewise. The update fires only while x<1x < 1x<1. Summing the per-day increments therefore does not telescope uniformly: days before xxx reaches 111 contribute 1+1/c1 + 1/c1+1/c each and later days contribute nothing, and the index at which the switch happens is exactly BBB — which is a theorem about the recurrence, not an assumption.

The constant is forced, not chosen. c=(1+1/B)B−1c = (1+1/B)^B - 1c=(1+1/B)B−1 is not a free tuning parameter; it is the unique value for which the geometric sequence xj=((1+1/B)j−1)/cx_j = \bigl((1+1/B)^j - 1\bigr)/cxj​=((1+1/B)j−1)/c hits 111 at j=Bj = Bj=B, which is in turn what makes the dual solution feasible (∑jyj≤B\sum_j y_j \le B∑j​yj​≤B). Dual feasibility and the choice of ccc are the same fact.

Formalization scope

Conventions this development commits to. The purchase price is a natural number BBB with 0<B0 < B0<B, because Chapter 3 uses BBB simultaneously as a price, as a day index ("buy skis on the BBBth day"), and as the exponent in (1+1/B)B(1+1/B)^B(1+1/B)B; costs are real numbers, with BBB and kkk coerced. Days are indexed from 000, so day j+1j+1j+1 of the prose is index jjj, and Fin k indexes the kkk days. Real division is total, so 1/0=01/0 = 01/0=0; the hypothesis 0<B0 < B0<B is what keeps every reciprocal in the development genuine, and without it ccc would evaluate to 000 and the recurrence would collapse to the constant zero sequence. The algorithm's x < 1 guard is part of the formalized definition, not an informal aside: without it the cost would keep growing past day BBB.

A documented discrepancy in the source. The prose on p. 17 relaxes the integer program by letting xxx and each zjz_jzj​ range over [0,1][0,1][0,1]; Figure 3.1 on p. 18 prints only x≥0x \ge 0x≥0, zj≥0z_j \ge 0zj​≥0. This mission takes the prose version, 0≤x≤10 \le x \le 10≤x≤1 and 0≤zj≤10 \le z_j \le 10≤zj​≤1, as the canonical fractional program, and also records the nonnegativity-only region exactly as printed. Two separate theorems establish that both have least value min⁡(B,k)\min(B,k)min(B,k), so the discrepancy is resolved inside the mission rather than silently chosen. Solvers should note which of the two predicates a given statement uses.

Ruling out a trivializing formalization. The offline optimum is defined independently, as min⁡(B,k)\min(B,k)min(B,k), and is not derived from the algorithm's own behaviour; a separate theorem certifies that this value really is the least attainable objective value of the canonical program, so the goal cannot be satisfied by redefining the benchmark. The goal inequality is also tight — both sides are equal to (1+1/c)(1+1/c)(1+1/c) times the number of days on which x<1x < 1x<1 — so it cannot be weakened into vacuity without becoming false.

Infrastructure, and what is reusable. The development needs only Mathlib big operators over Fin k, basic real analysis for the limit, and IsLeast. Two items are explicitly infrastructure rather than ski-rental content: the specialized weak-duality theorem for this covering/packing pair, and the Figure 3.1 optimum. Both are candidates for generalization by the later mission on Chapter 2's general linear-programming duality, and a solver who proves the general form there should expect this instance to be derivable from it rather than duplicated.

Out of scope here. The final paragraph of p. 19 rounds the fractional solution into a randomized algorithm by sampling a threshold α∈[0,1]\alpha \in [0,1]α∈[0,1] uniformly and buying on the day whose increment of xxx contains α\alphaα. That step needs a probability space and an expectation argument, and is deferred to the immediate follow-up mission, Primal-Dual Online Algorithms II: Randomized Rounding for Ski Rental. Contributions here should not anticipate it.

Selected references

  • Niv Buchbinder, Designing Competitive Online Algorithms via a Primal-Dual Approach, PhD thesis, Tel Aviv University, 2008. Chapter 3, pp. 17–19. https://www.tau.ac.il/~nivb/download/phd-thsis.pdf
  • Niv Buchbinder and Joseph (Seffi) Naor, The Design of Competitive Online Algorithms via a Primal-Dual Approach, Foundations and Trends in Theoretical Computer Science 3(2–3), 2009. https://doi.org/10.1561/0400000024
  • Anna R. Karlin, Mark S. Manasse, Lyle A. McGeoch and Susan Owicki, Competitive randomized algorithms for nonuniform problems, Algorithmica 11(6), 1994, 542–571. https://doi.org/10.1007/BF01294260
  • Allan Borodin and Ran El-Yaniv, Online Computation and Competitive Analysis, Cambridge University Press, 1998.
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ProbabilityStatistics·Captain: burkh4rt

Discriminative Kalman Filter asymptoticsResearch Paper

Motivation

Bayesian filtering estimates an unobserved state from measurements arriving over time. A filter combines what the state dynamics predict with what the newest observation says. In neural decoding, for example, the state may describe an intended movement while the observation contains activity from many recorded neurons. The observation can have many more coordinates than the state and need not follow a linear Gaussian observation model.

The Discriminative Kalman Filter (DKF) uses a Gaussian approximation to the state conditional on the newest observation. It combines that approximation with a Gaussian state transition and a correction for the stationary state distribution. The resulting recursion retains a mean vector and covariance matrix. Burkhart et al. developed this construction and proved an asymptotic justification in Theorem 2 of Appendix B.

The historical starting point is the linear Gaussian filter of Kalman (1960). The 2020 DKF paper changes how observation information enters the update and establishes a corresponding approximation theorem. The present mission concerns formal verification of that published theorem.

Setting

The state space is Rd\mathbb R^dRd for a positive finite dimension ddd. Write ηd(z;m,C)\eta_d(z;m,C)ηd​(z;m,C) for the ordinary multivariate Gaussian density with mean mmm and symmetric positive-definite covariance CCC. Densities and their L1L^1L1 distances are with respect to Lebesgue measure.

The state model has a matrix AAA and positive-definite covariance matrices Γ,S\Gamma,SΓ,S satisfying

S=ASA⊤+Γ.S=ASA^\top+\Gamma.S=ASA⊤+Γ.

Its stationary density and transition density are

p(z)=ηd(z;0,S),τ(y,z)=ηd(z;Ay,Γ).p(z)=\eta_d(z;0,S),\qquad \tau(y,z)=\eta_d(z;Ay,\Gamma).p(z)=ηd​(z;0,S),τ(y,z)=ηd​(z;Ay,Γ).

For an integrable density sss, prediction gives

(τs)(z)=∫τ(y,z)s(y) dy.(\tau s)(z)=\int\tau(y,z)s(y)\,dy.(τs)(z)=∫τ(y,z)s(y)dy.

The discriminative update combines a previous filtering density sss with a density uuu for the state given the current observation:

u τs/p∥u τs/p∥1.\frac{u\,\tau s/p}{\|u\,\tau s/p\|_1}.∥uτs/p∥1​uτs/p​.

This expression is a probability density when its nonnegative weight has a finite, strictly positive integral. Dividing by ppp is part of the standard DKF under consideration.

For Gaussian inputs with parameters (a,V)(a,V)(a,V) and (b,U)(b,U)(b,U), define

G=AVA⊤+Γ,T=(U−1+G−1−S−1)−1,G=AVA^\top+\Gamma,\qquad T=(U^{-1}+G^{-1}-S^{-1})^{-1},G=AVA⊤+Γ,T=(U−1+G−1−S−1)−1, c=T(U−1b+G−1Aa).c=T(U^{-1}b+G^{-1}Aa).c=T(U−1b+G−1Aa).

The DKF step returns mean ccc and covariance TTT when the precision is invertible and the covariance is positive definite. The recursive filter starts from mean zero and covariance SSS, using the current observation's functions fff and QQQ as the Gaussian input mean and covariance. These are the updates in equation (2.7) of the paper.

Formalization targets

Fix sequences of probability densities sn,uns_n,u_nsn​,un​, indexed by positive integers, whose exact normalized updates

pn=unτsn/p∥unτsn/p∥1p_n=\frac{u_n\tau s_n/p}{\|u_n\tau s_n/p\|_1}pn​=∥un​τsn​/p∥1​un​τsn​/p​

are well defined for every index. Fix Gaussian density sequences sn′,un′s'_n,u'_nsn′​,un′​, a point bbb, and a probability measure PPP. The five assumptions are

A1:sn⇒P,A2:∥sn−sn′∥1⟶0,A3:un⇒δb,A4:∥un−un′∥1⟶0,A5:pn⇒δb.\begin{aligned} \mathrm{A1}:&\quad s_n\Rightarrow P,\\ \mathrm{A2}:&\quad \|s_n-s'_n\|_1\longrightarrow0,\\ \mathrm{A3}:&\quad u_n\Rightarrow\delta_b,\\ \mathrm{A4}:&\quad \|u_n-u'_n\|_1\longrightarrow0,\\ \mathrm{A5}:&\quad p_n\Rightarrow\delta_b. \end{aligned}A1:A2:A3:A4:A5:​sn​⇒P,∥sn​−sn′​∥1​⟶0,un​⇒δb​,∥un​−un′​∥1​⟶0,pn​⇒δb​.​

Here ⇒\Rightarrow⇒ denotes weak convergence, characterized by convergence of expectations of every bounded continuous real function; δb\delta_bδb​ is the unit point mass at bbb. The measure PPP need not have a density and may be degenerate.

The main goal is the complete conjunction of Theorem 2's conclusions, with separate milestones for each:

  • C1: sn′⇒Ps'_n\Rightarrow Psn′​⇒P.
  • C2: un′⇒δbu'_n\Rightarrow\delta_bun′​⇒δb​.
  • C3: the specific update
pn′=un′τsn′/p∥un′τsn′/p∥1p'_n=\frac{u'_n\tau s'_n/p}{\|u'_n\tau s'_n/p\|_1}pn′​=∥un′​τsn′​/p∥1​un′​τsn′​/p​

is a well-defined Gaussian density for all sufficiently large nnn.

  • C4: pn′⇒δbp'_n\Rightarrow\delta_bpn′​⇒δb​.
  • C5: ∥pn−pn′∥1⟶0\|p_n-p'_n\|_1\longrightarrow0∥pn​−pn′​∥1​⟶0.

A sixth milestone is Lemma 1 (DKF equation): the normalized Gaussian-input update has the explicit mean and covariance above whenever valid, and the two input weak limits imply its eventual validity and convergence to δb\delta_bδb​. It includes both the exact equation and the asymptotic assertion from the source.

Significance

In addition to neurodecoding with intracortical brain-computer interfaces, the DKF has also found applications in optimization and sequential data augmentation (see references). The original study was successfully reproduced by Casco-Rodriguez, et al. in ReScience C.

Difficulty

The inverse stationary density can grow in the tails, so small L1L^1L1 errors in the input densities do not immediately control the error after division and renormalization. Normalizing constants must remain finite and nonzero. The candidate Gaussian covariance must also become positive definite as a conclusion of the assumptions, rather than through an extra validity assumption imposed at every index.

There is a second distinction between convergence to a point mass and approximation in L1L^1L1. Two sequences may concentrate at the same point while retaining different shapes at shrinking scales. The C5 target therefore requires the full approximation argument, beyond the weak-convergence conclusions.

Formalization scope

Lean represents states as Fin d → ℝ and covariance matrices as real square matrices. The Gaussian density is the standard determinant-and-quadratic-form formula. The definition of Gaussian PDF includes positive-definite covariance and equality of densities almost everywhere. Thus null-set changes do not constrain the theorem artificially.

Probability density validity explicitly includes nonnegativity almost everywhere, integrability and total integral one. The L1L^1L1 quantity is an extended nonnegative integral. The update's validity explicitly requires measurable weight and a finite, strictly positive normalizer. Total expressions outside that domain supply no assumed probability interpretation; C3 establishes validity on a tail.

All density sequences use positive integer indices. The limit measure is a Mathlib probability measure. Weak convergence is tested against Mathlib bounded continuous functions using the actual measures generated by the densities. The stationary model, standard recursive DKF, exact update and approximate update are defined independently of the theorem conclusions.

The mission addresses deterministic Theorem 2 of Appendix B. The random-sequence extension in Remark 4, conditions implying a Bernstein–von Mises theorem, and induction over filtering time are separate developments. The proof plan follows the appendix through C1–C2, Lemma 1, C3–C4, and the five-term comparison for C5.

Selected references

  • M. C. Burkhart, D. M. Brandman, B. Franco, L. R. Hochberg and M. T. Harrison, The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models, Neural Computation 32(5), 969–1017, 2020. DOI: 10.1162/neco_a_01275.
  • R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering 82(1), 35–45, 1960. DOI: 10.1115/1.3662552.
  • M. C. Burkhart, A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding, Ph.D. dissertation, Brown University, 2019. DOI: 10.26300/nhfp-xv22.
  • D. M. Brandman, M. C. Burkhart, J. Kelemen, B. Franco, M. T. Harrison and L. R. Hochberg, Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression, Neural Computation 30(11), 2986–3008, 2018. DOI: 10.1162/neco_a_01129.
  • D. M. Brandman, T. Hosman, J. Saab, M. C. Burkhart, B. E. Shanahan, J. G. Ciancibello et al., Rapid calibration of an intracortical brain–computer interface for people with tetraplegia, Journal of Neural Engineering 15(2), 026007, 2018. DOI: 10.1088/1741-2552/aa9ee7.
  • J. Casco-Rodriguez, C. Kemere and R. G. Baraniuk, [Re] The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Non-Gaussian Observation Models, ReScience C 10(1), article 3, 2025. DOI: 10.5281/zenodo.15172014, published PDF.
  • M. C. Burkhart, Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions, Optimization Letters 17, 657–673, 2023. DOI: 10.1007/s11590-022-01895-5.
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Statistics·Captain: burkh4rt

Formalized SCOPE and REACH estimatorsResearch Paper

Motivation

A foundation model trained on tokenized electronic health record (EHR) timelines can be used to predict clinical outcomes without ever being finetuned for a specific prediction task: condition the model on a patient's observed timeline, autoregressively sample many possible futures, and report the fraction of sampled futures in which the outcome of interest occurs. This generative approach to inference powers a growing family of EHR foundation models— including Event Stream GPT (McDermott et al., 2023), Foresight (Kraljevic et al., 2024), ETHOS (Renc et al., 2024), and Curiosity (Waxler et al., 2025)—and it is attractive for its zero-shot approach to predicting a variety of outcomes.

It is also expensive. Reproducing one published pipeline required more than 150015001500 A100 GPU-hours of inference. Worse, the estimator built from nnn sampled futures takes values in {0,1/n,…,1}\{0, 1/n, \dots, 1\}{0,1/n,…,1}, so its resolution is tied to the sampling budget: for an outcome of prevalence 1/10,0001/10{,}0001/10,000, 100100100 sampled futures fail more than 90%90\%90% of the time to rank a patient at ten times average risk above an average one. The most consequential clinical decisions turn on exactly such low-prevalence, high-impact outcomes.

Solo et al. (arXiv:2602.03730) observe that Monte Carlo discards almost everything the model produces: at every step the model emits a full next-token distribution and the sampler keeps only the token it drew. The paper introduces two estimators that consume the discarded probabilities instead, and proves that doing so costs no bias and—for one of them—never costs variance.

Setting and estimators

Let PPP generate token sequences from a countable vocabulary VVV, with designated outcome token OOO. The next-token probabilities may depend on the complete preceding history. The time threshold is initially unexceeded. Its crossing may depend on several kinds of time-spacing tokens and on their accumulated duration.

For a sampled timeline XXX, TO(X)T_O(X)TO​(X) is the first position occupied by OOO, or ∞\infty∞ if it never appears. The time TE(X)T_E(X)TE​(X) is the first position at which the threshold has been exceeded. Assume TO≠TET_O\ne T_ETO​=TE​ almost surely. Timelines are retained through the actual threshold crossing, even if the outcome appears earlier. Thus TET_ETE​ is not reassigned after an outcome.

The threshold is reached almost surely, but the number of tokens required may be arbitrarily large. No deterministic bound or finite expected token count is assumed. For REACH, assume also that removing the outcome token and renormalizing defines a sampler that reaches the same threshold almost surely.

For n≥1n\ge1n≥1 independent original timelines, the Monte Carlo estimator is

M0=1n∑i=1n1{TO(X(i))<TE(X(i))}.M_0=\frac1n\sum_{i=1}^n 1_{\{T_O(X^{(i)})<T_E(X^{(i)})\}}.M0​=n1​i=1∑n​1{TO​(X(i))<TE​(X(i))}​.

The SCOPE estimator is

S=1n∑i=1n∑t=1min⁡{TE(X(i)),TO(X(i))}P(Xt=O∣X1:t−1(i)).\mathcal S=\frac1n\sum_{i=1}^n\sum_{t=1}^{\min\{T_E(X^{(i)}),T_O(X^{(i)})\}}P(X_t=O\mid X_{1:t-1}^{(i)}).S=n1​i=1∑n​t=1∑min{TE​(X(i)),TO​(X(i))}​P(Xt​=O∣X1:t−1(i)​).

For REACH, sample independent outcome-free timelines by setting the next-token probability of OOO to zero and renormalizing the probabilities of the other tokens. Using the original model probabilities along those timelines, define

R=1n∑i=1n[1−∏t=1TE(X^(i))(1−P(Xt=O∣X^1:t−1(i)))].\mathcal R=\frac1n\sum_{i=1}^n\left[1-\prod_{t=1}^{T_E(\hat X^{(i)})}\left(1-P(X_t=O\mid\hat X_{1:t-1}^{(i)})\right)\right].R=n1​i=1∑n​​1−t=1∏TE​(X^(i))​(1−P(Xt​=O∣X^1:t−1(i)​))​.

Formalization targets

The targets are:

  1. SCOPE unbiasedness: E[S]=P(TO<TE)\mathbb E[\mathcal S]=P(T_O<T_E)E[S]=P(TO​<TE​).
  2. Equal probabilities milestone: P(A)=P(B)P(A)=P(B)P(A)=P(B) from Appendix C, where AAA is the original outcome-before-threshold event and BBB is at least one successful Bernoulli trial along an outcome-free timeline.
  3. REACH unbiasedness: E[R]=P(TO<TE)\mathbb E[\mathcal R]=P(T_O<T_E)E[R]=P(TO​<TE​).
  4. Rao–Blackwell identity: for every positive sample count, conditioning the average of the two-stage event indicators on the entire pool of outcome-free timelines equals R\mathcal RR almost surely.
  5. Main goal: Var⁡(R)≤Var⁡(M0)\operatorname{Var}(\mathcal R)\le\operatorname{Var}(M_0)Var(R)≤Var(M0​) at the same positive sample count, with finite second moments for both estimators.

Expectations and variances use each estimator's specified sampling law.

What the formalization establishes

The claims concern the probability assigned by the generative model. They provide unbiasedness and a comparison of sampling variance. SCOPE is kept unclipped, as in the paper's unbiasedness result. All five target statements have accompanying local Lean proofs.

Main mathematical difficulty

A pathwise finite stopping time need not have a common finite bound or a finite mean. An expectation involving the stopped SCOPE sum therefore needs justification beyond finite-sum linearity. REACH uses a different sampling law, so its unbiasedness and variance comparison also require a proved connection between the original event and the two-stage experiment. The equal-probabilities milestone records that connection explicitly.

Formalization scope

Lean represents each sampled timeline by a finite list ending at its first threshold crossing. Arbitrary finite lengths are included in the same sample space. Path probabilities are products of next-token probabilities, and the laws are countable sums of these path masses. Requiring each law to have total mass one expresses almost-sure termination of that sampler; it is not a uniform length bound. The vocabulary can be finite or countably infinite.

The stopping predicate examines a complete prefix and is not restricted to a single terminal token. The original law continues through outcomes until the threshold. A separate almost-everywhere hypothesis excludes equal outcome and threshold times. The code proves that the actual threshold time is finite almost surely and that the strict event TO<TET_O<T_ETO​<TE​ is the event used by the internal calculations.

The two-stage experiment explicitly samples conditionally independent Bernoulli trials using the original hazards. Its conditioning information retains the complete indexed pool of outcome-free timelines. The Rao–Blackwell target uses Mathlib's conditional expectation. The variance target uses Mathlib's variance, and proves square integrability rather than assuming it.

Selected references

  • Luke Solo, Matthew B. A. McDermott, William F. Parker, Bashar Ramadan, Michael C. Burkhart, Brett K. Beaulieu-Jones, Efficient Generative Prediction for EHR Foundation Models: The SCOPE and REACH Estimators, 2026. arXiv:2602.03730
  • M. B. A. McDermott, B. Nestor, P. Argaw, I. S. Kohane, Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events, Advances in Neural Information Processing Systems 36, pp. 24322–24334, 2023. arXiv:2306.11547
  • Z. Kraljevic, D. Bean, A. Shek, R. Bendayan, H. Hemingway, J. A. Yeung, A. Deng, A. Baston, J. Ross, E. Idowu, J. T. Teo, R. J. B. Dobson, Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study, Lancet Digital Health 6(4), pp. e281–e290, 2024. doi:10.1016/S2589-7500(24)00025-6
  • P. Renc, Y. Jia, A. E. Samir, J. Was, Q. Li, D. W. Bates, A. Sitek, Zero shot health trajectory prediction using transformer, npj Digital Medicine 7(1), p. 256, 2024. doi:10.1038/s41746-024-01235-0
  • S. Waxler, P. Blazek, D. White, D. Sneider, K. Chung, M. Nagarathnam, P. Williams, H. Voeller, K. Wong, M. Swanhorst, S. Zhang, N. Usuyama, C. Wong, T. Naumann, H. Poon, A. Loza, D. Meeker, S. Hain, R. Shah, Generative medical event models improve with scale, 2025. Introduces the Curiosity model family. arXiv:2508.12104
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AnalysisDynamical Systems·Captain: Lucas

Curso de EDO I: Picard Existence and UniquenessTextbook

Motivation

Essentially every quantitative model written as a rate of change — a mechanical system, a chemical reaction network, a population model, a control loop — is an ordinary differential equation (ODE) together with an initial condition. Before anything can be computed about such a model, two questions must be settled: does a solution through the given initial state exist, and is it the only one? The classical answer is the Picard–Lindelöf theorem: a Lipschitz right-hand side yields a unique local solution. Uniqueness is not a technicality — it is what licenses speaking of the trajectory through a point, hence of a flow, and so it underwrites the entire qualitative theory of dynamical systems that the source text builds afterwards (vector fields, tubular flow, ω\omegaω-limit sets, Poincaré–Bendixson, Grobman–Hartman, stable manifolds).

This mission is the first in a series formalizing Augusto Armando de Castro Júnior's lecture notes Curso de Equações Diferenciais Ordinárias (2009), a graduate ODE course that develops the theory directly in Banach spaces, not only in Rn\mathbb R^nRn. The book proves existence and uniqueness once, in that general setting, and then reuses it throughout; this mission formalizes that foundation.

Setting

Let EEE be a real Banach space, t0∈Rt_0 \in \mathbb Rt0​∈R, x0∈Ex_0 \in Ex0​∈E, and a,b>0a, b > 0a,b>0. Write Bˉ(x0,b)={x∈E:∥x−x0∥≤b}\bar B(x_0,b) = \{x \in E : \|x - x_0\| \le b\}Bˉ(x0​,b)={x∈E:∥x−x0​∥≤b} for the closed ball and

U  =  [t0−a, t0+a]×Bˉ(x0,b)  ⊂  R×E.U \;=\; [t_0-a,\,t_0+a] \times \bar B(x_0,b) \;\subset\; \mathbb R \times E .U=[t0​−a,t0​+a]×Bˉ(x0​,b)⊂R×E.

A map f:U→Ef : U \to Ef:U→E is Lipschitz with respect to the second variable with constant c>0c>0c>0 if

∥f(z,y1)−f(z,y2)∥  ≤  c ∥y1−y2∥whenever (z,y1),(z,y2)∈U,\|f(z,y_1) - f(z,y_2)\| \;\le\; c\,\|y_1-y_2\| \qquad\text{whenever } (z,y_1),(z,y_2)\in U ,∥f(z,y1​)−f(z,y2​)∥≤c∥y1​−y2​∥whenever (z,y1​),(z,y2​)∈U,

the same ccc serving for every zzz (Definição 2.1.1 of the source).

Given fff, the Cauchy problem (initial value problem) with initial data (t0,x0)(t_0,x_0)(t0​,x0​) asks for a curve φ:I→E\varphi : I \to Eφ:I→E, defined on a nondegenerate interval I∋t0I \ni t_0I∋t0​, such that (t,φ(t))∈U(t,\varphi(t)) \in U(t,φ(t))∈U for all t∈It \in It∈I, φ(t0)=x0\varphi(t_0)=x_0φ(t0​)=x0​, and φ′(t)=f(t,φ(t))\varphi'(t) = f(t,\varphi(t))φ′(t)=f(t,φ(t)) for all t∈It \in It∈I — one-sided derivatives at the endpoints of III (Definições 1.0.1 and 1.1.1). Equivalently, by the Fundamental Theorem of Calculus, φ\varphiφ is continuous with graph in UUU and satisfies the integral equation

φ(t)  =  x0+∫t0tf(s,φ(s)) ds,t∈I.\varphi(t) \;=\; x_0 + \int_{t_0}^{t} f\bigl(s,\varphi(s)\bigr)\,ds , \qquad t \in I .φ(t)=x0​+∫t0​t​f(s,φ(s))ds,t∈I.

Finally, put M=sup⁡{∥f(t,x)∥:(t,x)∈U}M = \sup\{\|f(t,x)\| : (t,x) \in U\}M=sup{∥f(t,x)∥:(t,x)∈U} and α=min⁡{a, b/M}\alpha = \min\{a,\ b/M\}α=min{a, b/M}.

Target

The goal theorem is Teorema 2.1.2 (Picard) of the source: if f:U→Ef : U \to Ef:U→E is continuous, bounded, and Lipschitz in the second variable, then the Cauchy problem x′=f(t,x)x' = f(t,x)x′=f(t,x), x(t0)=x0x(t_0)=x_0x(t0​)=x0​ has a solution on [t0−α, t0+α][t_0-\alpha,\,t_0+\alpha][t0​−α,t0​+α], and any two solutions on that interval whose graphs stay in UUU coincide there:

∃ φ solution on [t0−α,t0+α],∀ ψ solution on [t0−α,t0+α]: φ=ψ on [t0−α,t0+α].\exists\,\varphi \ \text{solution on } [t_0-\alpha,t_0+\alpha], \qquad \forall\,\psi \ \text{solution on } [t_0-\alpha,t_0+\alpha]: \ \varphi = \psi \ \text{on } [t_0-\alpha,t_0+\alpha].∃φ solution on [t0​−α,t0​+α],∀ψ solution on [t0​−α,t0​+α]: φ=ψ on [t0​−α,t0​+α].

The milestones are the results the source uses to get there, in its own order: the contraction fixed point theorem (Teorema 0.2.10), the equivalence between the Cauchy problem and the integral equation (Capítulo 2, opening paragraphs), the sufficient condition for Lipschitz dependence via a bounded partial derivative (Proposição 2.1.4), and the global version on a whole interval (Corolário 2.1.3).

Significance

Picard's theorem is what makes the initial value problem well posed in the sense of Hadamard's first two requirements. Downstream in the same book it is the hypothesis behind maximal solutions and the escape-from-compacts property (§2.3), continuous and differentiable dependence on initial conditions and parameters (Chapter 3), and the local flow of a vector field (Chapter 4). Without uniqueness none of these statements can even be phrased.

As for formalization status: Mathlib already contains a Picard–Lindelöf development (IsPicardLindelof, ODE_solution_unique and relatives) and a Banach fixed point theorem (ContractingWith.exists_fixedPoint). The work this mission asks for is therefore not the discovery of a proof but a faithful bridge: stating the source's hypotheses as the source states them — a closed ball, a supremum bound MMM, the radius α=min⁡{a,b/M}\alpha=\min\{a,b/M\}α=min{a,b/M}, Lipschitz in the second variable with one constant for all times, uniqueness among solutions whose graph stays in the domain — and deriving them from, or proving them alongside, the library's own formulation. Such bridges are where unfaithful formalizations usually hide, and they are reusable by every later mission in the series.

Difficulty

The obvious route is "cite the library and close the goal". It does not go through unchanged, for three reasons. First, the hypothesis shapes differ: the library packages its assumptions in a structure with its own choice of ball, bound and time radius, and matching α=min⁡{a,b/M}\alpha=\min\{a,b/M\}α=min{a,b/M} with a supremum-defined MMM requires the boundedness argument to be redone at the interface. Second, uniqueness here is asserted for solutions in the sense of this mission's definition — derivative within the interval, one-sided at the two endpoints, graph inside UUU — so a Grönwall-type uniqueness statement must be transported to that formulation, including the endpoint cases. Third, the source works with an arbitrary Banach space EEE and only assumes fff bounded, rather than assuming finite dimension; compactness arguments are unavailable by design.

The remaining genuine mathematical content sits in the milestones: the iterate estimate ∥Fm(φ1)−Fm(φ2)∥≤cmαmm! ∥φ1−φ2∥\|F^m(\varphi_1)-F^m(\varphi_2)\| \le \frac{c^m\alpha^m}{m!}\,\|\varphi_1-\varphi_2\|∥Fm(φ1​)−Fm(φ2​)∥≤m!cmαm​∥φ1​−φ2​∥ used by the source to make some iterate of the Picard operator a contraction, and the mean value inequality on a convex open set behind Proposição 2.1.4.

Formalization scope

Conventions this proposal fixes, all visible in the definitions item:

  1. Solutions are total functions R→E\mathbb R \to ER→E whose behaviour is constrained only on the interval III; uniqueness is therefore stated as agreement on III, never as equality of functions.
  2. Differentiability is the derivative relative to III, which is exactly the source's convention of lateral derivatives at endpoints.
  3. The ball Bˉ(x0,b)\bar B(x_0,b)Bˉ(x0​,b) is closed — the source's proof needs the function space C0([t0−α,t0+α],Bˉ(x0,b))C^0([t_0-\alpha,t_0+\alpha], \bar B(x_0,b))C0([t0​−α,t0​+α],Bˉ(x0​,b)) to be a closed subset of C0C^0C0.
  4. MMM is a least upper bound of {∥f(t,x)∥:(t,x)∈U}\{\|f(t,x)\| : (t,x)\in U\}{∥f(t,x)∥:(t,x)∈U}, which encodes both the boundedness hypothesis and the definition of MMM; the extra hypothesis M>0M > 0M>0 is stated explicitly because the quotient b/Mb/Mb/M is otherwise a junk value.
  5. The Lipschitz constant ccc is an explicit parameter with c>0c>0c>0, the same for all times, as in Definição 2.1.1.
  6. Vacuity is ruled out: the hypotheses are satisfiable — for instance by a nonzero constant fff — so the goal is not true by default.

A complete development needs the Bochner and interval integrals, the contraction mapping API, the mean value inequality for Fréchet derivatives, and the ODE files. Contributions of independent interest to the series: the Picard iterate factorial estimate, and the conversion between the library's Picard–Lindelöf hypotheses and the ones above.

Selected references

  • A. A. de Castro Júnior, Curso de Equações Diferenciais Ordinárias, lecture notes, 6 January 2009. Teorema 0.2.10 (p. 10), Definição 1.1.1 (p. 34), Definição 2.1.1 (p. 43), Teorema 2.1.2 (p. 44), Corolário 2.1.3 (p. 46), Proposição 2.1.4 (p. 47).
  • E. Lindelöf, Sur l'application de la méthode des approximations successives aux équations différentielles ordinaires du premier ordre, C. R. Acad. Sci. Paris 114 (1894), 454–457.
  • Mathlib 4, Mathlib/Analysis/ODE/PicardLindelof.lean and Mathlib/Analysis/ODE/Gronwall.lean, https://github.com/leanprover-community/mathlib4.
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Calculus of VariationsMathematical Physics·Captain: Lucas

Noether 1918: Invariant Variation ProblemsResearch Paper

Motivation

In 1918 Emmy Noether published Invariante Variationsprobleme (Nachrichten der Königlichen Gesellschaft der Wissenschaften zu Göttingen, Math.-phys. Klasse, 235–257), answering a question raised by Hilbert and Klein about the status of energy conservation in the general theory of relativity. The paper proves two theorems that tie the symmetries of a variational integral to structural properties of its Euler–Lagrange equations: continuous symmetries depending on finitely many parameters produce divergence identities ("conservation laws"), while symmetries depending on arbitrary functions produce identities among the Euler–Lagrange expressions themselves, so that some of the field equations are consequences of the others. The first theorem is the source of the correspondence between time translation and energy, space translation and momentum, rotation and angular momentum; the second underlies the Bianchi-type identities of generally covariant theories and the gauge identities of field theory.

This mission formalizes the two theorems of §1 of the paper, together with the chain of identities of §2 and §3 by which Noether derives them, in the case of first-order Lagrangians.

Setting

Fix integers nnn (independent variables), mmm (dependent variables). Points of the base are x=(x1,…,xn)∈Rnx = (x_1,\dots,x_n) \in \mathbb{R}^nx=(x1​,…,xn​)∈Rn, and a field is a map u:Rn→Rmu : \mathbb{R}^n \to \mathbb{R}^mu:Rn→Rm, written componentwise ui(x)u_i(x)ui​(x). Write ∂lg\partial_l g∂l​g for the derivative of a scalar function ggg on Rn\mathbb{R}^nRn along the lll-th coordinate direction, and

Div⁡A  =  ∑l=1n∂lAl\operatorname{Div} A \;=\; \sum_{l=1}^{n} \partial_l A_lDivA=l=1∑n​∂l​Al​

for the divergence of a vector field A=(A1,…,An)A = (A_1,\dots,A_n)A=(A1​,…,An​) on Rn\mathbb{R}^nRn.

A Lagrangian is a function f(x,q,v)f(x, q, v)f(x,q,v) of the point x∈Rnx \in \mathbb{R}^nx∈Rn, of the field value q∈Rmq \in \mathbb{R}^mq∈Rm, and of the array of first derivatives v=(vli)∈Rn×mv = (v_{l i}) \in \mathbb{R}^{n \times m}v=(vli​)∈Rn×m. Along a field uuu one writes f[u](x)=f(x,u(x),(∂lui(x))l,i)f[u](x) = f\bigl(x, u(x), (\partial_l u_i(x))_{l,i}\bigr)f[u](x)=f(x,u(x),(∂l​ui​(x))l,i​), and the integral under study is I=∫f[u] dxI = \int f[u]\,dxI=∫f[u]dx. The momenta are

pli[u](x)  =  ∂f∂vli(x,u(x),(∂u)(x)),p_{l i}[u](x) \;=\; \frac{\partial f}{\partial v_{l i}}\bigl(x, u(x), (\partial u)(x)\bigr),pli​[u](x)=∂vli​∂f​(x,u(x),(∂u)(x)),

and the Lagrange expressions — the left-hand sides of the Euler–Lagrange equations — are

ψi[u](x)  =  ∂f∂qi(x,u(x),(∂u)(x))  −  ∑l=1n∂l pli[u](x).\psi_i[u](x) \;=\; \frac{\partial f}{\partial q_i}\bigl(x, u(x), (\partial u)(x)\bigr) \;-\; \sum_{l=1}^{n} \partial_l\, p_{l i}[u](x).ψi​[u](x)=∂qi​∂f​(x,u(x),(∂u)(x))−l=1∑n​∂l​pli​[u](x).

An infinitesimal transformation is given by generators Δx=(Δxl)\Delta x = (\Delta x_l)Δx=(Δxl​) on the independent variables and Δu=(Δui)\Delta u = (\Delta u_i)Δu=(Δui​) on the dependent ones. Noether's equation (9) replaces them by the variation at fixed xxx,

δui  =  Δui  −  ∑l=1n∂ui∂xl Δxl,\delta u_i \;=\; \Delta u_i \;-\; \sum_{l=1}^{n} \frac{\partial u_i}{\partial x_l}\,\Delta x_l ,δui​=Δui​−l=1∑n​∂xl​∂ui​​Δxl​,

and the corresponding variation of the Lagrangian is

δf  =  ∑i=1m(∂f∂qi δui+∑l=1n∂f∂vli ∂lδui).\delta f \;=\; \sum_{i=1}^{m}\Bigl( \frac{\partial f}{\partial q_i}\,\delta u_i + \sum_{l=1}^{n} \frac{\partial f}{\partial v_{l i}}\, \partial_l \delta u_i \Bigr).δf=i=1∑m​(∂qi​∂f​δui​+l=1∑n​∂vli​∂f​∂l​δui​).

Two vector fields organise the boundary terms: the partial-integration vector Al=−∑ipli δuiA_l = -\sum_i p_{l i}\,\delta u_iAl​=−∑i​pli​δui​ of equation (3), and Noether's

Bl  =  Al  −  f[u] Δxl(equation (12)).B_l \;=\; A_l \;-\; f[u]\,\Delta x_l \qquad\text{(equation (12))}.Bl​=Al​−f[u]Δxl​(equation (12)).

Invariance of III enters through Noether's equation (11), the pointwise identity

δf  +  Div⁡(f[u] Δx)  =  0,\delta f \;+\; \operatorname{Div}\bigl(f[u]\,\Delta x\bigr) \;=\; 0 ,δf+Div(f[u]Δx)=0,

which the paper derives in §2 from the vanishing of ΔI\Delta IΔI over every region.

Target

The goal theorem is Theorem I of the paper, in the first-order case, in the form Noether states as equation (13). Given ρ\rhoρ generators (Δx(r),Δu(r))\bigl(\Delta x^{(r)}, \Delta u^{(r)}\bigr)(Δx(r),Δu(r)), r=1,…,ρr = 1,\dots,\rhor=1,…,ρ, each satisfying the invariance identity (11) with its own δu(r)\delta u^{(r)}δu(r) from (9), one has for every rrr and every xxx

∑i=1mψi[u]  δui(r)  =  Div⁡B(r),Bl(r)  =  −∑i=1mpli δui(r)  −  f[u] Δxl(r).\sum_{i=1}^{m} \psi_i[u]\;\delta u^{(r)}_i \;=\; \operatorname{Div} B^{(r)}, \qquad B^{(r)}_l \;=\; -\sum_{i=1}^m p_{l i}\,\delta u^{(r)}_i \;-\; f[u]\,\Delta x^{(r)}_l .i=1∑m​ψi​[u]δui(r)​=DivB(r),Bl(r)​=−i=1∑m​pli​δui(r)​−f[u]Δxl(r)​.

The milestones are the intermediate statements of the paper, in the order in which it proves them: the central identity (3), the passage from the invariance of the integral to the pointwise identity (11), the single-generator divergence identity (12), the conservation law Div⁡B=0\operatorname{Div} B = 0DivB=0 on solutions of the Euler–Lagrange equations (§3), the converse of Theorem I (§3), and Theorem II in the form of the dependency relations (16) for a group depending on arbitrary functions entering to first order.

Significance

Theorem I is the general statement behind every "first integral from a symmetry" argument in mechanics and field theory; Theorem II is the statement that a variational theory invariant under a group of arbitrary functions has field equations that are not independent — ρ\rhoρ of them follow from the rest — which is the group-theoretic form of the failure of a proper energy conservation law in general relativity that Hilbert had asserted. The two theorems are used constantly and stated loosely; a formal version fixes exactly which hypotheses are needed and what the conclusion says.

Mathlib contains the differential-calculus and measure-theoretic infrastructure used here (Fréchet derivatives, integration on Rn\mathbb{R}^nRn, compactly supported test functions, and the standard vanishing lemma for locally integrable functions tested against smooth compactly supported functions), but no calculus of variations: there is no Euler–Lagrange operator, no first-variation formula, and no Noether theorem. This mission supplies the first-order, finite-dimensional-base version of that material, with definitions that later missions (higher-order Lagrangians, the κ\kappaκ-th order identity (6), mixed groups) can extend.

Difficulty

The algebraic core — the central identity (3) and the passage to (12) — is a product rule plus a reindexing, and the real work is elsewhere.

Two steps are genuinely analytic. First, Noether's inference from "the integral of the integrand vanishes over every region" to "the integrand vanishes pointwise" (equations (10)–(11)) requires the regularity of the integrand to be used explicitly. Second, Theorem II's equation (16) requires integrating by parts against an arbitrary function and then applying the fundamental lemma of the calculus of variations in Rn\mathbb{R}^nRn: the arbitrary functions of the group must be specialized to compactly supported test functions before the boundary terms can be discarded.

The remaining difficulty is bookkeeping: every statement must carry the differentiability hypotheses that make each derivative in it meaningful, since in Lean an undefined derivative silently evaluates to zero rather than failing.

Formalization scope

The formalization is in the first-order setting: the Lagrangian depends on xxx, on uuu, and on the first derivatives of uuu only. The base is Rn\mathbb{R}^nRn with nnn fixed but arbitrary, and fields are globally defined maps Rn→Rm\mathbb{R}^n \to \mathbb{R}^mRn→Rm; no boundary conditions, no manifolds, and no jet bundles are used. The group is not formalized as a group: as in §2 of the paper, only its infinitesimal generators Δx\Delta xΔx, Δu\Delta uΔu enter, and the invariance hypothesis is Noether's identity (11). The linear independence of the ρ\rhoρ divergence relations, which Noether argues from the essentiality of the parameters, is not part of the formal statements.

Derivatives are Fréchet derivatives: ∂lg(x)\partial_l g(x)∂l​g(x) is the derivative of ggg at xxx applied to the lll-th standard basis vector, and partial derivatives of the Lagrangian are derivatives of the corresponding partially applied function. Because Lean's derivative operator returns 000 at points of non-differentiability, each statement carries explicit differentiability hypotheses for exactly the functions whose derivatives it mentions; a solver may not assume more.

The statements are not vacuous: the hypotheses of every milestone are satisfied, for instance, by smooth Lagrangians and smooth fields, and the invariance hypothesis (11) is satisfied by the classical examples (a Lagrangian independent of xlx_lxl​ with Δx=el\Delta x = e_lΔx=el​, Δu=0\Delta u = 0Δu=0). Degenerate parameter values are admitted and behave as expected: for m=0m = 0m=0 or n=0n = 0n=0 the sums are empty and the identities reduce to 0=00 = 00=0, and for ρ=0\rho = 0ρ=0 the goal quantifies over an empty index set.

Contributions of independent interest that this mission would welcome: a reusable statement of the fundamental lemma of the calculus of variations on Rn\mathbb{R}^nRn in the form needed for (16), and the higher-order analogue of the central identity, Noether's equation (6).

Selected references

  • E. Noether, Invariante Variationsprobleme, Nachr. d. König. Gesellsch. d. Wiss. zu Göttingen, Math-phys. Klasse (1918), 235–257. English translation by M. A. Tavel, Invariant Variation Problems, Transport Theory and Statistical Physics 1 (3) (1971), 183–207; arXiv:physics/0503066.
  • Y. Kosmann-Schwarzbach, The Noether Theorems: Invariance and Conservation Laws in the Twentieth Century, Springer (2011), DOI:10.1007/978-0-387-87868-3.
  • P. J. Olver, Applications of Lie Groups to Differential Equations, 2nd ed., Springer (1993), DOI:10.1007/978-1-4612-4350-2.
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