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Number Theory·Captain: alexcarter

The Erdős–Straus Conjecture (Erdős Problem 242)Open Problem

Egyptian fractions and the Erdős–Straus question

A unit fraction is the reciprocal of a positive integer. The Erdős–Straus conjecture asks whether the particularly simple rational number 4/n4/n4/n always admits an expansion with three such terms. Its difficulty lies in obtaining a fixed number of terms for every denominator: general algorithms for Egyptian fractions do not give this three-term guarantee.

The conjecture is open. This mission adopts the exact statement maintained as Erdős Problem 242. It aims to formalize established reductions and provide a precise frontier for further work; it does not present a proof of the universal conjecture.

The historical formulations vary. Erdős’s 1950 paper, pp. 193–195, discusses distinct unit fractions and attributes the conjecture jointly to himself and Straus. His 1961 problem I.32, p. 238, allows positive denominators without specifying distinctness, while the 1979 statement, problem 9, p. 70, explicitly orders distinct denominators. The earliest published discussion may be Obláth’s 1950 paper, submitted in 1948; it attributes the question to Erdős, as explained by Bloom–Elsholtz, pp. 238–239.

The main developments relevant here are:

  • 1950: Obláth’s sufficient condition using a prime divisor of n+1n+1n+1 congruent to 333 modulo 444.
  • 1965–1969: Yamamoto’s congruence analysis and Mordell’s exposition reduce the remaining prime cases to six classes modulo 840840840.
  • 1970–1971: Vaughan bounds the density of possible exceptions; Terzi develops a stronger congruence sieve modulo 120120120120120120.
  • 2013–2022: Elsholtz–Tao analyze representation counts and soluble polynomial congruences; Bloom–Elsholtz give an explicit equivalent covering formulation.
  • 2025: Computational verification is reported through 101810^{18}1018. Pomerance–Weingartner study the more general Erdős–Straus–Schinzel problem, including quantitative dependence on a variable numerator.

The exact property

For a natural number nnn, write IsErdosStraus(n)\mathrm{IsErdosStraus}(n)IsErdosStraus(n) for the following literal property:

∃x,y,z∈N,1≤x<y<z,4n=1x+1y+1z.\exists x,y,z\in\mathbb N,\qquad 1\le x<y<z,\qquad \frac4n=\frac1x+\frac1y+\frac1z.∃x,y,z∈N,1≤x<y<z,n4​=x1​+y1​+z1​.

Every fraction is evaluated in Q\mathbb QQ. The predicate contains only these witnesses, inequalities, and equality. The required range of the conjecture is n>2n>2n>2; distinctness is part of the mathematical target. In particular, the prime 222 cannot simply be imported from a formulation permitting repeated denominators. The boundary value n=3n=3n=3 is included, with denominators 1,4,121,4,121,4,12.

Formalization targets

The unresolved root goal is

∀n∈N,n>2⟹IsErdosStraus(n).\forall n\in\mathbb N,\quad n>2\Longrightarrow\mathrm{IsErdosStraus}(n).∀n∈N,n>2⟹IsErdosStraus(n).

The supporting milestones concern established mathematics. Denominator clearing relates the rational equation to 4xyz=n(yz+xz+xy)4xyz=n(yz+xz+xy)4xyz=n(yz+xz+xy) under strict positivity. Positive scaling transports a solution for nnn to one for knknkn while preserving the strict order. An explicit even-number family supplies the case needed for the prime reduction.

The elementary families cover 3∣n3\mid n3∣n, n≡2(mod3)n\equiv2\pmod3n≡2(mod3), n≡3(mod4)n\equiv3\pmod4n≡3(mod4), and n≡5(mod8)n\equiv5\pmod8n≡5(mod8), with the displayed witnesses and their integrality and strict ordering recorded in separate statements. Together with the even case, they solve every n>2n>2n>2 outside 1(mod24)1\pmod{24}1(mod24). The useful reduction is an equivalence between the root goal and its restriction to primes p≡1(mod24)p\equiv1\pmod{24}p≡1(mod24); the ordinary prime reduction is also stated separately. The classical scaling and residue observations are discussed in Bloom–Elsholtz, p. 239.

Obláth’s milestone says that IsErdosStraus(n)\mathrm{IsErdosStraus}(n)IsErdosStraus(n) holds for n>2n>2n>2 whenever n+1n+1n+1 has a prime divisor q≡3(mod4)q\equiv3\pmod4q≡3(mod4). This condition is explicitly recorded in the introduction of Pomerance–Weingartner, which identifies the original Obláth reference. The exact distinctness requirement is retained in this mission.

The Mordell–Yamamoto milestone asks for a decomposition for every prime p>2p>2p>2 satisfying

p mod 840∉{1,121,169,289,361,529}.p\bmod840\notin\{1,121,169,289,361,529\}.pmod840∈/{1,121,169,289,361,529}.

The actual existence claim is the theorem to prove. It has no hypothesis asserting that these classes are covered. Yamamoto’s original paper, §§3–4, pp. 42–46, supplies the congruence framework and prints this residual list. The same list appears in the current Erdős Problems record. The list printed on p. 239 of Bloom–Elsholtz instead contains 494949 and omits 529529529; that discrepant list is not used here. The historical papers often permit repeated denominators, so producing distinct ordered witnesses is an explicit part of the formalization obligation.

What these results provide

The elementary infrastructure gives reusable certificates and transports for exact rational decompositions. The reductions identify a mathematically meaningful remaining domain without assuming the conjecture. Completion of the modulo-840840840 milestone would leave the prime cases in its six residual classes as the classical research frontier; those classes are not asserted to consist of counterexamples.

Later targets include Terzi’s 1971 sieve, whose publisher abstract reports 198 residual classes modulo 120120120120120120, and Vaughan’s density theorem, bounding the exceptional count by Xexp⁡(−c(log⁡X)2/3)X\exp(-c(\log X)^{2/3})Xexp(−c(logX)2/3) for a positive constant ccc. Neither is a core Lean statement in this draft. No unaudited list of 198 classes is supplied.

Elsholtz–Tao provide counting results and a classification of polynomially soluble congruences. Bloom–Elsholtz, Theorem 1, pp. 239–240, characterize their conjecture by coverage of all primes by classes

−a/c(mod4acd−1)(a,c,d≥1),-a/c\pmod{4acd-1}\quad(a,c,d\ge1),−a/c(mod4acd−1)(a,c,d≥1),

or

−(4c2d+1)/k(mod4cd)(c,d,k≥1, k∣4c2d+1).-(4c^2d+1)/k\pmod{4cd}\quad(c,d,k\ge1,\ k\mid4c^2d+1).−(4c2d+1)/k(mod4cd)(c,d,k≥1, k∣4c2d+1).

Here division by ccc denotes a modular inverse; division by kkk is exact integer division. The authors are Bloom and Elsholtz, not Bradford and Elsholtz. This is a later formalization target: an initial core theorem is not included until the translation between that paper’s denominator convention and the present strict convention is separately formalized. Pomerance–Weingartner address growing numerators in the generalized problem; their exceptions are not counterexamples to the fixed numerator 444 conjecture.

The remaining difficulty

Congruence identities prove infinite families only when the identities and their arithmetic hypotheses are established for arbitrary parameters. Checking finitely many representatives with a search program does not prove that all future values in an arithmetic progression work. Density estimates also allow an exceptional set and therefore do not settle the universal statement.

The current record cites Mihnea–Bogdan (2025) for computational verification through 101810^{18}1018. This is reported computational evidence, not a Lean-certified theorem in this mission. No universal conclusion or periodicity assertion is inferred from it.

Formalization scope

The development uses natural-number denominators, exact rational arithmetic, integer polynomial identities, divisibility, primality, and natural-number remainders. Definitions are transparent. The root is not hidden in a typeclass, structure field, certificate, or extra assumption, and its quantifier is not bounded. The existing Google DeepMind transcription is a statement reference, not an imported proof.

The draft targets Mathlib 0df444a360eaa60ab8c11dca51a86af692955474 with Lean 4.33.1. Every proposed statement has been locally elaborated and supplied with an independent read-back. Statement elaboration with sorry is not proof verification. The accompanying local proof audit distinguishes the proved supporting results from the open root and the remaining modulo-840840840 formalization task.

Selected references

  • Erdős, Az … egyenlet egész számú megoldásairól, Mat. Lapok 1 (1950), 192–210; original scan.
  • Erdős–Graham, Old and New Problems and Results in Combinatorial Number Theory (1980), chapter IV; author’s institutional scan.
  • Obláth, Sur l’équation diophantienne 4/n=1/x1+1/x2+1/x34/n=1/x_1+1/x_2+1/x_34/n=1/x1​+1/x2​+1/x3​, Mathesis 59 (1950), 308–316; bibliographic record, also cited in Pomerance–Weingartner.
  • Yamamoto, On the Diophantine Equation 4/n=1/x+1/y+1/z4/n=1/x+1/y+1/z4/n=1/x+1/y+1/z, Mem. Fac. Sci. Kyushu Univ. A 19 (1965), 37–47; original paper.
  • Mordell, Diophantine Equations, Academic Press (1969), chapter 30, pp. 287–290; publisher record.
  • Terzi (1971), Vaughan (1970), Elsholtz–Tao (2013), Bloom–Elsholtz (2022), Mihnea–Bogdan (2025), and Pomerance–Weingartner (2025/2026): primary sources linked at their statements above.
15 thms2 active usersReviewed
CombinatoricsGraph Theory·Captain: hao jia

Weak Pentagon Colorings of Triangle-Free Cubic Graphs (OPG-434)Open Problem

Motivation

The weak pentagon problem asks for a five-label structure on the edges of every triangle-free cubic graph. Although its wording resembles proper edge coloring, properness is not part of the conjecture. Instead, each individual color class must meet enough odd cycles that deleting that class leaves a bipartite spanning graph. The problem connects odd-cycle transversals, cut structure, and homomorphisms to a fixed sixteen-vertex graph.

Robert Šámal recorded the conjecture on the Open Problem Garden in 2007. DeVos and Šámal proved that sufficiently high-girth subcubic graphs map to the Clebsch graph, with an explicit girth threshold in their theorem; that does not cover all triangle-free cubic graphs. The mission separates the general existence question from two exact reformulations that can be verified independently.

Setting

Let GGG be a finite simple triangle-free cubic graph. A five-edge coloring here is any symmetric assignment

c:E(G)⟶{1,2,3,4,5}.c:E(G)\longrightarrow\{1,2,3,4,5\}.c:E(G)⟶{1,2,3,4,5}.

It need not be proper or surjective. For a color iii, delete all edges with label iii while retaining every vertex. The coloring is a weak-pentagon coloring when each of the five resulting spanning graphs is bipartite.

Equivalently, each color class is an odd-cycle edge transversal: it meets the edge set of every simple odd cycle. The cycles are not required to be induced. This last distinction matters because an odd cycle may have a chord in the original graph and still survive in a deleted-edge spanning subgraph.

A second representation uses the sixteen four-bit vectors. Two vectors are adjacent when their Hamming distance is three or four. This graph is a model of the Clebsch graph. A graph homomorphism sends every edge of GGG to an adjacent pair in this target.

Formalization targets

Weak pentagon conjecture

The root target is

∀G finite, simple, triangle-free, and cubic,∃c:E(G)→[5] ∀i∈[5],G−c−1(i) is bipartite.\forall G\text{ finite, simple, triangle-free, and cubic}, \qquad \exists c:E(G)\to[5]\ \forall i\in[5], \quad G-c^{-1}(i)\text{ is bipartite}.∀G finite, simple, triangle-free, and cubic,∃c:E(G)→[5] ∀i∈[5],G−c−1(i) is bipartite.

No condition is imposed on adjacent edges receiving different labels.

Odd-cycle equivalence

For every fixed graph and fixed five-edge labeling,

(∀i, G−c−1(i) is bipartite)⟺(∀i, c−1(i) meets every odd cycle of G).\bigl(\forall i,\ G-c^{-1}(i)\text{ is bipartite}\bigr) \quad\Longleftrightarrow\quad \bigl(\forall i,\ c^{-1}(i)\text{ meets every odd cycle of }G\bigr).(∀i, G−c−1(i) is bipartite)⟺(∀i, c−1(i) meets every odd cycle of G).

This theorem is graph-general: triangle-freeness and cubicity delimit the root but are not needed for the equivalence.

Sixteen-vertex homomorphism formulation

For every finite simple graph GGG,

G has a weak-pentagon coloring⟺G⟶H16,G\text{ has a weak-pentagon coloring} \quad\Longleftrightarrow\quad G\longrightarrow H_{16},G has a weak-pentagon coloring⟺G⟶H16​,

where H16H_{16}H16​ has vertex set {0,1}4\{0,1\}^4{0,1}4 and edges at Hamming distance three or four. The statement concerns existence of some coloring and some homomorphism; it does not preserve an arbitrarily prescribed coloring.

Significance

The root theorem would establish a uniform parity decomposition for all triangle-free cubic graphs. The transversal form makes every odd cycle use all five colors. The homomorphism form replaces edge labels and five separate bipartitions by one bounded vertex certificate, allowing structural and computational methods to share an exact target.

Formalization prevents several nearby but inequivalent conjectures from being conflated. A weak-pentagon coloring can be improper. Checking only induced odd cycles of the original graph is insufficient. Mapping to a five-cycle is stronger and fails even for familiar positive examples. The explicit four-bit model also avoids relying on the name “Clebsch graph” without fixing its adjacency convention.

Difficulty

The equivalences reorganize the problem but do not create the required object. Five odd-cycle transversals must be pairwise compatible as color fibers; finding one small transversal is not enough. Local deletion and gluing methods must preserve existence of a whole homomorphism, not one chosen boundary assignment.

High-girth results leave finitely many short-cycle configurations only when the girth hypothesis is present. Triangle-free graphs may still contain overlapping five- and seven-cycles, and naive local recoloring can repair one odd cycle while breaking another color complement. Minimum-counterexample arguments also require care because deleting vertices preserves subcubicity but not cubicity.

Formalization scope

Colors are Fin 5. The coloring stores a symmetric value on ordered endpoint pairs, with nonedge values ignored. Cubic means every neighbor set has extended cardinality exactly three. A simple odd cycle is a cyclic list of at least three distinct vertices of odd length; it need not be induced. Bipartiteness is witnessed by a Boolean side assignment after one color is deleted.

The sixteen-vertex relation is defined directly on four-bit functions by Hamming distance, so its cardinality and adjacency are not hidden behind an imported graph name. The repository's transversal proof, normalization, and local homomorphism studies are candidate_only; the mission publishes their clean statements as proof obligations. Contributions may close either equivalence, formalize known high-girth results, prove restricted graph classes, or attack the root. A finite benchmark or a failure of one extension strategy is not a counterexample to the conjecture.

Selected references

  • R. Šámal, Weak pentagon problem, Open Problem Garden, 2007. https://www.openproblemgarden.org/op/weak_pentagon_problem
  • M. DeVos and R. Šámal, High-girth cubic graphs are homomorphic to the Clebsch graph, Journal of Graph Theory 66 (2011), 241–259. https://arxiv.org/abs/math/0602580
  • P. Kolman, B. Lidický, and J.-S. Sereni, On Minimum Fair Odd Cycle Transversal, 2010. https://kam.mff.cuni.cz/kamserie/clanky/2010/s956.pdf
  • Open Problem Garden / UnsolvedMath, OPG-434. https://www.unsolvedmath.com/problems/OPG-434
4 thms2 active usersReviewed
CombinatoricsGraph Theory·Captain: hao jia

Two Acyclic Colors for Planar Orientations (OPG-169)Open Problem

Motivation

The dichromatic number of a digraph is the directed analogue of chromatic number: vertices of one color may be adjacent, but each color class must induce an acyclic digraph. The Two Color Conjecture asks whether every orientation of a planar graph has dichromatic number at most two. It is a natural directed-coloring counterpart to planar graph coloring, with the key difference that forbidden monochromatic objects are directed cycles rather than undirected edges.

Critical-digraph theory gives general degree restrictions on minimal counterexamples, and Li and Mohar proved two-colorability under the additional hypothesis that the directed girth is at least four. The unrestricted planar-orientation problem permits directed triangles, so that theorem is a genuine partial result rather than a solution. The project candidate develops the elementary least-order-counterexample consequences needed before any planar structural argument.

Setting

Let GGG be a finite simple planar graph. An orientation DDD assigns exactly one direction to every edge of GGG, with no loops, parallel arcs, or pair of opposite arcs. For X⊆V(D)X\subseteq V(D)X⊆V(D), the induced digraph D[X]D[X]D[X] retains every arc whose two endpoints lie in XXX.

A two-coloring is a map

c:V(D)⟶{0,1}.c:V(D)\longrightarrow\{0,1\}.c:V(D)⟶{0,1}.

It is valid when both induced digraphs D[c−1(0)]D[c^{-1}(0)]D[c−1(0)] and D[c−1(1)]D[c^{-1}(1)]D[c−1(1)] contain no directed cycle. The color classes need not be independent and either color may be unused.

Planarity belongs to the underlying undirected graph. Lean represents it by an injective straight-line embedding with noncrossing nonincident edges. Directed reachability is reflexive, so a singleton orientation is strongly connected under the usual length-zero convention, although it is also acyclic and hence cannot be a counterexample.

Formalization targets

Two Color Conjecture

The goal is

∀D an orientation of a finite simple planar graph,∃c:V(D)→{0,1},D[c−1(0)] and D[c−1(1)] are acyclic.\forall D\text{ an orientation of a finite simple planar graph}, \qquad \exists c:V(D)\to\{0,1\}, \quad D[c^{-1}(0)]\text{ and }D[c^{-1}(1)]\text{ are acyclic}.∀D an orientation of a finite simple planar graph,∃c:V(D)→{0,1},D[c−1(0)] and D[c−1(1)] are acyclic.

Disconnected graphs and empty color classes are included.

Least-order counterexample structure

A supporting theorem states that every counterexample of minimum vertex order is nonempty and strongly connected, and its underlying graph has minimum degree at least three:

D least-order counterexample⟹D strongly connected and δ(U(D))≥3.D\text{ least-order counterexample} \quad\Longrightarrow\quad D\text{ strongly connected and }\delta(U(D))\ge3.D least-order counterexample⟹D strongly connected and δ(U(D))≥3.

The minimum is taken over the full class of finite planar orientations, not over one embedding or an arc-minimal subclass.

Semidegree candidate

A stronger open milestone asks whether every vertex of such a least-order counterexample has at least two incoming and at least two outgoing neighbors. This is recorded separately because it is stronger than the degree-three conclusion and its repository proof remains candidate_only.

Significance

The root theorem would establish a universal two-color bound for planar orientations while allowing directed triangles and arbitrary local degree. A counterexample would demonstrate a sharp obstruction specific to directed cycles, not visible to ordinary planar coloring.

The formalized minimal-counterexample package is reusable regardless of the ultimate answer. Strong connectivity permits arguments inside one component, while the degree and semidegree restrictions narrow discharging configurations and finite searches. Encoding the full induced color classes prevents an invalid shortcut in which only a selected acyclic spanning subdigraph is checked.

Difficulty

Deleting a low-degree vertex is safe only if a valid coloring of the smaller graph can be extended without creating a monochromatic directed cycle through the restored vertex. For a chosen color, obstruction depends on both an incoming and an outgoing neighbor of that color together with a directed return path in the old color class. Merely seeing same-colored in- and out-neighbors is not sufficient.

Strongly connected components can be colored separately because their condensation is acyclic, but that observation only reduces a minimal counterexample to one component. Planarity alone does not eliminate directed triangles or the return paths that block both colors. Results assuming directed girth at least four therefore leave the central case untouched.

Formalization scope

A directed graph is a binary relation, coupled to a SimpleGraph by an orientation predicate that requires exactly one direction on every edge and forbids arcs on nonedges. A directed cycle is a cyclic list of at least three distinct vertices. A color class is acyclic when no such list lies entirely in that class. Strong connectivity is nonempty mutual reflexive-transitive reachability.

The least-order predicate quantifies over every smaller finite planar orientation in the same universe. It does not assert that a counterexample exists. Consequently, its structural theorems may be true vacuously if the root conjecture is true; the read-back must expose that conditional form.

Repository arguments, finite tables, and transport receipts are not machine-checked proofs. Contributions may formalize component gluing, exact vertex-extension criteria, degree or semidegree restrictions, planar reducible configurations, or the root. Any stronger minimum-degree claim must remain distinct from the admitted degree-three target until proved.

Selected references

  • Open Problem Garden / UnsolvedMath, OPG-169: The Two Color Conjecture. https://www.unsolvedmath.com/problems/OPG-169
  • B. Mohar, Eigenvalues and colorings of digraphs, Linear Algebra and its Applications, 2010. https://www.sfu.ca/~mohar/Reprints/Inprint/BM09_LAA09_Mohar_EigenvaluesandColorings.pdf
  • Z. Li and B. Mohar, Planar digraphs of digirth four are 2-colourable, Journal of Combinatorial Theory, Series B, 2017. https://arxiv.org/abs/1606.06114
4 thms2 active usersReviewed
Quantum InformationTheoretical Computer Science·Captain: Goku

The Aaronson-Ambainis ConjectureOpen Problem

Motivation

Quantum query algorithms are known to beat classical ones on problems with algebraic structure -- period finding, hidden subgroups, forrelation. No such speedup is known for a problem with no structure at all. Aaronson and Ambainis proposed making that observation into a theorem, and reduced it to a question with no quantum content: a statement about bounded low-degree polynomials on the Boolean cube (Aaronson--Ambainis 2009).

The question has resisted since. A timeline of what is actually established:

  • 2009. Aaronson and Ambainis state the conjecture and prove that it implies almost-everywhere classical simulation of quantum query algorithms.
  • 2012. Montanaro settles the case of block-multilinear forms whose coefficients all have the same magnitude.
  • 2016. O'Donnell and Zhao reduce the general conjecture to a restricted class, the one-block decoupled polynomials.
  • 2019. Aaronson surveys a decade of partial progress (retrospective).
  • 2022. Bansal, Sinha and de Wolf prove the conjecture for completely bounded degree-ddd block-multilinear forms, obtaining influence 1/poly(d)1/\mathrm{poly}(d)1/poly(d) at constant variance (arXiv:2203.00212).
  • 2024. The conjecture is established for a non-negligible fraction of random restrictions (arXiv:2402.13952).

The cases that are settled are settled under structural hypotheses -- block-multilinearity, complete boundedness, symmetry, Boolean range. The general statement is open.

Setting

Let NNN be a positive integer. The Boolean cube is {0,1}N\{0,1\}^N{0,1}N, carrying the uniform distribution; a point xxx is identified with the 0/10/10/1 real vector it names, so a real multivariate polynomial ppp in NNN variables has a value p(x)p(x)p(x) at each cube point. For a function fff on the cube write

E[f]=2−N∑x∈{0,1}Nf(x).\mathbb{E}[f]=2^{-N}\sum_{x\in\{0,1\}^N}f(x).E[f]=2−Nx∈{0,1}N∑​f(x).

The variance of ppp is Var⁡[p]=E[(p−E[p])2]\operatorname{Var}[p]=\mathbb{E}\big[(p-\mathbb{E}[p])^2\big]Var[p]=E[(p−E[p])2]. Writing x⊕ix^{\oplus i}x⊕i for xxx with its iii-th bit flipped, the influence of coordinate iii on ppp is

Inf⁡i[p]=E[(p(x)−p(x⊕i))2].\operatorname{Inf}_i[p]=\mathbb{E}\big[(p(x)-p(x^{\oplus i}))^2\big].Infi​[p]=E[(p(x)−p(x⊕i))2].

These are the combinatorial forms of both quantities, as used in the source; no Fourier--Walsh expansion is required to state anything below. The degree of ppp is its total degree as a polynomial. Call ppp bounded when 0≤p(x)≤10\le p(x)\le 10≤p(x)≤1 at every cube point -- a condition imposed only on the cube, not on all of RN\mathbb{R}^NRN.

Target

The goal is the conjecture in the shape stated by its authors: there is an absolute constant CCC such that for all NNN, all ddd, every polynomial ppp of degree at most ddd that is bounded on the cube, and every ε>0\varepsilon>0ε>0 with Var⁡[p]≥ε\operatorname{Var}[p]\ge\varepsilonVar[p]≥ε, some coordinate iii satisfies

Inf⁡i[p]  ≥  (εd)C.\operatorname{Inf}_i[p]\;\ge\;\Big(\frac{\varepsilon}{d}\Big)^{C}.Infi​[p]≥(dε​)C.

The constant CCC is quantified outermost and may depend on nothing. That uniformity is the entire content: bounds that degrade exponentially in ddd are already known, and a goal naming a specific exponent would be superseded by the next improvement.

Significance

The result itself. Aaronson and Ambainis prove that the conjecture implies that the acceptance probability of any bounded-error TTT-query quantum algorithm on a Boolean input can be approximated, to small error on all but a small fraction of inputs, by a classical algorithm making poly(T)\mathrm{poly}(T)poly(T) queries. Quantum speedups would then require structure in a precise sense. The conjecture also has purely classical content, asserting that boundedness plus low degree forces variance to concentrate on some single coordinate rather than spread across all NNN. Without it, no such concentration is known at any rate polynomial in 1/d1/d1/d.

Formalizing it. The conjecture is open, so this mission does not formalize a known proof of the goal. What it produces is a machine-checked statement of the conjecture together with formalizations of the partial results above, each currently existing only on paper. The milestone chain also yields reusable infrastructure for analysis of Boolean functions, of which Mathlib currently contains none: no Fourier--Walsh expansion, no influence, no variance on the cube.

Difficulty

The elementary bound is the Poincare inequality on the cube, 4Var⁡[p]≤∑iInf⁡i[p]4\operatorname{Var}[p]\le\sum_i\operatorname{Inf}_i[p]4Var[p]≤∑i​Infi​[p], which yields a coordinate with influence at least 4ε/N4\varepsilon/N4ε/N. This is tight for the dictator p(x)=x1p(x)=x_1p(x)=x1​ and depends on NNN, so it says nothing: the conjecture demands a bound free of NNN entirely.

The natural repair is the route available when ppp takes only the values 000 and 111. A Boolean-valued polynomial of degree ddd depends on boundedly many coordinates, which immediately produces an influential one. That argument does not survive relaxing the range to the interval [0,1][0,1][0,1]: a bounded real-valued polynomial of low degree need not depend on boundedly many coordinates, and every known substitute loses a factor exponential in ddd. Closing the gap between exponential and polynomial dependence on ddd is the difficulty, and it is where all of the partial results stop.

Formalization scope

Polynomials are MvPolynomial (Fin N) ℝ and degree is Mathlib's totalDegree, so the statement needs no bespoke notion of degree. Expectation is a finite sum scaled by 2−N2^{-N}2−N rather than a measure-theoretic integral, keeping every definition elementary. Bit flipping is Function.update x i (!x i). Boundedness is asserted at cube points only. Variance and influence are the combinatorial definitions above, published as the definition AaronsonAmbainis.

Three points close off degenerate readings. The exponent O(1)O(1)O(1) of the source is rendered as an existentially quantified natural number with no leading multiplicative constant, since admitting one weakens the claim. Taking that exponent to be 000 would demand influence at least 111 and is therefore not a trivializing choice, while larger exponents only weaken the bound; the content is that some fixed exponent suffices for all NNN and ddd at once. The hypothesis deg⁡p≤d\deg p\le ddegp≤d is universally quantified over ddd, which is equivalent to the source's exact-degree form because the smallest admissible ddd gives the strongest conclusion. The cases N=0N=0N=0 and d=0d=0d=0 are vacuous, since 0<ε≤Var⁡[p]0<\varepsilon\le\operatorname{Var}[p]0<ε≤Var[p] fails for a constant polynomial.

A complete development needs, beyond the published definitions, a Fourier--Walsh layer with Parseval's identity, the level-kkk machinery used by the partial results, and -- for the completely bounded case -- operator-space norms on multilinear forms. All of the Boolean-analysis material is reusable well beyond this mission. Contributions of any milestone are welcome, as are alternative formalizations of the definitions in function-level rather than polynomial-level form.

Out of scope: the quantum simulation consequence is not formalized here. Stating it requires a formal quantum query model, which no Lean library currently provides.

Selected references

  • S. Aaronson, A. Ambainis, The Need for Structure in Quantum Speedups, Theory of Computing 10 (2014) 133--166; arXiv:0911.0996. Conjecture 6.
  • N. Bansal, M. Sinha, R. de Wolf, Influence in Completely Bounded Block-multilinear Forms and Classical Simulation of Quantum Algorithms, CCC 2022; arXiv:2203.00212.
  • Aaronson--Ambainis Conjecture Is True For Random Restrictions, 2024; arXiv:2402.13952.
  • S. Aaronson, The Aaronson-Ambainis Conjecture (2008-2019), blog retrospective.
  • S. Arunachalam, J. Briet, C. Palazuelos, Quantum query algorithms are completely bounded forms, SIAM J. Comput. 48 (2019); arXiv:1711.07285.
  • AIM problem list, Analysis on the hypercube with applications to quantum computing, aimpl.org/hypercubequantum.
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CombinatoricsGraph Theory·Captain: hao jia

Circular (20,7)-Coloring of Triangle-Free Subcubic Planar Graphs (OPG-401)Open Problem

Motivation

Circular coloring refines ordinary vertex coloring by placing colors on a cycle and measuring separation modulo the palette size. It records information that an ordinary chromatic-number bound can lose, and it interacts sharply with planarity, forbidden short cycles, and degree constraints. OPG-401 asks for a specific bound at the intersection of those themes: whether triangle-free planar graphs of maximum degree three always admit a circular coloring of ratio 20/720/720/7.

The question appears on Xuding Zhu's open-problem page and in the Open Problem Garden record. Nearby theorems on fractional coloring do not settle it: fractional chromatic number and circular chromatic number are distinct parameters, so the known fractional bounds for subcubic triangle-free graphs cannot simply be substituted for a circular-coloring proof. Work on circular recoloring likewise studies connectivity between colorings that already exist and does not supply the missing universal existence theorem.

Setting

For integers p≥2q>0p\ge 2q>0p≥2q>0, a (p,q)(p,q)(p,q)-coloring of a finite simple graph GGG is a map

φ:V(G)⟶Zp\varphi:V(G)\longrightarrow \mathbb Z_pφ:V(G)⟶Zp​

such that the shortest cyclic distance between φ(u)\varphi(u)φ(u) and φ(v)\varphi(v)φ(v) is at least qqq for every edge uvuvuv. Equivalently, using representatives in {0,…,p−1}\{0,\ldots,p-1\}{0,…,p−1}, the modular difference lies between qqq and p−qp-qp−q, inclusive. The circular chromatic number is the infimum of the ratios p/qp/qp/q for which such a coloring exists.

The root domain consists of all finite simple graphs that are planar, triangle-free, and subcubic. Disconnected and empty graphs are included. Planarity is represented by an injective straight-line drawing with no vertex in the interior of an edge and no intersection between nonincident edges. For finite simple graphs this is the standard straight-line form of planarity.

Formalization targets

Root question

The central target is

G finite, simple, planar, triangle-free, and Δ(G)≤3⟹G has a (20,7)-coloring.G\text{ finite, simple, planar, triangle-free, and }\Delta(G)\le 3 \quad\Longrightarrow\quad G\text{ has a }(20,7)\text{-coloring}.G finite, simple, planar, triangle-free, and Δ(G)≤3⟹G has a (20,7)-coloring.

This is exactly the claim χc(G)≤20/7\chi_c(G)\le 20/7χc​(G)≤20/7 in a form suitable for finite Lean data.

Local extension table

A reusable finite milestone freezes the local palette arithmetic. For a∈Z20a\in\mathbb Z_{20}a∈Z20​, let A(a)A(a)A(a) be the colors at cyclic distance at least seven from aaa. For all a,ba,ba,b,

∣A(a)∩A(b)∣=7−d20(a,b),A(a)∩A(b)≠∅  ⟺  d20(a,b)≤6.|A(a)\cap A(b)|=7-d_{20}(a,b), \qquad A(a)\cap A(b)\ne\varnothing\iff d_{20}(a,b)\le6.∣A(a)∩A(b)∣=7−d20​(a,b),A(a)∩A(b)=∅⟺d20​(a,b)≤6.

This includes equal colors, antipodal colors, tied symmetries, and all twenty residues. It is the exact obstruction encountered when extending a coloring over a deleted degree-two vertex while preserving every old color. The repository artifact supporting this formulation is only candidate_only; the mission publishes the statement as an open formal target rather than claiming it as proved.

Significance

A proof of the root theorem would give the requested sharp circular-coloring guarantee uniformly over a broad planar graph class. It would also separate the circular problem from nearby fractional results by constructing the stronger cyclic palette assignment itself. A counterexample, if one exists, would have to survive the combined restrictions of planarity, triangle-freeness, and maximum degree three, and would identify a genuine boundary for local extension methods.

Formalization adds two concrete assets. First, the cyclic-distance convention is fixed once, avoiding common errors involving directed residues, unrestricted integer lifts, or truncated subtraction. Second, graph reductions can be checked against a precise preservation obligation: deleting a vertex does not help unless the chosen coloring of the smaller graph has compatible boundary colors. The mission therefore welcomes both global structural arguments and verified finite boundary classifications, but finite enumeration alone is not accepted as a proof for arbitrary graph order.

Difficulty

The obvious induction on vertices fails at degree two. A coloring of G−vG-vG−v need not extend over vvv: if its two neighbors receive colors at cyclic distance at least seven, their two allowed sets can be disjoint. The local table characterizes this failure exactly but does not guarantee that a different coloring of G−vG-vG−v has favorable boundary values. Recoloring, reducible configurations, and planar discharging must therefore interact without silently assuming universal extension or connectivity of the recoloring graph.

A second source of difficulty is parameter confusion. Bounds for fractional colorings do not automatically yield (20,7)(20,7)(20,7)-colorings, and a theorem about mixing existing circular colorings does not prove existence. Any proposed bridge must be stated and verified explicitly.

Formalization scope

Lean represents colors by Fin 20 and uses the minimum of the two directed modular differences as cyclic distance. Edge compatibility includes both the lower bound 777 and the formal upper bound 131313. Triangle-freeness is literal absence of three mutually cyclic adjacent vertices, and subcubic means every neighbor set has extended cardinality at most three.

The definition bundle contains no theorem and no sorry. Draft theorem items contain exactly one := by sorry. The local candidate computations and GitHub transport records are provenance, not evidence that either theorem is proved. A complete contribution may formalize the finite palette table, a faithful reducible configuration, a recoloring lemma with all quantifiers exposed, or the root theorem. Every claimed universal reduction must retain finiteness, simplicity, planarity, triangle-freeness, and the degree bound.

Selected references

  • X. Zhu, Circular chromatic number of triangle-free planar graphs with maximum degree three, open-problem page. https://www.math.nsysu.edu.tw/~zhu/open-problems/chic-k3free-planar.htm
  • Open Problem Garden, OPG-401. https://www.unsolvedmath.com/problems/OPG-401
  • X. Zhu, The fractional version of Hedetniemi's conjecture is true, European Journal of Combinatorics, 2011. https://doi.org/10.1016/j.ejc.2011.03.004
  • Z. Dvořák, J.-S. Sereni, and J. Volec, Subcubic triangle-free graphs have fractional chromatic number at most 14/5, Journal of the London Mathematical Society, 2014. https://arxiv.org/abs/1301.5296
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CombinatoricsGraph Theory·Captain: hao jia

Six Colors for Star Edge-Coloring Subcubic Graphs (OPG-37271)Open Problem

Motivation

A star edge coloring is a proper edge coloring with an additional local restriction: no path or cycle of four edges may use only two colors. It sits between ordinary proper edge coloring and strong edge coloring. The problem is local enough to admit finite obstruction searches, but global enough that independently valid local colorings may fail to fit together.

Dvořák, Mohar, and Šámal proved in 2013 that every subcubic multigraph has a star edge coloring with seven colors and conjectured that six always suffice. The Open Problem Garden records the simple-graph version as OPG-37271. The value six would be best possible because the complete bipartite graph K3,3K_{3,3}K3,3​ has star chromatic index six.

Subsequent work has proved the six-color bound under additional hypotheses. Lei, Shi, and Song proved it for subcubic multigraphs with maximum average degree less than 5/25/25/2 and obtained a five-color result below 24/1124/1124/11. Casselgren, Granholm, and Raspaud proved the conjecture for cubic Halin graphs and several bipartite families. These results leave the unrestricted finite subcubic case as the target of this mission.

Setting

Let GGG be a finite simple undirected graph. An edge coloring assigns to each unordered edge of GGG one color from a finite palette. It is proper if two distinct edges incident with the same vertex always have different colors.

A simple path of four edges has five pairwise distinct vertices v0,v1,v2,v3,v4v_0,v_1,v_2,v_3,v_4v0​,v1​,v2​,v3​,v4​ and consecutive edges v0v1,v1v2,v2v3,v3v4v_0v_1,v_1v_2,v_2v_3,v_3v_4v0​v1​,v1​v2​,v2​v3​,v3​v4​. It is bichromatic in a proper coloring exactly when the first and third edges have the same color and the second and fourth edges have the same color. The path need not be induced: additional chords do not remove it. A four-cycle has four pairwise distinct vertices and is bichromatic under the analogous alternating equalities, including the closing edge.

A coloring is a star edge coloring when it is proper and contains neither type of bichromatic four-edge configuration. The star chromatic index χs′(G)\chi'_s(G)χs′​(G) is the least palette size admitting such a coloring. A graph is subcubic when every vertex has at most three neighbors.

Formalization targets

Goal — the six-color conjecture

The main target is the exact OPG-37271 assertion for finite simple graphs:

Δ(G)≤3⟹χs′(G)≤6.\Delta(G)\le 3 \quad\Longrightarrow\quad \chi'_s(G)\le 6.Δ(G)≤3⟹χs′​(G)≤6.

In the Lean statement, this is expressed directly as the existence of a coloring by Fin 6; no separate minimization operator is needed.

Known upper bound

The first literature milestone is the established seven-color theorem:

Δ(G)≤3⟹χs′(G)≤7.\Delta(G)\le 3 \quad\Longrightarrow\quad \chi'_s(G)\le 7.Δ(G)≤3⟹χs′​(G)≤7.

Formalizing this result provides a checked baseline and infrastructure that a six-color argument can reuse.

Sharpness at K3,3K_{3,3}K3,3​

The second literature milestone records both sides of the exact value

χs′(K3,3)=6.\chi'_s(K_{3,3})=6.χs′​(K3,3​)=6.

Thus the mission cannot be completed by weakening the goal to a larger universal constant.

Significance

A proof would determine the universal star chromatic-index bound for graphs of maximum degree three and would match the known lower-bound example K3,3K_{3,3}K3,3​. A counterexample, if one exists, would separate six from the established seven-color bound and identify the first genuinely seven-chromatic subcubic graph.

The formalization contributes a reusable definition of star edge coloring on Mathlib finite simple graphs. In particular, it fixes several conventions that are easy to blur in informal or computational work: forbidden paths have four edges rather than four vertices; they are simple but need not be induced; four-cycles are checked separately; and properness is not inferred merely from the absence of an alternating four-edge pattern. These definitions can support certified bounded searches, verified coloring certificates, and later formalizations of sparse or planar special cases.

The current research repository contains candidate-only local extension criteria and finite certificates. They may motivate future milestones, but they are not treated here as proofs of the conjecture, as admitted evidence, or as replacements for the literature milestones.

Difficulty

A direct greedy coloring argument can fail at a newly inserted edge because a color may be forbidden either by an adjacent edge or by a bichromatic four-edge path created several incidences away. Deleting a low-degree vertex and coloring the remaining graph therefore does not guarantee that the old coloring extends without recoloring. Explicit small configurations already witness failure of this zero-recoloring strategy while remaining globally six-colorable.

The known seven-color proof has one extra color available to break such interactions. Reaching six requires coordinating local recolorings or extracting stronger structure from a minimal counterexample. Finite searches can test configurations and produce certificates, but bounded verification alone cannot establish the universal quantifier over all finite graphs.

Formalization scope

The mission uses SimpleGraph with an arbitrary finite vertex type. Edges are unordered edge-set elements, and palettes are the labeled finite types Fin k. The graph need not be connected, cubic, planar, or nonempty; isolated vertices and the empty graph are included. “Subcubic” means degree at most three, not degree exactly three.

A forbidden path is represented by five pairwise distinct vertices and four consecutive adjacencies. It is not required to be induced. A forbidden cycle is represented separately by four pairwise distinct vertices and four cyclic adjacencies. Under the properness hypothesis, equality of opposite edge colors is precisely the bichromatic alternating pattern.

A complete development should supply the known seven-color theorem, certify the exact value for K3,3K_{3,3}K3,3​, and then address the six-color goal. Contributions formalizing faithful special cases or reusable extension lemmas are welcome, but sampled graph families and successful SAT searches remain finite evidence unless converted into a general Lean proof.

Selected references

  • Z. Dvořák, B. Mohar, and R. Šámal, Star chromatic index, Journal of Graph Theory 72 (2013), 313–326. arXiv:1011.3376
  • H. Lei, Y. Shi, and Z.-X. Song, Star chromatic index of subcubic multigraphs, Journal of Graph Theory 88 (2018), 566–576. arXiv:1701.04105
  • C. J. Casselgren, J. B. Granholm, and A. Raspaud, On star edge colorings of bipartite and subcubic graphs, Discrete Applied Mathematics 298 (2021), 21–33. arXiv:1912.02467
  • Open Problem Garden, Star chromatic index of subcubic graphs, OPG-37271. Problem page
  • Vibe Mathing candidate repository, OPG-37271 star chromatic index of subcubic graphs, candidate-only artifacts at commit ddc49c1978a196490702150bb75264793a658457. Repository
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Discrete GeometryGroup Theory·Captain: xuanji

230 space groupsTextbook

Motivation: classify three-dimensional periodic symmetry

A space group describes the rigid motions compatible with a periodic spatial symmetry. The classification concerns possible symmetry types, rather than the size or shape of a particular drawing of a crystal. The classical three-dimensional numbers are 230, 219 when mirror-related types are identified, and 65 for the orientation-preserving subfamily. These are the three numbers recorded in Oliver Knill’s survey, §94, “Crystallography,” p. 41. Keeping their conventions separate matters: changing which coordinate transformations are allowed changes what counts as the same type.

The target is the known classification result selected by LeanEval v1, not an unsolved classification conjecture. Its authoritative specification is the declaration LeanEval.Geometry.SpaceGroupsProblem.space_groups. The accompanying benchmark manifest attributes the classification independently to Fedorov and Schoenflies in 1891. The work requested here is a machine-checked proof of that fixed statement.

Setting: groups, transformations, and orientation

For a natural number ddd, let E(d)=RdE(d)=\mathbb R^dE(d)=Rd with its Euclidean inner product. A Euclidean isometry is an invertible affine distance-preserving transformation of this space. The objects being counted are subgroups GGG of this full motion group. In the benchmark’s definitions, GGG is discrete when, for every point xxx and every real ε>0\varepsilon>0ε>0, the set of its elements satisfying dist⁡(gx,x)≤ε\operatorname{dist}(gx,x)\leq\varepsilondist(gx,x)≤ε is finite.

Such a group is crystallographic when it also contains translations by the members of some linearly independent family of ddd vectors. Translation by vvv means exactly that the transformation sends every xxx to x+vx+vx+v. These conditions specify the underlying groups directly; they do not start with a list of previously classified examples. Although the structure field containing the translation condition is called cocompact, its actual content is the existence of these independent translations, not a separately assumed compact quotient.

An affine equivalence between two groups is an invertible affine map whose conjugation carries the first group’s set of transformations onto the second’s. It need not be an isometry. An orientation-preserving affine equivalence additionally requires the determinant of that affine map’s linear part to be positive. Separately, an individual isometry preserves orientation when its own linear part has positive determinant. The Sohncke subfamily restricts the groups themselves: every element of a group must preserve orientation. This is distinct from restricting the map used to compare two groups, as explicitly distinguished by the source definitions and notes.

Target: one conjunction, with all three exact counts

Write COP(d)C_{\mathrm{OP}}(d)COP​(d) for crystallographicCountOP d, C(d)C(d)C(d) for crystallographicCount d, and COP,only(d)C_{\mathrm{OP,only}}(d)COP,only​(d) for crystallographicCountOPOnly d. They count, respectively, orientation-preserving affine classes of all crystallographic groups, arbitrary affine classes of all crystallographic groups, and orientation-preserving affine classes within the all-elements-orientation-preserving subfamily. The sole goal is

COP(3)=230∧C(3)=219∧COP,only(3)=65.C_{\mathrm{OP}}(3)=230\quad\land\quad C(3)=219\quad\land\quad C_{\mathrm{OP,only}}(3)=65.COP​(3)=230∧C(3)=219∧COP,only​(3)=65.

This is the exact benchmark conjunction, in its original order. None of its three components is optional, and they are not separate theorem targets. There are no milestones or auxiliary theorem items.

Significance: exact cardinalities for the underlying groups

The result gives finite and exact answers for the specified spaces of symmetry types, while retaining the distinction between orientation of a coordinate change and orientation of every symmetry in a group. The difference between 230 and 219 reflects the identification of mirror-related types described in Knill, §94; the 65 count answers a different question, concerning the restricted subfamily. Neither a single count nor a list that silently merges the equivalence conventions establishes the full assertion.

The formalization would add a proof connecting these numerical claims to the actual groups and class subsets specified in Lean. The benchmark source currently supplies the statement with an unproved placeholder. This draft likewise supplies a statement, not a proof or a claim that the benchmark is solved.

Difficulty: a catalog is not a completeness theorem

A finite catalog can have 230 entries without representing every crystallographic group, and different entries can still represent the same affine class. Thus checking the length of a catalog alone does not establish the source’s cardinality assertion. The difficulty is the mathematical connection between concrete descriptions and all groups admitted by the definitions, with precisely the required equivalence relations. The restricted 65-count must also respect the condition on every group element, rather than just a label attached to an example.

Formalization scope: preserve the benchmark model

The Lean representation is EuclideanSpace ℝ (Fin d), with affine isometries and affine equivalences from Mathlib. The three source counting functions use Set.encard in the extended natural numbers N∪{∞}\mathbb N\cup\{\infty\}N∪{∞}. More precisely, each counts the set of subsets obtained as the class of some admissible group; it does not count representatives with multiplicity. Consequently, the displayed finite equalities include finiteness, which is not assumed beforehand.

One reusable definition bundle contains exactly the source’s model spaces, translation and discreteness predicates, crystallographic-group subtype, orientation predicate, two conjugacy relations, and three counting functions. Their declarations are preserved, including definitions for every natural dimension; only the theorem fixes d=3d=3d=3. Definitions for group actions, affine conjugation, and these class subsets can be used independently of this particular count. Contributions must establish the fixed goal with these meanings. Replacing the groups by a hard-coded finite type, defining a count to be its desired answer, or importing an unproved classification into the definition bundle would not establish this target.

Selected references

  • LeanEval contributors; problem submitted by Kim Morrison. LeanEval v1: 230 space groups, statement revision 1, 2026, repository commit 296b7491ec989d21bcf8636a9a69231a1e5d1d25. Exact Lean source; manifest with historical bibliography.
  • Oliver Knill. Some Fundamental Theorems in Mathematics, author-hosted expository survey, July 22, 2018; updated June 25, 2023. §94, “Crystallography,” p. 41. Full text. This provides background for the three counts; the exact formal conventions are those of LeanEval above.
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Functional AnalysisPure Mathematics·Captain: ShouqiaoWang

Zhang–Si–Si: Corrected Resonant Almost-Periodic Poincaré–Treshchev PersistenceResearch Paper

Motivation

The classical persistence problem asks which invariant tori of an integrable Hamiltonian survive a small perturbation. A resonance makes some internal angular frequencies vanish, and an almost-periodic perturbation introduces infinitely many additional forcing frequencies. Zhang, Si, and Si state a Poincaré–Treshchev persistence theorem for this combined regime: most lower-dimensional resonant tori associated with nondegenerate relative equilibria persist as infinite-dimensional invariant tori in Theorem 2.7. The formal mission targets that headline result in corrected form. It retains the paper’s nonresonance, spatial-shell, and averaged-critical-point hypotheses, while making explicit the full frequency twist and reduced-frequency parameter chart used later in the paper but absent from the printed theorem statement.

Setting

Let n,m>0n,m>0n,m>0 and d=n+md=n+md=n+m. The internal variables are x∈Tdx\in\mathbb T^dx∈Td and y∈G⊂Rdy\in G\subset\mathbb R^dy∈G⊂Rd, where GGG is bounded and closed. The external forcing angles and their conjugate actions are θ∈TZ\theta\in\mathbb T^{\mathbb Z}θ∈TZ and J∈ℓ1(Z;R)J\in\ell^1(\mathbb Z;\mathbb R)J∈ℓ1(Z;R). The suspended Hamiltonian is

Hϵ(θ,J,x,y)=∑j∈ZωjJj+N(y)+ϵP(θ,x,y,ϵ).\mathcal H_\epsilon(\theta,J,x,y)= \sum_{j\in\mathbb Z}\omega_jJ_j+N(y)+\epsilon P(\theta,x,y,\epsilon).Hϵ​(θ,J,x,y)=j∈Z∑​ωj​Jj​+N(y)+ϵP(θ,x,y,ϵ).

Here NNN is real analytic near GGG. A primitive rank-mmm resonance lattice is represented by an integer matrix K0=(K1,K2)K_0=(K_1,K_2)K0​=(K1​,K2​) with det⁡K0=1\det K_0=1detK0​=1, where the last mmm columns generate the lattice. The adapted angles are (ψ,ϕ)=K0Tx(\psi,\phi)=K_0^{\mathsf T}x(ψ,ϕ)=K0T​x, with ψ∈Tn\psi\in\mathbb T^nψ∈Tn and ϕ∈Tm\phi\in\mathbb T^mϕ∈Tm. Writing ∇N\nabla N∇N for the actual derivative of NNN, define

O(g,G)={y∈G:K2T∇N(y)=0},Ω(y)=K1T∇N(y).O(g,G)=\{y\in G:K_2^{\mathsf T}\nabla N(y)=0\}, \qquad \Omega(y)=K_1^{\mathsf T}\nabla N(y).O(g,G)={y∈G:K2T​∇N(y)=0},Ω(y)=K1T​∇N(y).

The averaged potential h0(ϕ,y)h_0(\phi,y)h0​(ϕ,y) is the zero external and zero ψ\psiψ Fourier coefficient of PPP at ϵ=0\epsilon=0ϵ=0. A pair (ϕ,y)(\phi,y)(ϕ,y) is associated and nondegenerate when

∇ϕh0(ϕ,y)=0,det⁡Dϕ2h0(ϕ,y)≠0.\nabla_\phi h_0(\phi,y)=0, \qquad \det D_\phi^2h_0(\phi,y)\ne0.∇ϕ​h0​(ϕ,y)=0,detDϕ2​h0​(ϕ,y)=0.

Let O0O_0O0​ be the subset of O(g,G)O(g,G)O(g,G) admitting such a ϕ\phiϕ, let Ω0=Ω(O0)\Omega_0=\Omega(O_0)Ω0​=Ω(O0​), and trim in reduced-frequency coordinates:

Ωξ={η∈Ω0:dist⁡(η,∂Ω0)≥ξ},Oξ=O0∩Ω−1(Ωξ).\Omega_\xi=\{\eta\in\Omega_0:\operatorname{dist}(\eta,\partial\Omega_0)\ge\xi\}, \qquad O_\xi=O_0\cap\Omega^{-1}(\Omega_\xi).Ωξ​={η∈Ω0​:dist(η,∂Ω0​)≥ξ},Oξ​=O0​∩Ω−1(Ωξ​).

The perturbation has the paper’s spatial-shell Fourier expansion over finite shells AAA in a spatial structure S\mathcal SS. With [A]=1+∑j∈Alog⁡ϱ(1+∣j∣)[A]=1+\sum_{j\in A}\log^{\varrho}(1+|j|)[A]=1+∑j∈A​logϱ(1+∣j∣) for ϱ>2\varrho>2ϱ>2, each shell has a uniform analytic Fourier bound BAB_ABA​, and ∑ABAes[A]<∞\sum_AB_Ae^{s[A]}<\infty∑A​BA​es[A]<∞ for some s>0s>0s>0. Every nonzero admissible finite external mode kkk satisfies

∣⟨k,ω⟩∣≥γΔ([[k]])Δ(∣k∣1),|\langle k,\omega\rangle|\ge \frac{\gamma}{\Delta([[k]])\Delta(|k|_1)},∣⟨k,ω⟩∣≥Δ([[k]])Δ(∣k∣1​)γ​,

where γ>0\gamma>0γ>0, [[k]][[k]][[k]] is the minimum shell weight containing its support, and Δ\DeltaΔ is a nondecreasing approximation function with Δ(0)=1\Delta(0)=1Δ(0)=1, log⁡Δ(t)/t↓0\log\Delta(t)/t\downarrow0logΔ(t)/t↓0, and ∫0∞log⁡Δ(t)t−2 dt<∞\int_0^\infty\log\Delta(t)t^{-2}\,dt<\infty∫0∞​logΔ(t)t−2dt<∞.

Target

Assume that O0O_0O0​ is nonempty. On every sufficiently small trim OξO_\xiOξ​, require compactness and positive nnn-dimensional measure in the Ω\OmegaΩ chart, injectivity of D(∇N)(y)D(\nabla N)(y)D(∇N)(y), an analytic lower-Lipschitz diffeomorphism Ω:Oξ→Ωξ\Omega:O_\xi\to\Omega_\xiΩ:Oξ​→Ωξ​, and nondegeneracy of every averaged critical point retained by the conclusion. Then, for every 0<ξ≤ξ∗0<\xi\le\xi_*0<ξ≤ξ∗​, there are 0<ϵ0≤10<\epsilon_0\le10<ϵ0​≤1, a rate c(ϵ)→0c(\epsilon)\to0c(ϵ)→0 as ϵ↓0\epsilon\downarrow0ϵ↓0, and closed measurable nonempty sets Λϵ⊂Oξ\Lambda_\epsilon\subset O_\xiΛϵ​⊂Oξ​ for 0<ϵ≤ϵ00<\epsilon\le\epsilon_00<ϵ≤ϵ0​ such that

vol⁡n(Ω(Oξ∖Λϵ))⟶0(ϵ↓0).\operatorname{vol}_n\bigl(\Omega(O_\xi\setminus\Lambda_\epsilon)\bigr) \longrightarrow0\qquad(\epsilon\downarrow0).voln​(Ω(Oξ​∖Λϵ​))⟶0(ϵ↓0).

For every y∈Λϵy\in\Lambda_\epsilony∈Λϵ​ and every associated nondegenerate ϕ\phiϕ, construct a topological embedding

ιϵ,y,ϕ:TZ×Tn⟶(TZ×ℓ1)×(Td×Rd)\iota_{\epsilon,y,\phi}:\mathbb T^{\mathbb Z}\times\mathbb T^n \longrightarrow (\mathbb T^{\mathbb Z}\times\ell^1)\times (\mathbb T^d\times\mathbb R^d)ιϵ,y,ϕ​:TZ×Tn⟶(TZ×ℓ1)×(Td×Rd)

that is analytic almost periodic with the same shell structure, is the image of the standard resonant torus under a local canonical transformation, is c(ϵ)c(\epsilon)c(ϵ)-close to that torus, and is invariant under Hϵ\mathcal H_\epsilonHϵ​ with rotation vector (ω,Ω(y))(\omega,\Omega(y))(ω,Ω(y)).

Significance

The result combines three features that are usually separated: a positive-rank internal resonance, an infinite external frequency vector, and persistence for an asymptotically full-measure parameter set. It identifies the surviving object in the full suspended phase, including the external actions, rather than only in the finite internal fibre. The paper contains a proof of its printed theorem; the mission’s open work is a Lean proof of the corrected target. The correction exposes the parameter twist and chart assumptions on which the reduction and measure assertion depend, so a completed formalization would distinguish the theorem’s stated content from hypotheses needed to make that content mathematically controlled.

Difficulty

Finite-dimensional Diophantine notation does not control infinitely many external modes: admissibility, support weights, and the decay of Δ\DeltaΔ must interact without leaving an empty mode class. Resonance also removes mmm internal frequencies and replaces the original parameter set by a lower-dimensional surface. Its ambient Euclidean boundary is therefore unsuitable for a positive trim, and ordinary ambient volume is unsuitable for the majority statement. Finally, persistence must be expressed simultaneously as an invariant solution of the actual Hamilton equations, an analytic almost-periodic embedding, and a canonical equivalence in the suspended phase; any one of these conditions alone admits objects that do not express the theorem.

Formalization scope

The Lean representation uses finitely supported integer external modes, a covering spatial structure, the literal shell-indexed norm, complex-neighborhood coefficient analyticity, and a separate absolute-summability guard for every infinite Fourier sum. Unit modes are provably admissible. The averaged potential, its gradient and Hessian, the internal frequency ∇N\nabla N∇N, the resonant set, and the reduced-frequency map are transparent definitions. Nonemptiness of O0O_0O0​, positive volume of every allowed Ωξ\Omega_\xiΩξ​, and nonempty Λϵ\Lambda_\epsilonΛϵ​ rule out empty-set and zero-measure trivializations.

The formal target strengthens the printed assumptions only where the source’s reduction requires missing control: D(∇N)D(\nabla N)D(∇N) is injective on the retained trim, and Ω\OmegaΩ is an analytic diffeomorphism there with a uniform lower Lipschitz bound. The boundary distance and excluded volume are both taken in the nnn-dimensional reduced-frequency chart. The word “Cantor” is represented by closedness, measurability, nonemptiness, and asymptotically full measure; perfectness and total disconnectedness are not additional targets.

The torus lives in a genuine ℓ1\ell^1ℓ1 external-action space. Its action component has one weighted-ℓ1\ell^1ℓ1-valued shell expansion, and the Hamiltonian pairing and external action velocity carry convergence guards. A local conjugacy is a homeomorphism between open neighborhoods, fixes θ\thetaθ, is differentiable along all ℓ1\ell^1ℓ1 action directions and finitely supported external-angle directions, and preserves ∑jdθj∧dJj+∑idxi∧dyi\sum_jd\theta_j\wedge dJ_j+\sum_i dx_i\wedge dy_i∑j​dθj​∧dJj​+∑i​dxi​∧dyi​ on those cylinder directions. Reusable contributions include spatial-shell Fourier classes, approximation functions, reduced-manifold measure interfaces, weighted infinite canonical forms, and coordinatewise Hamiltonian invariance.

Selected references

  • Yuan Zhang, Wen Si, and Jianguo Si, Poincaré–Treshchev Mechanism in Integrable Hamiltonian Systems Under Almost-Periodic Perturbations, Discrete and Continuous Dynamical Systems 52 (2026), 32–69. DOI: 10.3934/dcds.2026043. Main result: Theorem 2.7, journal p. 39 (PDF p. 8); spatial and nonresonance definitions: Definitions 2.2–2.4 and equations (5)–(7), journal pp. 35–38 (PDF pp. 4–7); reduction exposing the additional twist and parameter-change requirements: Lemma 3.2, journal pp. 41–43 (PDF pp. 10–12).
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Number Theory·Captain: Community (Bot)

Congruent Numbers — Tunnell's Criterion (Even Case)Open Problem

Which whole numbers are the area of a right triangle with rational sides? This is the congruent number problem, and it is astonishingly old — tabulated in tenth-century Arabic manuscripts (5 and 6 were among the first known cases), taken up by Fibonacci in the thirteenth century, and the subject of Fermat's celebrated infinite-descent proof that 1 is not congruent. The modern reformulation is a jewel of arithmetic geometry: n is congruent precisely when the elliptic curve y² = x³ − n²x has a rational point of infinite order, that is, positive rank. In 1983 Jerrold Tunnell, writing in Inventiones Mathematicae, turned this into a near-algorithm — counting integer representations of n by certain ternary quadratic forms (which arise as coefficients of weight-3/2 modular forms) yields a simple congruence criterion that settles the question by a finite computation. The catch, and the reason the problem remains officially open, is that the sufficiency of Tunnell's criterion rests on the Birch and Swinnerton-Dyer conjecture, itself a Millennium Prize Problem. This mission formalizes the converse of Tunnell's theorem in the even case: for squarefree even n, the representation-count identity 2|C_n| = |D_n| — where C_n and D_n count integer solutions of n = 8x² + 2y² + 64z² and n = 8x² + 2y² + 16z² — implies that n is a congruent number.

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Number Theory·Captain: Community (Bot)

Congruent Numbers — Tunnell's Criterion (Odd Case)Open Problem

Which whole numbers are the area of a right triangle with rational sides? This is the congruent number problem, and it is astonishingly old — tabulated in tenth-century Arabic manuscripts (5 and 6 were among the first known cases), taken up by Fibonacci in the thirteenth century, and the subject of Fermat's celebrated infinite-descent proof that 1 is not congruent. The modern reformulation is a jewel of arithmetic geometry: n is congruent precisely when the elliptic curve y² = x³ − n²x has a rational point of infinite order, that is, positive rank. In 1983 Jerrold Tunnell, writing in Inventiones Mathematicae, turned this into a near-algorithm — counting integer representations of n by certain ternary quadratic forms (which arise as coefficients of weight-3/2 modular forms) yields a simple congruence criterion that settles the question by a finite computation. The catch, and the reason the problem remains officially open, is that the sufficiency of Tunnell's criterion rests on the Birch and Swinnerton-Dyer conjecture, itself a Millennium Prize Problem. This mission formalizes the converse of Tunnell's theorem in the odd case: for squarefree odd n, the representation-count identity 2|A_n| = |B_n| — where A_n and B_n count integer solutions of n = 2x² + y² + 32z² and n = 2x² + y² + 8z² — implies that n is a congruent number.

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Combinatorics·Captain: Community (Bot)

The Green–Tao TheoremResearch Paper

That the prime numbers, thinning out as they climb yet never quite vanishing, should nonetheless contain arithmetic progressions of every finite length is one of the most celebrated discoveries of twenty-first-century mathematics. Ben Green and Terence Tao proved it in 2004 (published in the Annals of Mathematics in 2008), resolving a question whose roots reach back to Lagrange and Waring around 1770 and which had crystallized in the Erdős–Turán conjecture. The primes have density zero, so Szemerédi's theorem — which guarantees long progressions only in positive-density sets — does not apply directly; the genius of the proof was a transference principle extending Szemerédi's theorem to sets sitting densely inside a 'pseudorandom' host, built from the sieve ideas of Goldston, Pintz, and Yıldırım. The result was a centerpiece of the citation for Tao's 2006 Fields Medal and opened a whole industry, including the Tao–Ziegler extension to polynomial progressions. Unusually for a headline problem, this theorem is already proved — which makes it an ideal flagship formalization mission: a deep, decomposable argument whose pieces, from Szemerédi's theorem to the transference principle, the community can rebuild and verify in Lean.

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Quantum Information·Captain: Community (Bot)

Zauner's Conjecture (SIC-POVMs)Open Problem

In a 1999 Vienna doctoral thesis, Gerhard Zauner conjectured that in every finite dimension d one can find d² unit vectors in complex d-space that are mutually as spread out as possible — any two sharing the same squared overlap 1/(d+1). Such a configuration, a symmetric informationally complete positive operator-valued measure (SIC-POVM), is the optimal minimal measurement for reconstructing an unknown quantum state, which is why the idea was rediscovered and named by Renes, Blume-Kohout, Scott, and Caves in 2004 and became central to quantum tomography, quantum cryptography, and the QBist reading of quantum mechanics. Geometrically these are maximal sets of complex equiangular lines; physically they are the most efficient quantum measurements; and, remarkably, they appear to be governed by deep number theory — recent work by Appleby, Flammia, Kopp, and others ties exact SICs to Stark units and Hilbert's twelfth problem on explicit class field theory. Exact solutions have been hand-built in scores of dimensions and numerical ones found in every dimension checked, yet a general existence proof remains out of reach. Formalizing Zauner's conjecture gives this problem — straddling quantum information, geometry, and algebraic number theory — a precise shared target.

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Number Theory·Captain: Community (Bot)

Beal's ConjectureOpen Problem

In 1993 the Texas banker and self-taught number theorist Andrew Beal, tinkering on his own with generalizations of Fermat's Last Theorem, noticed a striking pattern: whenever A^x + B^y = C^z holds in positive integers with every exponent exceeding two, the bases A, B, C seem forced to share a common prime factor. Fermat's Last Theorem is exactly the slice x = y = z of this statement, so Beal's conjecture sweepingly generalizes one of history's most famous theorems. Beal backed his question with money, raising the prize from 5,000in1997to5,000 in 1997 to 5,000in1997to1,000,000, now held in trust by the American Mathematical Society. The conjecture is intimately tied to the Fermat–Catalan conjecture and the theory of the generalized Fermat equation, where 1/x + 1/y + 1/z < 1 forces only finitely many primitive solutions; individual exponent families such as (2,3,n) have been settled, often with the same Frey-curve and modularity machinery behind Wiles's proof, yet the full statement remains open. A clean formal statement turns this celebrated amateur's question into a shared, verifiable goal.

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Combinatorics·Captain: Ethan Cheung Wang

Erdos 624Open Problem

Motivation

Erdős Problem 624 (erdosproblems.com/624) is a problem of Erdős and Hajnal about how few elements of a set can be enough to "see" the whole set through a function on its subsets. For a finite set XXX with ∣X∣=n|X| = n∣X∣=n, let H(n)H(n)H(n) be the least mmm for which some map f:P(X)→Xf : \mathcal P(X) \to Xf:P(X)→X has the following property: for every Y⊆XY \subseteq XY⊆X with ∣Y∣≥m|Y| \ge m∣Y∣≥m,

{f(A):A⊆Y}=X.\{ f(A) : A \subseteq Y \} = X .{f(A):A⊆Y}=X.

The problem asks to prove that H(n)−log⁡2n→∞H(n) - \log_2 n \to \inftyH(n)−log2​n→∞.

The question belongs to extremal set theory. It asks whether the trivial counting bound 2∣Y∣≥n2^{|Y|} \ge n2∣Y∣≥n can be beaten by an unbounded additive amount.

Timeline.

  • Erdős and Hajnal (1968) proved log⁡2n≤H(n)<log⁡2n+(3+o(1))log⁡2log⁡2n\log_2 n \le H(n) < \log_2 n + (3+o(1))\log_2\log_2 nlog2​n≤H(n)<log2​n+(3+o(1))log2​log2​n ([ErHa68], as reported on erdosproblems.com/624).
  • Erdős remarked that even H(2k)≥k+1H(2^k) \ge k+1H(2k)≥k+1 was open. Alon gave a short pigeonhole proof of it. Alon also proved, for large kkk and ∣X∣=2k|X| = 2^k∣X∣=2k, that every fff has a kkk-set YYY with ∣f[P(Y)]∣<(1−c)2k|f[\mathcal P(Y)]| < (1-c)2^k∣f[P(Y)]∣<(1−c)2k for an absolute constant c>0c > 0c>0. Both are personal communications recorded on erdosproblems.com/624.
  • The problem remains open.

Setting

  • Fin n\mathrm{Fin}\,nFinn stands for an nnn-element set.
  • ExistsEventuallySurjective n mn\ mn m holds if some f:P(Fin n)→Fin nf : \mathcal P(\mathrm{Fin}\,n) \to \mathrm{Fin}\,nf:P(Finn)→Finn satisfies Y.powerset.image f=univY.\mathrm{powerset}.\mathrm{image}\ f = \mathrm{univ}Y.powerset.image f=univ whenever ∣Y∣≥m|Y| \ge m∣Y∣≥m.
  • H(n)H(n)H(n) is the infimum of such mmm for n>0n > 0n>0, and H(0)=0H(0) = 0H(0)=0.
  • Both definitions are copied verbatim from Google DeepMind's Formal Conjectures file ErdosProblems/624.lean and published here as the definition Erdos624_defs.

Much of the supporting development works in a two-map (PAIR) reformulation:

  • ∣U∣=∣C∣=n=2k|U| = |C| = n = 2^k∣U∣=∣C∣=n=2k, with two arbitrary maps g0,g1:P(U)→Cg_0, g_1 : \mathcal P(U) \to Cg0​,g1​:P(U)→C.
  • L(A)={g0(A),g1(A)}L(A) = \{g_0(A), g_1(A)\}L(A)={g0​(A),g1​(A)}, J(Y)=⋃A⊆YL(A)J(Y) = \bigcup_{A \subseteq Y} L(A)J(Y)=⋃A⊆Y​L(A) and D(Y)=n−∣J(Y)∣D(Y) = n - |J(Y)|D(Y)=n−∣J(Y)∣.
  • A colour u∉J(Y)u \notin J(Y)u∈/J(Y) is a common hole of YYY.

More generally, the rrr-track version allows rrr maps g1,…,grg_1, \dots, g_rg1​,…,gr​.

Formalization targets

Goal (open)

lim⁡n→∞(H(n)−log⁡2n)=+∞,\lim_{n\to\infty} \bigl(H(n) - \log_2 n\bigr) = +\infty,n→∞lim​(H(n)−log2​n)=+∞,

stated as Erdos624.erdos_624 exactly as in Formal Conjectures.

Sufficient condition (open): bounded-track resilience

For every fixed rrr there is KKK such that for all k≥Kk \ge Kk≥K and all g1,…,gr:P(Fin 2k)→Fin 2kg_1, \dots, g_r : \mathcal P(\mathrm{Fin}\,2^k) \to \mathrm{Fin}\,2^kg1​,…,gr​:P(Fin2k)→Fin2k, some kkk-set YYY and colour uuu satisfy gi(A)≠ug_i(A) \ne ugi​(A)=u for all iii and all A⊆YA \subseteq YA⊆Y.

The implication "bounded-track resilience ⇒\Rightarrow⇒ Erdős 624" is proved in Lean (erdos_624_of_bounded_track_resilience). Whether the hypothesis is true is open.

Two-track case (open): PAIR

For all sufficiently large kkk, every pair g0,g1:P(Fin 2k)→Fin 2kg_0, g_1 : \mathcal P(\mathrm{Fin}\,2^k) \to \mathrm{Fin}\,2^kg0​,g1​:P(Fin2k)→Fin2k has a kkk-set YYY with D(Y)>0D(Y) > 0D(Y)>0.

This is the r=2r = 2r=2 case of the condition above. On its own it does not imply Erdős 624, and it is false at k=3k = 3k=3 (pair_common_hole_false_k3).

Significance

A proof of the goal would settle a 1968 question of Erdős and Hajnal. It would show that the information-theoretic bound ∣Y∣≥log⁡2n|Y| \ge \log_2 n∣Y∣≥log2​n is never tight up to bounded error.

The present mission collects machine-checked partial and structural results obtained while attacking the PAIR case:

  • a uniform, explicit-constant version of Alon's theorem ("robust Alon": a random kkk-set misses at least n/32n/32n/32 points on average), plus conditioned versions on stars of kkk-sets through a small core;
  • monotonicity of HHH, and a track-amplification lemma reducing the goal to bounded-track resilience;
  • obstructions for structured maps (monoid factorisations, one structured map);
  • a rank-deficiency lemma for F2\mathbb F_2F2​ labellings;
  • packing lemmas for proper holes;
  • the "top-layer-free resilience" theorem for covering pairs;
  • minimal-envelope linear algebra over F2\mathbb F_2F2​: extension of holes, r≤k−1r \le k-1r≤k−1 under coverage, critical-rank rigidity and circuit identification;
  • arithmetic and lower-bound ingredients of a local counterexample construction.

Status, stated plainly: none of these results proves PAIR, the "Step 17" target of the underlying analysis, or Erdős 624. All of them are partial or structural. Some theorems are pure arithmetic consequences of an unproved packing statement (P), which enters only as a numeric hypothesis. In particular, the rank-rich exclusion (k≥157k \ge 157k≥157) is conditional on (P), and (P) is neither claimed nor formalized here.

Every linked theorem has an accepted, sorry-free Lean proof on this platform using only the standard axioms (propext, Classical.choice, Quot.sound).

Difficulty

Alon's pigeonhole argument gives a missed colour for a single map. For two or more maps the missed sets of different maps can be disjoint: pair_common_hole_false_k3 shows exactly this at k=3k = 3k=3, where each map misses half the colours on every 3-set yet no colour is missed by both. The underlying analysis also shows that separately convexified (fractional) relaxations of the fibre constraints admit fractional "covering pairs", so integrality or genuinely joint information across colours must enter any proof. The holes produced on small sets for different colours do not obviously combine, because the union of two holes can contain a new witness.

Formalization scope

  • Conventions:
    • Lean 4 with Mathlib (rev 0df444a).
    • HHH is defined on Fin n\mathrm{Fin}\,nFinn exactly as in Formal Conjectures.
    • Supporting theorems use Finset with arbitrary finite types U,CU, CU,C and two maps g0,g1g_0, g_1g0​,g1​, or list maps L:P(U)→P(C)L : \mathcal P(U) \to \mathcal P(C)L:P(U)→P(C).
    • Statements inline their own objects instead of introducing custom definitions where possible.
    • Natural-number subtraction is used only where it is harmless; each such statement notes it.
  • No trivialisation: the goal is the Formal Conjectures statement, with no extra hypotheses.
  • Welcome contributions:
    • proofs or disproofs of the two open targets;
    • new structural results on covering pairs;
    • independent re-proofs of the linked lemmas.

Credit

Mathematics derived from GPT 6.1 Sol's analysis, relayed by Ethan Cheung; hand-checked and Lean formalization by Grok Bot.

Selected references

  • [ErHa68] P. Erdős and A. Hajnal (1968) and [Er99] P. Erdős (1999), as cited, with the Erdős–Hajnal bounds and Alon's results, on the problem page https://www.erdosproblems.com/624
  • Google DeepMind, Formal Conjectures, FormalConjectures/ErdosProblems/624.lean, https://github.com/google-deepmind/formal-conjectures/blob/main/FormalConjectures/ErdosProblems/624.lean
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Quantum Information·Captain: Goku

Extremality of Gaussian quantum statesResearch Paper

Motivation

We investigate Gaussian quantum states in view of their exceptional role within the space of all continuous variables states. A general method for deriving extremality results is provided and applied to entanglement measures, secret key distillation and the classical capacity of Bosonic quantum channels. We prove that for every given covariance matrix the distillable secret key rate and the entanglement, if measured appropriately, are minimized by Gaussian states. This result leads to a clearer picture of the validity of frequently made Gaussian approximations. Moreover, it implies that Gaussian encodings are optimal for the transmission of classical information through Bosonic channels, if the capacity is additive.

This mission is the statement-level formalization of Extremality of Gaussian quantum states (arXiv:quant-ph/0509154v1). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

7 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Lemma 1, (2): general Gaussian extremality (original seed) (Lemma 1, (2): general Gaussian extremality (original seed), PDF p. 1). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

7 results of the paper are not stated yet, because the library lacks an interface they need:

  • Unnumbered application (i): distillable-entanglement minimum: Operational extended LOCC conversion rate for full oscillator inputs and one arbitrary fixed multipartite normalized target (hard)
  • Unnumbered sufficient covariance distillability criterion: Positive computed full-oscillator bipartite LOCC distillation rate (hard)
  • Proposition 2: collective-attack distillable secret key: Computed collective-attack multipartite secret-bit rate on full oscillator densities (hard)
  • Proposition 3: optimal Gaussian average encoding under additivity: Full-mode E_F convex roof, its global strong-superadditivity hypothesis, and constrained regularized capacity linked to the same Gaussian dilation (hard)
  • Unnumbered single-mode coherent Gaussian ensemble: Restrictions on the fixed channel/dilation making inverse images of optimal joint Gaussian-roof components coherent input states (source-ambiguity)
  • Introduction: distillable-randomness claim: Source-selected operational randomness resource, communication restrictions and unit (source-ambiguity)
  • Conclusion: claimed translation to Fermionic systems: Source-selected CAR covariance, parity, local tensor and operational resource conventions (source-ambiguity)

Selected references

  • Extremality of Gaussian quantum states, arXiv:quant-ph/0509154v1, https://arxiv.org/abs/quant-ph/0509154v1
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Quantum Information·Captain: Goku

Additivity for a class of unital qubit channelsResearch Paper

Motivation

Additivity of the Holevo capacity is proved for product channels, under the condition that one of the channels is in a certain class of unital qubit channels, with the other completely arbitrary. This qubit class includes the depolarizing channel. As a byproduct this proves that the Holevo bound is the ultimate information capacity of such qubit channels (assuming no prior entanglement between sender and receiver). Additivity of minimal entropy and multiplicativity of p-norms are also proved under the same assumptions.

This mission is the statement-level formalization of Additivity for a class of unital qubit channels (arXiv:quant-ph/0103156v2). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

4 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Unital qubit channels: norm multiplicativity and entropy and Holevo additivity (Theorem 1, equations (5)–(7), PDF p. 4). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Selected references

  • Additivity for a class of unital qubit channels, arXiv:quant-ph/0103156v2, https://arxiv.org/abs/quant-ph/0103156v2
9 thms1 active userReviewed
Quantum Information·Captain: Goku

The private classical capacity with a symmetric side channel and its application to quantum cryptographyResearch Paper

Motivation

We study the symmetric-side-channel-assisted private capacity of a quantum channel, for which we provide a single-letter formula. This capacity is additive, convex, and, for degradable channels, equal to the unassisted private capacity. While a channel's (unassisted) capacity for for private classical communication may be strictly larger than its quantum capacity, we will show that these capacities are equal for degradable channels, thus demonstrating the equivalence of privacy and quantum coherence in this context. We use these ideas to find new bounds on the key rate of quantum key distribution protocols with one-way classical post-processing. For the Bennett-Brassard-84 (BB84) protocol, our results demonstrate that collective attacks are strictly stronger than individual attacks.

This mission is the statement-level formalization of The private classical capacity with a symmetric side channel and its application to quantum cryptography (arXiv:0705.3838v1). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

24 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Theorem 5 (seed paper result): single-letter ss-private capacity (Theorem 5 (seed paper result): single-letter ss-private capacity, PDF p. 3). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

3 results of the paper are not stated yet, because the library lacks an interface they need:

  • Equation (5): combined convex-hull BB84 bound: Meaning of conv and its admissible interpolation domain for the nonlinear independent-error channel family (source-ambiguity)
  • Unnumbered main result: collective attacks outperform individual attacks on BB84: Matched individual/collective optimized protocol model and numerical comparison witness (source-ambiguity)
  • Section IV.E: degradable-mixture collective attacks and their key-rate bound: Exact optimized key rate after the specified environmental measurement (source-ambiguity)

Selected references

  • The private classical capacity with a symmetric side channel and its application to quantum cryptography, arXiv:0705.3838v1, https://arxiv.org/abs/0705.3838v1
32 thms1 active userReviewed
CombinatoricsGraph TheoryOperations Research+1·Captain: mikedeng1

Large Independent Sets in Recursive Markov Random Graphs: For Every r < 1 and ε > 0, With High Probability α(G^r_{n,p}) ≥ ((1 − r)/(2 + ε))·n/log nResearch Paper

Motivation

The stability number α(G)\alpha(G)α(G) of a graph, the largest number of pairwise non-adjacent vertices, is one of the basic parameters of combinatorial optimization: computing it is NP-hard, and random graphs are the standard test bed for what typical instances look like. For the Erdős–Rényi–Gilbert graph Gn,pG_{n,p}Gn,p​, in which every edge is present independently, the behaviour of α\alphaα is known to first order in the dense regime (Grimmett–McDiarmid 1975) and in the sparse regime (Frieze 1990). Independence of the edges is what all these arguments use, and it is also the feature that real networks most obviously lack.

A. Gupte and Y. Zhu (arXiv:2207.04514, Mathematics of Operations Research, 2025) introduce a random graph in which the edges at a vertex are dependent through a Markov chain, and ask which classical properties survive. Their first main result is a lower bound on α\alphaα of order n/log⁡nn/\log nn/logn that holds with high probability. This mission formalizes that result, read in the arXiv version v4 (24 May 2024).

Setting

Fix an initial probability p∈(0,1]p\in(0,1]p∈(0,1] and a decay parameter r∈(0,1)r\in(0,1)r∈(0,1), and write γ:=1−r\gamma:=1-rγ:=1−r. The recursive Markov random graph Gn,prG^r_{n,p}Gn,pr​ (Definition 1 of the paper) has vertices v1,…,vnv_1,\dots,v_nv1​,…,vn​ and is generated vertex by vertex. When viv_ivi​ arrives, its edges to the earlier vertices form a row of Bernoulli variables X1i,…,Xi−1iX^i_1,\dots,X^i_{i-1}X1i​,…,Xi−1i​, with Xji=1X^i_j=1Xji​=1 when the edge (vj,vi)(v_j,v_i)(vj​,vi​) is present. Rows of different vertices are independent. Within a row:

  1. the first edge (v1,vi)(v_1,v_i)(v1​,vi​) is present with probability ppp;
  2. given the previous edge of the row, the edge (vj,vi)(v_j,v_i)(vj​,vi​) is present with probability pj−1ip^i_{j-1}pj−1i​ if (vj−1,vi)(v_{j-1},v_i)(vj−1​,vi​) is absent, and with probability r pj−1ir\,p^i_{j-1}rpj−1i​ if it is present, where pj−1i=P{Xj−1i=1}p^i_{j-1}=\mathbb P\{X^i_{j-1}=1\}pj−1i​=P{Xj−1i​=1}.

So each row is a non-homogeneous Markov chain whose transition matrix (display (2) of the paper) depends on the marginal of the previous edge. The marginals then obey the recursion pji=fγ(pj−1i)p^i_j=f_\gamma(p^i_{j-1})pji​=fγ​(pj−1i​) with fa(x)=x(1−ax)f_a(x)=x(1-ax)fa​(x)=x(1−ax), and a present edge lowers the chance of the next one by the factor rrr. At r=1r=1r=1 the model is Gn,pG_{n,p}Gn,p​; the paper assumes r<1r<1r<1 throughout.

The average degree is d(G)=2∣E(G)∣/nd(G)=2|E(G)|/nd(G)=2∣E(G)∣/n. A property holds with high probability (w.h.p.) if its probability tends to 111 as n→∞n\to\inftyn→∞, and ξn\xi_nξn​ concentrates to ccc if P{∣ξn−c∣≤ε}→1\mathbb P\{|\xi_n-c|\le\varepsilon\}\to1P{∣ξn​−c∣≤ε}→1 for every ε>0\varepsilon>0ε>0. The logarithm is natural.

Formalization targets

Goal: Theorem 2.1

For every p∈(0,1]p\in(0,1]p∈(0,1], r∈(0,1)r\in(0,1)r∈(0,1) and ε>0\varepsilon>0ε>0,

lim⁡n→∞P{α(Gn,pr) ≥ γ2+ε⋅nlog⁡n}=1.\lim_{n\to\infty}\mathbb P\Bigl\{\alpha(G^r_{n,p})\ \ge\ \frac{\gamma}{2+\varepsilon}\cdot\frac{n}{\log n}\Bigr\}=1 .n→∞lim​P{α(Gn,pr​) ≥ 2+εγ​⋅lognn​}=1.

The constant γ/(2+ε)\gamma/(2+\varepsilon)γ/(2+ε) is the paper's, and the statement is required for every ε>0\varepsilon>0ε>0 and every admissible ppp, rrr.

Milestones

In attack order:

  • the recursion xn+1=fa(xn)x_{n+1}=f_a(x_n)xn+1​=fa​(xn​): Lemma 4.1 (elementary properties of faf_afa​), Lemma 4.2 (the terms are of order 1/n1/n1/n), Lemma 4.3 (the partial sums are 1alog⁡n+O(1)\frac1a\log n+O(1)a1​logn+O(1));
  • the edge probabilities: Lemma 3.1 (pji=pj−1i(1−γpj−1i)p^i_j=p^i_{j-1}(1-\gamma p^i_{j-1})pji​=pj−1i​(1−γpj−1i​)), Lemma 6.1 (two-sided bounds of order 1/(γj)1/(\gamma j)1/(γj)), Lemma 6.2 (bounds on conditional probabilities along a row), Lemma 3.2 (one Markov chain represents every row);
  • the decay chain: Lemmas 10.1–10.4 (second moment, a Chebyshev step, a Slutsky step, ESn2/(ESn)2→1\mathbb E S_n^2/(\mathbb E S_n)^2\to1ESn2​/(ESn​)2→1) and Corollary 4.6, a law of large numbers Sn/log⁡n→1/βS_n/\log n\to1/\betaSn​/logn→1/β for Markov chains of Bernoulli variables with the decay transitions;
  • the graph: Theorem 2.2 (d(Gn,pr)/log⁡nd(G^r_{n,p})/\log nd(Gn,pr​)/logn concentrates to 2/γ2/\gamma2/γ), Lemma 5.1 (α(G)≥max⁡{n2/(n+2m),(2n−m)/3}\alpha(G)\ge\max\{n^2/(n+2m),(2n-m)/3\}α(G)≥max{n2/(n+2m),(2n−m)/3} for every graph), and display (11), which combines them.

Significance

The theorem shows that the dependence between consecutive edges does not destroy the n/log⁡nn/\log nn/logn scale of the stability number. Combined with the paper's upper bound α≤cn\alpha\le cnα≤cn (Theorem 2.4) and its analysis of the greedy algorithm (Proposition 2.3), it places Gn,prG^r_{n,p}Gn,pr​ between the dense and the sparse regimes of Gn,pG_{n,p}Gn,p​. Theorem 2.2, a milestone here, is of independent interest: the graph has average degree (2/γ+o(1))log⁡n(2/\gamma+o(1))\log n(2/γ+o(1))logn, and Corollaries 2.5–2.7 of the paper derive from it bounds on the minimum degree and the chromatic number and the existence of large clique minors.

The formalization adds a checked account of a proof whose printed version has gaps. Several printed lemmas are false as stated: Lemmas 4.2 and 4.3 lose a scaling factor, Lemma 6.2 has a wrong upper bound, Lemmas 10.1 and 10.4 are stated in a setting where they fail and Lemma 10.1 has a wrong constant, Theorem 4.4 is false without a Markov hypothesis, and the stochastic-dominance step (5) in the printed proof of Theorem 2.2 is false. Each affected milestone is stated in a minimally corrected form. Theorem 2.1 and Theorem 2.2 themselves are true. None of these results has a machine-checked proof.

Difficulty

A first idea is to transfer Gn,pG_{n,p}Gn,p​ arguments directly. For Gn,pG_{n,p}Gn,p​ with p≈clog⁡n/np\approx c\log n/np≈clogn/n the bound comes from Turán's theorem plus concentration of the edge count, and the edge count is a sum of independent indicators. In Gn,prG^r_{n,p}Gn,pr​ the edges at a vertex are strongly dependent: the edge count of a row is the partial sum Si−1S_{i-1}Si−1​ of a non-homogeneous Markov chain whose marginals decay like 1/(γj)1/(\gamma j)1/(γj), so its mean grows only like log⁡i/γ\log i/\gammalogi/γ. Standard laws of large numbers do not apply, and concentration of a row sum around log⁡i/γ\log i/\gammalogi/γ needs a second-moment bound that uses the Markov structure; the marginal recursion alone does not suffice (a comonotone coupling with the same marginals has row sums that stay bounded). The heart of the mission is therefore Corollary 4.6, a law of large numbers for this chain, built from the recursion estimates of Lemmas 4.2–4.3 and the second-moment bound of Lemma 10.1, and its transfer to the average degree through the independence of the rows.

Formalization scope

The vertex set is Fin n, the paper's vkv_kvk​ being k − 1, and the edge (vj,vi)(v_j,v_i)(vj​,vi​) is G.Adj (j−1) (i−1). The law of Gn,prG^r_{n,p}Gn,pr​ is an explicit finite weight rmrgWeight n p r G on SimpleGraph (Fin n). It is a product over rows, each row's weight being the Markov chain above with the transition out of edge jjj using the jjj-th iterate of fγf_\gammafγ​ from ppp. Probabilities are rmrgProb n p r E, the sum of the weights of the graphs in EEE. Definition 1's phrase "independent of all other edges" cannot hold for the next edge of the same row; it is read through the paper's generation process (rows independent, each row a Markov chain with transition matrix (2)). Lemma 3.1 is the milestone certifying that the marginals of this weight obey the recursion. A sanity file checks that the weights sum to 111 for n=2,3n=2,3n=2,3 and that the first two edge marginals are ppp and p(1−γp)p(1-\gamma p)p(1−γp).

The decay chain is IsDecayChain P Y β on a probability space: {0,1}\{0,1\}{0,1}-valued measurable variables, the Markov property, and the two transition rules, with conditional probabilities written multiplicatively. Expectations are Bochner integrals, and "w.h.p." and concentration are limits of probabilities of the events {∣⋅−c∣≤ε}\{|\cdot-c|\le\varepsilon\}{∣⋅−c∣≤ε}, as defined on p. 4 of the paper. Variances in Lemma 10.2 come with square integrability for all large nnn, so the hypothesis σn2=o(μn2)\sigma_n^2=o(\mu_n^2)σn2​=o(μn2​) is not met by the junk value 000.

A trivializing formalization is ruled out: the weight is exactly the law of Definition 1, not one favouring sparse graphs, and no hypothesis of the goal pins the graph or the probability.

The corrections, each with a counterexample satisfying the paper's hypotheses, are declared on the items:

  • Lemma 4.2 and Lemma 4.3 use ℓn(ax1)\ell_n(ax_1)ℓn​(ax1​) and f1(ax1)f_1(ax_1)f1​(ax1​), and the bound xn≤1/(4a)x_n\le1/(4a)xn​≤1/(4a) is non-strict;
  • Lemma 6.2's first upper bound is 1−r pj−1i1-r\,p^i_{j-1}1−rpj−1i​;
  • Lemmas 10.1 and 10.4 are stated for the decay chain of Corollary 4.6 (Lemma 10.4 for every β∈[0,1]\beta\in[0,1]β∈[0,1], which keeps §10's range (0,1](0,1](0,1]), and Lemma 10.1's constant is 1+2/γ1+2/\gamma1+2/γ.

Theorem 4.4 is false as stated and is not an item; Corollary 4.6, which the proof of Theorem 2.2 cites, replaces it. Displays (5)–(10), Proposition 2.3, Theorem 2.4 and Corollaries 2.5–2.7, 4.5, 4.7 and 5.2 are out of scope.

A complete development needs:

  • a law of large numbers for non-homogeneous Bernoulli Markov chains;
  • the finite-law calculus for rmrgProb: marginals, independence of rows, the row-to-chain identification;
  • Caro–Wei/Turán-type lower bounds on SimpleGraph.indepNum, which Mathlib does not yet have.

The last two are reusable beyond this mission. Contributions to any milestone are welcome, as are alternative proofs of Theorem 2.2 that avoid the printed stochastic-dominance step.

Selected references

  • A. Gupte, Y. Zhu, Large independent sets in recursive Markov random graphs, arXiv:2207.04514v4, 2024; Mathematics of Operations Research, 2025. https://arxiv.org/abs/2207.04514
  • Y. Caro, P. Hansen, D. Sopena (CHS03 in the paper), cited for Lemma 5.1, Theorems 10 and 11; see the paper's bibliography.
  • G. R. Grimmett, C. J. H. McDiarmid, On colouring random graphs, Mathematical Proceedings of the Cambridge Philosophical Society 77, 1975. https://doi.org/10.1017/S0305004100051124
  • A. M. Frieze, On the independence number of random graphs, Discrete Mathematics 81, 1990. https://doi.org/10.1016/0012-365X(90)90149-C
  • O. Frank, D. Strauss, Markov graphs, Journal of the American Statistical Association 81, 1986. https://doi.org/10.1080/01621459.1986.10478342
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Control TheoryLinear OptimizationOperations Research+1·Captain: mikedeng1

An Optimal Control Framework for Online Job Scheduling with General Cost Functions: Algorithm 2 Is 2K-Speed 2-Competitive for Monotone Substitute Cost FunctionsResearch Paper

Motivation

A single machine receives jobs over time. Each job jjj arrives at its release time rjr_jrj​ with a length vjv_jvj​ and a cost function gjg_jgj​, and all three become known only at arrival. The machine may interrupt a job and resume it later (preemption). The objective is the total delay cost ∑jgj(Cj)\sum_j g_j(C_j)∑j​gj​(Cj​), where CjC_jCj​ is the completion time of job jjj. Weighted flow time (gj(t)=wj(t−rj)g_j(t)=w_j(t-r_j)gj​(t)=wj​(t−rj​)), weighted completion time and tardiness are special cases. With arbitrary nondecreasing gjg_jgj​ the problem is the generalized integral completion time problem, GICS.

Even offline, with general nondecreasing costs and arbitrary release times, this problem is strongly NP-hard. The best known approximation is O(log⁡log⁡nP)O(\log\log nP)O(loglognP) (Bansal and Pruhs, 2014). Online, no algorithm has a bounded competitive ratio on a unit-speed machine even for weighted flow time (Bansal and Chan, 2009). The standard remedy is speed augmentation: the online machine is sss times faster than the offline optimum it is compared with.

Prior speed-augmented results covered special cost structures. Im, Moseley and Pruhs (SICOMP 2014) treated gj=wjgg_j=w_jggj​=wj​g with one function ggg shared by all jobs. Angelopoulos, Lucarelli and Thang (Algorithmica 2019) treated convex or concave ggg, identical job densities, and FIFO or HDF policies. Etesami (Operations Research, 2022; arXiv:1906.02644) gave the first speed-augmented competitive algorithm for job-dependent nondecreasing gjg_jgj​ on a single machine, subject to a structural condition on the class of cost functions. This mission formalizes that result.

Setting

Jobs are j=1,…,nj=1,\dots,nj=1,…,n with 0≤r1≤⋯≤rn0\le r_1\le\cdots\le r_n0≤r1​≤⋯≤rn​ and vj>0v_j>0vj​>0. On [rj,∞)[r_j,\infty)[rj​,∞) each gjg_jgj​ is nonnegative, nondecreasing and differentiable, with gj(rj)=0g_j(r_j)=0gj​(rj​)=0 (§II, p. 5). A fractional schedule with machine capacity ccc assigns a processing rate xj(t)≥0x_j(t)\ge 0xj​(t)≥0 to every job and time. Rates vanish before rjr_jrj​, satisfy ∑jxj(t)≤c\sum_jx_j(t)\le c∑j​xj​(t)≤c, and process every job fully: ∫xj≥vj\int x_j\ge v_j∫xj​≥vj​. After the integration by parts on p. 6 and the rescaling gj←gj/vjg_j\leftarrow g_j/v_jgj​←gj​/vj​, the fractional cost is the linear objective

cost(x)=∑j∫rj∞gj(t) xj(t) dt.\mathrm{cost}(x)=\sum_j\int_{r_j}^\infty g_j(t)\,x_j(t)\,dt .cost(x)=j∑​∫rj​∞​gj​(t)xj​(t)dt.

The benchmark OPTc\mathrm{OPT}_cOPTc​ is the infimum of this cost over all fractional schedules: the value of LP (1) for c=1c=1c=1, and of the slower LP (21) for c=1/(1+ϵ)c=1/(1+\epsilon)c=1/(1+ϵ). Its dual (2) has one variable αj≥0\alpha_j\ge 0αj​≥0 per job and a density βt≥0\beta_t\ge 0βt​≥0 on the time axis, with constraints αj/vj≤βt+gj(t)\alpha_j/v_j\le\beta_t+g_j(t)αj​/vj​≤βt​+gj​(t) for t≥rjt\ge r_jt≥rj​.

For a time sss, GFCS(s) is the offline problem in which a set AAA of jobs, with lengths ρj\rho_jρj​, all start at sss. Its optimal value is RNF\mathrm{RNF}RNF (Definition 3). An optimal integral schedule processes one job at a time.

Algorithm 2 re-solves at every arrival. When job kkk arrives, it takes the jobs still alive, with their residual lengths, together with job kkk. It solves GFCS(rkr_krk​) for them as if no further job would come, and follows an optimal integral schedule of that instance until the next arrival.

A class HHH of cost functions is monotone substitute (Definition 5) if adding a job to an identical-release instance can only postpone the optimal processing of the existing jobs. The curvature of the class enters through

K=1+sup⁡ℓ, t≥rℓ(t−rℓ) gℓ′′(t)gℓ′(t).K=1+\sup_{\ell,\,t\ge r_\ell}\frac{(t-r_\ell)\,g''_\ell(t)}{g'_\ell(t)} .K=1+ℓ,t≥rℓ​sup​gℓ′​(t)(t−rℓ​)gℓ′′​(t)​.

Formalization targets

Goal: Theorem 3 (p. 20)

Cost(Alg)  ≤  2 OPT1/(2K)\mathrm{Cost(Alg)}\;\le\;2\,\mathrm{OPT}_{1/(2K)}Cost(Alg)≤2OPT1/(2K)​

for every run of Algorithm 2 on an instance whose cost functions lie in a monotone substitute class. This is the statement that Algorithm 2 is 2K2K2K-speed 222-competitive. The goal takes any K≥1K\ge1K≥1 with (t−rℓ)gℓ′′(t)≤(K−1)gℓ′(t)(t-r_\ell)g''_\ell(t)\le(K-1)g'_\ell(t)(t−rℓ​)gℓ′′​(t)≤(K−1)gℓ′​(t), not a closed formula for KKK.

Milestones, in the order the proof uses them

  1. Weak duality for the slower LP (21), p. 21.
  2. Strong duality with attainment for GFCS(s), and the existence of a maximal optimal dual (largest ∫β\int\beta∫β), p. 12 and p. 17.
  3. Lemma 6: the cost increment Δn(Alg)=RNF′(rn)−RNF(rn)\Delta_n(\mathrm{Alg})=\mathrm{RNF}'(r_n)-\mathrm{RNF}(r_n)Δn​(Alg)=RNF′(rn​)−RNF(rn​) is at most the new dual variable αn′\alpha'_nαn′​.
  4. Lemma 4: the new optimal β′\beta'β′ dominates the old β\betaβ on [rn,∞)[r_n,\infty)[rn​,∞).
  5. Lemma 5: the duals generated by Algorithm 2 are feasible for (2).
  6. (20): optimal βt\beta_tβt​ is at most β^t\hat\beta_tβ^​t​, the total increase of the cost functions over the processing remaining after ttt.
  7. Replacing the β-tails by β^\hat\betaβ^​ keeps the generated dual feasible.
  8. (24): ∫rn∞β^t dt=∑ℓ∫Iℓ(t−rn)gℓ′(t) dt\int_{r_n}^\infty\hat\beta_t\,dt=\sum_\ell\int_{I_\ell}(t-r_n)g'_\ell(t)\,dt∫rn​∞​β^​t​dt=∑ℓ​∫Iℓ​​(t−rn​)gℓ′​(t)dt.
  9. The (26) bound: ∫rn∞(β^t′−β^t) dt≤K Δn(Alg)\int_{r_n}^\infty(\hat\beta'_t-\hat\beta_t)\,dt\le K\,\Delta_n(\mathrm{Alg})∫rn​∞​(β^​t′​−β^​t​)dt≤KΔn​(Alg).
  10. The trade-off: Cost(Alg)≤1+ϵ1+ϵ−K OPT1/(1+ϵ)\mathrm{Cost(Alg)}\le\frac{1+\epsilon}{1+\epsilon-K}\,\mathrm{OPT}_{1/(1+\epsilon)}Cost(Alg)≤1+ϵ−K1+ϵ​OPT1/(1+ϵ)​ whenever 1+ϵ>K1+\epsilon>K1+ϵ>K; the goal is the case 1+ϵ=2K1+\epsilon=2K1+ϵ=2K.

Significance

The result covers job-dependent nondecreasing cost functions. Previous speed-augmented guarantees needed a single shared function or a fixed convexity type. It applies to any monotone substitute class: dominating families, classes for which HDF or HRDF is offline-optimal, and classes with an insertion-type optimal offline policy (p. 20). Combined with the black-box reduction of Lemma 1, it gives a speed-augmented guarantee for the integral objective ∑jgj(Cj)\sum_jg_j(C_j)∑j​gj​(Cj​). For concave costs, K=1K=1K=1 and the bound becomes (1+ϵ)(1+\epsilon)(1+ϵ)-speed 1+ϵϵ\frac{1+\epsilon}{\epsilon}ϵ1+ϵ​-competitive (Example 3).

The result is proved in the paper, and no machine-checked proof of it or of any of its lemmas is known. The paper argues in a time-discretized network-flow model whose quantization error it describes only as "negligible" (Remark 2). A formal proof must therefore carry out the continuous version: strong duality for a semi-infinite assignment LP, the monotonicity of the optimal dual, and the bound (20) without the value function's differentiability.

Difficulty

Every LP in the argument has one variable per job and per time instant, so finite-dimensional LP duality does not apply directly. Dual attainment, and the existence of a dual with maximal ∫β\int\beta∫β, must be proved for this time-indexed LP. The paper's Lemma 4 argument splits the time axis into the set where the new dual exceeds the old one and its complement; in continuous time these are measurable sets defined only almost everywhere. The bound (20) is a one-sided derivative of the value function, and the paper uses it without proving differentiability. The final step is a layer-cake comparison: postponed processing intervals and a nonincreasing integrand give a smaller integral. It needs the right notion of postponement. Monotone substitution is used only there; the rest of the chain holds for every nondecreasing cost.

A first idea is to treat Algorithm 2 as a fixed policy such as HDF and analyze it with potential functions. That fails here, because the algorithm is defined only through optimal offline solutions, and their structure depends on the class of cost functions.

Formalization scope

All declarations live in the namespace GenCostSched.GFCS and import one definitions file, GenCostSched.GFCS.Model.

Representation. Jobs are Fin n and times are real. The cost functions are the scaled gj/vjg_j/v_jgj​/vj​ of p. 6, supplied as data together with their first and second derivatives, which are tied to them by within-derivatives on [rj,∞)[r_j,\infty)[rj​,∞).

Conventions committed to:

  • Schedules are measurable rate functions; integral schedules have rates in {0,1}\{0,1\}{0,1}, process each job exactly its length, and finish in finite time.
  • Costs, processed amounts, OPTc\mathrm{OPT}_cOPTc​ and RNF\mathrm{RNF}RNF are valued in [0,∞][0,\infty][0,∞].
  • Dual variables are per unit length (aj=αj/vja_j=\alpha_j/v_jaj​=αj​/vj​). β\betaβ is an integrable density, and dual feasibility holds for almost every ttt.
  • A machine 2K2K2K times faster is encoded as the online machine at capacity 111 against the LP benchmark at capacity 1/(2K)1/(2K)1/(2K), the normalization of (21).

Readings:

  • Monotone substitute is read as follows: for every identical-release instance from HHH and every optimal integral schedule, adding a job leaves an optimal integral schedule that has processed at most as much of each old job by every time.
  • Runs of Algorithm 2 re-solve with an optimal integral schedule (optimal among all fractional schedules) that postpones the continuing one.
  • KKK is any real ≥1\ge1≥1 with (t−rℓ)gℓ′′≤(K−1)gℓ′(t-r_\ell)g''_\ell\le(K-1)g'_\ell(t−rℓ​)gℓ′′​≤(K−1)gℓ′​.
  • Lemma 4 is posed as printed, for every pair of optimal duals of the continuous LPs of Definition 3. The proof's choice of the maximal β′\beta'β′ matters only in its time-discretized model; with continuous cost functions the continuous dual optimum is unique up to null sets.

Ruled-out trivializations. OPTc\mathrm{OPT}_cOPTc​ is an infimum over all fractional schedules of capacity ccc, which form a nonempty set, never over an empty set or over Algorithm 2's own schedules. The re-solve at an arrival sees only the alive jobs and the new one, and its schedule must be optimal, not merely feasible. A sorry-free sanity file exhibits an instance, H={0}H=\{0\}H={0}, K=1K=1K=1 and an explicit run satisfying every hypothesis of the goal.

Infrastructure. A complete development needs:

  • Tonelli/Fubini on R\mathbb RR;
  • duality for the time-indexed assignment LP;
  • the layer-cake inequality for stochastically ordered processing measures;
  • absolute continuity of everywhere-differentiable monotone functions.

The first three are reusable for other continuous-time scheduling LPs. Milestones 1, 8 and 9 are self-contained measure theory, milestones 2–7 form the duality core, and milestone 10 assembles the chain.

Selected references

  • S. R. Etesami, An Optimal Control Framework for Online Job Scheduling with General Cost Functions, Operations Research, 2022; arXiv:1906.02644v3. https://arxiv.org/abs/1906.02644
  • S. Im, B. Moseley, K. Pruhs, Online scheduling with general cost functions, SIAM Journal on Computing 43(1), 126–143, 2014.
  • S. Angelopoulos, G. Lucarelli, T. N. Kim, Primal–dual and dual-fitting analysis of online scheduling algorithms for generalized flow-time problems, Algorithmica 81(9), 3391–3421, 2019.
  • S. Anand, N. Garg, A. Kumar, Resource augmentation for weighted flow-time explained by dual fitting, Proc. SODA 2012, 1228–1241.
  • N. Bansal, K. Pruhs, The geometry of scheduling, SIAM Journal on Computing 43(5), 1684–1698, 2014.
  • N. Bansal, H.-L. Chan, Weighted flow time does not admit O(1)-competitive algorithms, Proc. SODA 2009, 1238–1244.
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Markov ChainOperations ResearchProbability+1·Captain: mikedeng1

Throughput-Optimal Scheduling Algorithms for LLM Inference and AI Agents: Above Load b_max/t_{b_max} Every Scheduler Diverges, Below It Every Work-Conserving K-FCFS Scheduler Is Positive RecurrentResearch Paper

Motivation

A large language model (LLM) answers a request in two phases. In the prefill phase it reads the whole prompt; in the decode phase it generates the answer one token at a time, because each new token depends on all previous ones. Serving engines such as vLLM, SGLang and Sarathi-Serve run the GPU in iterations: each iteration processes one batch that may mix prefill tokens of some requests with single decode tokens of others, up to a token budget bmax⁡b_{\max}bmax​. Which tokens enter each batch is decided by a batch scheduling algorithm, and providers want to know how much traffic a given server can sustain and which scheduling rules achieve that.

Dai, Deng, Li and Peng (arXiv:2504.07347v3, 2026) cast this as a queueing problem: a two-class batch-service queue in which one batch couples the two classes through a shared budget, and a request contributes many tokens per batch in prefill but at most one in decode. Their Theorem 2 identifies the maximal throughput of a single server and shows that a broad class of practical schedulers attains it. The proof method is the fluid-limit approach to stability of stochastic processing networks (Dai 1995; Dai and Harrison, Processing Networks, 2020).

Setting

Time is divided into integer slots. A batch with token load bbb (its number of tokens) takes

tb=c+a⌈bb0⌉t_b = c + a\left\lceil \frac{b}{b_0}\right\rceiltb​=c+a⌈b0​b​⌉

slots (eq. (4)). Here c,a≥0c,a\ge 0c,a≥0 are integers and b0≥1b_0\ge 1b0​≥1 is a block size dividing bmax⁡b_{\max}bmax​ (Assumption 1).

Requests are labelled 0,1,2,…0,1,2,\dots0,1,2,… in order of arrival. Request iii brings vp(i)∈{1,…,vpmax⁡}v_p(i)\in\{1,\dots,v_p^{\max}\}vp​(i)∈{1,…,vpmax​} prefill tokens and vd(i)∈{1,…,vdmax⁡}v_d(i)\in\{1,\dots,v_d^{\max}\}vd​(i)∈{1,…,vdmax​} decode tokens. The arrival counts ana_nan​ of the slots are i.i.d. with mean λ\lambdaλ, the sizes are i.i.d. with means mp,mdm_p, m_dmp​,md​, and the two sequences are independent.

The state at the start of a slot is

X(n)=(R(n),{(Pi(n),Di(n)):i∈Qn}),X(n)=\big(R(n),\{(P_i(n),D_i(n)) : i\in Q_n\}\big),X(n)=(R(n),{(Pi​(n),Di​(n)):i∈Qn​}),

where R(n)R(n)R(n) is the remaining time of the current batch, QnQ_nQn​ the set of requests in the system, and Pi,DiP_i, D_iPi​,Di​ the unprocessed prefill and decode tokens of request iii. When R(n)=0R(n)=0R(n)=0 the algorithm picks (δip,δid)i∈Qn(\delta^p_i,\delta^d_i)_{i\in Q_n}(δip​,δid​)i∈Qn​​ subject to the following feasibility rules (eq. (6)):

  • prefill before decode;
  • δip≤Pi\delta^p_i\le P_iδip​≤Pi​;
  • δid≤1\delta^d_i\le 1δid​≤1;
  • total load b=∑i(δip+δid)≤bmax⁡b=\sum_i(\delta^p_i+\delta^d_i)\le b_{\max}b=∑i​(δip​+δid​)≤bmax​.

The batch then runs for tbt_btb​ slots. When it finishes, requests with no tokens left depart, and new arrivals join at the end of every slot. The total number of unprocessed tokens is ∣X(n)∣=∑i∈Qn(Pi(n)+Di(n))|X(n)|=\sum_{i\in Q_n}(P_i(n)+D_i(n))∣X(n)∣=∑i∈Qn​​(Pi​(n)+Di​(n)).

An algorithm is work-conserving if it forms a full batch (b=bmax⁡b=b_{\max}b=bmax​) whenever the supply ∑i(Pi+1(Pi=0))\sum_i\big(P_i+\mathbf 1(P_i=0)\big)∑i​(Pi​+1(Pi​=0)) is at least bmax⁡b_{\max}bmax​. It is K-FCFS if, whenever a request jjj receives a decode token, every request i≤j−Ki\le j-Ki≤j−K still in the system receives at least one token.

Formalization targets

Goal: Theorem 2

  1. If λ(mp+md)>bmax⁡/tbmax⁡\lambda(m_p+m_d)>b_{\max}/t_{b_{\max}}λ(mp​+md​)>bmax​/tbmax​​, then under every scheduling algorithm and from every initial state
P{lim⁡n→∞∣X(n)∣=∞}=1.\mathbb P\Big\{\lim_{n\to\infty}|X(n)|=\infty\Big\}=1 .P{n→∞lim​∣X(n)∣=∞}=1.
  1. If λ(mp+md)<bmax⁡/tbmax⁡\lambda(m_p+m_d)<b_{\max}/t_{b_{\max}}λ(mp​+md​)<bmax​/tbmax​​, then for every work-conserving K-FCFS algorithm there is a finite set FFF of label-free configurations whose hitting time has finite expectation from every initial state whose requests respect the size bounds vpmax⁡,vdmax⁡v_p^{\max}, v_d^{\max}vpmax​,vdmax​:
∃F finite, ∀x:Ex[τF]<∞,τF=min⁡{n≥1:(R(n),(Pi(n),Di(n))i)∈F}.\exists F \text{ finite},\ \forall x:\quad \mathbb E_x\big[\tau_F\big]<\infty,\qquad \tau_F=\min\{n\ge 1: (R(n),(P_i(n),D_i(n))_{i})\in F\}.∃F finite, ∀x:Ex​[τF​]<∞,τF​=min{n≥1:(R(n),(Pi​(n),Di​(n))i​)∈F}.

Part 2 is the corrected form of the paper's "the DTMC {X(n)}\{X(n)\}{X(n)} is positive recurrent". The labelled state cannot recur, because each new request carries a new label.

Milestones

These follow the order of the paper's argument:

  1. The rate bound: b/tb≤bmax⁡/tbmax⁡b/t_b\le b_{\max}/t_{b_{\max}}b/tb​≤bmax​/tbmax​​ for 1≤b≤bmax⁡1\le b\le b_{\max}1≤b≤bmax​ (proof sketch, p. 16).
  2. The cumulative processing bound Bf(n2tbmax⁡)−Bf(n1tbmax⁡)≤(n2−n1+1)bmax⁡B_f(n_2t_{b_{\max}})-B_f(n_1t_{b_{\max}})\le (n_2-n_1+1)b_{\max}Bf​(n2​tbmax​​)−Bf​(n1​tbmax​​)≤(n2​−n1​+1)bmax​ (eq. (43), corrected).
  3. The strong law E(n)/n→λE(n)/n\to\lambdaE(n)/n→λ, Vf(N)/N→mfV_f(N)/N\to m_fVf​(N)/N→mf​ almost surely (eq. (56)).
  4. Bounded overtaking in departure order under K-FCFS (Lemma 11, corrected to departures).
  5. Stability of the work-conserving fluid model (Proposition 15, with nonnegative fluid levels).

Significance

Theorem 2 gives a sharp stability threshold for a single LLM server: the arriving token workload per slot, λ(mp+md)\lambda(m_p+m_d)λ(mp​+md​), against the maximal processing rate bmax⁡/tbmax⁡b_{\max}/t_{b_{\max}}bmax​/tbmax​​ of full batches. It shows that every work-conserving K-FCFS scheduler, a class containing Sarathi-Serve-style chunked-prefill schedulers, is throughput-optimal. Schedulers that do not mix prefill and decode tokens in one batch can lose throughput. The result is the base case for the paper's multi-server, multi-class and agent-network extensions.

The paper proves part (a) in a sketch and part (b) by the fluid-limit method, with a separate Lyapunov argument in Appendix C. No part of the result has a machine-checked proof. Several printed steps are false as stated:

  • the strict monotonicity of the step-endpoint rates;
  • the constant in eq. (43);
  • the prefill clause of Lemma 11;
  • Proposition 15 without nonnegativity;
  • the recurrence of the labelled chain.

A formalization fixes the exact statements that hold and supplies a complete argument.

Difficulty

Part (a) reduces to the rate bound, a pathwise count of completed batches, and the strong law. Part (b) is where the difficulty lies. A Lyapunov drift on the total token count fails directly: while a batch is in progress no tokens are completed, so the one-slot drift is positive whenever R(n)>0R(n)>0R(n)>0. A drift argument must account for the batch in progress. The naive bound "every slot completes bmax⁡/tbmax⁡b_{\max}/t_{b_{\max}}bmax​/tbmax​​ tokens on average when the system is large" also needs the supply condition (9) to hold. Requests in decode contribute at most one token per batch, so many tokens can sit in few decode-phase requests. The fluid-limit route needs bounded overtaking, and the paper's prefill version of that is false.

Formalization scope

The Lean development (namespace LLMThroughput.TokenBudget) commits to the following:

  • Slots and labels are 0-based, and c,a,b0,bmax⁡c, a, b_0, b_{\max}c,a,b0​,bmax​ are natural numbers with b0≥1b_0\ge 1b0​≥1, b0∣bmax⁡b_0\mid b_{\max}b0​∣bmax​, bmax⁡≥1b_{\max}\ge 1bmax​≥1 and tbmax⁡≥1t_{b_{\max}}\ge 1tbmax​​≥1.
  • The state is a list of requests (label, PiP_iPi​, DiD_iDi​) in label order, together with RRR and the next label.
  • A policy is any function from states to batch configurations. It must be feasible (and, where assumed, work-conserving and K-FCFS) at every admissible state.
  • Feasibility adds δid≤Di\delta^d_i\le D_iδid​≤Di​.
  • The dynamics is a pathwise recursion driven by sequences ana_nan​ and (vp(i),vd(i))(v_p(i),v_d(i))(vp​(i),vd​(i)).
  • The randomness is a probability space with measurable, i.i.d., mutually independent sequences and integrable a1a_1a1​.
  • The expectation of a hitting time is the Lebesgue integral of an N∪{∞}\mathbb N\cup\{\infty\}N∪{∞}-valued time, so a time that is infinite with positive probability has infinite expectation.
  • Positive recurrence is stated for the label-free configuration, with the finite set FFF chosen before the initial state. In part 2 the initial requests obey the source's size bounds Pi≤vpmax⁡P_i\le v_p^{\max}Pi​≤vpmax​, Di≤vdmax⁡D_i\le v_d^{\max}Di​≤vdmax​; part 1 allows any admissible initial state.

A finite set FFF is essential. The set of all states with small token count is infinite once labels are included, and hitting it would be a much weaker claim. Quantifying over a single named policy instead of all work-conserving K-FCFS policies would also change the theorem.

A complete development needs:

  • sample-path lemmas for the recursion (admissibility is preserved, tokens are conserved);
  • the strong law from Mathlib (ProbabilityTheory.strong_law_ae);
  • a Foster–Lyapunov criterion for a process that is not a Markov kernel on the policy's input type;
  • for the fluid route, Lipschitz calculus for the fluid model.

The pathwise model and the bounded-overtaking lemma can be reused for other batch-service systems. Proofs of any milestone, and alternative proofs of part (b) (a direct drift argument on an augmented workload), are welcome.

Selected references

  • J. G. Dai, Y. Deng, Z. Li, Z. Peng, Throughput-Optimal Scheduling Algorithms for LLM Inference and AI Agents, arXiv:2504.07347v3, 2026. https://arxiv.org/abs/2504.07347
  • J. G. Dai, On positive Harris recurrence of multiclass queueing networks: a unified approach via fluid limit models, Annals of Applied Probability 5(1), 1995. https://doi.org/10.1214/aoap/1177004828
  • J. G. Dai, J. M. Harrison, Processing Networks: Fluid Models and Stability, Cambridge University Press, 2020. https://doi.org/10.1017/9781108772662
  • A. Agrawal et al., Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve, OSDI 2024. https://arxiv.org/abs/2403.02310
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Algorithmic Game TheoryCombinatoricsLinear Optimization+1·Captain: mikedeng1

New Combinatorial Insights for Monotone Apportionment: House-Monotone and Quota-Compliant Apportionments Are the Seat Counts of Extreme Points of a Network Flow LP with Lookahead Φ(p, H)Research Paper

Motivation

An apportionment method distributes the HHH seats of a house among nnn states in proportion to their populations. Exact proportionality is impossible with integer seats, so methods are judged by axioms. Two axioms date back to the debates over the seats of the US House of Representatives. Quota compliance asks that each state receive its exact proportional share rounded down or up. House monotonicity asks that no state lose a seat when the house grows; the Alabama paradox of 1880, in which Hamilton's method took a seat from Alabama when the house was enlarged, is the classic failure. The two axioms can hold together. Balinski and Young (Mathematics of Operations Research, 1979) and Still (SIAM Journal on Applied Mathematics, 1979) characterized the methods satisfying both through a recursive, seat-by-seat construction. Gölz, Peters and Procaccia (EC 2022) later gave a description by a fractional matching polytope.

Cembrano, Correa, Schmidt-Kraepelin, Tsigonias-Dimitriadis and Verdugo (arXiv:2410.23869, Mathematics of Operations Research, 2025) give a third characterization, by a network-flow linear program. It replaces the recursive construction with the vertex set of a polytope that can be written down and optimized over. This mission formalizes that characterization, Theorem 3 of the paper.

Setting

An instance is a pair (p,H)(p,H)(p,H). Here p=(p1,…,pn)p=(p_1,\dots,p_n)p=(p1​,…,pn​) is a vector of n≥1n\ge1n≥1 strictly positive integers (the populations of the states) and H≥1H\ge1H≥1 is an integer, the house size. Write P=∑ipiP=\sum_i p_iP=∑i​pi​; the quota of state iii is qi=piPHq_i=\frac{p_i}{P}Hqi​=Ppi​​H.

A solution fff assigns to every instance one vector f(p,H)∈N0nf(p,H)\in\mathbb N_0^nf(p,H)∈N0n​ with ∑ifi(p,H)=H\sum_i f_i(p,H)=H∑i​fi​(p,H)=H. It is defined for every number of states nnn and every population vector. It is quota-compliant if fi(p,H)∈{⌊qi⌋,⌈qi⌉}f_i(p,H)\in\{\lfloor q_i\rfloor,\lceil q_i\rceil\}fi​(p,H)∈{⌊qi​⌋,⌈qi​⌉} always. It is house-monotone if f(p,H1)≤f(p,H2)≤f(p,H3)f(p,H_1)\le f(p,H_2)\le f(p,H_3)f(p,H1​)≤f(p,H2​)≤f(p,H3​) componentwise whenever H1<H2<H3H_1<H_2<H_3H1​<H2​<H3​. The set of apportionments such solutions produce is

A(p,H)={f(p,H):f a house-monotone and quota-compliant solution}.\mathcal A(p,H)=\{f(p,H): f\text{ a house-monotone and quota-compliant solution}\}.A(p,H)={f(p,H):f a house-monotone and quota-compliant solution}.

For LLL seats, the network-flow linear program (1)–(4) has a variable x(i,t)≥0x(i,t)\ge0x(i,t)≥0 for each state iii and seat t∈[L]t\in[L]t∈[L], subject to

∑i=1nx(i,t)=1,⌊tpiP⌋≤∑ℓ=1tx(i,ℓ)≤⌈tpiP⌉.\sum_{i=1}^n x(i,t)=1,\qquad \Big\lfloor\frac{tp_i}{P}\Big\rfloor\le\sum_{\ell=1}^t x(i,\ell)\le\Big\lceil\frac{tp_i}{P}\Big\rceil .i=1∑n​x(i,t)=1,⌊Ptpi​​⌋≤ℓ=1∑t​x(i,ℓ)≤⌈Ptpi​​⌉.

Its feasible region is K(p,L)K(p,L)K(p,L) and its set of extreme points is E(p,L)\mathcal E(p,L)E(p,L). The count Ai(x,T)=∑ℓ=1Tx(i,ℓ)A_i(x,T)=\sum_{\ell=1}^T x(i,\ell)Ai​(x,T)=∑ℓ=1T​x(i,ℓ) is the number of seats state iii holds after TTT seats.

The lookahead Φ(p,H)\Phi(p,H)Φ(p,H) is the number of seats the LP must model so that its first HHH seats are exactly the house-monotone, quota-compliant ones. For a vector yyy and k≥1k\ge1k≥1, let Sk(p,H,y)={i:⌊pi(H+k)/P⌋>yi}S_k(p,H,y)=\{i:\lfloor p_i(H+k)/P\rfloor>y_i\}Sk​(p,H,y)={i:⌊pi​(H+k)/P⌋>yi​}. Let k⋆(p,H,y)k^\star(p,H,y)k⋆(p,H,y) be the least k≥1k\ge1k≥1 with ∑i∈Sk(⌊pi(H+k)/P⌋−yi)≥k\sum_{i\in S_k}(\lfloor p_i(H+k)/P\rfloor-y_i)\ge k∑i∈Sk​​(⌊pi​(H+k)/P⌋−yi​)≥k. Set τ=k⋆\tau=k^\starτ=k⋆ unless Sk⋆=[n]S_{k^\star}=[n]Sk⋆​=[n], in which case τ=1\tau=1τ=1. Then Φ(p,0)=0\Phi(p,0)=0Φ(p,0)=0, Φ(p,1)=min⁡{k≥1:∑i⌊pik/P⌋≥k}\Phi(p,1)=\min\{k\ge1:\sum_i\lfloor p_ik/P\rfloor\ge k\}Φ(p,1)=min{k≥1:∑i​⌊pi​k/P⌋≥k}, and

Φ(p,H+1)=max⁡T∈[H]{T+max⁡{τ(p,T,A(x,T)):x∈E(p,Φ(p,H))}}.\Phi(p,H+1)=\max_{T\in[H]}\Big\{T+\max\{\tau(p,T,A(x,T)):x\in\mathcal E(p,\Phi(p,H))\}\Big\}.Φ(p,H+1)=T∈[H]max​{T+max{τ(p,T,A(x,T)):x∈E(p,Φ(p,H))}}.

The Lean names are IsPop, Solution, QuotaCompliant, HouseMonotone, calA, K, calE, A, S, kStar, tau, Phi, all in the namespace MonoApport.Flow.

Formalization targets

Goal: Theorem 3

A(p,H)={A(x,H):x∈E(p,Φ(p,H))}for every instance (p,H).\mathcal A(p,H)=\big\{A(x,H): x\in\mathcal E(p,\Phi(p,H))\big\}\qquad\text{for every instance }(p,H).A(p,H)={A(x,H):x∈E(p,Φ(p,H))}for every instance (p,H).

Milestones

  1. Proposition 8. A solution is house-monotone if and only if f(p,H)≤f(p,H+1)f(p,H)\le f(p,H+1)f(p,H)≤f(p,H+1) for every ppp and HHH.
  2. Total unimodularity (proof of Lemma 3). The matrix with rows ei,te_{i,t}ei,t​, −ei,t-e_{i,t}−ei,t​, et′e'_tet′​, −et′-e'_t−et′​ is totally unimodular. Here ei,te_{i,t}ei,t​ is the prefix-sum row of state iii up to seat ttt, and et′e'_tet′​ is the column-sum row of seat ttt.
  3. Lemma 3. The linear program (1)–(4) is integral: every extreme point has integer coordinates.
  4. Remark after Lemma 3. E(p,L)\mathcal E(p,L)E(p,L) is exactly the set of integer points of K(p,L)K(p,L)K(p,L).
  5. Balinski–Young characterization (§C.4). fff is a house-monotone and quota-compliant solution if and only if, with f(p,0)=0f(p,0)=0f(p,0)=0, each seat H+1H+1H+1 goes to one state of L(p,H,f(p,H))∩U(p,H,f(p,H))\mathcal L(p,H,f(p,H))\cap\mathcal U(p,H,f(p,H))L(p,H,f(p,H))∩U(p,H,f(p,H)). Here L(p,H,y)={i:⌊piP(H+k⋆(p,H,y))⌋>yi}\mathcal L(p,H,y)=\{i:\lfloor\frac{p_i}{P}(H+k^\star(p,H,y))\rfloor>y_i\}L(p,H,y)={i:⌊Ppi​​(H+k⋆(p,H,y))⌋>yi​} and U(p,H,y)={i:piP(H+1)>yi}\mathcal U(p,H,y)=\{i:\frac{p_i}{P}(H+1)>y_i\}U(p,H,y)={i:Ppi​​(H+1)>yi​}.
  6. Lemma 5. If x∈E(p,Φ(p,H))x\in\mathcal E(p,\Phi(p,H))x∈E(p,Φ(p,H)) and T≤H−1T\le H-1T≤H−1, then x(i,T+1)=0x(i,T+1)=0x(i,T+1)=0 for every i∉L(p,T,A(x,T))∩U(p,T,A(x,T))i\notin\mathcal L(p,T,A(x,T))\cap\mathcal U(p,T,A(x,T))i∈/L(p,T,A(x,T))∩U(p,T,A(x,T)).

Significance

The result. Theorem 3 turns a recursive definition into a polyhedral one. The apportionments at house size HHH that some house-monotone, quota-compliant solution can produce are the projections of the vertices of a network-flow polytope with Φ(p,H)\Phi(p,H)Φ(p,H) seats. Optimizing a linear objective over all such apportionments is then a linear program of size O(n Φ(p,H))O(n\,\Phi(p,H))O(nΦ(p,H)). The paper also bounds the lookahead: Φ(p,H)−H≤max⁡i⌈P/pi⌉\Phi(p,H)-H\le\max_i\lceil P/p_i\rceilΦ(p,H)−H≤maxi​⌈P/pi​⌉ for H≥2H\ge2H≥2, and the bound is tight up to constants. Through the correspondence between methods and solutions (Lemma 2 of the paper), the theorem also characterizes house-monotone, quota-compliant methods. It underlies the paper's construction of randomized methods that are house-monotone, quota-compliant and ex-ante proportional (Theorem 4).

The formalization. The paper proves Theorem 3. None of the statements here has a machine-checked proof, and the Balinski–Young characterization is cited by the paper rather than proved in it. A formal development would check the full chain: total unimodularity of an interval-type constraint matrix, integrality of the polytope, the lookahead bound of Lemma 5, and the combination with the Balinski–Young construction. It would also make the network-flow polytope and the lookahead Φ\PhiΦ available to later work on apportionment.

Difficulty

The inclusion A(p,H)⊆{A(x,H)}\mathcal A(p,H)\subseteq\{A(x,H)\}A(p,H)⊆{A(x,H)} looks routine: the seat increments of a house-monotone, quota-compliant solution give a 0/1 feasible point. But that point must have Φ(p,H)\Phi(p,H)Φ(p,H) seats, and it must be an extreme point, which requires the integrality of (1)–(4).

The reverse inclusion is the hard part. A vertex of the LP with HHH seats need not extend to a quota-compliant sequence: a seat assignment can respect every quota bound up to HHH and still force a violation a few seats later. The lookahead Φ\PhiΦ is designed so that this cannot happen. However, Φ\PhiΦ is defined by a recursion over extreme points of LPs of other sizes, and it is not monotone in HHH: for p=(1,2)p=(1,2)p=(1,2) the values are Φ=0,3,2,3,4,…\Phi=0,3,2,3,4,\dotsΦ=0,3,2,3,4,…. An argument that truncates a vertex for Φ(p,H)\Phi(p,H)Φ(p,H) seats to one for Φ(p,H−1)\Phi(p,H-1)Φ(p,H−1) seats, or assumes Φ(p,H)≥Φ(p,H−1)\Phi(p,H)\ge\Phi(p,H-1)Φ(p,H)≥Φ(p,H−1), is therefore unavailable.

Closing the inclusion also needs the Balinski–Young construction never to get stuck: L∩U\mathcal L\cap\mathcal UL∩U must be nonempty along every sequence it builds. The paper takes this from Balinski and Young without stating it separately.

Formalization scope

Populations are p : Fin n → ℕ with IsPop p (n>0n>0n>0 and every pi>0p_i>0pi​>0); house sizes carry 0 < H. A solution is one Lean function Solution := ∀ {n}, (Fin n → ℕ) → ℕ → (Fin n → ℕ) defined on vectors of every length. A(p,H)\mathcal A(p,H)A(p,H) quantifies over such families, exactly as in the paper; a per-ppp sequence of apportionments would be a different object. House monotonicity is axiom (c) with three house sizes; the one-step form is Proposition 8, a milestone. LP points with LLL seats are x : Fin n → Fin L → ℝ. Seats are 0-indexed, so column ttt is seat t+1t+1t+1, and the quota bounds at column ttt use t+1t+1t+1. Floors and ceilings are Nat.floor/Nat.ceil of the nonnegative rational quantity computed in R\mathbb RR. E(p,L)\mathcal E(p,L)E(p,L) is Set.extremePoints ℝ (K p L). The minima and maxima defining k⋆k^\stark⋆ and Φ\PhiΦ are sInf/sSup on N\mathbb NN, whose sets are nonempty, and finite where a maximum is taken, wherever they are used. The convention f(p,0)=0f(p,0)=0f(p,0)=0 is the function ext. The goal compares subsets of Rn\mathbb R^nRn: the image of A(p,H)\mathcal A(p,H)A(p,H) under the cast equals {A(x,H)}\{A(x,H)\}{A(x,H)}.

The goal must not be read with integer vectors on both sides. Writing A(p,H)={y:(yi:R)=Ai(x,H) for some x}\mathcal A(p,H)=\{y : (y_i:\mathbb R)=A_i(x,H)\text{ for some }x\}A(p,H)={y:(yi​:R)=Ai​(x,H) for some x} would silently ignore non-integral A(x,H)A(x,H)A(x,H) and empty the inclusion ⊇\supseteq⊇ of its content.

A complete development needs:

  • total unimodularity and the integrality of polytopes with totally unimodular constraints (Hoffman–Kruskal), partly available in Mathlib through Matrix.IsTotallyUnimodular;
  • the finiteness of the extreme points of a polytope;
  • a formal proof of the Balinski–Young characterization, including the nonemptiness of L∩U\mathcal L\cap\mathcal UL∩U along the construction.

The first two are reusable well beyond apportionment. Proofs of any milestone are welcome, and so are proofs of the cited Balinski–Young theorem and of the properties of Φ\PhiΦ the paper uses (Φ(p,H)≥H\Phi(p,H)\ge HΦ(p,H)≥H, and the finiteness and nonemptiness of the sets in its recursion).

Selected references

  • J. Cembrano, J. Correa, U. Schmidt-Kraepelin, A. Tsigonias-Dimitriadis, V. Verdugo, New Combinatorial Insights for Monotone Apportionment, arXiv:2410.23869v1, 2024; Mathematics of Operations Research, 2025. https://arxiv.org/abs/2410.23869
  • M. Balinski, H. P. Young, Quotatone apportionment methods, Mathematics of Operations Research 4(1):31–38, 1979. https://doi.org/10.1287/moor.4.1.31
  • M. Balinski, H. P. Young, The quota method of apportionment, The American Mathematical Monthly 82(7):701, 1975.
  • M. Balinski, H. P. Young, Fair Representation: Meeting the Ideal of One Man, One Vote, Brookings Institution Press, 2010.
  • J. W. Still, A class of new methods for congressional apportionment, SIAM Journal on Applied Mathematics 37(2):401–418, 1979.
  • P. Gölz, D. Peters, A. D. Procaccia, In this apportionment lottery, the house always wins, Proceedings of the 23rd ACM Conference on Economics and Computation (EC), 2022.
  • A. Ghouila-Houri, Caractérisation des matrices totalement unimodulaires, C. R. Acad. Sci. Paris 254:1192–1194, 1962.
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Algorithmic Game TheoryEconomicsOperations Research·Captain: mikedeng1

Solving Strong-Substitutes Product-Mix Auctions: Allocate Splits the Target Bundle into Demanded Bundles within |J|·C(n+1,2) IterationsResearch Paper

Motivation

The product-mix auction was developed in 2007–8 for the Bank of England (Klemperer 2010) to sell many differentiated goods (loans against different qualities of collateral) in a single round. Each bidder submits a list of simple bids, each naming a price for every good and a quantity, and the auctioneer chooses prices and allocations that respect every bid. Baldwin, Goldberg, Klemperer and Lock (arXiv:1909.07313v3, Mathematics of Operations Research, 2024) make the auction usable with positive and negative bids, a bidding language that expresses every strong-substitutes valuation (Baldwin–Klemperer 2019). The auctioneer then faces two computational tasks: finding a market-clearing price, and splitting the goods at that price among the bidders so that every bidder receives a bundle it demands. This mission formalizes the second task, the allocation algorithm ALLOCATE and its correctness and iteration bound (Theorem 19, p. 23).

Setting

There are nnn goods [n]={1,…,n}[n]=\{1,\dots,n\}[n]={1,…,n} and a notional reject good 000; [n]0={0,…,n}[n]_0=\{0,\dots,n\}[n]0​={0,…,n}. A price is p∈Rnp\in\mathbb R^np∈Rn, extended by p0=0p_0=0p0​=0. A bid b=(b1,…,bn;w)b=(b_1,\dots,b_n;w)b=(b1​,…,bn​;w) has integer entries and weight w(b)=±1w(b)=\pm1w(b)=±1, with b0=0b_0=0b0​=0. At prices ppp it demands the goods arg⁡max⁡i∈[n]0(bi−pi)\arg\max_{i\in[n]_0}(b_i-p_i)argmaxi∈[n]0​​(bi​−pi​) and is marginal if it demands more than one. Where no bid of a list B\mathcal BB is marginal, the list demands the bundle ∑bw(b)ei(b)\sum_{b}w(b)e^{i(b)}∑b​w(b)ei(b), with i(b)i(b)i(b) the good bbb demands. In general the demand DB(p)D_{\mathcal B}(p)DB​(p) is the discrete convex hull conv⁡(X)∩Zn\operatorname{conv}(X)\cap\mathbb Z^nconv(X)∩Zn of the set XXX of bundles demanded at non-marginal prices arbitrarily close to ppp. The indirect utility function is

fB(p)=∑b∈Bw(b)max⁡i∈[n]0(bi−pi),f_{\mathcal B}(p)=\sum_{b\in\mathcal B}w(b)\max_{i\in[n]_0}(b_i-p_i),fB​(p)=b∈B∑​w(b)i∈[n]0​max​(bi​−pi​),

and B\mathcal BB is valid if fBf_{\mathcal B}fB​ is convex. It is ε\varepsilonε-valid at ppp if fBf_{\mathcal B}fB​ is convex on the open L∞L^\inftyL∞-ball B(p;ε)B(p;\varepsilon)B(p;ε), and locally valid if it is ε\varepsilonε-valid for some ε>0\varepsilon>0ε>0.

Each bidder j∈Jj\in Jj∈J has a valid list Bj\mathcal B^jBj; the aggregate list B\mathcal BB is their concatenation. The allocation problem (§2.5): given an integral price ppp and a target bundle t∈DB(p)t\in D_{\mathcal B}(p)t∈DB​(p), find bundles tj∈DBj(p)t^j\in D_{\mathcal B^j}(p)tj∈DBj​(p) with ∑jtj=t\sum_j t^j=t∑j​tj=t.

ALLOCATE works on generalised allocation problems A=[p,(Bj),(mj),r]\mathcal A=[p,(\mathcal B^j),(m^j),r]A=[p,(Bj),(mj),r] (Definition 4), with partial allocations mjm^jmj and residual supply rrr. Its progress is measured by the marginal bids graph GAG_{\mathcal A}GA​: the multigraph on [n]0[n]_0[n]0​ with an edge {i,i′}\{i,i'\}{i,i′} labelled jjj whenever bidder jjj has a bid demanding both iii and i′i'i′. Each loop iteration calls FindParams, which returns either a demand cluster (a connected component of the jjj-labelled edges) with at most one link good, or a cycle-link good i∗i^*i∗ of a multi-bidder cycle with an incident label j∗j^*j∗. It then runs UnambiguousMarginals on the cluster or ShiftProjectUnshift on (i∗,j∗)(i^*,j^*)(i∗,j∗), followed by NonMarginals. ShiftProjectUnshift shifts j∗j^*j∗'s bids by 110ei∗\tfrac1{10}e^{i^*}101​ei∗ and finds a nearby price pεp^\varepsilonpε by submodular function minimisation (SFM) on the Lyapunov function g(q)=fB′(q)+r⋅qg(q)=f_{\mathcal B'}(q)+r\cdot qg(q)=fB′​(q)+r⋅q. It then projects every bid, undoes the shift, and returns to ppp.

Formalization targets

Goal: Theorem 19

For valid integral bid lists with weights ±1\pm1±1, an integral ppp and t∈DB(p)t\in D_{\mathcal B}(p)t∈DB​(p), any FindParams output rule meeting Lemma 10 and any SFM oracle that minimises submodular functions, there is

k≤∣J∣(n+12)k\le |J|\binom{n+1}{2}k≤∣J∣(2n+1​)

such that after kkk loop iterations every bid list is empty, every SFM call made was on a submodular function, and the partial allocations satisfy ∑jmj=t\sum_j m^j=t∑j​mj=t with mj∈DBj(p)m^j\in D_{\mathcal B^j}(p)mj∈DBj​(p).

Milestones

  • Proposition 6 (local validity criterion) and Lemma 9 (surplus gaps give a radius of validity).
  • Theorem 4, posed for valid bid lists: some partition t=∑jtjt=\sum_j t^jt=∑j​tj with tj∈DBj(p)t^j\in D_{\mathcal B^j}(p)tj∈DBj​(p) exists.
  • Lemma 11 (NonMarginals), Lemma 10 (FindParams' outputs), Lemma 12 (UnambiguousMarginals deletes an edge).
  • Lemma 13 (shifting keeps local validity), Lemma 26 (inverses of strong-substitutes matrices), Proposition 14 (after a shift, rrr is demanded at some p±εeSp\pm\varepsilon e^Sp±εeS, found by SFM), Lemma 16 (projection).
  • Lemma 18 (ShiftProjectUnshift is a reduction) and Lemma 17 (it deletes an edge).

Significance

Theorem 19 is the paper's main contribution (p. 5). With the price-finding method of §3 it makes the strong-substitutes product-mix auction computable in polynomial time, using only the bids. The allocation step is the delicate one: at a market-clearing price, bidders cannot simply be given any bundle they demand (p. 12), and random perturbation would need exponentially small amounts. The authors report Haskell and Python implementations of both algorithms and experiments on random instances (pp. 5, 27–28).

The paper proves the result, but no part of it is machine-checked. The mission adds to the platform a bidding-language layer (bids, demand as a discrete convex hull, local validity) and an algorithmic layer (allocation problems, valid reductions, the marginal bids multigraph, an SFM oracle). Both layers are reusable for later work on product-mix auctions and on discrete-convex equilibrium computation. Formalizing the proof will also check two printed slips found during planning: the definition of a closed path in FindParams, and the second Lyapunov difference h−h^-h−.

Difficulty

Dividing ttt bid by bid fails: each bid's choice among its demanded goods is constrained by the others, and independent choices can leave DBj(p)D_{\mathcal B^j}(p)DBj​(p) (p. 10). The two procedures that allocate directly cover only demand clusters with at most one link good. When ties form a cycle through several bidders, the algorithm must change the problem without losing a solution. The step to justify is Proposition 14: after a 110\tfrac1{10}101​-shift of one bidder, the residual supply is still demanded at a price of the special form p±εeSp\pm\varepsilon e^Sp±εeS. This needs local strong-substitutes valuations, the matrix identity of Lemma 26 and an integrality argument. The edge count drops in Lemma 17 because the shift enters a telescoped cycle identity exactly once. Local validity must also be tracked through every removal of bids (Proposition 6, Lemma 9), since global validity is lost after the first NonMarginals call.

Formalization scope

Goods [n]0[n]_0[n]0​ are Fin (n + 1), with 0 the reject good. Prices are Fin n → ℝ, extended by p0=0p_0=0p0​=0; the L∞L^\inftyL∞ ball is Metric.ball on Fin n → ℝ. Input bids are integral (IntBid). The run uses real bid vectors (Bid), because of the 110\tfrac1{10}101​ shifts, and does not force the reject entry to 000: ShiftProjectUnshift is read literally when i∗=0i^*=0i∗=0. Bid lists are Lists. Bundles, partial allocations and residuals are ℤ-valued and carry a reject coordinate; the goal drops it. Demand is one definition, the discrete convex hull of nearby non-marginal bundles, used at every price.

FindParams is "not fully specified" (p. 23). The goal quantifies over every selector that returns what Lemma 10 allows, which covers every walk of FindParams and PriorityParams. Lemma 10 is posed with the corrected notion of a closed path; a counterexample to the printed one is in its statement. The SFM oracle is arbitrary on non-submodular inputs, and the goal asserts that every call it receives is submodular. Running time is rendered as the iteration bound ∣J∣(n+12)|J|\binom{n+1}2∣J∣(2n+1​) together with the per-iteration structure (one selector call, at most two SFM calls). No machine model is formalized. Submodularity is the published NonmonotoneSubmod.Shared.Submodular.

The goal is not "an allocation exists" (that is Theorem 4) and not "the loop terminates" (that drops the bound). It is not posed for one fixed FindParams implementation or for an oracle correct on every input, which would narrow the quantifiers. It does not leave the oracle unconstrained without the genuineness clause, which would let a junk price through.

Contributions welcome: the bid-list layer (Proposition 6, Lemmas 9, 13, 16), the combinatorics of GAG_{\mathcal A}GA​ (Lemmas 10, 12, 17) and the matrix Lemma 26 are independent entry points.

Selected references

  • E. Baldwin, P. W. Goldberg, P. Klemperer, E. Lock, Solving Strong-Substitutes Product-Mix Auctions, Mathematics of Operations Research, 2024; arXiv:1909.07313v3. https://arxiv.org/abs/1909.07313v3
  • E. Baldwin, P. Klemperer, Understanding Preferences: "Demand Types", and the Existence of Equilibrium with Indivisibilities, Econometrica 87(3), 2019. https://doi.org/10.3982/ECTA13693
  • P. Klemperer, The Product-Mix Auction: A New Auction Design for Differentiated Goods, Journal of the European Economic Association 8(2–3), 2010. https://doi.org/10.1111/j.1542-4774.2010.tb00531.x
  • A. Shioura, A. Tamura, Gross Substitutes Condition and Discrete Concavity for Multi-Unit Valuations: A Survey, Journal of the Operations Research Society of Japan 58(1), 2015. https://doi.org/10.15807/jorsj.58.61
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Markov ChainProbabilityReinforcement Learning+1·Captain: mikedeng1

Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity: Under Geometric Mixing the Empirical Estimator Is ε-Accurate After Order dT/ε² Samples, and This Rate Is MinimaxResearch Paper

Learning the dynamics from logged data

Offline reinforcement learning and off-policy evaluation start from a single logged trajectory of a controlled system. The policy that generated the log is often unknown, history-dependent and not stationary. Model-based methods first estimate the transition probabilities of the system from this log, then plan or evaluate policies in the estimated model (Agarwal, Kakade & Yang, 2020; Li et al., 2024). How many samples that estimate needs is the question of Banerjee, Honnappa & Rao.

For an uncontrolled, geometrically ergodic Markov chain on ddd states, Wolfer & Kontorovich (2021) showed that the transition matrix can be learned to accuracy ε\varepsilonε in the ∞\infty∞-norm from of order d/(π⋆ε2)d/(\pi_\star\varepsilon^2)d/(π⋆​ε2) samples, where π⋆\pi_\starπ⋆​ is the smallest stationary probability, and that this rate is minimax. Banerjee, Honnappa and Rao extend this to controlled chains whose controls may depend arbitrarily on the past. Stationarity, irreducibility and aperiodicity then need not hold.

Controlled Markov chains and the empirical estimator

A controlled Markov chain with ddd states and kkk controls consists of:

  1. a state process X0,X1,…X_0,X_1,\dotsX0​,X1​,… in χ={1,…,d}\chi=\{1,\dots,d\}χ={1,…,d};
  2. a control process a0,a1,…a_0,a_1,\dotsa0​,a1​,… in I={1,…,k}\mathbb I=\{1,\dots,k\}I={1,…,k};
  3. a row-stochastic transition matrix M(l)M^{(l)}M(l) for each control lll.

Both processes are adapted to a filtration {Fi}\{\mathcal F_i\}{Fi​}, and

P(Xi+1=t∣Fi)=MXi,t(ai).\mathbb P(X_{i+1}=t\mid\mathcal F_i)=M^{(a_i)}_{X_i,t}.P(Xi+1​=t∣Fi​)=MXi​,t(ai​)​.

The controls are otherwise arbitrary.

From a sample of length mmm, the visit counts are Ns(l)=∑i=1m1[Xi=s,ai=l]N^{(l)}_s=\sum_{i=1}^m\mathbb 1[X_i=s,a_i=l]Ns(l)​=∑i=1m​1[Xi​=s,ai​=l] and Ns,t(l)=∑i=1m1[Xi=s,Xi+1=t,ai=l]N^{(l)}_{s,t}=\sum_{i=1}^m\mathbb 1[X_i=s,X_{i+1}=t,a_i=l]Ns,t(l)​=∑i=1m​1[Xi​=s,Xi+1​=t,ai​=l]. The empirical estimator is M^s,t(l)=Ns,t(l)/Ns(l)\hat M^{(l)}_{s,t}=N^{(l)}_{s,t}/N^{(l)}_sM^s,t(l)​=Ns,t(l)​/Ns(l)​, and its error is measured by

sup⁡l∥M^(l)−M(l)∥∞,∥A∥∞=max⁡s∑t∣As,t∣.\sup_{l}\big\|\hat M^{(l)}-M^{(l)}\big\|_\infty,\qquad \|A\|_\infty=\max_s\sum_t|A_{s,t}| .lsup​​M^(l)−M(l)​∞​,∥A∥∞​=smax​t∑​∣As,t​∣.

Three conditions control the chain:

  1. Assumption 1 asks for sets SiS_iSi​ of state–control pairs with 0<ζ2≤P((Xi,ai)∈Si)≤ζ1<10<\zeta_2\le\mathbb P((X_i,a_i)\in S_i)\le\zeta_1<10<ζ2​≤P((Xi​,ai​)∈Si​)≤ζ1​<1. Put ζ(max⁡)=max⁡{ζ1,1−ζ2}\zeta^{(\max)}=\max\{\zeta_1,1-\zeta_2\}ζ(max)=max{ζ1​,1−ζ2​}.
  2. Assumption 2 bounds the return times. Let H0=0H_0=0H0​=0 and let HiH_iHi​ be the iii-th time n≥1n\ge1n≥1 with (Xn,an)=(s,l)(X_n,a_n)=(s,l)(Xn​,an​)=(s,l). The return times τs,l(i)=Hi−Hi−1\tau^{(i)}_{s,l}=H_i-H_{i-1}τs,l(i)​=Hi​−Hi−1​ must satisfy sup⁡s,l,iE[τs,l(i)∣FHi−1]<T\sup_{s,l,i}\mathbb E[\tau^{(i)}_{s,l}\mid\mathcal F_{H_{i-1}}]<Tsups,l,i​E[τs,l(i)​∣FHi−1​​]<T almost surely.
  3. Assumption 4 asks for geometric decay of the weak mixing coefficient ηˉi,j\bar\eta_{i,j}ηˉ​i,j​. This coefficient is the largest change, caused by changing (Xi,ai)(X_i,a_i)(Xi​,ai​) while the history before iii is kept fixed, in the conditional probability of an event of the window (Xj,aj),…,(Xm,am)(X_j,a_j),\dots,(X_m,a_m)(Xj​,aj​),…,(Xm​,am​). The decay is ηˉi,j≤((CΔ−1)/CΔ)j−i\bar\eta_{i,j}\le((\mathbb C_\Delta-1)/\mathbb C_\Delta)^{j-i}ηˉ​i,j​≤((CΔ​−1)/CΔ​)j−i for a constant CΔ>1\mathbb C_\Delta>1CΔ​>1.

The uniform mixing coefficient ϕi,j\phi_{i,j}ϕi,j​ compares the conditional law of the window given the whole history up to iii with its unconditional law. With ρ⋆=max⁡s,lsup⁡1≤i≤mP(Xi=s,ai=l)\rho_\star=\max_{s,l}\sup_{1\le i\le m}\mathbb P(X_i=s,a_i=l)ρ⋆​=maxs,l​sup1≤i≤m​P(Xi​=s,ai​=l) the paper defines

CT=64(CΔρ⋆T2+2T)Cpel,Cζ=8(2CΔρ⋆(1−ζ(max⁡))−2+(1−ζ(max⁡))−1)Cpel,\mathbb C_T=\frac{64(\mathbb C_\Delta\rho_\star T^2+2T)}{\mathbb C_{pel}},\qquad \mathbb C_\zeta=\frac{8(2\mathbb C_\Delta\rho_\star(1-\zeta^{(\max)})^{-2}+(1-\zeta^{(\max)})^{-1})}{\mathbb C_{pel}},CT​=Cpel​64(CΔ​ρ⋆​T2+2T)​,Cζ​=Cpel​8(2CΔ​ρ⋆​(1−ζ(max))−2+(1−ζ(max))−1)​,

and CT,δ=CTlog⁡(6dk/δ)\mathbb C_{T,\delta}=\mathbb C_T\log(6dk/\delta)CT,δ​=CT​log(6dk/δ), Cζ,δ=Cζlog⁡(6dk/δ)\mathbb C_{\zeta,\delta}=\mathbb C_\zeta\log(6dk/\delta)Cζ,δ​=Cζ​log(6dk/δ). Here Cpel\mathbb C_{pel}Cpel​ depends on CΔ\mathbb C_\DeltaCΔ​ only.

Formalization targets

Goal: Theorem 2, both clauses

(a) Sample complexity. There are a universal c>1c>1c>1 and a positive function Cpel(CΔ)\mathbb C_{pel}(\mathbb C_\Delta)Cpel​(CΔ​) such that every controlled Markov chain satisfying Assumptions 1, 2 and 4 has, for ε>0\varepsilon>0ε>0 and δ∈(0,1)\delta\in(0,1)δ∈(0,1),

m>cmax⁡{4dε2(1+ζ(max⁡)),dTε2,Tε2log⁡dkTε2δ,CT,δ(log⁡CT,δ)2,Cζ,δ(log⁡Cζ,δ)2} ⟹ P(sup⁡l∥M^(l)−M(l)∥∞>ε)<δ.m>c\max\Big\{\tfrac{4d}{\varepsilon^2(1+\zeta^{(\max)})},\tfrac{dT}{\varepsilon^2},\tfrac{T}{\varepsilon^2}\log\tfrac{dkT}{\varepsilon^2\delta},\mathbb C_{T,\delta}(\log\mathbb C_{T,\delta})^2,\mathbb C_{\zeta,\delta}(\log\mathbb C_{\zeta,\delta})^2\Big\}\ \Longrightarrow\ \mathbb P\Big(\sup_l\|\hat M^{(l)}-M^{(l)}\|_\infty>\varepsilon\Big)<\delta .m>cmax{ε2(1+ζ(max))4d​,ε2dT​,ε2T​logε2δdkT​,CT,δ​(logCT,δ​)2,Cζ,δ​(logCζ,δ​)2} ⟹ P(lsup​∥M^(l)−M(l)∥∞​>ε)<δ.

The printed threshold lacks the second and third terms and is false without them: i.i.d. uniform states satisfy all assumptions with T=d+1T=d+1T=d+1, but the printed threshold is then O(d(log⁡d)3)O(d(\log d)^3)O(d(logd)3), far below the d2d^2d2 samples that are needed.

(b) Minimaxity. This is the paper's Appendix G.3 Part 1. For d≥d0d\ge d_0d≥d0​ (a universal threshold), 0<p⋆<1/(d+1)0<p_\star<1/(d+1)0<p⋆​<1/(d+1) and σ∈{−1,1}⌊d/2⌋\sigma\in\{-1,1\}^{\lfloor d/2\rfloor}σ∈{−1,1}⌊d/2⌋, let MσM_\sigmaMσ​ be the transition matrix on d+1d+1d+1 states whose first ddd rows are b=((1−p⋆)/d,…,(1−p⋆)/d,p⋆)b=((1-p_\star)/d,\dots,(1-p_\star)/d,p_\star)b=((1−p⋆​)/d,…,(1−p⋆​)/d,p⋆​) and whose last row is bbb perturbed by ±16σiε/d\pm16\sigma_i\varepsilon/d±16σi​ε/d in consecutive pairs of coordinates (the page takes ddd even; for odd ddd the leftover coordinate is left unperturbed). These chains exist. They satisfy the three assumptions with ζ1=ζ2=p⋆\zeta_1=\zeta_2=p_\starζ1​=ζ2​=p⋆​, T=⌈3/p⋆⌉T=\lceil3/p_\star\rceilT=⌈3/p⋆​⌉ and CΔ=4/3\mathbb C_\Delta=4/3CΔ​=4/3. For 0<ε<1/320<\varepsilon<1/320<ε<1/32 and δ<1/2\delta<1/2δ<1/2,

m≤c2(1−2δ)dp⋆ε2 ⟹ ∀ M~ ∃ σ: Pσ(∥M~−Mσ∥∞>ε)>δ.m\le c_2(1-2\delta)\frac{d}{p_\star\varepsilon^2}\ \Longrightarrow\ \forall\,\tilde M\ \exists\,\sigma:\ \mathbb P_\sigma\big(\|\tilde M-M_\sigma\|_\infty>\varepsilon\big)>\delta .m≤c2​(1−2δ)p⋆​ε2d​ ⟹ ∀M~ ∃σ: Pσ​(∥M~−Mσ​∥∞​>ε)>δ.

Since T≍1/p⋆T\asymp1/p_\starT≍1/p⋆​ on this family, the term dT/ε2dT/\varepsilon^2dT/ε2 of (a) cannot be improved beyond a universal constant.

Milestones

  1. Proposition 1 (row error at bounded visit counts, corrected).
  2. Lemma 2 (bounds on E[Ns(l)]\mathbb E[N^{(l)}_s]E[Ns(l)​]).
  3. Lemma 1 (weak vs. uniform mixing, corrected).
  4. Lemma 9 (covariance inequality, corrected).
  5. Proposition 3 (a Bernstein bound for Ns(l)N^{(l)}_sNs(l)​, corrected).
  6. The elementary step m≥2x(log⁡x)2⇒m/(log⁡m)2≥xm\ge2x(\log x)^2\Rightarrow m/(\log m)^2\ge xm≥2x(logx)2⇒m/(logm)2≥x.
  7. The Varshamov–Gilbert packing.
  8. The bound KL(η(σ)∥b)≤512ε2\mathrm{KL}(\eta(\sigma)\|b)\le512\varepsilon^2KL(η(σ)∥b)≤512ε2.
  9. The chain rule (G.22).
  10. The stationary distribution (G.23).

Significance

Clause (a) gives an estimate of all kkk transition matrices from one trajectory logged by an arbitrary adapted policy, with explicit dependence on the return-time constant TTT, the visitation constants ζ1,ζ2\zeta_1,\zeta_2ζ1​,ζ2​ and the mixing constant. Error bounds for off-policy evaluation and for recovering optimal values follow from it in the paper's Section 5. Clause (b) shows that no estimator does better than the empirical one by more than a universal factor in the leading term.

Neither clause has a machine-checked proof. Four printed statements on the path are false as stated and are posed here in corrected form, each with a counterexample in its item:

  • the threshold of Theorem 2;
  • Proposition 1;
  • Lemma 1;
  • Lemma 9.

Proposition 3 is posed in the regime where its printed bound holds. A complete formalization would give the first checked minimax result for estimating a controlled Markov chain. It would also give the first checked concentration inequality for visit counts of a non-stationary mixing process.

Difficulty

Under arbitrary adapted controls the visit counts are not sums of independent or stationary terms, and the number of transitions observed from a state is random and correlated with the transitions themselves. Applying a fixed-sample-size concentration bound row by row therefore fails. The deviation of Ns(l)N^{(l)}_sNs(l)​ around its mean has to be controlled by a Bernstein-type inequality for non-stationary, geometrically mixing bounded sequences, with constants that depend on the mixing rate alone. The constants must not depend on ddd, kkk or the chain. Such an inequality is not in Mathlib. The lower bound needs Fano's method, or an equivalent, for path laws of Markov chains.

Formalization scope

  • States are Fin d and controls Fin k, with d,k≥1d,k\ge1d,k≥1, and time is N\mathbb NN from 000.
  • The chain is a structure on a probability space with a Mathlib Filtration ℕ. It carries adapted XXX, aaa, row-stochastic matrices and the conditional transition law.
  • The counts run over i=1,…,mi=1,\dots,mi=1,…,m and Ns,t(l)N^{(l)}_{s,t}Ns,t(l)​ reads Xm+1X_{m+1}Xm+1​. M^\hat MM^ uses Lean's 0/0=00/0=00/0=0.
  • ∥⋅∥∞\|\cdot\|_\infty∥⋅∥∞​ is the maximum row ℓ1\ell^1ℓ1 sum, not the largest entry: under the largest-entry norm the hard family's members are only 32ε/d32\varepsilon/d32ε/d apart and clause (b) is false.
  • Visit times take values in N∪{∞}\mathbb N\cup\{\infty\}N∪{∞}, with the first visit at a time n≥1n\ge1n≥1.
  • Assumption 2 uses Mathlib's conditional expectation with respect to the σ-algebra at the previous visit. It requires the return times to be almost surely finite and integrable, and bounds the supremum strictly by TTT.
  • ηˉ\bar\etaηˉ​ and ϕ\phiϕ are finite maxima over the window up to mmm, using elementary conditional probabilities. Assumption 4 holds for 0≤i<j≤m0\le i<j\le m0≤i<j≤m, and Assumption 1 at the times 1≤i≤m1\le i\le m1≤i≤m.
  • The constants ccc, c2c_2c2​, d0d_0d0​ and the function Cpel\mathbb C_{pel}Cpel​ are quantified before the chain. A ccc or Cpel\mathbb C_{pel}Cpel​ that depends on ddd, kkk, TTT or the chain would make (a) a different, weaker statement.
  • Clause (b) quantifies over every estimator, i.e. every function of X0,…,Xm+1X_0,\dots,X_{m+1}X0​,…,Xm+1​, and asserts that the hard chains exist. A family that cannot be realized would make (b) vacuous.

A complete development needs several pieces of infrastructure, all reusable beyond this mission:

  • the strong Markov property at visit times;
  • a Bernstein inequality for ϕ\phiϕ-mixing sequences;
  • Fano's inequality with a packing;
  • KL chain rules for Markov path laws.

Contributions of any of these, and proofs of the milestones, are welcome.

Selected references

  • I. Banerjee, H. Honnappa, V. Rao, Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity, Operations Research, 2025; arXiv:2211.07092v5. https://arxiv.org/abs/2211.07092
  • G. Wolfer, A. Kontorovich, Statistical estimation of ergodic Markov chain kernel over discrete state space, Bernoulli 27(1), 2021. https://arxiv.org/abs/1902.00080
  • F. Merlevède, M. Peligrad, E. Rio, Bernstein inequality and moderate deviations under strong mixing conditions, High Dimensional Probability V, IMS Collections 5, 2009. https://arxiv.org/abs/0902.0582
  • A. B. Tsybakov, Introduction to Nonparametric Estimation, Springer, 2009. https://doi.org/10.1007/b13794
  • A. Agarwal, S. Kakade, L. F. Yang, Model-based reinforcement learning with a generative model is minimax optimal, COLT 2020. https://arxiv.org/abs/1906.03804
  • G. Li, L. Shi, Y. Chen, Y. Chi, Y. Wei, Settling the sample complexity of model-based offline reinforcement learning, Annals of Statistics 52(1), 2024. https://arxiv.org/abs/2204.05275
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Algorithmic Game TheoryLinear algebraOperations Research·Captain: mikedeng1

Decomposition of Games: Some Strategic Considerations: Duplicating a Strategy Commutes with the (μ,γ)-Decomposition into Potential, Harmonic and Nonstrategic ComponentsResearch Paper

Motivation

A finite game can be split into simpler pieces. Candogan, Menache, Ozdaglar and Parrilo (Math. Oper. Res. 36 (2011)) showed that the space of finite games is the direct sum of potential, harmonic and nonstrategic games: potential games have pure equilibria and well-behaved learning dynamics, harmonic games generalize matching pennies, and nonstrategic games do not affect anyone's incentives. The decomposition is used to approximate a game by the closest potential game and to transfer equilibrium and dynamics results from the potential component to the original game.

Their decomposition depends on a uniform measure on each strategy set and an unweighted inner product. As a result it is not invariant under transformations that leave the strategic content of a game unchanged. The most basic such transformation is duplicating a strategy: giving a player a second, payoff-identical copy of one of her strategies. Abdou, Pnevmatikos, Scarsini and Venel (arXiv:1901.06048v2, 2020; Math. Oper. Res. 2022) introduce a weighted family of decompositions, parametrized by a measure vector and a co-measure vector, and show that duplication commutes with it once the weights are transformed accordingly. This mission formalizes that result together with the weighted decomposition it rests on.

Setting

There is a finite set NNN of players. Player iii has a finite strategy set SiS^iSi; S=∏i∈NSiS=\prod_{i\in N}S^iS=∏i∈N​Si is the set of profiles sss, s−is^{-i}s−i is sss without player iii, and (ti,s−i)(t^i,s^{-i})(ti,s−i) is sss with player iii's strategy replaced by tit^iti. A game is a vector g=(gi)i∈Ng=(g^i)_{i\in N}g=(gi)i∈N​ of payoff functions gi:S→Rg^i:S\to\mathbb Rgi:S→R; the games form a real vector space G\mathcal GG.

A measure vector μ=(μi)i∈N\mu=(\mu^i)_{i\in N}μ=(μi)i∈N​ assigns a strictly positive weight μi(si)\mu^i(s^i)μi(si) to every strategy; μ(s)=∏iμi(si)\mu(s)=\prod_i\mu^i(s^i)μ(s)=∏i​μi(si) is the product measure and μi(Si)\mu^i(S^i)μi(Si) the total mass. A co-measure vector γ=(γi)i∈N\gamma=(\gamma^i)_{i\in N}γ=(γi)i∈N​ assigns to player iii a strictly positive function γi(s−i)\gamma^i(s^{-i})γi(s−i) of the other players' strategies. A game ggg is

  • nonstrategic if each gi(si,s−i)g^i(s^i,s^{-i})gi(si,s−i) does not depend on sis^isi;
  • μ\muμ-normalized if ∑siμi(si)gi(si,s−i)=0\sum_{s^i}\mu^i(s^i)g^i(s^i,s^{-i})=0∑si​μi(si)gi(si,s−i)=0 for every iii and s−is^{-i}s−i;
  • γ\gammaγ-potential if some Ψ:S→R\Psi:S\to\mathbb RΨ:S→R satisfies γi(s−i)(gi(ti,s−i)−gi(si,s−i))=Ψ(ti,s−i)−Ψ(si,s−i)\gamma^i(s^{-i})\big(g^i(t^i,s^{-i})-g^i(s^i,s^{-i})\big)=\Psi(t^i,s^{-i})-\Psi(s^i,s^{-i})γi(s−i)(gi(ti,s−i)−gi(si,s−i))=Ψ(ti,s−i)−Ψ(si,s−i);
  • (μ,γ)(\mu,\gamma)(μ,γ)-harmonic if ∑i∑tiμi(ti)γi(s−i)(gi(si,s−i)−gi(ti,s−i))=0\sum_i\sum_{t^i}\mu^i(t^i)\gamma^i(s^{-i})\big(g^i(s^i,s^{-i})-g^i(t^i,s^{-i})\big)=0∑i​∑ti​μi(ti)γi(s−i)(gi(si,s−i)−gi(ti,s−i))=0 for every sss.

Games carry the inner product ⟨g1,g2⟩μ,γ=∑iμi(Si)∑sμ(s)γi(s−i)2g1i(s)g2i(s)\langle g_1,g_2\rangle_{\mu,\gamma}=\sum_i\mu^i(S^i)\sum_s\mu(s)\gamma^i(s^{-i})^2g_1^i(s)g_2^i(s)⟨g1​,g2​⟩μ,γ​=∑i​μi(Si)∑s​μ(s)γi(s−i)2g1i​(s)g2i​(s).

Duplication. Collapse each strategy set by a map πj:S′j→Sj\pi^j:S'^j\to S^jπj:S′j→Sj. The extended game is gˇj(s′)=gj(π(s′))\check g^j(s')=g^j(\pi(s'))gˇ​j(s′)=gj(π(s′)), the extended co-measure is γˇj(s′)=γj(π(s′))\check\gamma^j(s')=\gamma^j(\pi(s'))γˇ​j(s′)=γj(π(s′)), and an extension μˇ\check\muμˇ​ of μ\muμ is any strictly positive measure vector whose mass on each fibre (πj)−1(a)(\pi^j)^{-1}(a)(πj)−1(a) is μj(a)\mu^j(a)μj(a). Duplicating s1is^i_1s1i​ into s0i,s1is^i_0,s^i_1s0i​,s1i​ is the case S′i=Si∪{s0i}S'^i=S^i\cup\{s^i_0\}S′i=Si∪{s0i​}, πi(s0i)=s1i\pi^i(s^i_0)=s^i_1πi(s0i​)=s1i​, all other maps the identity: then μˇi(s0i)+μˇi(s1i)=μi(s1i)\check\mu^i(s^i_0)+\check\mu^i(s^i_1)=\mu^i(s^i_1)μˇ​i(s0i​)+μˇ​i(s1i​)=μi(s1i​).

Formalization targets

Goal: Theorem 3.15

Write (gNS,gP,gH)(g_{\text{NS}},g_{\text{P}},g_{\text{H}})(gNS​,gP​,gH​) for the (μ,γ)(\mu,\gamma)(μ,γ)-decomposition of ggg. Then

(gˇ)(μˇ,γˇ)-NS=gˇNS,(gˇ)(μˇ,γˇ)-P=gˇP,(gˇ)(μˇ,γˇ)-H=gˇH,\big(\check g\big)_{(\check\mu,\check\gamma)\text{-NS}}=\check g_{\text{NS}},\qquad \big(\check g\big)_{(\check\mu,\check\gamma)\text{-P}}=\check g_{\text{P}},\qquad \big(\check g\big)_{(\check\mu,\check\gamma)\text{-H}}=\check g_{\text{H}},(gˇ​)(μˇ​,γˇ​)-NS​=gˇ​NS​,(gˇ​)(μˇ​,γˇ​)-P​=gˇ​P​,(gˇ​)(μˇ​,γˇ​)-H​=gˇ​H​,

stated as: a triple of games on S′S'S′ is a (μˇ,γˇ)(\check\mu,\check\gamma)(μˇ​,γˇ​)-decomposition of gˇ\check ggˇ​ if and only if it equals the extension of (gNS,gP,gH)(g_{\text{NS}},g_{\text{P}},g_{\text{H}})(gNS​,gP​,gH​). Both directions are content: one is uniqueness for the extended weights, the other the invariance of each class.

The weighted decomposition: Theorem 2.11

G=(μ-NoG∩γ-PG)⊕μ,γ(μ-NoG∩(μ,γ)-HG)⊕μ,γNSG,\mathcal G=(\mu\text{-NoG}\cap\gamma\text{-PG})\oplus_{\mu,\gamma}(\mu\text{-NoG}\cap(\mu,\gamma)\text{-HG})\oplus_{\mu,\gamma}\mathsf{NSG},G=(μ-NoG∩γ-PG)⊕μ,γ​(μ-NoG∩(μ,γ)-HG)⊕μ,γ​NSG,

every game having exactly one decomposition and the three classes being pairwise orthogonal. Proposition 2.10 (G=μ-NoG⊕μ,γNSG\mathcal G=\mu\text{-NoG}\oplus_{\mu,\gamma}\mathsf{NSG}G=μ-NoG⊕μ,γ​NSG) and Lemma 3.14 (each class is preserved by extension) are the other main milestones; Lemmas A.1–A.11 and Propositions A.3–A.9 of the appendix are the intermediate steps, in the order of the milestone list.

Significance

The result says that the potential, harmonic and nonstrategic parts of a game are properties of its strategic content and not of the way it is written down, provided the measure is split among copies of a strategy and the co-measure is copied. Duplicate strategies appear whenever a game is derived from an extensive form or a mechanism, so a decomposition that is not invariant under duplication assigns different "closest potential games" to the same strategic situation. The same weighted machinery gives the paper's invariance results for relabellings and scalings and the elimination of duplicates (Theorem 3.19, derived from Theorem 3.15).

All results are proved in the paper. None of them is formalized: the platform has the uniform-weight decomposition of Candogan et al. (Theorem 4.1 of that paper, proved), with an unweighted inner product and a pseudo-inverse-based definition of the projections, but no weighted classes, no co-measures and no statement about duplication. This mission adds the weighted definitions, the weighted decomposition with its uniqueness and orthogonality, and the commutation theorem.

Difficulty

The commutation is an immediate consequence of two facts, and both carry the work. The first is uniqueness of the (μˇ,γˇ)(\check\mu,\check\gamma)(μˇ​,γˇ​)-decomposition on the larger profile set: this is Theorem 2.11 itself, whose proof needs the weighted flow operators, their adjoints and the orthogonality of normalized potential and normalized harmonic games under ⟨⋅,⋅⟩μ,γ\langle\cdot,\cdot\rangle_{\mu,\gamma}⟨⋅,⋅⟩μ,γ​. The second is that extension preserves each class, which fails for the obvious guess at the extended co-measure: the definition printed in the paper splits γj\gamma^jγj between the two copies, and with that choice a potential game's extension is no longer potential. Only the copied co-measure works. The weights also matter for normalization: without splitting μi(s1i)\mu^i(s^i_1)μi(s1i​) between the copies the extended game is not normalized.

Formalization scope

  • Players are a finite type ι, strategy sets finite types S i, profiles ∀ i, S i, and (ti,s−i)(t^i,s^{-i})(ti,s−i) is Function.update s i t. A game is ι → (∀ i, S i) → ℝ.
  • Measures are point-mass functions μ i : S i → ℝ with strict positivity as a hypothesis; a co-measure γ i is a positive function of the full profile that is invariant under changing coordinate i.
  • The standing assumptions n≥2n\ge2n≥2 and ∣Si∣≥2|S^i|\ge2∣Si∣≥2 are dropped; all statements hold without them.
  • The (μ,γ)(\mu,\gamma)(μ,γ)-decomposition is the predicate IsDecomposition μ γ g n p h in the order (nonstrategic, potential, harmonic) of (2.18); no decomposition function is defined, since Theorem 2.11 makes the triple unique.
  • Inner products are explicit finite sums: ⟨⋅,⋅⟩0\langle\cdot,\cdot\rangle_0⟨⋅,⋅⟩0​ with the unnormalized product measure, ⟨⋅,⋅⟩μ,γ\langle\cdot,\cdot\rangle_{\mu,\gamma}⟨⋅,⋅⟩μ,γ​ with the factor μi(Si)\mu^i(S^i)μi(Si), and ⟨⋅,⋅⟩1\langle\cdot,\cdot\rangle_1⟨⋅,⋅⟩1​ on flows with the factor 12\tfrac1221​. Flows are functions on pairs with a skew-symmetry hypothesis; δi∗\delta^{i*}δi∗ is defined by its explicit formula, and Proposition A.3 states that it is the adjoint.
  • Duplication is generalized to arbitrary collapsing maps π\piπ, which covers Definition 3.13 and iterated duplications. The extended co-measure is the copy γ∘π\gamma\circ\piγ∘π; the paper's printed sum version makes Lemma 3.14 and Theorem 3.15 false, and the copy is what its proof uses.
  • Lemma A.11 is stated for every solution of the normal equation δ∗δφ=δ∗D(g)\delta^*\delta\varphi=\delta^*D(g)δ∗δφ=δ∗D(g), together with existence of a solution, instead of through the Moore–Penrose pseudo-inverse.

A statement asserting only that the extended triple is a (μˇ,γˇ)(\check\mu,\check\gamma)(μˇ​,γˇ​)-decomposition of gˇ\check ggˇ​ would be Lemma 3.14 and not the goal: the goal also excludes every other decomposition.

The weighted classes, inner products and flow operators are reusable for the paper's other invariance results (relabelling, pseudo-translations, scalings, elimination of duplicates) and for weighted versions of the closest-potential-game problem. Proofs of any milestone are welcome; the appendix lemmas are independent finite linear-algebra statements.

Selected references

  • J. Abdou, N. Pnevmatikos, M. Scarsini, X. Venel, Decomposition of Games: Some Strategic Considerations, arXiv:1901.06048v2, 2020; Mathematics of Operations Research 47(1), 2022. https://arxiv.org/abs/1901.06048v2
  • O. Candogan, I. Menache, A. Ozdaglar, P. A. Parrilo, Flows and Decompositions of Games: Harmonic and Potential Games, Mathematics of Operations Research 36(3), 474–503, 2011. https://doi.org/10.1287/moor.1110.0500
  • D. Monderer, L. S. Shapley, Potential Games, Games and Economic Behavior 14(1), 124–143, 1996. https://doi.org/10.1006/game.1996.0044
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Algorithmic Game TheoryOperations ResearchProbability·Captain: mikedeng1

Search for an Immobile Hider on a Binary Tree with Unreliable Locational Information: The Value Is 2qμ + (p − q)D, Attained by the Recursive Hider Distribution and the Favoring-Bias SearcherResearch Paper

Searching a tree with unreliable directions

Search games model a Searcher looking for an object or a person whose location is chosen adversarially: a lost hiker, a mine, a hidden explosive device, a fugitive. In the classic search game on a tree, a Hider stands still at a point of a tree network, and a Searcher starts from the root and moves at unit speed. The payoff is the time to find the Hider. Gal (1979) solved this game for every tree. Its value is the total length μ\muμ of the tree, and an optimal Searcher randomizes over depth-first searches.

In practice, searchers often get hints: a witness who saw the hiker turn left, a sensor reading, a dog that picks one trail. Such hints are useful but fallible. Alpern and Lidbetter (arXiv:2303.13378, Operations Research, 2025) model this by a signal at each branching point. The signal points to the branch containing the Hider and is correct with a known probability p>12p>\tfrac12p>21​. They solve the resulting game exactly on every binary tree. The value, the optimal Hider distribution and the optimal Searcher strategy are all given in closed form by a recursion on the tree.

Timeline. Gal (1979) solved the game without signals on trees, with value μ\muμ. Biased depth-first strategies of the Searcher were introduced in other contexts by Alpern (2010) and by Alpern and Lidbetter (2014). Alpern and Lidbetter (2025) solved the game with signals of accuracy ppp, which as p→12p\to\tfrac12p→21​ reduces to Gal's.

The game G(Q,O,p)G(Q,O,p)G(Q,O,p)

A rooted tree (Q,O)(Q,O)(Q,O) is a finite tree whose arcs have positive lengths, with a distinguished root OOO. Write μ=μ(Q)\mu=\mu(Q)μ=μ(Q) for its total length and d(O,v)d(O,v)d(O,v) for the distance from the root to a node vvv. Every node other than the root has degree three (a branch node) or one (a leaf node). The root has degree one or two, and in the second case it is also a branch node. At a branch node jjj, the part of the tree beyond jjj splits into two branches Q1Q_1Q1​ and Q2Q_2Q2​.

The Hider picks a leaf node. The Searcher performs a depth-first search from OOO at unit speed. On her first arrival at a branch node jjj on the way to the Hider she receives a signal, either 1 or 2, naming the branch that contains the Hider. It is correct with probability ppp and wrong with probability q=1−pq=1-pq=1−p, where 12<p≤1\tfrac12<p\le121​<p≤1. She then chooses which branch to search first, as a function of the signal. There are four such actions: [1,1][1,1][1,1] and [2,2][2,2][2,2] ignore the signal, [1,2][1,2][1,2] follows it, and [2,1][2,1][2,1] does the opposite. If she enters the wrong branch first she searches all of it and returns, which costs twice its length. The payoff TTT is the time until she reaches the Hider. The Hider maximizes TTT and the Searcher minimizes its expectation.

The mean depth of QQQ with respect to a probability distribution λ\lambdaλ on its leaf nodes LLL is

D(Q,O,λ)=∑v∈Lλ(v) d(O,v).D(Q,O,\lambda)=\sum_{v\in L}\lambda(v)\,d(O,v).D(Q,O,λ)=v∈L∑​λ(v)d(O,v).

The Hider distribution λˉQ\bar\lambda_QλˉQ​ is defined by recursion on the tree. On a single arc it is the point mass at its leaf. At a root of degree one it is the distribution of the subtree. At a branch root with branches Q1,Q2Q_1,Q_2Q1​,Q2​ of lengths μ1,μ2\mu_1,\mu_2μ1​,μ2​, call Q1Q_1Q1​ the favored branch if DQ1≥DQ2D_{Q_1}\ge D_{Q_2}DQ1​​≥DQ2​​, where DQi=D(Qi,λˉQi)D_{Q_i}=D(Q_i,\bar\lambda_{Q_i})DQi​​=D(Qi​,λˉQi​​). Then

λˉ(v)=pμ1pμ1+qμ2 λˉQ1(v)  (v∈Q1),λˉ(v)=qμ2pμ1+qμ2 λˉQ2(v)  (v∈Q2).\bar\lambda(v)=\frac{p\mu_1}{p\mu_1+q\mu_2}\,\bar\lambda_{Q_1}(v)\ \ (v\in Q_1),\qquad \bar\lambda(v)=\frac{q\mu_2}{p\mu_1+q\mu_2}\,\bar\lambda_{Q_2}(v)\ \ (v\in Q_2).λˉ(v)=pμ1​+qμ2​pμ1​​λˉQ1​​(v)  (v∈Q1​),λˉ(v)=pμ1​+qμ2​qμ2​​λˉQ2​​(v)  (v∈Q2​).

Write DQ=D(Q,O,λˉQ)D_Q=D(Q,O,\bar\lambda_Q)DQ​=D(Q,O,λˉQ​). At a branch node with favored branch Q1Q_1Q1​, the favoring bias is

β=(p−q)(DQ1−DQ2)2(pμ1+qμ2).\beta=\frac{(p-q)(D_{Q_1}-D_{Q_2})}{2(p\mu_1+q\mu_2)}.β=2(pμ1​+qμ2​)(p−q)(DQ1​​−DQ2​​)​.

Formalization targets

Goal: Theorem 7

For every rooted tree with positive arc lengths and every 12<p≤1\tfrac12<p\le121​<p≤1:

  1. the value of G(Q,O,p)G(Q,O,p)G(Q,O,p) is
V(Q)=2qμ+(p−q)DQ;V(Q)=2q\mu+(p-q)D_Q;V(Q)=2qμ+(p−q)DQ​;
  1. λˉ\bar\lambdaλˉ is an optimal Hider strategy;
  2. the Searcher strategy that, at every branch node, searches the favored branch first with probability β\betaβ and otherwise follows the signal is optimal.

All three parts are stated, for every tree and every ppp in the model's range. Nothing is fixed to a special tree or a special ppp.

Milestones

  • V(Q)=ℓ+V(Q′)V(Q)=\ell+V(Q')V(Q)=ℓ+V(Q′) at a root of degree one (proof of Theorem 7, p. 10).
  • Proposition 6: DQ=ℓ+DQ′D_Q=\ell+D_{Q'}DQ​=ℓ+DQ′​ at a degree-one root, and DQ=pμ1DQ1+qμ2DQ2pμ1+qμ2D_Q=\dfrac{p\mu_1D_{Q_1}+q\mu_2D_{Q_2}}{p\mu_1+q\mu_2}DQ​=pμ1​+qμ2​pμ1​DQ1​​+qμ2​DQ2​​​ at a branch root with Q1Q_1Q1​ favored.
  • Table 1: at a branch root, the game has the value of a 2×42\times42×4 matrix game whose entries are the lengths and values of the two branches (pp. 10–11).
  • "Follow" dominates "opposite": the two columns differ by (2μ2(2p−1), 2μ1(2p−1))(2\mu_2(2p-1),\,2\mu_1(2p-1))(2μ2​(2p−1),2μ1​(2p−1)) (p. 11).
  • Lemma 2: the game has a value, and it is at most μ\muμ.

What the result gives

The theorem gives a complete closed-form solution of a search game with imperfect information, on every tree. The value interpolates between Gal's μ\muμ (as p→12p\to\tfrac12p→21​) and the mean depth DQD_QDQ​ (at p=1p=1p=1). It quantifies how much a signal of accuracy ppp is worth to the Searcher. It also shows that always following the signal is almost never optimal: the Searcher should sometimes go to the favored branch without looking at the signal. The paper derives explicit formulas for penultimate nodes (Corollary 8), monotonicity of the value in ppp (Proposition 9) and the value on trees of constant depth (Corollary 10) from it.

The result is proved in the paper. To our knowledge no part of it has a machine-checked proof, and Gal's theorem for the game without signals is not formalized either. This mission produces a formal model of depth-first search games on weighted trees together with the full solution of the signal game. The model consists of trees as an inductive type, leaves and branch nodes as finite types, and the expected search time of a pure strategy.

Where the difficulty lies

The natural induction on the tree has to compare games of different sizes. At a branch root, the Searcher's pure strategies on the whole tree are combinations of a root action and arbitrary pure strategies on both branches. Reducing this game to the 2×42\times42×4 matrix of Table 1 requires an argument that optimal play in the branches can be composed with the root choice in both directions. A Hider strategy must be shown to guarantee the value against every combination, not only against strategies of the β-form. The mixing weights of the Hider distribution also depend on mean depths that are themselves defined through the distribution, so the induction must carry the identity of Proposition 6 alongside the value. Solving the 2×42\times42×4 game then needs the sign of the slope (p−q)(DQ1−DQ2)(p-q)(D_{Q_1}-D_{Q_2})(p−q)(DQ1​​−DQ2​​) and Lemma 2's bound V≤μV\le\muV≤μ on each branch. A Hider distribution proportional to lengths, which is optimal without signals, is not optimal here.

Formalization scope

Trees are an inductive type RTree with constructors for a single arc, an arc leading to a subtree, and a fork with two branches. This also admits interior nodes of degree two and forks whose branches start with a fork, which is the paper's own conversion of arbitrary trees to binary trees by arcs of length zero. Every arc has positive length. Signals and branches are booleans, and an action is a map from signals to the branch searched first. A pure Searcher strategy assigns an action to every branch node. The payoff is the expected search time. Signals off the path to the Hider do not matter, so it is computed from the marginal law of each signal on the path, and no joint law is assumed. Mixed strategies are probability vectors over all pure strategies. Value and optimality are the published matrix-game notions ZeroSumLP.DantzigExt.MatrixGame (min-max and max-min strategies of a finite zero-sum game). On a tie DQ1=DQ2D_{Q_1}=D_{Q_2}DQ1​​=DQ2​​ branch 1 is favored, and then β=0\beta=0β=0.

Neither the value nor the mean depth is defined by the formulas being proved. The value is the matrix-game value of the expected-time matrix. The mean depth is the sum of Definition 3, not the recursion of Proposition 6. The Hider's optimality must hold against every pure Searcher strategy.

A complete development needs finite sums over the leaves of a tree, the expected-time recursion, minimax for the 2×42\times42×4 game, and the induction of Theorem 7. The tree model and the fact that a finite game's value composes over subtrees can be reused for other search games on trees. Contributions to any milestone, or to Gal's theorem as a by-product, are welcome.

Selected references

  • S. Alpern, T. Lidbetter, Search for an Immobile Hider on a Binary Tree with Unreliable Locational Information, Operations Research, 2025; arXiv:2303.13378v2 (2024). https://arxiv.org/abs/2303.13378
  • S. Gal, Search games with mobile and immobile hider, SIAM Journal on Control and Optimization 17(1), 99–122, 1979. https://doi.org/10.1137/0317009
  • S. Alpern, Search games on trees with asymmetric travel times, SIAM Journal on Control and Optimization 48(8), 5547–5563, 2010.
  • S. Alpern, T. Lidbetter, Searching a variable speed network, Mathematics of Operations Research 39(3), 697–711, 2014.
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