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Combinatorics

627 missions · 347 completed

The mathematics of finite and discrete structures — counting the arrangements of a set, deciding when a configuration meeting prescribed constraints can exist, and characterizing the patterns such structures are forced to contain. It encompasses enumerative and extremal combinatorics, graph theory, design theory, and additive combinatorics, with deep ties to algebra, probability, and computer science.

Missions

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AlgebraAnalysisFunctional Analysis+3·Captain: Lucas

Formal Conjectures Portfolio: Bateman-Horn and CompanionsOpen Problem

1. Motivation

Wikipedia's pages on open problems are, for many mathematicians, the first contact with a conjecture: a one-paragraph statement, a short history, a list of partial results. The Formal Conjectures library (Google DeepMind, Apache-2.0) turned a large part of that material into Lean 4 statements, so that the conjectures can be attacked — and, just as importantly, stated unambiguously — by machine.

This mission ports a coherent slice of that material to Prove2Me. It is deliberately a portfolio mission: the goal theorem is the Bateman–Horn conjecture, the strongest single statement in the collection, and the milestone list gathers the other conjectures and the landmark theorems that surround them. Some milestones are genuine steps toward the goal (the Bunyakovsky conjecture is literally the one-polynomial case); most are independent open problems from other fields, grouped here because they share a source, a level of difficulty, and a need for faithful formal statements. A reader should not assume that proving a milestone advances the goal theorem. The mission's value is that every statement in it has been written against the same Mathlib revision, checked to compile, and documented well enough to be attacked.

A rough timeline of the collection's landmarks:

  • 1947 — Mills: a real A>1A>1A>1 with ⌊A3n⌋\lfloor A^{3^n}\rfloor⌊A3n⌋ always prime.
  • 1962 — Radó: the busy beaver function outgrows every computable function.
  • 1971 — Davies: planar Kakeya sets have Hausdorff dimension 222.
  • 1978 — Apéry: ζ(3)\zeta(3)ζ(3) is irrational.
  • 1985 — Read (after Enflo, 1981): an operator on ℓ1\ell^1ℓ1 with no nontrivial closed invariant subspace.
  • 2001 — Zudilin: one of ζ(5),ζ(7),ζ(9),ζ(11)\zeta(5),\zeta(7),\zeta(9),\zeta(11)ζ(5),ζ(7),ζ(9),ζ(11) is irrational.
  • 2002 — Mihăilescu: 888 and 999 are the only consecutive perfect powers (Catalan's conjecture).
  • 2009 / 2021 — Dvir; Bukh–Chao: the finite-field Kakeya bound and its sharp density constant.
  • 2021 — Gardam: Kaplansky's unit conjecture is false (its zero-divisor and idempotent companions remain open).
  • 2024 — Saito: Mills' constant is irrational; bbchallenge: BB(5)=47 176 870\mathrm{BB}(5)=47\,176\,870BB(5)=47176870.
  • 2025 — Wang–Zahl: the Kakeya set conjecture in R3\mathbb{R}^3R3.

2. Setting

The goal theorem concerns prime values of polynomials. Fix a finite set S={f1,…,fk}⊆Z[X]S=\{f_1,\dots,f_k\}\subseteq\mathbb{Z}[X]S={f1​,…,fk​}⊆Z[X] of distinct polynomials. Say that fff satisfies the Bunyakovsky condition if its leading coefficient is positive, deg⁡f≥1\deg f\ge 1degf≥1, and fff is irreducible over Z\mathbb{Z}Z; say that SSS satisfies the Schinzel condition if for every prime ppp there is an integer nnn with p∤f1(n)⋯fk(n)p\nmid f_1(n)\cdots f_k(n)p∤f1​(n)⋯fk​(n) — i.e. no fixed prime divides the product at every argument.

For a prime ppp let ωp(S)\omega_p(S)ωp​(S) be the number of residue classes n mod pn \bmod pnmodp at which some fif_ifi​ vanishes, let D=∏ideg⁡fiD=\prod_i \deg f_iD=∏i​degfi​, and let

πS(x)=#{ n≤x:∣fi(n)∣ is prime for every i }.\pi_S(x)=\#\{\,n\le x : |f_i(n)| \text{ is prime for every } i\,\}.πS​(x)=#{n≤x:∣fi​(n)∣ is prime for every i}.

The Bateman–Horn constant is the (conditionally convergent) Euler product

C=lim⁡N→∞ ∏p<N(1−1p)−k(1−ωp(S)p).C=\lim_{N\to\infty}\ \prod_{p<N}\Big(1-\tfrac1p\Big)^{-k}\Big(1-\tfrac{\omega_p(S)}{p}\Big).C=N→∞lim​ p<N∏​(1−p1​)−k(1−pωp​(S)​).

The other groups use their own vocabulary, each fixed in a definition item of this mission: Kakeya sets in Rn\mathbb{R}^nRn and over Fq\mathbb{F}_qFq​; Mills' property ⌊A3n⌋∈P\lfloor A^{3^n}\rfloor \in \mathbb{P}⌊A3n⌋∈P; Wagstaff primes and Catalan–Mersenne numbers; polynomial self-maps and their Jacobian matrix; nontrivial closed invariant subspaces; linear extensions of a finite poset; Catalan's constant; and an explicit two-symbol Turing machine model with its maximum-shifts function BB\mathrm{BB}BB.

3. Target

The goal theorem is the Bateman–Horn asymptotic: under the Bunyakovsky and Schinzel hypotheses, CCC exists and is positive and

πS(x) ∼ CD x(log⁡x)k(x→∞).\pi_S(x)\ \sim\ \frac{C}{D}\,\frac{x}{(\log x)^{k}}\qquad (x\to\infty).πS​(x) ∼ DC​(logx)kx​(x→∞).

Weaker statements in the same direction appear as milestones, first of all Bunyakovsky's conjecture: under the same hypotheses with k=1k=1k=1, fff takes prime values infinitely often. The remaining milestones are listed in the milestone panel and are grouped by subject: Diophantine equations (Brocard, Pillai, Lebesgue–Nagell, Catalan/Mihăilescu), Mersenne-type primality (New Mersenne, infinitude of Mersenne primes, Catalan–Mersenne), prime-representing constants (Mills), geometric measure theory (Kakeya in Rn\mathbb{R}^nRn, Kakeya over Fq\mathbb{F}_qFq​, Falconer), operator theory (invariant subspace problem and Read's ℓ1\ell^1ℓ1 counterexample), group algebras (Kaplansky's zero-divisor and idempotent conjectures), affine algebraic geometry (the two-variable Jacobian conjecture), irrationality and transcendence (ζ(5)\zeta(5)ζ(5), all odd zeta values, Zudilin's theorem, e+πe+\pie+π, eπe\pieπ, γ\gammaγ, Catalan's constant), order theory (the 1/31/31/3–2/32/32/3 conjecture), and computability (Radó's theorem).

4. Significance

The results themselves. Bateman–Horn is the quantitative form of Schinzel's hypothesis H: it contains the twin prime conjecture, the infinitude of primes of the form n2+1n^2+1n2+1, and Bunyakovsky as special cases, and it is the standard heuristic behind prime-counting predictions. The other targets are each the headline question of their area: whether every bounded Hilbert-space operator has an invariant subspace; whether group algebras of torsion-free groups are domains; whether Kakeya sets must have full dimension. The solved milestones (Mihăilescu, Davies, Dvir, Zudilin, Read, Saito, Radó) are landmarks whose formal proofs would be significant library contributions in their own right.

Formalizing them. None of the open statements is expected to fall here; the concrete deliverable is a set of faithful, compiling, reusable statements plus formal proofs of the solved milestones, most of which are not in Mathlib today. Several are realistically in reach: the finite-field Kakeya bound (Dvir's polynomial method is short), the elementary fact that π+e\pi+eπ+e and πe\pi eπe cannot both be algebraic, and Radó's diagonal argument.

5. Difficulty

For Bateman–Horn, the obstruction is visible already for k=1k=1k=1, deg⁡f=2\deg f = 2degf=2: sieve methods bound πS(x)\pi_S(x)πS​(x) from above by a constant times the conjectured main term and produce almost-primes, but the parity problem blocks every known sieve from producing a single prime value of an irreducible quadratic. The conditional convergence of the Euler product is a second, smaller trap: the product over p<Np<Np<N must be taken in order, so any reformulation as an unordered infinite product changes the statement.

Each other group has its own obstruction, and they do not transfer: the parity problem says nothing about Kakeya, where the difficulty is that dimension is not stable under the natural compactness arguments, nor about the invariant subspace problem, where the known counterexamples on ℓ1\ell^1ℓ1 show that no soft argument can work.

6. Formalization scope

Conventions this mission commits to, all fixed in the definition items:

  • Polynomials are elements of ℤ[X]; primality of a polynomial value is primality of its absolute value, and the counting function ranges over natural numbers n≤⌊x⌋n \le \lfloor x\rfloorn≤⌊x⌋.
  • The Bateman–Horn constant is the limit of the ordered partial products over p<Np<Np<N, not an unordered infinite product.
  • Kakeya sets carry no compactness or measurability hypothesis, matching the source; the conjecture is stated as an equality of Hausdorff dimensions in [0,∞][0,\infty][0,∞].
  • Falconer's hypothesis is written d<2dim⁡HEd < 2\dim_H Ed<2dimH​E to avoid division in [0,∞][0,\infty][0,∞].
  • Torsion-freeness of a group is spelled out as "every element of finite order is the identity", which is the hypothesis the source intends (it is weaker than Mathlib's IsMulTorsionFree).
  • Linear extensions are order-preserving bijections onto {0,…,∣P∣−1}\{0,\dots,|P|-1\}{0,…,∣P∣−1}, and probabilities are quotients of set cardinalities in Q\mathbb{Q}Q.
  • The busy beaver model is an explicit nnn-state, 222-symbol machine with a bi-infinite Boolean tape; BB\mathrm{BB}BB counts transitions performed (maximum shifts), the halting transition included, and BB(0)=0\mathrm{BB}(0)=0BB(0)=0.
  • Several source statements are phrased as "is XXX true?" with an unknown answer. Prove2Me statements must be definite, so each such question is recorded in its affirmative form (e.g. "e+πe+\pie+π is irrational"); a solver who can refute one should submit a disproof. The one question with no statable answer, "what is BB(6)\mathrm{BB}(6)BB(6)?", is replaced by Radó's growth theorem rather than guessed at.
  • Nothing here is vacuous: each hypothesis set is satisfiable (e.g. closed unit balls are Kakeya sets, and X2+1X^2+1X2+1 satisfies the Bunyakovsky and Schinzel conditions).

Contributions welcome: proofs of the solved milestones; sharper variants; and additional faithful statements from the same source library, which contains far more than fits in one mission.

7. Selected references

  • P. T. Bateman and R. A. Horn, A heuristic asymptotic formula concerning the distribution of prime numbers, Math. Comp. 16 (1962), 363–367. DOI
  • T. Radó, On non-computable functions, Bell System Tech. J. 41 (1962), 877–884. DOI
  • R. O. Davies, Some remarks on the Kakeya problem, Math. Proc. Cambridge Philos. Soc. 69 (1971), 417–421. DOI
  • C. J. Read, A solution to the invariant subspace problem on the space ℓ1\ell_1ℓ1​, Bull. London Math. Soc. 17 (1985), 305–317. DOI
  • K. Falconer, On the Hausdorff dimensions of distance sets, Mathematika 32 (1985), 206–212. DOI
  • W. Zudilin, One of the numbers ζ(5),ζ(7),ζ(9),ζ(11)\zeta(5),\zeta(7),\zeta(9),\zeta(11)ζ(5),ζ(7),ζ(9),ζ(11) is irrational, Russian Math. Surveys 56 (2001), 774–776. DOI
  • P. Mihăilescu, Primary cyclotomic units and a proof of Catalan's conjecture, J. reine angew. Math. 572 (2004), 167–195. DOI
  • Z. Dvir, On the size of Kakeya sets in finite fields, J. Amer. Math. Soc. 22 (2009), 1093–1097. DOI
  • B. Bukh and T.-W. Chao, Sharp density bounds on the finite field Kakeya problem, Discrete Analysis 26 (2021). DOI
  • G. Gardam, A counterexample to the unit conjecture for group rings, Ann. of Math. 194 (2021), 967–979. DOI
  • K. Saito, Mills' constant is irrational, Mathematika 71 (2025), e70027. arXiv:2404.19461
  • H. Wang and J. Zahl, Volume estimates for unions of convex sets, and the Kakeya set conjecture in three dimensions, arXiv:2502.17655
  • Google DeepMind, Formal Conjectures, Apache-2.0, github.com/google-deepmind/formal-conjectures

Provenance note. The Lean statements in this mission are adaptations of the Formal Conjectures library (Apache-2.0), rewritten to depend only on Mathlib and on this mission's own definition items, and checked to compile against the platform's Mathlib revision. Each draft item carries a read-back; those read-backs are non-blind — they were written by the same agent that drafted the statements, and each says so in its first line. They are documentation, not independent testimony.

93 thms14 active usersReviewed
Graph Theory·Captain: Gabewhigham

Conway's 99-graph problemOpen Problem

Motivation

A strongly regular graph with parameters (n,k,λ,μ)(n,k,\lambda,\mu)(n,k,λ,μ) is a finite simple graph on nnn vertices in which every vertex has exactly kkk neighbours, every pair of adjacent vertices has exactly λ\lambdaλ common neighbours, and every pair of non-adjacent vertices has exactly μ\muμ common neighbours. For most parameter tuples the elementary counting and integrality conditions already decide existence; the interesting cases are those that survive every known feasibility test and still resist construction. The tuple (99,14,1,2)(99,14,1,2)(99,14,1,2) is the smallest such case in the family λ=1\lambda = 1λ=1, μ=2\mu = 2μ=2, and its existence has been open for more than fifty years. John Horton Conway offered $1000 for a resolution, as one of five problems posed at the 2014 DIMACS conference on Challenges of Identifying Integer Sequences (Conway, Five $1,000 Problems (Update 2017)).

Timeline of the problem and of what is known about it:

  • 1969/1971 — the parameter set is raised by Norman Biggs in his Southampton lectures (Finite Groups of Automorphisms, LMS Lecture Note Series 6, p. 111).
  • 1973 — Berlekamp, van Lint and Seidel construct a strongly regular graph with parameters (243,22,1,2)(243,22,1,2)(243,22,1,2) as the coset graph of the perfect ternary Golay code, settling one of the five feasible parameter tuples in this family.
  • 1975 — the existence question appears as Problem 7 (attributed to J. J. Seidel) in R. K. Guy's problem list, The Geometry of Metric and Linear Spaces, Springer LNM 490, pp. 237–238; Conway had worked on it by then.
  • 1984 — H. A. Wilbrink, On the (99,14,1,2)(99,14,1,2)(99,14,1,2) strongly regular graph, shows that such a graph cannot be vertex-transitive: no group of automorphisms can act transitively on its 99 vertices.
  • 1988 — Brouwer and Neumaier, A remark on partial linear spaces of girth 5 with an application to strongly regular graphs, Combinatorica 8, 57–61.
  • 2004 — Makhnev and Minakova, On automorphisms of strongly regular graphs with λ=1\lambda=1λ=1, μ=2\mu=2μ=2, Discrete Math. Appl. 14(2), and 2011 — Behbahani and Lam, Strongly regular graphs with non-trivial automorphisms, Discrete Math. 311, 132–144: further restrictions on the possible automorphism groups.
  • 2014/2017 — Conway's prize offer publicises the problem.

No graph with these parameters has been found, and no non-existence proof is known.

Setting

Fix a finite vertex set VVV and a simple graph ggg on VVV (irreflexive, symmetric adjacency Adj\mathrm{Adj}Adj). For vertices v,wv,wv,w write N(v)={u:Adj(v,u)}N(v) = \{u : \mathrm{Adj}(v,u)\}N(v)={u:Adj(v,u)} for the neighbourhood of vvv and N(v)∩N(w)N(v)\cap N(w)N(v)∩N(w) for the set of common neighbours. The graph ggg is strongly regular with parameters (n,k,λ,μ)(n,k,\lambda,\mu)(n,k,λ,μ), written IsSRGWith g n k λ μ\mathrm{IsSRGWith}\ g\ n\ k\ \lambda\ \muIsSRGWith g n k λ μ, when

  • ∣V∣=n|V| = n∣V∣=n;
  • ∣N(v)∣=k|N(v)| = k∣N(v)∣=k for every vertex vvv;
  • ∣N(v)∩N(w)∣=λ|N(v)\cap N(w)| = \lambda∣N(v)∩N(w)∣=λ whenever vvv and www are adjacent;
  • ∣N(v)∩N(w)∣=μ|N(v)\cap N(w)| = \mu∣N(v)∩N(w)∣=μ whenever v≠wv \neq wv=w are non-adjacent.

The case λ=1\lambda = 1λ=1 says that every edge lies in exactly one triangle — equivalently, the neighbourhood of each vertex induces a perfect matching, so such graphs are locally linear. The case μ=2\mu = 2μ=2 says that every non-adjacent pair is the pair of opposite corners of exactly one 444-cycle. Conway's problem asks for (n,k)=(99,14)(n,k) = (99,14)(n,k)=(99,14) with these two local conditions.

Counting paths of length two from a fixed vertex gives k(k−λ−1)=(n−k−1)μk(k-\lambda-1) = (n-k-1)\muk(k−λ−1)=(n−k−1)μ, which for λ=1\lambda=1λ=1, μ=2\mu=2μ=2 reduces to 2n=k2+22n = k^2 + 22n=k2+2; with k=14k = 14k=14 this yields n=99n = 99n=99. Writing AAA for the adjacency matrix, III for the identity and JJJ for the all-ones matrix, strong regularity is equivalent to the matrix identity A2=kI+λA+μ(J−I−A)A^2 = kI + \lambda A + \mu(J - I - A)A2=kI+λA+μ(J−I−A), which for (99,14,1,2)(99,14,1,2)(99,14,1,2) reads A2+A=12I+2JA^2 + A = 12I + 2JA2+A=12I+2J; the eigenvalues of AAA other than k=14k=14k=14 are then 333 and −4-4−4, and integrality of their multiplicities (545454 and 444444) is one of the feasibility conditions that (99,14,1,2)(99,14,1,2)(99,14,1,2) passes.

Formalization targets

Goal

∃ α, ∃ g a simple graph on α,IsSRGWith g 99 14 1 2.\exists\ \alpha,\ \exists\ g \text{ a simple graph on } \alpha,\quad \mathrm{IsSRGWith}\ g\ 99\ 14\ 1\ 2 .∃ α, ∃ g a simple graph on α,IsSRGWith g 99 14 1 2.

The goal is Mathlib's own proof_wanted conway_99 in Mathlib/Combinatorics/SimpleGraph/StronglyRegular.lean, stated verbatim: existence of a finite type carrying a strongly regular graph with parameters (99,14,1,2)(99,14,1,2)(99,14,1,2). A resolution in either direction is welcome — a proof settles the existence half, and a proof of the negation settles the non-existence half; the platform records the two as proof and disproof of the same statement.

Supporting targets

2n=k2+2,k even,k∈{2,4,14,22,112,994}2n = k^2 + 2, \qquad k \text{ even}, \qquad k \in \{2,4,14,22,112,994\}2n=k2+2,k even,k∈{2,4,14,22,112,994}

for every strongly regular graph with λ=1\lambda = 1λ=1, μ=2\mu = 2μ=2: the counting identity, local linearity, and the integrality restriction that cuts the family down to five non-degenerate parameter tuples.

∃ g, IsSRGWith g 9 4 1 2,∃ g, IsSRGWith g 243 22 1 2\exists\, g,\ \mathrm{IsSRGWith}\ g\ 9\ 4\ 1\ 2, \qquad \exists\, g,\ \mathrm{IsSRGWith}\ g\ 243\ 22\ 1\ 2∃g, IsSRGWith g 9 4 1 2,∃g, IsSRGWith g 243 22 1 2

the two members of the family that are known to exist: the 3×33\times 33×3 rook's graph (the Paley graph on 999 vertices) and the Berlekamp–van Lint–Seidel graph.

∣E(g)∣=693,∣{triangles of g}∣=231,A2+A=12I+2J,g not vertex-transitive|E(g)| = 693, \qquad |\{\text{triangles of } g\}| = 231, \qquad A^2 + A = 12I + 2J, \qquad g \text{ not vertex-transitive}∣E(g)∣=693,∣{triangles of g}∣=231,A2+A=12I+2J,g not vertex-transitive

structural consequences for a hypothetical 999999-graph, the last one being Wilbrink's theorem.

Significance

A (99,14,1,2)(99,14,1,2)(99,14,1,2) graph, if it exists, is a locally linear graph of maximal density in its parameter range and a partial linear space of girth 555 with 999999 points and 231231231 lines of size 333; its existence would also produce new association schemes and new examples for the general classification of strongly regular graphs. A non-existence proof would be the first case in this family ruled out by anything other than the classical feasibility conditions, and would say something new about how far local conditions (λ=1\lambda=1λ=1, μ=2\mu=2μ=2) constrain global structure.

Nothing in this mission is presently formalized. Mathlib defines SimpleGraph.IsSRGWith, proves the counting identity IsSRGWith.param_eq, the complement rule IsSRGWith.compl, and the matrix identity IsSRGWith.matrix_eq, and records the 999999-graph problem as a proof_wanted. The supporting targets are of three kinds: results that are proved in the literature and only need formalizing (existence at (9,4,1,2)(9,4,1,2)(9,4,1,2) and (243,22,1,2)(243,22,1,2)(243,22,1,2); Wilbrink's non-vertex-transitivity; the integrality restriction on kkk); routine consequences that supply reusable infrastructure (edge and triangle counts, the spectral identity, evenness of kkk); and the goal itself, which is open mathematics.

Difficulty

The obvious approaches fail for concrete reasons. Exhaustive search is out of range: the graph has 693693693 edges among (992)=4851\binom{99}{2} = 4851(299​)=4851 pairs, and no isomorph-free generation of locally linear graphs on 999999 vertices is feasible. Algebraic constructions are blocked by Wilbrink's theorem — the graph cannot be vertex-transitive, so it is not a Cayley graph and cannot be produced by the group-theoretic constructions that yield most known strongly regular graphs, including the two that work at (9,4,1,2)(9,4,1,2)(9,4,1,2) and (243,22,1,2)(243,22,1,2)(243,22,1,2). On the non-existence side, every classical feasibility test (the counting identity, integrality of the eigenvalue multiplicities, the Krein conditions, the absolute bound) is passed by (99,14,1,2)(99,14,1,2)(99,14,1,2), so a proof of non-existence needs an argument that does not factor through the parameters alone.

Formalization scope

All statements are phrased with Mathlib's SimpleGraph.IsSRGWith on a Fintype vertex type with DecidableRel adjacency, and use Fintype.card, SimpleGraph.edgeFinset, SimpleGraph.cliqueFinset 3 (triangles as 333-cliques), SimpleGraph.adjMatrix over Z\mathbb{Z}Z, and graph isomorphisms g ≃g g for automorphisms. The goal quantifies over α : Type together with a Fintype α instance, so the vertex set is finite by construction and the empty type does not satisfy the cardinality clause; the statement is therefore not vacuously satisfiable. Note that Mathlib's definition constrains λ\lambdaλ only through pairs that are actually adjacent and μ\muμ only through pairs that are actually distinct and non-adjacent, so degenerate small graphs (the one-vertex graph, K3K_3K3​) do satisfy IsSRGWith with λ=1\lambda = 1λ=1, μ=2\mu = 2μ=2; the supporting statements carry the cardinality hypotheses (0<n0 < n0<n, 1<n1 < n1<n) that exclude them where needed, and the degenerate degree k=2k = 2k=2 is listed explicitly in the classification of feasible degrees.

Infrastructure a complete development needs, and which is reusable beyond this mission: interface lemmas for counting common neighbours in a strongly regular graph; the spectral theory of the adjacency matrix (multiplicities of the two non-principal eigenvalues, and their integrality), which is the missing ingredient for the classification of feasible degrees; a Lean construction of the perfect ternary Golay code and its coset graph, for the (243,22,1,2)(243,22,1,2)(243,22,1,2) case; and decision procedures for strong regularity of an explicitly given small graph, for the (9,4,1,2)(9,4,1,2)(9,4,1,2) case. Contributions to any of these are welcome, as are partial non-existence results (for instance, restrictions on automorphisms of prime order) submitted as separate statements.

Selected references

  • N. Biggs, Finite Groups of Automorphisms: Course Given at the University of Southampton, October–December 1969, London Mathematical Society Lecture Note Series 6, Cambridge University Press, 1971, p. 111.
  • E. R. Berlekamp, J. H. van Lint, J. J. Seidel, A strongly regular graph derived from the perfect ternary Golay code, in: A Survey of Combinatorial Theory, North-Holland, 1973, pp. 25–30.
  • R. K. Guy, Problems, in: The Geometry of Metric and Linear Spaces, Springer Lecture Notes in Mathematics 490, 1975, pp. 233–244 (Problem 7, J. J. Seidel, pp. 237–238). doi:10.1007/BFb0081147
  • H. A. Wilbrink, On the (99,14,1,2)(99,14,1,2)(99,14,1,2) strongly regular graph, in: Papers dedicated to J. J. Seidel, EUT Report 84-WSK-03, Eindhoven University of Technology, 1984, pp. 342–355. PDF
  • A. E. Brouwer, A. Neumaier, A remark on partial linear spaces of girth 5 with an application to strongly regular graphs, Combinatorica 8 (1988), 57–61. doi:10.1007/BF02122552
  • A. A. Makhnev, I. M. Minakova, On automorphisms of strongly regular graphs with λ=1\lambda=1λ=1, μ=2\mu=2μ=2, Discrete Mathematics and Applications 14 (2004), no. 2. doi:10.1515/156939204872374
  • M. Behbahani, C. Lam, Strongly regular graphs with non-trivial automorphisms, Discrete Mathematics 311 (2011), 132–144. doi:10.1016/j.disc.2010.10.005
  • J. H. Conway, Five $1,000 Problems (Update 2017), OEIS. PDF
24 thms7 active usersReviewed
Operations ResearchProbability·Captain: Shuze Chen

The Komlos ConjectureOpen Problem

Motivation

Discrepancy theory asks how evenly a collection of objects can be split into two parts. Its central open question is a conjecture of Komlós, first circulated in the 1980s: any finite family of vectors of Euclidean length at most one can be signed ±1\pm 1±1 so that the signed sum is bounded in every coordinate by a universal constant — independent of how many vectors there are and of the dimension they live in.

Timeline

  • 1963. Steinitz-type vector balancing questions circulate; Bárány and Grinberg later (1981) show any norm admits a dimension-dependent bound 2d2d2d, setting the theme: how much of the dependence on dimension is real?
  • 1981. Beck and Fiala (Discrete Appl. Math.) prove degree-ttt set systems have discrepancy at most 2t−12t - 12t−1, by the floating-colors argument, and conjecture O(t)O(\sqrt{t})O(t​).
  • 1980s. Komlós poses the vector form — unit ℓ2\ell^2ℓ2-norm columns, constant ℓ∞\ell^\inftyℓ∞ discrepancy — which implies the Beck–Fiala conjecture; it circulates through Spencer's Ten Lectures (1987) as the central open problem of the area.
  • 1985. Spencer (Trans. AMS) proves "six standard deviations suffice": discrepancy 6n6\sqrt{n}6n​ for nnn sets on nnn points, beating random signing via the partial-coloring method.
  • 1998. Banaszczyk (Random Struct. Algorithms) proves the Komlós bound O(log⁡n)O(\sqrt{\log n})O(logn​) by a recursive Gaussian-measure argument over convex bodies.
  • 2010–2016. The constructive era: Bansal (2010) makes Spencer algorithmic by SDP random walks, Lovett and Meka (2012) simplify, and Bansal, Dadush, and Garg (STOC 2016) give a polynomial-time algorithm matching Banaszczyk's bound.
  • 2023. Kunisky (SIAM J. Discrete Math.) constructs instances from unsatisfiable formulas with discrepancy approaching 1+21+\sqrt{2}1+2​ — the strongest lower bound on the conjectured constant.
  • 2025. Bansal and Jiang (arXiv:2508.03961) break the Banaszczyk barrier: O~((log⁡n)1/4)\tilde{O}((\log n)^{1/4})O~((logn)1/4) for Komlós, and the Beck–Fiala conjecture resolved for t≥log⁡2nt \ge \log^2 nt≥log2n — the first movement in nearly thirty years. The gap between 2.414…2.414\ldots2.414… and O~((log⁡n)1/4)\tilde{O}((\log n)^{1/4})O~((logn)1/4) is the conjecture.

Setting

Fix nnn vectors v1,…,vn∈Rmv_1, \dots, v_n \in \mathbb{R}^mv1​,…,vn​∈Rm with Euclidean norm ∥vi∥2≤1\lVert v_i \rVert_2 \le 1∥vi​∥2​≤1. A sign vector is an ε∈{−1,+1}n\varepsilon \in \{-1, +1\}^nε∈{−1,+1}n: one sign εi∈{±1}\varepsilon_i \in \{\pm 1\}εi​∈{±1} per vector. Writing vijv_{ij}vij​ for the jjj-th coordinate of the vector viv_ivi​, the discrepancy of the family under ε\varepsilonε is the largest coordinate, in absolute value, of the signed sum ∑iεivi\sum_i \varepsilon_i v_i∑i​εi​vi​ — that is, max⁡j≤m∣∑i≤nεivij∣\max_{j \le m} \lvert \sum_{i \le n} \varepsilon_i v_{ij} \rvertmaxj≤m​∣∑i≤n​εi​vij​∣, the ℓ∞\ell^\inftyℓ∞ norm of the signed sum. The Komlós property at constant KKK — KomlosBound K — says that every such family, in every nnn and every mmm, admits a sign vector with every coordinate of the signed sum at most KKK in absolute value.

Set systems embed as the special case of 0/10/10/1-incidence matrices: if AAA is an m×nm \times nm×n matrix of 000s and 111s in which every column has at most ttt ones (every element lies in at most ttt sets), the columns scaled by 1/t1/\sqrt{t}1/t​ have norm at most one, so the Komlós property gives discrepancy KtK\sqrt{t}Kt​ — the Beck–Fiala conjecture.

Formalization targets

Goal — the Komlós conjecture

∃ K∈R:every v1,…,vn∈Rm with ∥vi∥2≤1 admits ε∈{±1}n with max⁡j∣∑iεivij∣≤K.\exists\, K \in \mathbb{R}: \quad \text{every } v_1, \dots, v_n \in \mathbb{R}^m \text{ with } \lVert v_i\rVert_2 \le 1 \text{ admits } \varepsilon \in \{\pm 1\}^n \text{ with } \max_j \Big|\sum_i \varepsilon_i v_{ij}\Big| \le K.∃K∈R:every v1​,…,vn​∈Rm with ∥vi​∥2​≤1 admits ε∈{±1}n with jmax​​i∑​εi​vij​​≤K.

The goal fixes no value of KKK: any finite universal constant settles it, so the statement survives every improvement in the constant.

Milestones — the known ladder

Eight results over the same definitions: Beck–Fiala's 2t−12t - 12t−1 for degree-ttt set systems; Spencer's 6n6\sqrt{n}6n​ for nnn sets on nnn points; Banaszczyk's O(log⁡n)O(\sqrt{\log n})O(logn​) for the Komlós setting; its corollary O(tlog⁡n)O(\sqrt{t \log n})O(tlogn​) for set systems; the reduction "Komlós at KKK implies Beck–Fiala at KtK\sqrt{t}Kt​"; Kunisky's lower bound K≥1+2K \ge 1 + \sqrt{2}K≥1+2​; and the two 2025 Bansal–Jiang breakthroughs — O~((log⁡n)1/4)\tilde{O}((\log n)^{1/4})O~((logn)1/4) for the Komlós setting, and the Beck–Fiala conjecture's bound O(t)O(\sqrt{t})O(t​) in the regime t=Ω(log⁡2n)t = \Omega(\log^2 n)t=Ω(log2n).

Significance

The conjecture is the meeting point of the two main techniques of discrepancy theory — partial coloring and the Gaussian/convex-geometric method — and each further improvement has forced a new technique into existence. A proof would resolve the Beck–Fiala conjecture in full and sharpen the hereditary-discrepancy landscape; a disproof would break the widely-shared expectation that vector balancing is dimension-free. The problem is also a benchmark for algorithmic discrepancy: every known bound now has a polynomial-time counterpart, and the constructive tools built for it (random-walk roundings, spectral partial colorings) are used across approximation algorithms and ranging into differential privacy.

None of this literature is formalized anywhere; Mathlib has no discrepancy theory at all. The definitions here are elementary — finite sums, absolute values, one norm hypothesis — so the mission's entry cost is unusually low for an open-problem mission: the Beck–Fiala theorem and the scaling reduction are self-contained finite combinatorics, while Spencer and Banaszczyk each force a genuinely new proof technique (pigeonhole partial coloring; Gaussian measure on convex bodies) into Lean.

Difficulty

Random signs lose: they give Θ(n)\Theta(\sqrt{n})Θ(n​), not a constant, so the naive probabilistic argument is ruled out from the start. The Beck–Fiala argument caps discrepancy by degree, not by norm, and provably cannot be pushed below 2t−O(1)2t - O(1)2t−O(1) by its own bookkeeping. Partial coloring alone loses a logarithm through its iteration, and Banaszczyk's method is blocked at log⁡n\sqrt{\log n}logn​ by the Gaussian measure of the cube. The 2025 advance decouples the two methods but still pays iterated polylogarithmic factors. Nothing currently known contracts the remaining gap to a constant, and the lower bound says the constant, if it exists, is at least 1+21 + \sqrt{2}1+2​ — so any proof must handle instances strictly harder than the set-system case.

Formalization scope

The Lean model commits to: vectors as EuclideanSpace ℝ (Fin m), whose norm is the ℓ2\ell^2ℓ2 norm (the hypothesis ∥vi∥≤1\lVert v_i \rVert \le 1∥vi​∥≤1 reads ‖v i‖ ≤ 1); the ℓ∞\ell^\inftyℓ∞ conclusion written coordinatewise as ∀ j, |∑ i, ε i * v i j| ≤ K, avoiding any auxiliary sup-norm structure; sign vectors as real vectors with ε i = 1 ∨ ε i = -1; and set systems as matrices A : Fin m → Fin n → ℝ with an entrywise 0/10/10/1 hypothesis and column-degree counted by Set.ncard. Quantifier order matters everywhere: in KomlosBound K the constant is fixed before nnn and mmm — a KKK depending on nnn would make the statement the trivial n\sqrt{n}n​ bound. In beck_fiala the hypothesis t≥1t \ge 1t≥1 is required (the degree-000 system has discrepancy 0>2t−10 > 2t-10>2t−1 otherwise); the Banaszczyk-form bounds use log⁡(n+2)\log(n+2)log(n+2) so that the bound is positive already at n≤1n \le 1n≤1. In the Bansal–Jiang milestones the asymptotic O~\tilde{O}O~ and Ω\OmegaΩ are rendered by existential constants quantified before all instances: the hidden poly(log⁡log⁡n)\mathrm{poly}(\log\log n)poly(loglogn) factor becomes (log⁡log⁡(n+8))γ(\log\log(n+8))^{\gamma}(loglog(n+8))γ for some fixed γ>0\gamma > 0γ>0 (the inner shift +8+8+8 keeps the iterated logarithm positive), and the threshold t=Ω(log⁡2n)t = \Omega(\log^2 n)t=Ω(log2n) becomes C0log⁡2(n+2)≤tC_0 \log^2(n+2) \le tC0​log2(n+2)≤t for some fixed C0>0C_0 > 0C0​>0.

Welcome contributions: any milestone in any order — beck_fiala and komlos_implies_beck_fiala are self-contained finite arguments and the natural entry points; spencer_six_deviations and banaszczyk_bound each import a major technique; komlos_lower_bound needs an explicit construction and a case analysis over all sign vectors. Reusable infrastructure — partial colorings, Gaussian measure bounds for convex bodies, hereditary discrepancy — is welcome as platform theorems. The matrix Spencer conjecture, prefix discrepancy, and the Steinitz problem are related but deliberately left to future missions.

Selected references

  • J. Beck, T. Fiala, "Integer-making" theorems, Discrete Applied Mathematics 3 (1981). doi:10.1016/0166-218X(81)90022-6
  • J. Spencer, Six standard deviations suffice, Trans. Amer. Math. Soc. 289 (1985). doi:10.1090/S0002-9947-1985-0784009-0
  • W. Banaszczyk, Balancing vectors and Gaussian measures of n-dimensional convex bodies, Random Structures & Algorithms 12 (1998). doi link
  • N. Bansal, D. Dadush, S. Garg, An algorithm for Komlós conjecture matching Banaszczyk's bound, FOCS 2016 / SIAM J. Comput. arXiv:1605.02882
  • N. Bansal, H. Jiang, Decoupling via affine spectral-independence: Beck–Fiala and Komlós bounds beyond Banaszczyk, 2025. arXiv:2508.03961
  • D. Kunisky, The discrepancy of unsatisfiable matrices and a lower bound for the Komlós conjecture constant, SIAM J. Discrete Math. 37 (2023). arXiv:2111.02974
  • B. Chazelle, The Discrepancy Method, Cambridge University Press, 2000. author's page
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Number Theory·Captain: Lucas

Erdős Problem 3: arithmetic progressions in sets with divergent reciprocal sumOpen Problem

Motivation

Which sets of positive integers are forced to contain long arithmetic progressions? Van der Waerden (1927) showed that in any finite colouring of N\mathbb NN some colour class does; Erdős and Turán (1936) asked for a density version, which became Szemerédi's theorem. Erdős then proposed the strongest natural size condition: divergence of the reciprocal sum. Erdős Problem #3 (erdosproblems.com/3) asks whether every A⊆NA\subseteq\mathbb NA⊆N with ∑n∈A1/n=∞\sum_{n\in A}1/n=\infty∑n∈A​1/n=∞ contains arbitrarily long arithmetic progressions. Erdős attached one of his largest prizes to it. The primes are the motivating example: ∑p1/p=∞\sum_p 1/p=\infty∑p​1/p=∞, so a positive answer would contain the Green–Tao theorem.

Timeline.

  • 1936 — Erdős and Turán conjecture that sets of positive density contain arbitrarily long progressions.
  • 1953 — Roth proves the case k=3k=3k=3 of the density conjecture by Fourier analysis.
  • 1975 — Szemerédi proves the density conjecture for all kkk.
  • 2001 — Gowers gives the first quantitative bounds for all kkk: rk(N)≪N/(log⁡log⁡N)ckr_k(N)\ll N/(\log\log N)^{c_k}rk​(N)≪N/(loglogN)ck​.
  • 2008 — Green and Tao prove that the primes contain arbitrarily long progressions.
  • 2020 — Bloom and Sisask prove r3(N)≪N/(log⁡N)1+cr_3(N)\ll N/(\log N)^{1+c}r3​(N)≪N/(logN)1+c, which settles the case k=3k=3k=3 of Erdős Problem #3.
  • 2023 — Kelley and Meka prove r3(N)≤Nexp⁡(−c(log⁡N)1/12)r_3(N)\le N\exp(-c(\log N)^{1/12})r3​(N)≤Nexp(−c(logN)1/12).
  • 2024 — Leng, Sah and Sawhney prove rk(N)≤Nexp⁡(−(log⁡log⁡N)ck)r_k(N)\le N\exp(-(\log\log N)^{c_k})rk​(N)≤Nexp(−(loglogN)ck​) for every k≥5k\ge5k≥5.

The problem is open for every k≥4k\ge4k≥4.

Setting

A set S⊆NS\subseteq\mathbb NS⊆N is an arithmetic progression of length kkk if ∣S∣=k|S|=k∣S∣=k and S={a,a+d,…,a+(k−1)d}S=\{a,a+d,\dots,a+(k-1)d\}S={a,a+d,…,a+(k−1)d} for some a,d∈Na,d\in\mathbb Na,d∈N (for k≥2k\ge2k≥2 the size condition forces d>0d>0d>0). For k,N∈Nk,N\in\mathbb Nk,N∈N, rk(N)r_k(N)rk​(N) denotes the largest size of a subset of {1,…,N}\{1,\dots,N\}{1,…,N} containing no arithmetic progression of length kkk. A set AAA has divergent reciprocal sum if ∑n∈A1/n=∞\sum_{n\in A}1/n=\infty∑n∈A​1/n=∞.

Formalization targets

Goal (Erdős Problem #3)

For every A⊆NA\subseteq\mathbb NA⊆N,

∑n∈A1n=∞ ⟹ A contains arithmetic progressions of arbitrarily large length.\sum_{n\in A}\frac1n=\infty\ \Longrightarrow\ A\ \text{contains arithmetic progressions of arbitrarily large length}.n∈A∑​n1​=∞ ⟹ A contains arithmetic progressions of arbitrarily large length.

This is the formal-conjectures statement erdos_3 with its answer(sorry) instantiated to the conjectured answer yes. A disproof of the goal on the platform settles the problem negatively.

Milestones

  • Szemerédi's theorem for sets of positive upper density (the density case), and the existing platform statement rk(N)=o(N)r_k(N)=o(N)rk​(N)=o(N).
  • The Green–Tao theorem (the case A=A=A= primes).
  • The Bloom–Sisask bound on r3(N)r_3(N)r3​(N) and its corollary, the case k=3k=3k=3 of the goal; the Kelley–Meka bound (existing platform statement).
  • The Leng–Sah–Sawhney bound for k≥5k\ge5k≥5.
  • The partial-summation reduction: bounds rk(N)≤N/(log⁡N)1+ckr_k(N)\le N/(\log N)^{1+c_k}rk​(N)≤N/(logN)1+ck​ for all k≥3k\ge3k≥3 imply the goal.

Significance

A positive answer would be a common strengthening of Szemerédi's theorem and the Green–Tao theorem, obtained from a single size condition with no arithmetic structure. Through the reduction milestone, it is closely tied to the quantitative theory of rk(N)r_k(N)rk​(N): bounds of the shape N/(log⁡N)1+cN/(\log N)^{1+c}N/(logN)1+c for every kkk would suffice. Formalizing the milestones would also give reusable Lean statements of Szemerédi-type theorems in a common language.

Difficulty

Divergence of ∑1/n\sum 1/n∑1/n is a very weak condition: such sets can have density zero, and the natural approach through rk(N)r_k(N)rk​(N) requires bounds just past N/log⁡NN/\log NN/logN. For k=3k=3k=3 this barrier was only broken in 2020. For k≥4k\ge4k≥4 the best known bounds (Leng–Sah–Sawhney) save only a power of log⁡log⁡N\log\log NloglogN in the exponent, far from what is needed. The Green–Tao method uses pseudorandom majorants specific to the primes and does not apply to arbitrary sets.

Formalization scope

All statements import the published definition file Erdos142Basic, which reproduces the formal-conjectures definitions IsAPOfLengthWith, IsAPOfLength and the counting function r k N (over {1,…,N}\{1,\dots,N\}{1,…,N}). The reciprocal-sum hypothesis is ¬ Summable (fun a : A ↦ 1 / (a : ℝ)); the element 000, if present, contributes 1/0=01/0=01/0=0. "Arbitrarily long" is written as ∃ᶠ k in atTop, which is equivalent to "every length" because sub-progressions of progressions are progressions. Bounds stated in the literature with ≪\ll≪ are written without a multiplicative constant and with "for all sufficiently large NNN"; the constant can be absorbed into the exponent. Contributions formalizing partial summation over sets of naturals and the equivalence of the "frequently" and "for every kkk" forms are welcome.

Selected references

  • P. Erdős and P. Turán, On some sequences of integers, J. London Math. Soc. 11 (1936).
  • K. F. Roth, On certain sets of integers, J. London Math. Soc. 28 (1953).
  • E. Szemerédi, On sets of integers containing no k elements in arithmetic progression, Acta Arith. 27 (1975).
  • W. T. Gowers, A new proof of Szemerédi's theorem, Geom. Funct. Anal. 11 (2001).
  • B. Green and T. Tao, The primes contain arbitrarily long arithmetic progressions, Ann. of Math. 167 (2008).
  • T. F. Bloom and O. Sisask, Breaking the logarithmic barrier in Roth's theorem on arithmetic progressions, arXiv:2007.03528 (2020).
  • Z. Kelley and R. Meka, Strong bounds for 3-progressions, FOCS 2023, arXiv:2302.05537.
  • J. Leng, A. Sah and M. Sawhney, Improved bounds for Szemerédi's theorem, arXiv:2402.17995 (2024).
  • T. F. Bloom, Erdős Problem #3, https://www.erdosproblems.com/3
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Captain: Lucas

Erdős Problem 77: the limit of R(k)^(1/k)Open Problem

Motivation

The diagonal Ramsey number R(k)R(k)R(k) is the least nnn such that every red/blue colouring of the edges of the complete graph KnK_nKn​ contains a monochromatic copy of KkK_kKk​. Ramsey's theorem guarantees that R(k)R(k)R(k) is finite; the question of how fast it grows is one of the central problems of extremal and probabilistic combinatorics. Erdős asked repeatedly ([Er88], [Er93]; see erdosproblems.com/77) for the value of

lim⁡k→∞R(k)1/k.\lim_{k\to\infty} R(k)^{1/k}.k→∞lim​R(k)1/k.

It is not even known whether this limit exists.

Timeline.

  • 1935 — Erdős and Szekeres prove R(k)≤(2k−2k−1)R(k)\le\binom{2k-2}{k-1}R(k)≤(k−12k−2​), so R(k)≤4kR(k)\le 4^{k}R(k)≤4k and lim sup⁡kR(k)1/k≤4\limsup_k R(k)^{1/k}\le 4limsupk​R(k)1/k≤4 ([ES35]).
  • 1947 — Erdős proves R(k)>2k/2R(k) > 2^{k/2}R(k)>2k/2 for k≥3k\ge 3k≥3 by a counting (probabilistic) argument, so lim inf⁡kR(k)1/k≥2\liminf_k R(k)^{1/k}\ge\sqrt2liminfk​R(k)1/k≥2​ ([Er47]).
  • 1975 — Spencer improves the lower bound by a factor of 222: R(k)≥(1+o(1))2e k 2k/2R(k)\ge(1+o(1))\frac{\sqrt2}{e}\,k\,2^{k/2}R(k)≥(1+o(1))e2​​k2k/2 ([Sp75]). The exponential base 2\sqrt22​ has not been improved since.
  • 2009, 2023 — Conlon ([Co09]) and then Sah ([Sa23]) obtain super-polynomial savings over 4k4^k4k, but still with exponential base 444.
  • 2023 — Campos, Griffiths, Morris and Sahasrabudhe prove R(k)≤(4−ε)kR(k)\le(4-\varepsilon)^kR(k)≤(4−ε)k for some constant ε>0\varepsilon>0ε>0 and all large kkk: the first exponential improvement on the upper bound ([CGMS23]).
  • 2024 — Gupta, Ndiaye, Norin and Wei optimise the CGMS method and obtain R(k)≤3.8k+o(k)R(k)\le 3.8^{k+o(k)}R(k)≤3.8k+o(k) ([GNNW24]). Balister et al. extend exponential improvements to the multicolour setting ([BBCGHMST24]).

So today, if the limit exists, it lies in [2, 3.8][\sqrt2,\,3.8][2​,3.8].

Setting

For n∈Nn\in\mathbb Nn∈N consider simple graphs GGG on the vertex set {0,…,n−1}\{0,\dots,n-1\}{0,…,n−1}. A red/blue colouring of the edges of KnK_nKn​ is the same as such a graph GGG (the red edges) together with its complement GcG^{c}Gc (the blue edges). A kkk-clique of GGG is a set of exactly kkk vertices, any two of which are adjacent in GGG. Define

R(k)=min⁡{ n∈N: every graph G on n vertices has a k-clique in G or in Gc }.R(k)=\min\bigl\{\,n\in\mathbb N:\ \text{every graph } G \text{ on } n \text{ vertices has a } k\text{-clique in } G \text{ or in } G^{c}\,\bigr\}.R(k)=min{n∈N: every graph G on n vertices has a k-clique in G or in Gc}.

In the Lean development this is Erdos77.diagonalRamsey k. Small values: R(0)=0R(0)=0R(0)=0, R(1)=1R(1)=1R(1)=1, R(2)=2R(2)=2R(2)=2, R(3)=6R(3)=6R(3)=6, R(4)=18R(4)=18R(4)=18.

Formalization targets

Goal: existence of the limit

∃ L∈R:R(k)1/k ⟶ L(k→∞).\exists\,L\in\mathbb R:\qquad R(k)^{1/k}\ \longrightarrow\ L\qquad (k\to\infty).∃L∈R:R(k)1/k ⟶ L(k→∞).

The original problem asks for the value of the limit, which is unknown; a goal with a hard-coded value cannot be stated honestly. The goal therefore asserts only that the limit exists (as a real number). Determining LLL remains the ultimate aim; any proof of a specific value would in particular prove this goal.

Milestones (results from the literature)

  1. Erdős 1947: R(k)>2k/2R(k)>2^{k/2}R(k)>2k/2 for all k≥3k\ge 3k≥3.
  2. Spencer 1975: for every ε>0\varepsilon>0ε>0, eventually R(k)≥(1−ε)2e k 2k/2R(k)\ge(1-\varepsilon)\frac{\sqrt2}{e}\,k\,2^{k/2}R(k)≥(1−ε)e2​​k2k/2.
  3. Erdős–Szekeres 1935: R(k)≤(2k−2k−1)R(k)\le\binom{2k-2}{k-1}R(k)≤(k−12k−2​) for all k≥1k\ge1k≥1.
  4. Campos–Griffiths–Morris–Sahasrabudhe 2023: there is ε>0\varepsilon>0ε>0 with R(k)≤(4−ε)kR(k)\le(4-\varepsilon)^kR(k)≤(4−ε)k for all sufficiently large kkk.
  5. Gupta–Ndiaye–Norin–Wei 2024: for every δ>0\delta>0δ>0, eventually R(k)≤3.8(1+δ)kR(k)\le 3.8^{(1+\delta)k}R(k)≤3.8(1+δ)k, i.e. R(k)≤3.8k+o(k)R(k)\le 3.8^{k+o(k)}R(k)≤3.8k+o(k).

Significance

The result itself. Existence of the limit would say that diagonal Ramsey numbers have a well-defined exponential growth rate — a regularity statement that is currently unknown in either direction. Even the bounds 2≤lim inf⁡\sqrt2\le\liminf2​≤liminf and lim sup⁡≤3.8\limsup\le 3.8limsup≤3.8 are the products of decades of work, and the lower bound base 2\sqrt22​ has resisted improvement since 1947.

Formalizing it. The goal is open. The milestones are proved results in the literature; the classical ones (Erdős–Szekeres, Erdős 1947) are natural first formalization targets, and the recent upper bounds (CGMS, GNNW) are substantial formalization projects in their own right. The status of existing machine-checked formalizations of these results is not asserted here.

Difficulty

There is no known sub- or super-multiplicativity for R(k)R(k)R(k) that would give existence of the limit via Fekete's lemma: the natural product constructions relate R(kℓ)R(k\ell)R(kℓ) to R(k)R(k)R(k) and R(ℓ)R(\ell)R(ℓ) only with losses that are too large, and the best lower and upper bounds come from entirely different methods (random colourings versus the book algorithm), so neither side controls the other.

Formalization scope

  • R(k)R(k)R(k) is defined as an infimum over nnn of the property "every graph on Fin n\mathrm{Fin}\,nFinn has a kkk-clique in GGG or in GcG^{c}Gc". Lean's sInf of an empty set of naturals is 000; the Erdős–Szekeres milestone shows the set is nonempty, so the infimum is the genuine Ramsey number.
  • R(k)1/kR(k)^{1/k}R(k)1/k is the real power of the real number R(k)R(k)R(k) with exponent 1/k1/k1/k; the value at k=0k=0k=0 is irrelevant for the limit.
  • The limit is required to be a real number LLL; given the known bounds this loses nothing.
  • Asymptotic statements ("for all sufficiently large kkk") are expressed with the atTop filter on N\mathbb NN; "o(k)o(k)o(k)" in GNNW is encoded as "for every δ>0\delta>0δ>0, eventually with exponent (1+δ)k(1+\delta)k(1+δ)k".
  • Needed infrastructure: basic Ramsey theory for graphs on Fin n, binomial estimates, the probabilistic method for the lower bounds (Spencer uses the Lovász Local Lemma), and the CGMS book algorithm for the upper bounds. All of these are reusable beyond this mission.

Selected references

  • [Er47] P. Erdős, Some remarks on the theory of graphs, Bull. Amer. Math. Soc. 53 (1947), 292–294. https://doi.org/10.1090/S0002-9904-1947-08785-1
  • [ES35] P. Erdős and G. Szekeres, A combinatorial problem in geometry, Compositio Math. 2 (1935), 463–470. http://www.numdam.org/item/CM_1935__2__463_0/
  • [Sp75] J. Spencer, Ramsey's theorem — a new lower bound, J. Combin. Theory Ser. A 18 (1975), 108–115. https://doi.org/10.1016/0097-3165(75)90071-0
  • [Co09] D. Conlon, A new upper bound for diagonal Ramsey numbers, Ann. of Math. 170 (2009), 941–960. https://doi.org/10.4007/annals.2009.170.941
  • [Sa23] A. Sah, Diagonal Ramsey via effective quasirandomness, Duke Math. J. 172 (2023). https://arxiv.org/abs/2005.09251
  • [CGMS23] M. Campos, S. Griffiths, R. Morris, J. Sahasrabudhe, An exponential improvement for diagonal Ramsey, arXiv:2303.09521 (2023). https://arxiv.org/abs/2303.09521
  • [GNNW24] P. Gupta, N. Ndiaye, S. Norin, L. Wei, Optimizing the CGMS upper bound on Ramsey numbers, arXiv:2407.19026 (2024). https://arxiv.org/abs/2407.19026
  • [BBCGHMST24] P. Balister, B. Bollobás, M. Campos, S. Griffiths, E. Hurley, R. Morris, J. Sahasrabudhe, M. Tiba, Upper bounds for multicolour Ramsey numbers, arXiv:2410.17197 (2024). https://arxiv.org/abs/2410.17197
  • [Er88] P. Erdős, Problems and results in combinatorial analysis and graph theory, Discrete Math. 72 (1988), 81–92.
  • [Er93] P. Erdős, Some of my favorite solved and unsolved problems in graph theory, Quaestiones Math. 16 (1993), 333–350.
  • Erdős Problems, Problem #77. https://www.erdosproblems.com/77
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Captain: Lucas

Erdős Problem 20: The Sunflower ConjectureOpen Problem

Motivation

A sunflower (also called a Δ\DeltaΔ-system) with kkk petals is a family of kkk sets whose pairwise intersections are all equal to one common set, the kernel. In 1960 Erdős and Rado proved the sunflower lemma: every sufficiently large family of nnn-element sets contains a sunflower with kkk petals, and they asked how large "sufficiently large" must be (Erdős–Rado 1960). The conjecture that the threshold is only exponential in nnn is one of Erdős' best-known problems in extremal combinatorics; it is listed as Erdős Problem 20, and Erdős offered a $1000 prize for it. Sunflower bounds are used, for example, in Razborov's monotone circuit lower bounds and in the study of set systems with restricted intersections.

Timeline.

  • 1960 — Erdős and Rado prove (k−1)n<f(n,k)≤(k−1)n n!+1(k-1)^n < f(n,k) \le (k-1)^n\, n! + 1(k−1)n<f(n,k)≤(k−1)nn!+1 and conjecture f(n,k)≤ck nf(n,k) \le c_k^{\,n}f(n,k)≤ckn​ (ErRa60).
  • 2019 — Alweiss, Lovett, Wu and Zhang prove f(n,k)≤(Ck3log⁡nlog⁡log⁡n)nf(n,k) \le (C k^3 \log n \log\log n)^nf(n,k)≤(Ck3lognloglogn)n, the first bound of the form (log⁡n)n(1+o(1))(\log n)^{n(1+o(1))}(logn)n(1+o(1)) for fixed kkk (arXiv:1908.08483).
  • 2020 — Rao simplifies the argument via Shannon's noiseless coding theorem and obtains (αklog⁡(kn))n(\alpha k \log(kn))^n(αklog(kn))n (arXiv:1909.04774); Tao gives an entropy proof of the same bound.
  • 2021 — Bell, Chueluecha and Warnke obtain f(n,k)≤(Cklog⁡n)nf(n,k) \le (C k \log n)^nf(n,k)≤(Cklogn)n for n,k≥2n,k \ge 2n,k≥2 (arXiv:2009.09327).

The conjecture itself remains open, even for k=3k = 3k=3.

Setting

Fix natural numbers nnn (the uniformity) and kkk (the number of petals). A family F\mathcal FF of sets is nnn-uniform if every member of F\mathcal FF has exactly nnn elements. A subfamily S⊆F\mathcal S \subseteq \mathcal FS⊆F is a kkk-sunflower if ∣S∣=k|\mathcal S| = k∣S∣=k and there is a set YYY with A∩B=YA \cap B = YA∩B=Y for all distinct A,B∈SA, B \in \mathcal SA,B∈S.

The sunflower threshold f(n,k)f(n,k)f(n,k) is the least natural number mmm such that every nnn-uniform family F\mathcal FF (over any ground set) with ∣F∣≥m|\mathcal F| \ge m∣F∣≥m contains a kkk-sunflower.

Formalization targets

Goal — the sunflower conjecture (Erdős Problem 20)

∃ c:N→N∀n≥1, ∀k:f(n,k)<ck n.\exists\, c:\mathbb N\to\mathbb N\quad \forall n \ge 1,\ \forall k:\qquad f(n,k) < c_k^{\,n}.∃c:N→N∀n≥1, ∀k:f(n,k)<ckn​.

The constants ckc_kck​ are left unspecified; only the exponential shape in nnn is asked for. A disproof (the negation of this statement) would equally settle the problem.

Milestones from the literature

  1. Erdős–Rado upper bound: f(n,k)≤(k−1)n n!+1f(n,k) \le (k-1)^n\, n! + 1f(n,k)≤(k−1)nn!+1 for n≥1n \ge 1n≥1, k≥2k \ge 2k≥2.
  2. Erdős–Rado lower bound: (k−1)n<f(n,k)(k-1)^n < f(n,k)(k−1)n<f(n,k) for n≥1n \ge 1n≥1, k≥2k \ge 2k≥2.
  3. Rao's bound: there is α>1\alpha > 1α>1 with f(n,k)≤(αklog⁡(kn))n+1f(n,k) \le (\alpha k \log(kn))^n + 1f(n,k)≤(αklog(kn))n+1 for n≥1n \ge 1n≥1, k≥2k \ge 2k≥2.
  4. Bell–Chueluecha–Warnke bound: there is C≥4C \ge 4C≥4 with f(n,k)≤(Cklog⁡n)nf(n,k) \le (C k \log n)^nf(n,k)≤(Cklogn)n for n,k≥2n, k \ge 2n,k≥2.

A supporting sanity check, f(0,1)=1f(0,1) = 1f(0,1)=1, is taken from the source formalization.

Significance

A positive answer would show that sunflower-free nnn-uniform families have at most exponential size, the correct order of magnitude by the Erdős–Rado lower bound; this would sharpen every application that currently loses a log⁡n\log nlogn factor per coordinate, including monotone circuit lower bounds. A negative answer would show the (log⁡n)n(\log n)^n(logn)n-type bounds of 2019–2021 are essentially the truth.

On the formal side, the Erdős–Rado upper bound has a Lean formalization recorded in the source file; the lower-bound construction and the spread-family / coding arguments behind the Rao and Bell–Chueluecha–Warnke bounds are, as far as this proposal records, not yet formalized. Formalizing them produces reusable infrastructure on spread families and random-subset (or entropy) arguments.

Difficulty

The classical induction on nnn (pick a maximal family of pairwise disjoint members; if it is small, some element lies in many members, recurse on the link) loses a factor of about nnn at each of nnn steps, which is where n!n!n! comes from. The modern arguments replace the recursion by an analysis of spread families, but each still loses a factor log⁡n\log nlogn per level, and no known technique removes it. The case k=3k = 3k=3 is already open.

Formalization scope

  • The ground set is an arbitrary type in the lowest universe; set families are Set (Set α) and sizes are measured with Set.ncard, which returns 000 on infinite sets. Consequently, for n≥1n \ge 1n≥1 only finite members can be "nnn-element", and the condition m≤∣F∣m \le |\mathcal F|m≤∣F∣ with m≥1m \ge 1m≥1 only applies to finite families. All targets assume n≥1n \ge 1n≥1 (except the sanity check), so the n=0n = 0n=0 quirks do not affect them.
  • f(n,k)f(n,k)f(n,k) is defined as an infimum over natural numbers; if no admissible mmm existed the infimum would be 000. The Erdős–Rado upper bound shows the admissible set is non-empty for n≥1n \ge 1n≥1.
  • Logarithms are natural logarithms; changing the base only rescales the unspecified constants.
  • The goal is stated as the positive claim of the conjecture, not as a yes/no answer(·) statement.

Contributions of general lemmas on sunflowers, spread families and the Erdős–Rado construction are welcome and reusable beyond this mission.

Selected references

  • P. Erdős, R. Rado, Intersection theorems for systems of sets, J. London Math. Soc. 35 (1960), 85–90. doi:10.1112/jlms/s1-35.1.85
  • R. Alweiss, S. Lovett, K. Wu, J. Zhang, Improved bounds for the sunflower lemma, Annals of Mathematics 194 (2021). arXiv:1908.08483
  • A. Rao, Coding for sunflowers, Discrete Analysis 2020:2. arXiv:1909.04774
  • T. Bell, S. Chueluecha, L. Warnke, Note on sunflowers, Discrete Mathematics 344 (2021). arXiv:2009.09327
  • Erdős Problem 20. erdosproblems.com/20
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Number Theory·Captain: Zexuan Liu

Erdős Problem 142: Asymptotics for Sets Free of k-Term Arithmetic ProgressionsOpen Problem

Motivation

Erdős asked, repeatedly and with a rising price tag, for an asymptotic formula for the largest subset of {1,…,N}\{1,\dots,N\}{1,…,N} that contains no arithmetic progression of a given length. He offered 1000 dollars for it in [Er97c] and 10000 dollars in [Er81, p.4], where he called the question "probably enormously difficult"; elsewhere he described it as "probably unattackable at present". Most of modern additive combinatorics — the density increment method, the triangle removal lemma, Gowers uniformity norms, the arithmetic regularity lemma — grew out of attempts on this single question, and the answer is still unknown, even in the first non-trivial case k=3k=3k=3.

Timeline.

  • 1936: Erdős and Turán conjecture that rk(N)=o(N)r_k(N)=o(N)rk​(N)=o(N) for every kkk.
  • 1946: Behrend constructs large progression-free sets, giving r3(N)≥Nexp⁡(−clog⁡N)r_3(N)\ge N\exp(-c\sqrt{\log N})r3​(N)≥Nexp(−clogN​).
  • 1953: Roth proves r3(N)=o(N)r_3(N)=o(N)r3​(N)=o(N), with the quantitative form r3(N)≪N/log⁡log⁡Nr_3(N)\ll N/\log\log Nr3​(N)≪N/loglogN.
  • 1961: Rankin generalises Behrend, giving rk(N)≥Nexp⁡(−ck(log⁡N)1/(k−1))r_k(N)\ge N\exp(-c_k(\log N)^{1/(k-1)})rk​(N)≥Nexp(−ck​(logN)1/(k−1)).
  • 1969, 1975: Szemerédi proves r4(N)=o(N)r_4(N)=o(N)r4​(N)=o(N) and then rk(N)=o(N)r_k(N)=o(N)rk​(N)=o(N) for all kkk, settling Erdős–Turán.
  • 1977: Furstenberg reproves Szemerédi's theorem ergodically, with no effective bound.
  • 1998, 2001: Gowers introduces uniformity norms and obtains rk(N)≪N(log⁡log⁡N)−ckr_k(N)\ll N(\log\log N)^{-c_k}rk​(N)≪N(loglogN)−ck​, the first effective bound for general kkk.
  • 2017: Green and Tao obtain r4(N)≪N(log⁡N)−cr_4(N)\ll N(\log N)^{-c}r4​(N)≪N(logN)−c.
  • 2020: Bloom and Sisask obtain r3(N)≪N(log⁡N)−1−cr_3(N)\ll N(\log N)^{-1-c}r3​(N)≪N(logN)−1−c, the first bound past the N/log⁡NN/\log NN/logN barrier.
  • 2023: Kelley and Meka obtain r3(N)≤Nexp⁡(−c(log⁡N)1/12)r_3(N)\le N\exp(-c(\log N)^{1/12})r3​(N)≤Nexp(−c(logN)1/12).
  • 2024: Leng, Sah and Sawhney obtain rk(N)≪Nexp⁡(−(log⁡log⁡N)ck)r_k(N)\ll N\exp(-(\log\log N)^{c_k})rk​(N)≪Nexp(−(loglogN)ck​) for k≥5k\ge5k≥5.

Every upper bound in this list is still astronomically far from Behrend's lower bound, and no candidate asymptotic formula has been proposed for any k≥3k\ge3k≥3.

Setting

Fix an integer kkk. A non-trivial kkk-term arithmetic progression is a list a, a+d, a+2d,…,a+(k−1)da,\,a+d,\,a+2d,\dots,a+(k-1)da,a+d,a+2d,…,a+(k−1)d of natural numbers with common difference d>0d>0d>0; the requirement d>0d>0d>0 is what "non-trivial" means, and it forces the kkk terms to be distinct. A finite set A⊆NA\subseteq\mathbb NA⊆N is kkk-AP-free if it contains no such progression. Write

rk(N)  =  max⁡{ ∣A∣  :  A⊆{1,…,N}, A is k-AP-free }.r_k(N)\;=\;\max\bigl\{\,|A| \;:\; A\subseteq\{1,\dots,N\},\ A\ \text{is}\ k\text{-AP-free}\,\bigr\}.rk​(N)=max{∣A∣:A⊆{1,…,N}, A is k-AP-free}.

The mission takes its formal definition of rkr_krk​ verbatim from the formal-conjectures entry for this problem, so that the goal below is literally the statement recorded there. In that development a set is called free of progressions of length lll when every subset of it that is an arithmetic progression of length lll forces l≤1l\le1l≤1. Progressions of length 000 and 111 count as trivial, so under that convention every set is free of them and r0(N)=r1(N)=Nr_0(N)=r_1(N)=Nr0​(N)=r1​(N)=N; the interesting range begins at k≥2k\ge2k≥2. Every statement in this mission that depends on the convention carries an explicit hypothesis on kkk.

On that range, rk(N)r_k(N)rk​(N) is non-decreasing in both NNN and kkk, satisfies rk(M+N)≤rk(M)+rk(N)r_k(M+N)\le r_k(M)+r_k(N)rk​(M+N)≤rk​(M)+rk​(N), and hence, by Fekete's subadditivity lemma, rk(N)/Nr_k(N)/Nrk​(N)/N converges. Szemerédi's theorem is the statement that the limit is 000; the whole difficulty of this mission lies in how fast it goes to 000.

Formalization targets

Goal

rk(N)  =  ok ⁣(Nlog⁡N)for every k>1.r_k(N)\;=\;o_k\!\left(\frac{N}{\log N}\right)\qquad\text{for every }k>1 .rk​(N)=ok​(logNN​)for every k>1.

This is erdos_142.variants.lower of the formal-conjectures file for Erdős 142, reproduced binder for binder, over that file's own definition of rkr_krk​.

The headline theorem in that file, erdos_142, states rk(N)=Θ(f)r_k(N)=\Theta(f)rk​(N)=Θ(f) with the comparison function left as an answer(sorry) placeholder, and the same is true of its variants.upper and variants.three. Those are not closed propositions and cannot serve as a mission goal: the literal request of Erdős Problem #142 — "prove an asymptotic formula for rk(N)r_k(N)rk​(N)" — has no known right-hand side for any k≥3k\ge3k≥3, which is exactly why the file leaves a hole there. variants.lower is the one formalizable target in the file, and it is also the strongest precisely-stated form the problem page attaches to #142: Erdős offered 5000 dollars for (essentially) exactly it, as recorded under Erdős Problem #3. It is known for k=3k=3k=3 — it follows from Bloom–Sisask 2020, and a fortiori from Kelley–Meka 2023 — trivial for k=2k=2k=2, where r2(N)=1r_2(N)=1r2​(N)=1, and open for every k≥4k\ge4k≥4. It fixes no constants, so no future improvement can invalidate it.

A weaker open question

rk(n)rk+1(n)⟶0for some k≥3.\frac{r_k(n)}{r_{k+1}(n)}\longrightarrow 0\qquad\text{for some }k\ge3 .rk+1​(n)rk​(n)​⟶0for some k≥3.

Erdős remarked in [Er80, p.92] that even this separation between consecutive progression lengths is not known. Here [Er80] is the erdosproblems.com bibliography key for Erdős's 1980 paper; it is a citation, not a pointer to Erdős Problem #80, which is an unrelated question about books in graphs. This statement has no counterpart in formal-conjectures: the file for #142 contains only the Θ\ThetaΘ, ooo and OOO variants above, and the only two files in that repository that mention rkr_krk​ at all are the ones for #142 and #139.

Significance

Proving rk(N)=ok(N/log⁡N)r_k(N)=o_k(N/\log N)rk​(N)=ok​(N/logN) for all kkk yields, by a standard summation argument, Erdős's conjecture that every A⊆NA\subseteq\mathbb NA⊆N with ∑a∈A1/a=∞\sum_{a\in A}1/a=\infty∑a∈A​1/a=∞ contains arbitrarily long arithmetic progressions — the 5000-dollar Erdős Problem #3, of which the Green–Tao theorem on primes is the best-known special case. Below that threshold, quantitative bounds on rkr_krk​ control the density at which progressions must appear in any concrete set, and are the input to results on progressions in the primes, in sumsets, and in sparse random subsets of the integers.

Formalization status is uneven, and this mission is designed around that gap. Mathlib already contains the k=3k=3k=3 theory in a usable form: the predicate ThreeAPFree, the Roth number rothNumberNat, its subadditivity, and a complete formalization of Behrend's construction (Behrend.roth_lower_bound). Mathlib does not contain Roth's theorem, Szemerédi's theorem, or any of the modern upper bounds; to the best of current knowledge none of Roth, Szemerédi, Gowers, Green–Tao, Kelley–Meka or Leng–Sah–Sawhney has a machine-checked proof anywhere. The formal-conjectures entry states the problem but proves nothing: every declaration in it is a sorry. Two of this mission's targets are taken from that repository — the goal from its file for #142, and the Szemerédi milestone from its file for #139, which uses the same rkr_krk​; those are the only two files there that mention rkr_krk​. The milestones therefore split cleanly: the first six are reachable now on top of Mathlib, and the last five are open formalization projects of independent value.

Difficulty

Every known upper bound for rkr_krk​ runs a density increment: if A⊆{1,…,N}A\subseteq\{1,\dots,N\}A⊆{1,…,N} of density δ\deltaδ has no kkk-term progression, find a long subprogression on which AAA has density δ(1+c(δ))\delta(1+c(\delta))δ(1+c(δ)), and iterate. The bound this produces is governed entirely by two quantities — how large the increment c(δ)c(\delta)c(δ) is, and how much of the interval survives one step. For k≥4k\ge4k≥4 the increment is extracted from an inverse theorem for the Gowers Uk−1U^{k-1}Uk−1-norm, and the best available correlation bounds there are quasipolynomial in δ\deltaδ; iterating a quasipolynomial increment cannot do better than Nexp⁡(−(log⁡log⁡N)c)N\exp(-(\log\log N)^{c})Nexp(−(loglogN)c), which is nowhere near N/log⁡NN/\log NN/logN. Reaching N/log⁡NN/\log NN/logN requires an increment with polynomial dependence on δ\deltaδ together with a subprogression of polynomial length, and that combination is currently available only for k=3k=3k=3, through the sifting and almost-periodicity machinery of Kelley–Meka. No soft or averaging argument can substitute: Behrend's construction shows the truth at k=3k=3k=3 is Nexp⁡(−Θ(log⁡N))N\exp(-\Theta(\sqrt{\log N}))Nexp(−Θ(logN​)), so the answer is not a power of log⁡N\log NlogN and cannot be produced by any argument whose output has that shape.

Formalization scope

The mission's definition file Erdos142Basic carries two layers, and every statement in the mission is written against them.

  1. The source definitions, ported verbatim. IsAPOfLengthWith, IsAPOfLength, IsAPOfLengthFree and r are the declarations of the formal-conjectures entry, transcribed unchanged into the mission's namespace: a set is an arithmetic progression of length lll with first term aaa and difference ddd when it has exactly lll elements and equals {a+nd:n<l}\{a+nd : n<l\}{a+nd:n<l}; it is free of length-lll progressions when every progression of length lll inside it forces l≤1l\le1l≤1; and rk(N)r_k(N)rk​(N) is the supremum of ∣S∣|S|∣S∣ over subsets S⊆{1,…,N}S\subseteq\{1,\dots,N\}S⊆{1,…,N} free of length-kkk progressions. The ground set is Finset.Icc 1 N, and the supremum is sSup over N\mathbb NN; the file proves the two facts that make it a genuine maximum (le_r and r_le).

  2. An elementary handle. HasAP k A is ∃ a d, 0 < d ∧ ∀ i < k, a + i * d ∈ A, and APFree k A its negation. This form carries no cardinality side condition in N∪{∞}\mathbb N\cup\{\infty\}N∪{∞} and is what a solver actually wants to induct on. The first milestone is exactly the bridge between the two layers.

Two consequences of the source convention are worth stating plainly, because the prose is silent about them. Length-000 and length-111 progressions are trivial, so every set is free of them and r0(N)=r1(N)=Nr_0(N)=r_1(N)=Nr0​(N)=r1​(N)=N; monotonicity of rkr_krk​ in kkk therefore holds only from k≥2k\ge2k≥2 onward, and the corresponding milestone carries that hypothesis. Asymptotic statements use Asymptotics.IsLittleO and Filter.atTop over N\mathbb NN with real-valued casts, and real division is Lean's, so (N : ℝ) / Real.log N is 000 at N=1N=1N=1; this is invisible to atTop.

A trivializing formalization is ruled out by construction: one milestone asserts r3(N)=r_3(N)=r3​(N)= rothNumberNat N, pinning this development against Mathlib's independently written definition of the Roth number, so a vacuous or mis-quantified notion of progression-freeness cannot survive. That milestone, the bridge milestone above it, and the monotonicity milestone have all been checked to be provable before this proposal was drafted.

A full development needs: discrete Fourier analysis on Z/NZ\mathbb Z/N\mathbb ZZ/NZ, Bohr sets and their regularity, the arithmetic regularity lemma, Gowers uniformity norms and the inverse theorem for them, and — for the lower bounds — sphere-counting in high-dimensional boxes (already in Mathlib via Behrend). All of this is reusable well beyond this mission. Contributions of any kind are welcome, including partial results: quantitative bounds weaker than the cited ones, the k=3k=3k=3 case of a general-kkk milestone, and reusable Fourier-analytic infrastructure are all valuable even when they do not close a milestone.

Selected references

  • Erdős Problem #142. https://www.erdosproblems.com/142
  • Erdős Problem #3. https://www.erdosproblems.com/3
  • Erdős Problem #139 (Szemerédi's theorem in the rkr_krk​ formulation), linked from #142. https://www.erdosproblems.com/139
  • Google DeepMind, formal-conjectures, FormalConjectures/ErdosProblems/142.lean — the source of the goal statement and of the definition of rkr_krk​. https://github.com/google-deepmind/formal-conjectures/blob/main/FormalConjectures/ErdosProblems/142.lean
  • F. A. Behrend, On sets of integers which contain no three terms in arithmetical progression, Proc. Nat. Acad. Sci. USA 32 (1946), 331–332. https://doi.org/10.1073/pnas.32.12.331
  • K. F. Roth, On certain sets of integers, J. London Math. Soc. 28 (1953), 104–109. https://doi.org/10.1112/jlms/s1-28.1.104
  • R. A. Rankin, Sets of integers containing not more than a given number of terms in arithmetical progression, Proc. Roy. Soc. Edinburgh Sect. A 65 (1961), 332–344.
  • E. Szemerédi, On sets of integers containing no kkk elements in arithmetic progression, Acta Arith. 27 (1975), 199–245. https://doi.org/10.4064/aa-27-1-199-245
  • W. T. Gowers, A new proof of Szemerédi's theorem, Geom. Funct. Anal. 11 (2001), 465–588. https://doi.org/10.1007/s00039-001-0332-9
  • B. Green and T. Tao, New bounds for Szemerédi's theorem, III: A polylogarithmic bound for r4(N)r_4(N)r4​(N), Mathematika 63 (2017), 944–1040. https://arxiv.org/abs/1705.01703
  • T. F. Bloom and O. Sisask, Breaking the logarithmic barrier in Roth's theorem on arithmetic progressions, arXiv:2007.03528. https://arxiv.org/abs/2007.03528
  • Z. Kelley and R. Meka, Strong bounds for 3-progressions, arXiv:2302.05537. https://arxiv.org/abs/2302.05537
  • J. Leng, A. Sah and M. Sawhney, Improved bounds for Szemerédi's theorem, arXiv:2402.17995. https://arxiv.org/abs/2402.17995
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Graph Theory·Captain: hao jia

P3-Partitions of Cubic 3-Connected Graphs (OPG-46613)Open Problem

Motivation

A P3P_3P3​-packing in a graph is a collection of pairwise vertex-disjoint paths on three vertices. Determining the largest such packing is NP-hard even in restricted graph classes, so structural hypotheses that force an optimal packing are of independent interest in graph factor theory. The present question asks whether 3-vertex-connectivity and cubicity force the strongest possible packing whenever the vertex count permits a perfect partition.

A. Kelmans attributes the broader packing problem to 1984. In Problem 1.10 of Packing 3-vertex Paths in Cubic 3-connected Graphs, the question is whether every cubic 3-connected graph GGG satisfies λ(G)=⌊∣V(G)∣/3⌋\lambda(G)=\lfloor |V(G)|/3\rfloorλ(G)=⌊∣V(G)∣/3⌋. Theorem 3.1 of that paper proves that the divisible-order factor statement is equivalent to several apparently stronger deletion and prescribed-edge statements; it does not prove the open claim itself. OPG-46613 records the divisible-order form targeted here.

A 2026 candidate analysis in the Vibe Mathing problem repository investigated a tempting sufficient route: find a perfect matching whose complementary 2-factor has every cycle length divisible by three. Candidate C01 explains why that condition would yield a P3P_3P3​-factor. Candidate C02 gives an explicit proposed family HqH_qHq​ of order 18+12q18+12q18+12q that has P3P_3P3​-factors but is claimed not to satisfy the stronger matching condition. These candidate claims have computational and partial Lean checks, but no complete Lean kernel proof; they are milestones here, not declarations that the original problem or the candidate family has already been formally established.

Setting

All graphs are finite and simple. A graph is cubic when every vertex has exactly three neighbors. It is 3-vertex-connected here when it has at least four vertices and deleting any set of at most two vertices leaves a connected induced graph.

A P3P_3P3​-factor is represented by a natural number bbb, together with a bijection

Fin⁡(b)×Fin⁡(3)≃V(G),\operatorname{Fin}(b)\times\operatorname{Fin}(3)\simeq V(G),Fin(b)×Fin(3)≃V(G),

such that, in every block, positions 000 and 111 are adjacent and positions 111 and 222 are adjacent. The path is not required to be induced: an ambient edge between positions 000 and 222 is allowed because the two selected path edges still form a copy of P3P_3P3​.

A 2-factor is a spanning 2-regular subgraph. It is called divisible when every one of its connected components has order divisible by three. A divisible matching complement is a perfect matching MMM such that the relative complement G∖MG\setminus MG∖M is a divisible 2-factor.

The explicit graph HqH_qHq​ is defined on Fin⁡(18+12q)\operatorname{Fin}(18+12q)Fin(18+12q). Its first nine vertices form the fixed Petersen-minus-one-vertex brick from C02; the remaining vertices form the stated cycle-and-opposite-chord brick with three joining edges. The full adjacency relation is part of the Lean definition rather than an external data file.

Formalization targets

Main goal

For every finite simple graph GGG,

(G cubic)∧(G 3-vertex-connected)∧3∣∣V(G)∣⟹G has a P3-factor.\bigl(G\text{ cubic}\bigr)\land \bigl(G\text{ 3-vertex-connected}\bigr)\land 3\mid |V(G)| \quad\Longrightarrow\quad G\text{ has a }P_3\text{-factor}.(G cubic)∧(G 3-vertex-connected)∧3∣∣V(G)∣⟹G has a P3​-factor.

This is the OPG-46613 target. Cubicity forces the order to be even, so within this domain divisibility by three is equivalent to divisibility by six.

Literature and route milestones

The mission also formalizes the (z1)⇔(z8)(z1)\Leftrightarrow(z8)(z1)⇔(z8) part of Kelmans's Theorem 3.1: the divisible-order factor claim is equivalent to the assertion that deleting any specified 3-vertex path leaves a P3P_3P3​-factor. Two route lemmas state that divisible 2-factors split into P3P_3P3​-factors and that, in cubic graphs, divisible 2-factors are equivalent to divisible perfect-matching complements.

Candidate boundary milestones

The C02 milestones ask first for the complete 18-vertex statement and then for the full family:

∀q∈N,Hq is cubic and 3-vertex-connected, has a P3-factor, and has no divisible matching complement.\forall q\in\mathbb N,\quad H_q\text{ is cubic and 3-vertex-connected, has a }P_3\text{-factor, and has no divisible matching complement}.∀q∈N,Hq​ is cubic and 3-vertex-connected, has a P3​-factor, and has no divisible matching complement.

This separates a sufficient method from the root conclusion. It is not a counterexample to OPG-46613 because every HqH_qHq​ in the proposed family explicitly satisfies the desired P3P_3P3​ conclusion.

Significance

A proof of the main goal would settle the divisible-order form of a long-standing path-packing problem. Through Kelmans's equivalences it would also control several deletion and prescribed-edge variants for cubic 3-connected graphs. A disproof would require a graph satisfying all domain hypotheses but lacking a P3P_3P3​-factor; the C02 family does not claim this.

Formalizing the candidate boundary is useful even before the root is resolved. It turns a route exclusion into a checkable theorem and prevents a search campaign from silently assuming that every relevant graph possesses a divisible complementary 2-factor. The definitions of noninduced P3P_3P3​-factors, vertex connectivity by deletion, perfect matchings, 2-factors, and component-order divisibility are intended to be reusable in later graph-factor work.

Difficulty

The perfect-matching route is attractive because the complement of a perfect matching in a cubic graph is 2-regular. The obstruction is that its cycles need not have lengths divisible by three. The C02 candidate family is designed to expose exactly that gap: a persistent 5-cycle is claimed to occur in every complementary 2-factor even though an unrelated P3P_3P3​-factor exists. Consequently, proving the main theorem cannot simply assume that a favorable perfect matching always exists.

The formal difficulty is also semantic. Connectivity must mean vertex connectivity, the complement must be relative to GGG on the same vertex set, component sizes must refer to the 2-factor rather than the ambient graph, and P3P_3P3​ must remain noninduced. Weakening any of these points can create a materially different or vacuous theorem.

Formalization scope

The development targets Lean 4.33.1 and Mathlib revision 0df444a360eaa60ab8c11dca51a86af692955474. Graphs use SimpleGraph on finite vertex types. Degree is the cardinality of the actual neighbor subtype. Three-vertex-connectivity explicitly quantifies over all finite deletion sets of cardinality at most two and includes a four-vertex order guard.

The main theorem is universe-polymorphic and does not hard-code a finite graph enumeration. The HqH_qHq​ family includes q=0q=0q=0. The factor structure uses a bijection, so disjointness and coverage cannot be discharged by duplicate or omitted vertices. Ambient chords do not invalidate a block, while both required consecutive adjacencies must be genuine graph edges. The candidate family statements remain open theorem goals ending in sorry; the shared definition module itself is sorry-free.

Welcome contributions include proofs of the model lemmas, the finite H0H_0H0​ statement, the general C02 family, Kelmans's equivalence, or decompositions of the root theorem into faithful reusable lemmas. Numerical enumeration alone is supporting evidence and should not be presented as a kernel proof.

Selected references

  • A. Kelmans, Packing 3-vertex Paths In Cubic 3-connected Graphs, arXiv:0910.2766v2, 2011, Problem 1.10 (p. 3) and Theorem 3.1 (pp. 7–8). https://arxiv.org/abs/0910.2766v2
  • UnsolvedMath, OPG-46613: P3-partitions of cubic 3-connected graphs. https://www.unsolvedmath.com/problems/OPG-46613
  • Vibe Mathing, C01: divisible-cycle implication and a 30-vertex obstruction, fixed repository revision 14b8dc64ac2d89c98cf3a2bbb2fcba76ced0df6a. https://github.com/vibemathing/problem-opg-46613-cubic-p3-partition/blob/14b8dc64ac2d89c98cf3a2bbb2fcba76ced0df6a/research/artifacts/candidates/opg46613-c01/proof.md
  • Vibe Mathing, C02: an 18-vertex obstruction and an infinite family with P3-factors, fixed repository revision 14b8dc64ac2d89c98cf3a2bbb2fcba76ced0df6a. https://github.com/vibemathing/problem-opg-46613-cubic-p3-partition/blob/14b8dc64ac2d89c98cf3a2bbb2fcba76ced0df6a/research/artifacts/candidates/opg46613-c02/proof.md
16 thms5 active usersReviewed
Linear Optimization·Captain: Shuze Chen

The Polynomial Hirsch ConjectureOpen Problem

Motivation

The simplex method walks along edges of a polytope from vertex to vertex. Whether any pivot rule could ever make that walk short in the worst case is governed by a prior, purely geometric question: how far apart, in the edge graph, can two vertices of a polytope be? Warren Hirsch conjectured in 1957 that the diameter of a ddd-dimensional polytope with nnn facets is at most n−dn - dn−d. Half a century of upper bounds stalled at quasi-polynomial, and Santos disproved the conjecture itself in 2012 — but only by a constant factor. The surviving question, the subject of the Polymath 3 project, is the polynomial Hirsch conjecture: is the diameter bounded by a polynomial in nnn and ddd?

Timeline

  • 1957. Hirsch states the conjecture diam≤n−d\mathrm{diam} \le n - ddiam≤n−d in a letter to Dantzig, who publishes it in Linear Programming and Extensions (1963).
  • 1964–1966. Klee determines the exact maximum diameter of 333-polytopes with nnn facets, ⌊2n/3⌋−1\lfloor 2n/3\rfloor - 1⌊2n/3⌋−1 — the Hirsch bound holds up to dimension three.
  • 1967. Klee and Walkup (Acta Math.) refute the unbounded-polyhedron version, prove the bounded conjecture for n−d≤5n - d \le 5n−d≤5, and reduce the general case to the ddd-step conjecture (n=2dn = 2dn=2d).
  • 1970. Larman (Proc. LMS) proves diam≤n 2d−3\mathrm{diam} \le n\,2^{d-3}diam≤n2d−3 — linear in the number of facets for each fixed dimension, still the best bound of that shape.
  • 1989. Naddef (Math. Programming) proves 0/10/10/1-polytopes satisfy the Hirsch bound, with diameter at most ddd.
  • 1992. Kalai and Kleitman (Bull. AMS) prove diam≤nlog⁡2d+2\mathrm{diam} \le n^{\log_2 d + 2}diam≤nlog2​d+2 in under a page — the quasi-polynomial barrier every later bound refines. The same year brings subexponential pivot rules (Kalai; Matoušek–Sharir–Welzl), the algorithmic counterpart.
  • 2010. Eisenbrand, Hähnle, Razborov, and Rothvoß (Math. OR) show the known upper-bound arguments survive in a purely combinatorial abstraction — which admits almost-quadratic lower bounds, so a polynomial bound must use real geometry. Kalai launches Polymath 3 on the polynomial version.
  • 2010–2012. Santos (Annals of Math.) disproves the Hirsch conjecture: a 434343-dimensional polytope with 868686 facets and diameter at least 444444, via spindles of large width.
  • 2014–2019. Todd (SIAM J. Discrete Math.) sharpens Kalai–Kleitman to (n−d)log⁡2d(n-d)^{\log_2 d}(n−d)log2​d; Sukegawa refines further. Matschke, Santos, and Weibel (Proc. LMS 2015) shrink the counterexample to dimension 202020 with 404040 facets and diameter 212121. All known violations remain constant-factor; all known bounds remain quasi-polynomial.

Setting

Work in Rd\mathbb{R}^dRd. An H-polytope is a set cut out by finitely many linear inequalities: given vectors a1,…,an∈Rda_1, \dots, a_n \in \mathbb{R}^da1​,…,an​∈Rd and reals b1,…,bnb_1, \dots, b_nb1​,…,bn​, it is

P  =  { x∈Rd∣⟨ai,x⟩≤bi for i=1,…,n },P \;=\; \{\, x \in \mathbb{R}^d \mid \langle a_i, x\rangle \le b_i \text{ for } i = 1, \dots, n \,\},P={x∈Rd∣⟨ai​,x⟩≤bi​ for i=1,…,n},

where ⟨ai,x⟩=∑j=1daijxj\langle a_i, x\rangle = \sum_{j=1}^d a_{ij} x_j⟨ai​,x⟩=∑j=1d​aij​xj​ is the standard inner (dot) product — so each condition ⟨ai,x⟩≤bi\langle a_i, x\rangle \le b_i⟨ai​,x⟩≤bi​ is one linear inequality, with normal vector aia_iai​ and offset bib_ibi​. Throughout, PPP is assumed nonempty and bounded. The parameter nnn counts the inequalities in the given description; since every polytope with fff facets admits a description by exactly fff inequalities, bounds stated in terms of nnn over all descriptions are equivalent to bounds in terms of facet counts.

A vertex of PPP is an extreme point. Two vertices u≠vu \ne vu=v are adjacent when the segment [u,v][u, v][u,v] is an extreme subset of PPP; for a polytope the convex extreme subsets are exactly the faces, so this says precisely that [u,v][u,v][u,v] is a one-dimensional face — an edge. The combinatorial diameter of PPP is the diameter of the graph of vertices and edges. Throughout, "diameter at most BBB" is expressed as: every two vertices are joined by a walk of BBB steps, each step staying put or crossing an edge — a form that is monotone in BBB and asserts connectivity of the graph (Balinski's theorem) as part of the claim.

Formalization targets

Goal — the polynomial Hirsch conjecture

∃ c,k∈N: every nonempty bounded P={x∈Rd∣⟨ai,x⟩≤bi, i≤n} has diameter≤c (n+d)k.\exists\, c, k \in \mathbb{N}:\ \text{every nonempty bounded } P = \{x \in \mathbb{R}^d \mid \langle a_i, x \rangle \le b_i,\ i \le n\} \text{ has diameter} \le c\,(n + d)^k.∃c,k∈N: every nonempty bounded P={x∈Rd∣⟨ai​,x⟩≤bi​, i≤n} has diameter≤c(n+d)k.

Every polynomial in nnn and ddd is dominated by some c(n+d)kc(n+d)^kc(n+d)k and conversely, so this is exactly polynomiality, with no committed degree — the form that survives any future sharpening of constants or exponents.

Milestones — the known ladder

Six classical results over the same definitions: the Hirsch bound n−dn - dn−d in dimension d≤3d \le 3d≤3 (Klee; Klee–Walkup); Larman's bound n⋅2d−3n \cdot 2^{d-3}n⋅2d−3; Naddef's bound ddd for 0/10/10/1-polytopes; the Kalai–Kleitman bound nlog⁡2d+2n^{\log_2 d + 2}nlog2​d+2; Todd's bound (n−d)log⁡2d(n-d)^{\log_2 d}(n−d)log2​d for full-dimensional PPP with n≥d≥3n \ge d \ge 3n≥d≥3; and — in the other direction — the Santos counterexample: a nonempty bounded H-polytope whose diameter exceeds n−dn - dn−d.

Significance

A polynomial diameter bound is necessary for any pivot rule of the simplex method to run in polynomial time in the worst case: if vertices can be super-polynomially far apart, no edge-following algorithm can connect them quickly. A refutation would close off one of the main hoped-for routes to a strongly polynomial linear programming algorithm (Smale's ninth problem). The conjecture is also the test question of polyhedral graph theory: the Kalai–Kleitman argument uses so little about polytopes that it holds for far more general set systems, and Eisenbrand, Hähnle, Razborov, and Rothvoß (Math. OR 2010) showed such abstractions admit almost-quadratic lower bounds — so a proof of the conjecture must use geometry the abstract setting lacks, and a disproof must beat the abstraction barrier's constructions with actual polytopes.

None of these results has been formalized in any proof assistant; Mathlib has extreme points and faces of convex sets, but no polytope combinatorics — no vertex-edge graph, no diameter, no facet counting. This mission builds that layer: an H-polytope model, adjacency via faces, and walk-based diameter bounds, against which both the upper-bound ladder and the Santos disproof can be machine-checked. The Kalai–Kleitman proof is one page from first principles and is the natural summit; the Santos construction is a concrete finite object whose verification is a different, computational kind of challenge.

Difficulty

The naive approach — walk toward the target vertex by always improving some linear objective — is exactly the simplex method, and proving any polynomial bound on such walks is open for every known pivot rule; monotone variants of the diameter question have exponential lower bounds. The obvious inductive strategy (bound the diameter by recursing on facets) is precisely what Kalai–Kleitman optimizes, and it provably cannot go below quasi-polynomial without using metric or topological properties of actual polytopes, by the abstraction lower bound above. On the other side, making diameters large is blocked by the wedge/spindle calculus only producing constant-factor violations. The problem sits in a genuine gap: no technique on either side is known to reach polynomial.

Formalization scope

The Lean model commits to: ambient space EuclideanSpace ℝ (Fin d); the polytope as Hpoly a b = {x | ∀ i, ⟪a i, x⟫ ≤ b i} for a : Fin n → EuclideanSpace ℝ (Fin d), b : Fin n → ℝ, with nonemptiness and Bornology.IsBounded as explicit hypotheses (boundedness is essential: Klee–Walkup's unbounded counterexample would otherwise trivialize the Santos milestone); vertices as Set.extremePoints ℝ; adjacency as u ≠ v ∧ IsExtreme ℝ P (segment ℝ u v); and diameter bounds as the walk predicate DiamLE, whose stationary steps make it monotone in the bound. Real-exponent bounds enter through Real.logb and the natural floor. In larman_bound and the two Hirsch-form bounds the subtraction is natural-number (truncated) subtraction, which only weakens nothing: the stated forms are true as written for all n,dn, dn,d in scope. The dimension parameter ddd is the ambient dimension; lower-dimensional polytopes are included, and every milestone is stated so as to remain true for them, with todd_bound requiring full-dimensionality ((interior P).Nonempty) as in its source.

Welcome contributions: any milestone in any order (dimension_three_bound for d≤1d \le 1d≤1 cases and structural lemmas about Adj and DiamLE are natural entry points, and kalai_kleitman_bound is the summit); reusable infrastructure — polytopes have finitely many extreme points, faces of H-polytopes, Balinski connectivity — published as platform theorems; and, as a separate expedition, the explicit Santos or Matschke–Santos–Weibel polytope. Statements about unbounded polyhedra, the simplex method itself, and subexponential pivot rules are left to future missions.

Selected references

  • V. Klee, D. Walkup, The d-step conjecture for polyhedra of dimension d < 6, Acta Math. 117 (1967). doi:10.1007/BF02392971
  • D. Larman, Paths on polytopes, Proc. London Math. Soc. 20 (1970). doi:10.1112/plms/s3-20.2.249
  • D. Naddef, The Hirsch conjecture is true for (0,1)-polytopes, Math. Programming 45 (1989). doi:10.1007/BF01589418
  • G. Kalai, D. Kleitman, A quasi-polynomial bound for the diameter of graphs of polyhedra, Bull. AMS 26 (1992). arXiv:math/9204233
  • F. Santos, A counterexample to the Hirsch conjecture, Annals of Mathematics 176 (2012). arXiv:1006.2814
  • M. Todd, An improved Kalai–Kleitman bound for the diameter of a polyhedron, SIAM J. Discrete Math. 28 (2014). arXiv:1402.3579
  • B. Matschke, F. Santos, C. Weibel, The width of five-dimensional prismatoids, Proc. London Math. Soc. 110 (2015). arXiv:1202.4701
  • F. Eisenbrand, N. Hähnle, A. Razborov, T. Rothvoß, Diameter of polyhedra: limits of abstraction, Math. Oper. Res. 35 (2010). doi:10.1287/moor.1100.0470
  • F. Santos, Recent progress on the combinatorial diameter of polytopes and simplicial complexes, TOP 21 (2013) (survey). arXiv:1307.5900
81 thms5 active usersReviewed
Algorithmic Game TheoryEconomicsOperations Research+1·Captain: mikedeng1

Endogenous Risk Exposure and Systemic Instability II: Banks Take More Risk in the Least Equilibrium of a Complete Network than of a Ring NetworkResearch Paper

Motivation

Banks lend to one another, and the shape of the resulting network decides how a loss at one bank reaches the others. A long line of work, starting with Allen and Gale (2000) and continued by Acemoglu, Ozdaglar and Tahbaz-Salehi (2015), compares the ring network, where each bank is owed money by one neighbour, with the complete network, where every bank holds an equal share of every other bank's debt. That literature studies contagion after shocks hit and finds that the complete network spreads losses thinly and can absorb small shocks better.

Shu's paper (Management Science, doi:10.1287/mnsc.2022.01519) asks the ex-ante question: when banks choose how much risk to take, knowing the network they sit in, which network makes them take more? A successful bank subsidizes its failed counterparties through the clearing of interbank debt, and anticipating that subsidy pushes it toward riskier projects. Proposition 4 of the paper says that this effect is stronger in the complete network. This mission formalizes that proposition and the computation behind it.

Setting

There are N≥2N\ge2N≥2 banks. In a regular network with share matrix Θ=[θij]\Theta=[\theta_{ij}]Θ=[θij​], bank jjj owes bank iii the face value θijdˉ\theta_{ij}\bar dθij​dˉ, every bank has total interbank liability dˉ>0\bar d>0dˉ>0, and every bank also owes a senior deposit v>0v>0v>0. The two networks of Definition 3 are

ΘR: θij=1 iff j≡i−1(modN),ΘC=1N−1(1N,N−IN).\Theta^R:\ \theta_{ij}=1 \text{ iff } j\equiv i-1 \pmod N,\qquad \Theta^C=\tfrac{1}{N-1}\big(\mathbb 1_{N,N}-I_N\big).ΘR: θij​=1 iff j≡i−1(modN),ΘC=N−11​(1N,N​−IN​).

Each bank iii chooses a project ZiZ_iZi​ in a common interval [Z‾,Z‾][\underline Z,\overline Z][Z​,Z]. The project pays ZiZ_iZi​ with probability P(Zi)P(Z_i)P(Zi​) and 000 otherwise, independently across banks. Assumption 1 requires P(Z)∈(0,1)P(Z)\in(0,1)P(Z)∈(0,1), PPP decreasing, and P(Z) ZP(Z)\,ZP(Z)Z concave. The standing assumption Z‾≥v+dˉ\underline Z\ge v+\bar dZ​≥v+dˉ says a successful bank never defaults.

After the state ω∈{s,f}N\omega\in\{s,f\}^Nω∈{s,f}N is realized, interbank payments clear. A payment equilibrium is a vector ddd with

di={min⁡[∑jθijdj+ei−v, dˉ]}+,d_i=\Big\{\min\Big[\textstyle\sum_j\theta_{ij}d_j+e_i-v,\ \bar d\Big]\Big\}^+,di​={min[∑j​θij​dj​+ei​−v, dˉ]}+,

where ei=Zie_i=Z_iei​=Zi​ if bank iii succeeds and 000 if it fails. Bank iii's profit is (∑jθijdj∗+ei−v−di∗)+\big(\sum_j\theta_{ij}d^*_j+e_i-v-d^*_i\big)^+(∑j​θij​dj∗​+ei​−v−di∗​)+. A risk-taking equilibrium is a profile Z∗Z^*Z∗ in which each Zi∗Z^*_iZi∗​ maximizes bank iii's expected profit given Z−i∗Z^*_{-i}Z−i∗​. Equilibria need not be unique, and the paper selects the least one.

The key quantity is the network risk-taking distortion

Di(Z−i)=∑ω−i(dˉ−∑jθijdj∗(ωi=s))Pr⁡(ω−i),\mathcal D_i(Z_{-i})=\sum_{\omega_{-i}}\Big(\bar d-\sum_j\theta_{ij}d^*_j(\omega^{i=s})\Big)\Pr(\omega_{-i}),Di​(Z−i​)=ω−i​∑​(dˉ−j∑​θij​dj∗​(ωi=s))Pr(ω−i​),

which is bank iii's expected net interbank payment to the other banks when its own project succeeds. In a regular network, expected profit equals P(Zi)(Zi−v)−P(Zi) Di(Z−i)P(Z_i)(Z_i-v)-P(Z_i)\,\mathcal D_i(Z_{-i})P(Zi​)(Zi​−v)−P(Zi​)Di​(Z−i​), so Di\mathcal D_iDi​ acts like extra debt that the bank repays only when it succeeds. Superscripts CCC and RRR mark the complete and ring networks.

Formalization targets

Goal: Proposition 4

With ZCZ^CZC the least equilibrium of the complete network and ZRZ^RZR the least equilibrium of the ring,

ZiR≤ZiCfor every bank i.Z^R_i\le Z^C_i\qquad\text{for every bank } i.ZiR​≤ZiC​for every bank i.

Milestones

  1. Clearing in the complete network (IA p. 15): a failed bank pays (dˉ−N−1Sv)+\big(\bar d-\frac{N-1}{S}v\big)^+(dˉ−SN−1​v)+, where SSS is the number of successful banks.
  2. (I.13): with qqq the common success probability of the other banks and Pr⁡(m)=(N−1m)(1−q)mqN−1−m\Pr(m)=\binom{N-1}{m}(1-q)^mq^{N-1-m}Pr(m)=(mN−1​)(1−q)mqN−1−m,
DiC=∑m=1N−1min⁡(mvN−m,mdˉN−1)Pr⁡(m).\mathcal D^C_i=\sum_{m=1}^{N-1}\min\Big(\frac{mv}{N-m},\frac{m\bar d}{N-1}\Big)\Pr(m).DiC​=m=1∑N−1​min(N−mmv​,N−1mdˉ​)Pr(m).
  1. (I.14): in the ring, a failed bank pays (dˉ−Kiv)+(\bar d-K_iv)^+(dˉ−Ki​v)+, where KiK_iKi​ counts the consecutive failed banks upstream of iii, starting with iii itself.
  2. (I.15): the conditional distribution, given mmm failures, of what a successful bank receives in the ring.
  3. Closed form of DiR\mathcal D^R_iDiR​ (IA p. 16, also p. 14).
  4. Lemma I.A (ii): ∑l=rn(lr)(n−l)=(n+1r+1)n−rr+2\sum_{l=r}^n\binom lr(n-l)=\binom{n+1}{r+1}\frac{n-r}{r+2}∑l=rn​(rl​)(n−l)=(r+1n+1​)r+2n−r​. Part (i), the classical hockey-stick identity, is already published and proved on the platform and is included by reference.
  5. Lemma I.B: ∑iAi+≥(∑iAi−B)++B\sum_i A_i^+\ge(\sum_iA_i-B)^++B∑i​Ai+​≥(∑i​Ai​−B)++B whenever B<max⁡iAiB<\max_iA_iB<maxi​Ai​.
  6. DiR≤DiC\mathcal D^R_i\le\mathcal D^C_iDiR​≤DiC​ when all other banks choose the same project.

Significance

The result. Proposition 4 reverses the conventional reading of the complete network as the safer structure. The co-insurance that makes it resilient after a shock is the same cross-subsidy that weakens incentives before the shock. Two networks with the same face-value liabilities dˉ+v\bar d+vdˉ+v therefore induce different equilibrium risk-taking. Liabilities alone are not a sufficient statistic for risk incentives, and the network topology matters in its own right. The same comparison is the template for the paper's later results on the number of banks and on central clearing.

Formalizing it. The proposition is proved in the paper, not open, and none of it is machine-checked yet. A formal proof must establish the two clearing vectors, the counting identity behind (I.15), and the two closed forms of the distortion. The printed argument also leaves a step to be supplied: its final appeal to "the monotone selection theorem" needs the distortion comparison at every profile of the other banks. It is proved only when they all choose the same project, and it is false in general. The goal is nevertheless stated for least equilibria exactly as in the paper, and its formal proof has to bridge that gap. The milestones are true as stated.

Difficulty

The distortion of the ring is not a function of the number of failures alone. What a successful bank receives depends on the length of the run of failed banks immediately upstream of it, so the closed form needs an exact count of the binary strings with a given number of failures and a given initial run length. The comparison of the two distortions is not termwise either: the positive part sits inside a sum on the ring side and outside it on the complete side.

The obvious final step is to say that the distortion is larger in the complete network, and that a larger distortion raises the optimal risk exposure. That step does not go through as stated. The equilibrium comparison needs control of the best-response maps at every profile, while the distortion comparison holds only on the diagonal, where all other banks choose the same project. Some further structure of the two games is needed to restrict attention to that diagonal.

Formalization scope

Banks are Fin N with N≥2N\ge2N≥2, and states are Fin N → Bool with true meaning success. The paper's general model is specialized to a regular network given by (Θ,dˉ,v)(\Theta,\bar d,v)(Θ,dˉ,v), which covers both networks of Definition 3. Every expectation is a finite sum over the 2N2^N2N states, so no measure theory is involved. The payment equilibrium d∗d^*d∗ is the greatest solution of the clearing system, because the paper asserts only generic uniqueness. Every bank's project lies in [Z‾,Z‾][\underline Z,\overline Z][Z​,Z], and Z‾≥v+dˉ\underline Z\ge v+\bar dZ​≥v+dˉ is a hypothesis of every statement.

The distortion milestones (2, 5 and 8) are stated at the constant profile in which every bank chooses the same zzz, which is how the page's single P(Z−i)P(Z_{-i})P(Z−i​) reads. At m=N−1m=N-1m=N−1, where every other bank fails, the printed ring binomial has lower index −1-1−1, and the weight is fixed explicitly to 111 at l=N−1l=N-1l=N−1. Proposition 4 is stated for least equilibria and weak inequality. It adds one hypothesis the page does not state, continuity of PPP on [Z‾,Z‾][\underline Z,\overline Z][Z​,Z], so that best responses and least equilibria exist. Without it the IsLeast hypotheses could be unsatisfiable and the goal vacuous.

Trivializing encodings are ruled out: d∗d^*d∗ is an actual solution of the clearing system, not a junk supremum, and the run length KiK_iKi​ is only used in states where some bank succeeds.

A complete development needs Knaster–Tarski on the box [0,dˉ]N[0,\bar d]^N[0,dˉ]N, the clearing computations for the two networks, binomial identities, and a monotone comparative statics argument for the equilibria. Published Topkis results on supermodular games (existence of least equilibria, and monotonicity of least fixed points in a parameter) are included by reference as tools. The clearing and counting lemmas are reusable for the paper's other results on ring and complete networks. Proofs of any milestone, and of the goal, are welcome.

Not posed here: Proposition 1 (strategic complementarity in any network, a separate mission), Proposition 2 (existence), and the later comparative statics in the number of banks, central clearing and bailouts.

Selected references

  • C. Shu, Endogenous Risk Exposure and Systemic Instability, Management Science (accepted manuscript, 17 Sep 2024). https://doi.org/10.1287/mnsc.2022.01519
  • F. Allen and D. Gale, Financial Contagion, Journal of Political Economy 108(1), 2000. https://doi.org/10.1086/262109
  • D. Acemoglu, A. Ozdaglar and A. Tahbaz-Salehi, Systemic Risk and Stability in Financial Networks, American Economic Review 105(2), 2015. https://doi.org/10.1257/aer.20130456
  • L. Eisenberg and T. H. Noe, Systemic Risk in Financial Systems, Management Science 47(2), 2001. https://doi.org/10.1287/mnsc.47.2.236.9835
  • D. M. Topkis, Supermodularity and Complementarity, Princeton University Press, 1998. https://doi.org/10.1515/9781400822539
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Discrete GeometryLinear Optimization·Captain: mikedeng1

A Counterexample to the Hirsch Conjecture I: A 43-Dimensional Polytope with 86 Facets Whose Diameter Exceeds 43Research Paper

Motivation

The Hirsch conjecture, stated by Warren M. Hirsch in a 1957 letter to George Dantzig, asserts that the combinatorial diameter of a ddd-dimensional polytope with nnn facets is at most n−dn-dn−d. The diameter is the largest number of edge steps needed to walk between two vertices of the polytope. The question matters to linear optimization: every edge-following pivot rule of the simplex method, starting at a vertex of the feasible region, needs at least as many pivots as the graph distance to an optimal vertex. A polynomial bound on polytope diameters is a necessary condition for a strongly polynomial simplex method, which is still unknown.

Timeline of the conjecture before the counterexample:

  • 1967, Klee and Walkup: the conjecture fails for unbounded polyhedra, and for bounded polytopes it is equivalent to the ddd-step conjecture (the case n=2dn=2dn=2d). They also proved it for n−d≤5n-d\le 5n−d≤5.
  • 1992, Kalai and Kleitman: a quasi-polynomial upper bound nlog⁡2d+2n^{\log_2 d+2}nlog2​d+2 on the diameter.
  • 2010, Santos: a counterexample in dimension 43 with 86 facets (arXiv:1006.2814, published in Ann. of Math. 176 (2012) 383–412). It is the subject of this mission.
  • 2012, Matschke, Santos and Weibel: smaller 5-dimensional spindles, giving counterexamples in dimension 20 (arXiv:1202.4701).

The polynomial Hirsch conjecture, which asks only for a bound polynomial in nnn and ddd, remains open.

Setting

A polytope in Rd\mathbb R^dRd is written as Hpoly(a,b)={x:⟨ai,x⟩≤bi, i=1,…,n}\mathrm{Hpoly}(a,b)=\{x:\langle a_i,x\rangle\le b_i,\ i=1,\dots,n\}Hpoly(a,b)={x:⟨ai​,x⟩≤bi​, i=1,…,n}. A facet presentation is one in which Hpoly(a,b)\mathrm{Hpoly}(a,b)Hpoly(a,b) has nonempty interior and no inequality is redundant. For a bounded polytope it then has dimension ddd, and its nnn inequalities correspond one to one to its facets. A step between two vertices crosses an edge (a one-dimensional face). The polytope is non-Hirsch when its diameter exceeds n−dn-dn−d.

A spindle (Santos, Definition 1.4) is a polytope with two distinguished vertices u,vu,vu,v such that every facet contains exactly one of them. Its length is the graph distance from uuu to vvv. The polar objects are prismatoids: polytopes with two parallel facets Q+,Q−Q^+,Q^-Q+,Q− containing all vertices, whose width is the dual-graph distance between Q+Q^+Q+ and Q−Q^-Q−.

Santos's explicit object is the 5-prismatoid QQQ whose vertices are the 48 rows of Table 1 of the paper: 1+,…,24+1^+,\dots,24^+1+,…,24+ with x5=1x_5=1x5​=1 and 1−,…,24−1^-,\dots,24^-1−,…,24− with x5=−1x_5=-1x5​=−1. Its polar QΔ={x∈R5:⟨r,x⟩≤1 for each row r}Q^\Delta=\{x\in\mathbb R^5:\langle r,x\rangle\le1\ \text{for each row } r\}QΔ={x∈R5:⟨r,x⟩≤1 for each row r} is a spindle with apices e5e_5e5​ and −e5-e_5−e5​. Table 2 of the paper lists the 322 facets of QQQ, which are the 322 vertices of QΔQ^\DeltaQΔ: ±e5\pm e_5±e5​ and the points 1c0(±c1,±c2,±c3,±c4,c5)\frac1{c_0}(\pm c_1,\pm c_2,\pm c_3,\pm c_4,c_5)c0​1​(±c1​,±c2​,±c3​,±c4​,c5​) of twenty types B,B′,…,K,K′B,B',\dots,K,K'B,B′,…,K,K′ with sixteen sign patterns each. The symmetry group Σ\SigmaΣ of QQQ has order 64; its index-two subgroup Σ+\Sigma^+Σ+ preserves Q+Q^+Q+ and Q−Q^-Q−.

Formalization targets

Goal: Corollary 1.7

∃ a:Fin 86→R43, b∈R86:Hpoly(a,b) nonempty, bounded, a facet presentation, and diam⁡Hpoly(a,b)>43.\exists\, a:\mathrm{Fin}\,86\to\mathbb R^{43},\ b\in\mathbb R^{86}:\quad \mathrm{Hpoly}(a,b)\ \text{nonempty, bounded, a facet presentation, and}\ \operatorname{diam}\mathrm{Hpoly}(a,b)>43.∃a:Fin86→R43, b∈R86:Hpoly(a,b) nonempty, bounded, a facet presentation, and diamHpoly(a,b)>43.

The goal fixes the dimension (43) and the number of facets (86), as the paper does. It does not assert the exact diameter (44, remarked on p. 24).

Milestones

  1. §3, first bullet (polar form): QΔQ^\DeltaQΔ is a bounded 48-facet spindle with apices ±e5\pm e_5±e5​, the rows tight at e5e_5e5​ being 1+,…,24+1^+,\dots,24^+1+,…,24+.
  2. Theorem 4.1(1) (polar form): the 322 points of Table 2 are distinct vertices of QΔQ^\DeltaQΔ; the letter classes are Σ+\Sigma^+Σ+-orbits and the six Σ\SigmaΣ-orbits are A ∪ L, B ∪ K, C ∪ J, D ∪ I, E ∪ H, F ∪ G.
  3. Theorem 4.1(2) (polar form): QΔQ^\DeltaQΔ has no other vertices.
  4. Theorem 3.1 (polar form): the distance from e5e_5e5​ to −e5-e_5−e5​ in the graph of QΔQ^\DeltaQΔ is exactly 6.
  5. Theorem 1.6: a 5-spindle with 48 facets, 322 vertices and length exactly six exists.
  6. Inductive step of Theorem 2.6 (polar form): a ddd-spindle with n>2dn>2dn>2d facets and length at least lll yields a (d+1)(d+1)(d+1)-spindle with n+1n+1n+1 facets and length at least l+1l+1l+1.
  7. Theorem 1.5 (strong ddd-step theorem): a ddd-spindle with nnn facets and length at least lll yields an (n−d)(n-d)(n−d)-spindle with 2n−2d2n-2d2n−2d facets and length at least l+n−2dl+n-2dl+n−2d, non-Hirsch when l>dl>dl>d.

Significance

Corollary 1.7 settles the Hirsch conjecture in the negative. Theorem 1.5 is the reusable part: it turns any spindle whose length exceeds its dimension into a counterexample. That made the later search for small spindles (Matschke–Santos–Weibel) a finite, low-dimensional problem. Theorem 1.6 is the concrete certificate. Its claims about Tables 1 and 2 are finite statements about explicit rational data.

The result is proved, and several pieces are already formalized on Prove2Me. Hirsch.santos_counterexample (Proved) states that some polytope violates the bound n−dn-dn−d, with no dimension or facet count. Hirsch.strong_dstep_spindle (Proved) is the "in particular" clause of Theorem 1.5, counting inequalities rather than facets. Lemmas 2.2 and 2.4 of the paper are Proved in polar form (Hirsch.spindle_inward_row_push_graph, Hirsch.spindle_wedge_vertex_graph_projection), as is the bound n≥2dn\ge2dn≥2d for spindles (Hirsch.spindle_n_ge_two_d). This mission adds the dimension-43, 86-facet statement with facets counted exactly, the general length bound l+n−2dl+n-2dl+n−2d with the spindle structure preserved, and a machine-checkable account of Santos's specific spindle.

Difficulty

The obvious first idea for the 5-dimensional milestones is brute computation. This is feasible in principle but heavy in Lean. Showing that 322 given points are extreme requires exhibiting, for each, five linearly independent tight rows among 48. Showing that no other vertex exists requires a complete enumeration argument rather than a membership check. Exact distance 6 requires the full adjacency structure between the 322 vertices, not only a path.

For the general theorems, the difficulty is that the construction is a one-point suspension followed by a perturbation. Edges and facets of the new polytope must be controlled, with the face lattice changing only in a prescribed way. Diameter is not monotone under arbitrary perturbations, and in particular a walk can become shorter. Keeping the facet count exact (not merely the inequality count) adds an irredundancy argument at each step.

Formalization scope

Polytopes are H-presentations Hirsch.Hpoly a b with a : Fin n → EuclideanSpace ℝ (Fin d), from the platform definition Hirsch_model. "A ddd-polytope with nnn facets" is boundedness plus IsFacetPresentation: nonempty interior and every row irredundant. Without this predicate, padding with redundant rows would change the bound n−dn-dn−d, and the statement would collapse to the platform's dimension-free Hirsch.santos_counterexample. Diameter and length use the platform's padded walks (Hirsch.DiamLE, Hirsch.Reach). "Length at least LLL" means no walk of fewer than LLL steps, and "length exactly LLL" adds a walk of LLL steps. The junk-valued Hirsch.gdist is never used.

All prismatoid statements of the paper (§3, Theorems 2.6, 3.1, 4.1) are stated for the polar spindle, which the paper licenses (pp. 6, 8, 9). Vertices of QQQ become rows, facets of QQQ become vertices, the width becomes the apex distance, and Q±Q^\pmQ± become ±e5\pm e_5±e5​. Natural-number expressions n−dn-dn−d, 2n−2d2n-2d2n−2d, l+(n−2d)l+(n-2d)l+(n−2d) are faithful because spindles have n≥2dn\ge2dn≥2d. Theorem 1.5's "length lll" is read as "length at least lll", which is equivalent by monotonicity. Tables 1 and 2 are written entry by entry in Lean in the page's order.

Useful contributions include proofs of the finite Table 1/Table 2 facts by certified computation, reusable lemmas on one-point suspensions and on facet presentations under perturbation, and a proof of Corollary 1.7 from milestones 5 and 7.

Selected references

  • F. Santos, A counterexample to the Hirsch conjecture, Ann. of Math. 176 (2012) 383–412; cited version arXiv:1006.2814v3. https://arxiv.org/abs/1006.2814 , https://doi.org/10.4007/annals.2012.176.1.7
  • V. Klee and D. W. Walkup, The d-step conjecture for polyhedra of dimension d < 6, Acta Math. 117 (1967) 53–78. https://doi.org/10.1007/BF02395040
  • G. Kalai and D. J. Kleitman, A quasi-polynomial bound for the diameter of graphs of polyhedra, Bull. Amer. Math. Soc. 26 (1992) 315–316. https://arxiv.org/abs/math/9204233
  • B. Matschke, F. Santos and C. Weibel, The width of five-dimensional prismatoids, Proc. London Math. Soc. 110 (2015) 647–672. https://arxiv.org/abs/1202.4701
15 thms4 active usersReviewed
Graph TheoryOptimization·Captain: hao jia

Clique Partitions of Chordal Graphs (Erdos Problem 81)Open Problem

Motivation

An edge partition into cliques compresses the adjacency structure of a graph into complete pieces without allowing any edge to be counted twice. Erdős Problem 81 asks for the asymptotically sharp upper bound on the number of pieces needed when the graph is chordal. Chordal graphs have strong elimination structure, but that structure does not make the partition parameter additive under arbitrary edge deletion, and obtaining a linear error term remains substantially stronger than identifying the leading quadratic coefficient.

Erdős, Ordman, and Zalcstein studied clique partitions of chordal graphs in 1993. Their examples already exhibit the n2/6n^2/6n2/6 scale, while their general upper estimate had a larger quadratic coefficient. Later dense-packing results of Haxell–Rödl and Yuster compare fractional and integer triangle packings with an o(n2)o(n^2)o(n2) gap. The project candidate combines that interface with chordal elimination arguments to formulate a uniform n2/6+o(n2)n^2/6+o(n^2)n2/6+o(n2) milestone. It does not supply the O(n)O(n)O(n) remainder asked for by the root.

Setting

A finite simple graph is chordal when it has no induced cycle of length greater than three. The Lean definition uses the equivalent perfect-elimination form: vertices admit an injective ranking such that the later neighbors of every vertex form a clique.

An edge partition into cliques is a finite family P\mathcal PP of complete vertex sets such that every edge of GGG belongs to exactly one member of P\mathcal PP. Members may share vertices but may not share edges. Write cp⁡(G)\operatorname{cp}(G)cp(G) for the minimum possible number of pieces.

The asymptotic notation

n26+O(n)\frac{n^2}{6}+O(n)6n2​+O(n)

means that there are constants C>0C>0C>0 and n0≥1n_0\ge1n0​≥1, chosen independently of GGG and nnn, such that every chordal nnn-vertex graph with n≥n0n\ge n_0n≥n0​ has a clique partition with at most n2/6+Cnn^2/6+Cnn2/6+Cn pieces.

Formalization targets

Erdős Problem 81

The root theorem is

∃C>0 ∃n0≥1 ∀n≥n0 ∀G chordal on n vertices,cp⁡(G)≤n26+Cn.\exists C>0\ \exists n_0\ge1\ \forall n\ge n_0\ \forall G\text{ chordal on }n\text{ vertices}, \qquad \operatorname{cp}(G)\le \frac{n^2}{6}+Cn.∃C>0 ∃n0​≥1 ∀n≥n0​ ∀G chordal on n vertices,cp(G)≤6n2​+Cn.

The quantifier order is essential: CCC and n0n_0n0​ are universal and cannot depend on the graph.

Leading-coefficient milestone

The supporting target records the weaker uniform statement

∀ε>0 ∃n0 ∀n≥n0 ∀G chordal on n vertices,cp⁡(G)≤(16+ε)n2.\forall\varepsilon>0\ \exists n_0\ \forall n\ge n_0\ \forall G\text{ chordal on }n\text{ vertices}, \qquad \operatorname{cp}(G)\le \left(\frac16+\varepsilon\right)n^2.∀ε>0 ∃n0​ ∀n≥n0​ ∀G chordal on n vertices,cp(G)≤(61​+ε)n2.

This is the precise n2/6+o(n2)n^2/6+o(n^2)n2/6+o(n2) form. It is not equivalent to the root: choosing ε=1/n\varepsilon=1/nε=1/n is invalid because the cutoff may depend on the fixed value of ε\varepsilonε.

Significance

The root would determine the clique-partition extremum for chordal graphs up to a linear remainder, matching the scale of the complete-split examples that motivate the coefficient 1/61/61/6. It would refine a leading-order asymptotic theorem into a uniform estimate strong enough to distinguish second-order behavior.

Formalization creates a clean interface among perfect elimination orderings, exact edge partitions, fractional edge-and-triangle decompositions, and integer triangle packings. It also forces the proof to distinguish a partition from a cover and original graph order from the order of any auxiliary hypergraph. These definitions can support other decomposition problems on chordal and split graphs.

Difficulty

Perfect elimination does not by itself give the sharp partition count. Greedily taking maximal cliques may overlap in edges or accumulate too many singleton pieces. Similarly, a fractional edge-and-triangle partition can achieve the right leading coefficient while integer rounding loses o(n2)o(n^2)o(n2) pieces; the root requires that loss to be only O(n)O(n)O(n).

The dense-packing theorem has quantifiers of the form “for every fixed ε>0\varepsilon>0ε>0 there exists N(ε)N(\varepsilon)N(ε).” It therefore yields a uniform subquadratic error but no linear error. Any proof of the root must add a chordal-specific rounding or extremal reduction rather than treating the general packing theorem as if its ε\varepsilonε could vary with nnn.

Formalization scope

Graphs are finite and simple. Chordality is encoded by existence of a perfect-elimination ranking, including disconnected and edgeless graphs. A clique piece is a finite vertex set that spans a complete subgraph. Exactness means every actual edge occurs in exactly one piece; no nonedge can occur inside a piece. Bounds are compared in R\mathbb RR so the displayed asymptotic expressions retain their conventional form, while the number of parts remains a natural number.

The candidate derivation of the leading coefficient imports finite linear-programming duality and the Haxell–Rödl/Yuster fixed-triangle packing approximation. It is candidate_only, not an admitted result or kernel proof. Contributions may formalize the perfect-elimination lemmas, the fractional compression, the uniform packing interface, complete-split lower examples, or the root linear rounding theorem. A result for edge-and-triangle pieces only, a fractional partition, or one fixed order must not be presented as the unrestricted integer clique-partition theorem.

Selected references

  • P. Erdős, E. T. Ordman, and Y. Zalcstein, Clique Partitions of Chordal Graphs, Combinatorics, Probability and Computing 2(4), 1993. https://doi.org/10.1017/S0963548300000808
  • P. E. Haxell and V. Rödl, Integer and Fractional Packings in Dense Graphs, Combinatorica 21, 2001. https://doi.org/10.1007/s004930170003
  • R. Yuster, Integer and fractional packing of families of graphs, 2003. https://arxiv.org/abs/math/0305350
  • Erdős Problems, Problem 81. https://www.erdosproblems.com/81
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Complexity Theory·Captain: Robin Kothari

Block Sensitivity vs. Sensitivity: Reach 2.10Open Problem

How far apart can the two sensitivities be?

Sensitivity and block sensitivity measure two ways that a Boolean function can react to changes in its input. Both are basic measures in query complexity, where the input is accessed one coordinate at a time. Their relationship asks how much a function's response to changes involving several coordinates can exceed its response to individual coordinate changes.

Hao Huang's proof of the Sensitivity Conjecture established a polynomial relationship between sensitivity and polynomial degree in 2019, resolving the qualitative polynomial-relation question. The quantitative problem of obtaining the largest possible separation exponent remains the focus of this campaign. This mission asks for the specific exponent 2.10=21/102.10=21/102.10=21/10.

The relevant progression is:

  • The classical benchmark is a quadratic separation, included among the campaign's proved entries.
  • In 2026, OpenAI problem 132 supplied a superquadratic construction and a Lean formalization establishing an exponent strictly above 222.
  • On 16 October 2026, the campaign had accepted proofs at exponents 2.03912.03912.0391 and 2.052.052.05. The 2.05 entry is the proved benchmark when this proposal is drafted.
  • The present mission proposes 2.102.102.10 as the next research target. It does not assert that a construction achieving this exponent is already known.

Boolean functions and sensitive changes

A total Boolean function assigns an output bit to every input consisting of nnn bits:

f:{0,1}n⟶{0,1}.f:\{0,1\}^n\longrightarrow\{0,1\}.f:{0,1}n⟶{0,1}.

For an input xxx and a coordinate set BBB, write xBx^BxB for the input obtained by flipping exactly the coordinates in BBB. The sensitivity at an input counts the individual coordinates whose flip changes the output:

s(f,x)=∣{i:f(x{i})≠f(x)}∣.s(f,x)=\bigl|\{i:f(x^{\{i\}})\ne f(x)\}\bigr|.s(f,x)=​{i:f(x{i})=f(x)}​.

The block sensitivity at an input is the largest number of pairwise disjoint nonempty coordinate sets whose separate flips each change the output:

bs(f,x)=max⁡{m:there are disjoint nonempty B1,…,Bm with f(xBj)≠f(x) for all j}.bs(f,x)=\max\bigl\{m:\text{there are disjoint nonempty }B_1,\ldots,B_m \text{ with }f(x^{B_j})\ne f(x)\text{ for all }j\bigr\}.bs(f,x)=max{m:there are disjoint nonempty B1​,…,Bm​ with f(xBj​)=f(x) for all j}.

The two global measures maximize over all inputs:

s(f)=max⁡xs(f,x),bs(f)=max⁡xbs(f,x).s(f)=\max_x s(f,x),\qquad bs(f)=\max_x bs(f,x).s(f)=xmax​s(f,x),bs(f)=xmax​bs(f,x).

There is no restriction to monotone functions, symmetric functions, or any particular construction. The number of variables may vary with the desired sensitivity threshold and approximation to the exponent.

Target: separation exponent at least 2.10

Prove that for every real ε>0\varepsilon>0ε>0 and every integer S≥2S\ge2S≥2, there are a dimension nnn and a total Boolean function fff such that

s(f)≥S,bs(f)≥s(f)21/10−ε.s(f)\ge S,\qquad bs(f)\ge s(f)^{21/10-\varepsilon}.s(f)≥S,bs(f)≥s(f)21/10−ε.

The shared campaign predicate SensitivityCampaign.AchievesExponent (2.10 : ℝ) expresses exactly this statement. The decimal denotes the exact rational number 21/1021/1021/10. The witnesses may depend on both ε\varepsilonε and SSS; no fixed function is required to work for every threshold. The unbounded-threshold requirement is essential to the asymptotic separation being asked for.

What reaching 2.10 would establish

A solution would strengthen the campaign's proved lower bound on the possible separation exponent from 2.052.052.05 to 2.102.102.10. It would provide Boolean functions with arbitrarily large sensitivity and block sensitivity growing with the specified larger power, with an arbitrarily small loss in the exponent.

This is an open research target, rather than a request to transcribe an existing proof of 2.102.102.10. A complete contribution must establish the mathematical claim and provide a Lean proof using the shared definitions. Improvements to constructions, sharper bounds for existing constructions, and reusable supporting results are all relevant contributions. Acceptance of a proposal merely opens the target; a proof is needed to establish the new bound.

The main difficulty

A successful construction must support many disjoint sensitive blocks while controlling sensitivity at every input. A large collection of block witnesses at one input supplies only part of the required information: the upper bound on sensitivity is global. Bounds that lose too much in this global analysis can erase the desired exponent improvement.

The available campaign infrastructure already includes proved theorems converting suitable finite certificates into the asymptotic conclusion. The unresolved work is to obtain stronger certified bounds on a construction, or to establish the asymptotic target directly. A numerical estimate of a separation exponent does not replace a proof of the inequalities on which that estimate depends.

Formalization scope and existing foundations

Functions are represented as (Fin n → Bool) → Bool. Both complexity measures are natural-number maxima over finite sets; the comparison involving an exponent casts them to real numbers and uses real exponentiation. Since every requested witness satisfies s(f)≥S≥2s(f)\ge S\ge2s(f)≥S≥2, its power has a positive base. Zero-dimensional and constant functions cannot satisfy the sensitivity requirement.

The proposal retains the foundation definitions SensitivitySeparation and SensitivityCampaign. It also references two already-proved supporting theorems, rather than introducing alternative definitions or assuming any particular family of functions:

  • SensitivityCampaign.finite_certificate: bounds on sensitivity and block sensitivity at a chosen input, together with an exact power inequality, imply the campaign conclusion at the stated nonnegative exponent.
  • SensitivityCampaign.finite_certificate_log: the same finite bounds certify the exponent given by the ratio of their logarithms.

These statements are included as reusable foundation milestones. Their hypotheses require integer bounds greater than one, so the logarithmic denominator is positive. Solvers may use either criterion or prove the quantified target directly. The environment is Lean 4.33.1 with the campaign's pinned Mathlib revision.

Selected references

  • Hao Huang, Induced subgraphs of hypercubes and a proof of the Sensitivity Conjecture, Annals of Mathematics 190 (2019), 949–955. Article.
  • OpenAI, A superquadratic separation between sensitivity and block sensitivity, problem 132, 2026. Formalization scope and source links.
  • Prove2Me, Block Sensitivity vs. Sensitivity, 2026. Campaign, shared foundations, and verified entries.
5 thms3 active usersReviewed
Complexity Theory·Captain: abhishekdpseok

Block Sensitivity vs. Sensitivity: Reach 2.0635Open Problem

Establish a separation exponent of at least 2.06352.06352.0635 between block sensitivity and sensitivity of total Boolean functions.

For every ε>0\varepsilon>0ε>0 and integer S≥2S\ge2S≥2, construct fff with s(f)≥Ss(f)\ge Ss(f)≥S and bs(f)≥s(f)2.0635−εbs(f)\ge s(f)^{2.0635-\varepsilon}bs(f)≥s(f)2.0635−ε.

5 thms3 active usersReviewed
Graph Theory·Captain: mikedeng1

A Solution to the 1-2-3 Conjecture: Every Finite Graph Without a K2 Component Has an Edge-Weighting from {1,2,3} Whose Weighted Degrees Properly Color the VerticesResearch Paper

Motivation

Many graph-colouring questions ask whether a proper colouring can be produced indirectly, from local data, instead of being assigned vertex by vertex. In the 1-2-3 Conjecture, the colours are produced by weighting the edges of a graph and colouring each vertex with the sum of the weights at it. Karoński, Łuczak and Thomason asked in 2004 whether weights from {1,2,3}\{1,2,3\}{1,2,3} always suffice (J. Combin. Theory Ser. B 91 (2004)). The question started as a local version of the irregularity strength problem, where all vertices, not only adjacent ones, must receive different sums. It became one of the most studied problems on graph labellings. Surveys: Seamone (arXiv:1211.5122) and Grytczuk (DOI 10.1016/j.ejc.2020.103213).

Timeline.

  • 2004: Karoński, Łuczak, Thomason state the conjecture and prove it for 3-colourable graphs.
  • 2007: Addario-Berry, Dalal, McDiarmid, Reed and Thomason give the first finite bound, k=30k=30k=30; later k=16k=16k=16 (Addario-Berry et al.) and k=13k=13k=13 (Wang and Yu).
  • 2010: Kalkowski, Karoński and Pfender reach k=5k=5k=5 (J. Combin. Theory Ser. B 100 (2010)).
  • Before 2023: Przybyło proves the conjecture for ddd-regular graphs with d≥108d\ge 10^8d≥108 and for graphs with minimum degree δ(G)≥Clog⁡Δ(G)\delta(G)\ge C\log\Delta(G)δ(G)≥ClogΔ(G); Zhong proves it for graphs with minimum degree at least 0.99985∣V∣0.99985|V|0.99985∣V∣ (references [20], [21], [31] of the paper).
  • 2023: Keusch reaches k=4k=4k=4 with a maximum-cut and flow argument (Combinatorica 43 (2023), 651–658; reference [18] of the paper).
  • 2024: Keusch proves the conjecture, k=3k=3k=3 (J. Combin. Theory Ser. B (2024), arXiv:2303.02611).

Setting

Let G=(V,E)G=(V,E)G=(V,E) be a finite simple graph and write N(v)N(v)N(v) for the neighbourhood of a vertex vvv. A kkk-edge-weighting is a function ω:E→{1,…,k}\omega:E\to\{1,\dots,k\}ω:E→{1,…,k}. The weighted degree of vvv is

sω(v)=∑w∈N(v)ω({v,w}).s_\omega(v)=\sum_{w\in N(v)}\omega(\{v,w\}).sω​(v)=w∈N(v)∑​ω({v,w}).

Two adjacent vertices v,wv,wv,w have a coloring conflict if sω(v)=sω(w)s_\omega(v)=s_\omega(w)sω​(v)=sω​(w). The weighting is vertex-coloring if no edge is a coloring conflict, that is, if sωs_\omegasω​ is a proper vertex colouring of GGG.

If GGG has a connected component consisting of a single edge {v,w}\{v,w\}{v,w} (a component isomorphic to K2K_2K2​), then sω(v)=sω(w)=ω({v,w})s_\omega(v)=s_\omega(w)=\omega(\{v,w\})sω​(v)=sω​(w)=ω({v,w}) for every ω\omegaω, so no weighting is vertex-coloring. This is the only obstruction.

The proof uses a few further objects:

  • for W⊆VW\subseteq VW⊆V, deg⁡W(v)=∣N(v)∩W∣\deg_W(v)=|N(v)\cap W|degW​(v)=∣N(v)∩W∣;
  • for disjoint S,T⊆VS,T\subseteq VS,T⊆V, the bipartite subgraph G(S,T)G(S,T)G(S,T) with vertex set S∪TS\cup TS∪T and the edges of GGG between SSS and TTT;
  • a good R-B-partition (Definition 6): a partition V=R∪BV=R\cup BV=R∪B with RRR independent, G(R,B)G(R,B)G(R,B) connected and ∣B∣|B|∣B∣ even;
  • a maximum cut C=(S,T)C=(S,T)C=(S,T) and an auxiliary unit-capacity flow network GC,F,σG_{C,F,\sigma}GC,F,σ​ built from it (Lemma 4).

The Lean names are wdeg G ω v for sω(v)s_\omega(v)sω​(v), IsWeighting G k ω, IsVertexColoring G ω, NoK2Component G and IsGoodPartition G U R B.

Formalization targets

Goal: Theorem 1 (p. 2)

If no connected component of GGG is isomorphic to K2K_2K2​, then there is ω:E→{1,2,3}\omega:E\to\{1,2,3\}ω:E→{1,2,3} with

∑u∈N(v)ω({u,v})≠∑u∈N(w)ω({u,w})for every edge {v,w}∈E.\sum_{u\in N(v)}\omega(\{u,v\})\ne\sum_{u\in N(w)}\omega(\{u,w\})\qquad\text{for every edge }\{v,w\}\in E.u∈N(v)∑​ω({u,v})=u∈N(w)∑​ω({u,w})for every edge {v,w}∈E.

The connected case (p. 14)

Every finite connected graph with ∣V∣≥3|V|\ge 3∣V∣≥3 has a vertex-coloring {1,2,3}\{1,2,3\}{1,2,3}-edge-weighting. This is the statement the case analysis of the proof establishes. Theorem 1 follows by treating each component separately.

Milestones along the proof

In the order the proof uses them:

  • Lemma 2: an independent set RRR with G(R,V∖R)G(R,V\setminus R)G(R,V∖R) connected, alternating along a shortest path.
  • Lemma 4: a flow of value ∣F∣|F|∣F∣ in GC,F,σG_{C,F,\sigma}GC,F,σ​.
  • Lemma 3: a weighting and odd designated colours fff, proper on GGG, with ∣sω+h−f∣=1|s_\omega+h-f|=1∣sω​+h−f∣=1.
  • Lemma 5: a bipartite weighting with prescribed parities on RRR and exact weighted degrees on BBB, plus a second version with extra control along a fixed path.
  • Lemmas 7, 8 and 9: the three basic situations in which a good R-B-partition of GGG, or of GGG minus one vertex, yields a vertex-coloring weighting.
  • Lemma 10: every connected graph of minimum degree at least 222 has an edge whose removal, together with its two ends, leaves a connected graph.

Significance

The result. Theorem 1 settles a conjecture that was open for nineteen years. The constant is optimal: the triangle K3K_3K3​ has no vertex-coloring {1,2}\{1,2\}{1,2}-weighting (two of its three edges get the same weight, and then two vertices tie), and deciding whether a vertex-coloring {1,2}\{1,2\}{1,2}-weighting exists is NP-complete (Dudek and Wajc, 2011). Theorem 1 also closes the sequence of upper bounds 30,16,13,6,5,430,16,13,6,5,430,16,13,6,5,4 on the number of weights needed. Variants remain open: total weightings with vertex weights {1,2}\{1,2\}{1,2} and edge weights {1,2}\{1,2\}{1,2} (the 1-2 Conjecture), and list versions.

Formalizing it. The theorem is proved, in a 16-page paper. As far as is known it has no machine-checked proof. A formal proof would verify the case analysis at the end of the paper (pp. 11–14, about fifteen sub-cases with three auxiliary graphs), which is where errors in such arguments usually hide. The flow lemma is quoted from the 2023 paper and not reproved here. Formalizing it means proving an integral max-flow statement for a specific unit-capacity network, and the result can be reused for other flow-based weighting arguments.

Difficulty

The obvious approach is to fix the weights greedily, edge by edge or vertex by vertex. This fails because changing one edge weight changes two weighted degrees at once, so a repair at one vertex can create a conflict at a neighbour. With only three weights there is no room to absorb this. Every earlier bound above 333 used the extra weight as slack.

The difficulty in the paper is parity. The construction needs the set BBB of blue vertices to have even cardinality, and ∣B∣|B|∣B∣ cannot always be made even. The proof therefore removes one or two vertices, applies one of three base lemmas, and re-inserts the vertices. That re-insertion may need one more round of weight changes along a path. Each sub-case has to keep track of parities, of exact weighted degrees on BBB, and of the connectivity of G(R,B)G(R,B)G(R,B) simultaneously.

Formalization scope

Representation.

  • Graphs are SimpleGraph V on a finite vertex type V with decidable adjacency. The paper's "graph" is a finite simple graph: weighted degrees are finite sums, and the proof uses a maximum cut and vertex orderings.
  • An edge-weighting is a function ω : Sym2 V → ℕ on unordered pairs. IsWeighting G k ω requires ω(e)∈{1,…,k}\omega(e)\in\{1,\dots,k\}ω(e)∈{1,…,k} for every edge eee of GGG; values on non-edges are unconstrained and never used.
  • The weighted degree sums over G.neighborFinset v. Since {u,v}={v,u}\{u,v\}=\{v,u\}{u,v}={v,u} in Sym2, the single sum equals both sides of Theorem 1's displayed inequality.
  • "No component isomorphic to K2K_2K2​" uses Mathlib's connected components and graph isomorphism. A sanity file proves it equivalent to "no edge both of whose ends have degree 1".
  • G(R,B)G(R,B)G(R,B) is Mathlib's G.between R B, induced on R∪BR\cup BR∪B wherever that vertex set is not all of VVV.

Conventions that differ from the page. There are two, both disclosed in the item statements:

  1. The vertex type is finite.
  2. The flow of Lemma 4 is integral. The page says "an sss-ttt-flow"; the paper uses integrality immediately afterwards, and the two versions are equivalent by the integrality theorem.

Lemma 5's condition α(v)∈{2deg⁡(v)−1,2deg⁡(v),2deg⁡(v)+1}\alpha(v)\in\{2\deg(v)-1,2\deg(v),2\deg(v)+1\}α(v)∈{2deg(v)−1,2deg(v),2deg(v)+1} is stated in Z\mathbb ZZ, and Lemma 3's ∣sω+h−f∣=1|s_\omega+h-f|=1∣sω​+h−f∣=1 is computed in Z\mathbb ZZ.

Ruled out. None of the following formalizations is faithful:

  • weights allowed to be 000, which deletes edges;
  • weights constrained off the edges;
  • the K2K_2K2​ hypothesis strengthened to minimum degree at least 222, or to connectivity;
  • "adjacent vertices" replaced by "all pairs of vertices", which is the irregularity-strength problem.

Infrastructure. A complete development needs:

  • spanning trees and parity arguments on trees (Lemma 5);
  • depth-first search trees (Lemma 10);
  • maximum cuts and an integral max-flow or augmenting-path argument for GC,F,σG_{C,F,\sigma}GC,F,σ​ (Lemma 4), together with path decompositions of unit flows (the proof of Lemma 3).

The flow and cut definitions are stated for arbitrary finite graphs and can be reused. Contributions are welcome at every level: proofs of individual lemmas, general flow-decomposition results, and the final case analysis.

Selected references

  • R. Keusch, A Solution to the 1-2-3 Conjecture, J. Combin. Theory Ser. B, 2024. arXiv:2303.02611v4, DOI 10.1016/j.jctb.2024.01.002
  • R. Keusch, Vertex-coloring graphs with 4-edge-weightings, Combinatorica 43 (2023), 651–658. Cited as [18] in arXiv:2303.02611v4.
  • M. Karoński, T. Łuczak, A. Thomason, Edge weights and vertex colours, J. Combin. Theory Ser. B 91 (2004), 151–157. DOI 10.1016/j.jctb.2003.12.001
  • M. Kalkowski, M. Karoński, F. Pfender, Vertex-coloring edge-weightings: towards the 1-2-3-conjecture, J. Combin. Theory Ser. B 100 (2010). DOI 10.1016/j.jctb.2009.06.002
  • A. Dudek, D. Wajc, On the complexity of vertex-coloring edge-weightings, Discrete Math. Theor. Comput. Sci. 13 (2011), 45–50. Cited as [11] in arXiv:2303.02611v4.
  • B. Seamone, The 1-2-3 Conjecture and related problems: a survey, 2012. arXiv:1211.5122
  • J. Grytczuk, From the 1-2-3 Conjecture to the Riemann Hypothesis, European J. Combin. 91 (2021), 103213. DOI 10.1016/j.ejc.2020.103213
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Graph Theory·Captain: mikedeng1

Generalized Turán Problems for Even Cycles 6: The Maximum Number of (2l+1)-Cycles in a Graph with No Cycle of Length 3, …, 2l or 2k+1 Is Ω(n^{1+1/(2k+2)}) and O(n²)Research Paper

Motivation

Generalized Turán problems ask for the maximum number of copies of a fixed graph HHH in an nnn-vertex graph that avoids a family F\mathcal FF of forbidden subgraphs. The ordinary Turán number counts edges (H=K2H = K_2H=K2​); Alon and Shikhelman (arXiv:1506.03397) started the systematic study of the general function ex(n,H,F)\mathrm{ex}(n, H, \mathcal F)ex(n,H,F), building on earlier results of Erdős on cliques, Bollobás and Győri on pentagons in triangle-free graphs, and Győri and Li on triangles in graphs without a long odd or even cycle. Counting cycles in graphs with forbidden cycles is closely tied to extremal problems for Berge hypergraphs, and the constructions used are sparse hypergraphs of large girth.

Gerbner, Győri, Methuku and Vizer, Generalized Turán problems for even cycles (arXiv:1712.07079, J. Combin. Theory Ser. B, 2020), determine or bound ex(n,Cℓ,F)\mathrm{ex}(n, C_{\ell}, \mathcal F)ex(n,Cℓ​,F) for many cycle families. This mission covers one of their results in §6.2: the number of odd cycles C2l+1C_{2l+1}C2l+1​ in a graph in which every shorter cycle is forbidden and, in addition, one longer odd cycle C2k+1C_{2k+1}C2k+1​ is forbidden. Its companion (Theorem 17, a separate mission of this series) forbids a longer even cycle C2kC_{2k}C2k​ instead, and there the paper obtains a sub-quadratic upper bound; with an odd extra cycle only a quadratic upper bound is proved.

Setting

All graphs are finite and simple. For an integer a≥3a \ge 3a≥3, CaC_aCa​ is the cycle of length aaa. For a set AAA of integers a≥3a \ge 3a≥3, write CA={Ca:a∈A}\mathcal C_A = \{C_a : a \in A\}CA​={Ca​:a∈A}; a graph is CA\mathcal C_ACA​-free if it contains no CaC_aCa​ with a∈Aa \in Aa∈A as a (not necessarily induced) subgraph. The family C2l\mathcal C_{2l}C2l​ consists of all cycles C3,C4,…,C2lC_3, C_4, \dots, C_{2l}C3​,C4​,…,C2l​.

For graphs HHH and GGG, N(H,G)\mathcal N(H, G)N(H,G) is the number of copies of HHH in GGG: subgraphs of GGG isomorphic to HHH, counted without labelling. The generalized Turán number is

ex(n,H,F)=max⁡{N(H,G):G has n vertices and contains no member of F}.\mathrm{ex}(n, H, \mathcal F) = \max\{\mathcal N(H, G) : G \text{ has } n \text{ vertices and contains no member of } \mathcal F\}.ex(n,H,F)=max{N(H,G):G has n vertices and contains no member of F}.

For a vertex vvv of GGG, Ni(v)N_i(v)Ni​(v) is the set of vertices at distance exactly iii from vvv.

A hypergraph is a finite set of hyperedges (vertex sets); it is rrr-uniform if each hyperedge has rrr vertices. A Berge cycle of length j≥2j \ge 2j≥2 is a sequence v1,h1,v2,h2,…,vj,hj,v1v_1, h_1, v_2, h_2, \dots, v_j, h_j, v_1v1​,h1​,v2​,h2​,…,vj​,hj​,v1​ of distinct vertices and distinct hyperedges with vi,vi+1∈hiv_i, v_{i+1} \in h_ivi​,vi+1​∈hi​ and vj,v1∈hjv_j, v_1 \in h_jvj​,v1​∈hj​. The girth of a hypergraph is the length of its shortest Berge cycle; "girth at least sss" means there is no Berge cycle of length 2,…,s−12, \dots, s-12,…,s−1.

Formalization targets

Goal: Theorem 18

For all integers k>l≥2k > l \ge 2k>l≥2, as n→∞n \to \inftyn→∞,

Ω(n1+12k+2)=ex(n,C2l+1,C2l∪{C2k+1})=O(n2).\Omega\big(n^{1+\frac{1}{2k+2}}\big) = \mathrm{ex}\big(n, C_{2l+1}, \mathcal C_{2l} \cup \{C_{2k+1}\}\big) = O(n^2).Ω(n1+2k+21​)=ex(n,C2l+1​,C2l​∪{C2k+1​})=O(n2).

Both the lower and the upper bound are part of the goal; the implied constants may depend on kkk and lll only.

Milestones

  1. Theorem 29 (Nešetřil–Rödl). For r≥2r \ge 2r≥2, s≥3s \ge 3s≥3 and all large nnn there is an rrr-uniform hypergraph on nnn vertices of girth at least sss with at least n1+1/sn^{1+1/s}n1+1/s hyperedges.
  2. The construction. Replacing each hyperedge of a (2l+1)(2l+1)(2l+1)-uniform hypergraph of girth at least 2k+22k+22k+2 by a copy of C2l+1C_{2l+1}C2l+1​ yields a graph with no cycle of length at most 2k+12k+12k+1 other than 2l+12l+12l+1, and with at least as many copies of C2l+1C_{2l+1}C2l+1​ as hyperedges.
  3. Cycles through a vertex. In a C2l∪{C2k+1}\mathcal C_{2l} \cup \{C_{2k+1}\}C2l​∪{C2k+1​}-free graph there is no edge inside Ni(v)N_i(v)Ni​(v) for i<li < li<l, every C2l+1C_{2l+1}C2l+1​ through vvv uses exactly one edge inside Nl(v)N_l(v)Nl​(v), and so the number of C2l+1C_{2l+1}C2l+1​'s through vvv is at most the number of edges inside Nl(v)N_l(v)Nl​(v).
  4. Claim 19. In a C2l∪{C2k+1}\mathcal C_{2l} \cup \{C_{2k+1}\}C2l​∪{C2k+1​}-free graph the number of edges inside Nl(v)N_l(v)Nl​(v) is at most C(k,l) ∣Nl(v)∣≤C(k,l) nC(k,l)\,|N_l(v)| \le C(k,l)\, nC(k,l)∣Nl​(v)∣≤C(k,l)n.

The supporting published theorem erdos_gallai_path_conjecture (the Erdős–Gallai path theorem: a graph on nnn vertices with no path of m+1m+1m+1 edges has at most mn/2mn/2mn/2 edges) is included as a reference item; the paper's proof of Claim 19 invokes it.

Significance

Theorem 18 shows that forbidding all cycles up to length 2l2l2l together with one odd cycle C2k+1C_{2k+1}C2k+1​ still permits polynomially more than nnn copies of C2l+1C_{2l+1}C2l+1​, at least n1+1/(2k+2)n^{1 + 1/(2k+2)}n1+1/(2k+2), and at most O(n2)O(n^2)O(n2). Compared with Theorem 17, where an even cycle C2kC_{2k}C2k​ is forbidden and the count is O(n1+l/(l+1))O(n^{1+l/(l+1)})O(n1+l/(l+1)), it isolates the role of the parity of the extra forbidden cycle. The authors conjecture that the true order is sub-quadratic (Conjecture 34, §8 of the paper), so the gap between the two exponents in Theorem 18 is a stated open question; a formal development of the known bounds is the base on which any improvement would be checked.

The result is proved in the paper; to the best of current knowledge it has no machine-checked proof. Formalizing it produces: a reusable encoding of generalized Turán numbers for cycle families on top of Mathlib's copyCount; Berge cycles and the girth of uniform hypergraphs; distance layers Ni(v)N_i(v)Ni​(v); and a formal statement of the Nešetřil–Rödl existence theorem, which is used as a black box by many extremal constructions.

Difficulty

The upper bound requires careful bookkeeping of distance layers around a vertex in a graph without short cycles. The obvious estimate, bounding the copies of C2l+1C_{2l+1}C2l+1​ through vvv by the number of pairs of vertices of Nl(v)N_l(v)Nl​(v), gives only ∣Nl(v)∣2|N_l(v)|^2∣Nl​(v)∣2 per vertex and hence nothing better than the trivial bound overall; the content of Claim 19 is that forbidding the single cycle C2k+1C_{2k+1}C2k+1​ makes the edge count inside Nl(v)N_l(v)Nl​(v) linear, and the parity of 2k+12k+12k+1 is used essentially.

The lower bound rests on Theorem 29, a cited result that is not proved in the paper, and for which Mathlib has no supporting theory of uniform hypergraphs or Berge cycles. For the construction itself, the paper only asserts that the cycle-replacement graph has no short cycle other than the inserted C2l+1C_{2l+1}C2l+1​'s; a cycle of the graph may wander through many hyperedges, and relating it to the girth of the hypergraph is the step a formal proof has to supply.

Formalization scope

  • Vertices of an nnn-vertex graph are Fin n; graphs are SimpleGraph (Fin n). Cycles are Mathlib's SimpleGraph.cycleGraph, and N(H,G)\mathcal N(H, G)N(H,G) is Mathlib's unlabelled copyCount.
  • C2l∪{C2k+1}\mathcal C_{2l} \cup \{C_{2k+1}\}C2l​∪{C2k+1​} is encoded as the set of lengths Set.Icc 3 (2 * l) ∪ {2 * k + 1}: every length 3,…,2l3, \dots, 2l3,…,2l, not only 2l2l2l.
  • ex(n,H,CA)\mathrm{ex}(n, H, \mathcal C_A)ex(n,H,CA​) is the supremum of a nonempty finite set of natural numbers, hence a true maximum (checked in a sanity file).
  • Ni(v)N_i(v)Ni​(v) uses the extended distance edist, so vertices of other components lie in no layer.
  • Hypergraphs are Finset (Finset V); "girth at least sss" is a predicate (no Berge cycle of length 2,…,s−12, \dots, s-12,…,s−1), not an infimum, so a hypergraph without Berge cycles has every girth.
  • Ω\OmegaΩ and OOO are Mathlib's Asymptotics.IsBigO along atTop, with real exponents; both directions of the goal are stated.
  • Claim 19's O(⋅)O(\cdot)O(⋅) is a single constant chosen before nnn, the graph and the vertex. The milestone on cycles through vvv uses the page's C2l∪{C2k+1}\mathcal C_{2l} \cup \{C_{2k+1}\}C2l​∪{C2k+1​}-freeness assumption.

A formalization that states only the upper bound, uses labelled copies, lets a constant depend on the graph, omits the short cycles C3,…,C2l−1C_3, \dots, C_{2l-1}C3​,…,C2l−1​ from the forbidden family, or defines ex\mathrm{ex}ex by a supremum that can take a junk value, does not prove this theorem.

Contributions welcome: proofs of the milestones, a proof of Theorem 29, and general lemmas on breadth-first layers in graphs of large girth, which are reusable by the companion missions of this series.

Selected references

  • D. Gerbner, E. Győri, A. Methuku, M. Vizer, Generalized Turán problems for even cycles, J. Combin. Theory Ser. B (2020); arXiv:1712.07079v3. https://arxiv.org/abs/1712.07079, https://doi.org/10.1016/j.jctb.2020.05.005
  • N. Alon, C. Shikhelman, Many T copies in H-free graphs, J. Combin. Theory Ser. B 121 (2016). https://arxiv.org/abs/1506.03397
  • J. Nešetřil, V. Rödl, On a probabilistic graph-theoretical method, Proc. Amer. Math. Soc. 72(2), 417–421 (1978). https://www.ams.org/journals/proc/1978-072-02/
  • P. Erdős, T. Gallai, On maximal paths and circuits of graphs, Acta Math. Acad. Sci. Hungar. 10 (1959). https://doi.org/10.1007/BF02024498
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Machine Learning·Captain: mikedeng1

Online Learning and Online Convex Optimization 4: Finite Littlestone Dimension Gives Agnostic Online Regret 2√(Ldim·ln(eT/Ldim)·T)Research Paper

Motivation

Online classification is the simplest model of learning from a stream: on every round a learner sees an instance, guesses its binary label, and is then told the truth. Cover's impossibility argument shows that against an adversary no deterministic learner can compete with even two fixed hypotheses, so two relaxations are studied. In the realizable case the labels are generated by some unknown hypothesis of a known class HHH, and the question is how many mistakes a learner must make. In the agnostic (unrealizable) case the labels are arbitrary, the learner may randomize, and it is judged by its regret: its expected number of mistakes minus that of the best hypothesis in HHH in hindsight.

For the realizable case Littlestone (1988) introduced the dimension now named after him and showed that it is exactly the optimal mistake bound. Whether the same quantity also governs the agnostic case was settled by Ben-David, Pál and Shalev-Shwartz (COLT 2009, link): a class is agnostically online learnable if and only if its Littlestone dimension is finite. This mission formalizes the upper-bound half of that result, in the form given by Shalev-Shwartz's survey Online Learning and Online Convex Optimization (Found. Trends Mach. Learn. 4(2), 2011, doi:10.1561/2200000018), Theorem 3.6.

Timeline:

  • 1988 — Littlestone defines the dimension and the Standard Optimal Algorithm (SOA) for the realizable case (doi:10.1007/BF00116827).
  • 1994 — Littlestone and Warmuth publish the Weighted Majority algorithm for prediction with expert advice (doi:10.1006/inco.1994.1009).
  • 2009 — Ben-David, Pál, Shalev-Shwartz: agnostic online learnability is characterized by finite Littlestone dimension, regret O(Ldim(H) Tln⁡T)O(\sqrt{\mathrm{Ldim}(H)\, T \ln T})O(Ldim(H)TlnT​), with a lower bound Ω(Ldim(H) T)\Omega(\sqrt{\mathrm{Ldim}(H)\,T})Ω(Ldim(H)T​).
  • 2011 — the survey presents the expert construction and the upper bound in the form formalized here.

Setting

Let XXX be a set of instances and let a hypothesis class HHH be a set of functions h:X→{0,1}h : X \to \{0,1\}h:X→{0,1}. On round t=1,…,Tt = 1, \dots, Tt=1,…,T the learner receives xtx_txt​, outputs pt∈[0,1]p_t \in [0,1]pt​∈[0,1] (the probability of predicting 111), receives yt∈{0,1}y_t \in \{0,1\}yt​∈{0,1} and pays ∣pt−yt∣|p_t - y_t|∣pt​−yt​∣, the probability of a wrong randomized guess. The prediction ptp_tpt​ may depend on (x1,y1),…,(xt−1,yt−1)(x_1,y_1),\dots,(x_{t-1},y_{t-1})(x1​,y1​),…,(xt−1​,yt−1​) and xtx_txt​, never on yty_tyt​. The regret against h∈Hh \in Hh∈H is

∑t=1T∣pt−yt∣−∑t=1T∣h(xt)−yt∣.\sum_{t=1}^T |p_t - y_t| - \sum_{t=1}^T |h(x_t) - y_t|.t=1∑T​∣pt​−yt​∣−t=1∑T​∣h(xt​)−yt​∣.

A shattered tree of depth ddd assigns an instance to every node of a complete binary tree of depth ddd, so that for every labelling (y1,…,yd)(y_1,\dots,y_d)(y1​,…,yd​) some h∈Hh \in Hh∈H agrees with yty_tyt​ at the ttt-th node of the root-to-leaf path that the labels select. The Littlestone dimension Ldim(H)\mathrm{Ldim}(H)Ldim(H) is the largest depth of a shattered tree.

The Standard Optimal Algorithm keeps the version space VtV_tVt​ of hypotheses consistent so far, splits it as Vt(r)={h∈Vt:h(xt)=r}V_t^{(r)} = \{h \in V_t : h(x_t) = r\}Vt(r)​={h∈Vt​:h(xt​)=r}, and predicts the rrr with the larger Ldim(Vt(r))\mathrm{Ldim}(V_t^{(r)})Ldim(Vt(r)​). For a set I={i1<⋯<iL}I = \{i_1 < \dots < i_L\}I={i1​<⋯<iL​} of rounds, Expert(i1,…,iL)(i_1,\dots,i_L)(i1​,…,iL​) runs the same rule but flips its prediction on the rounds of III and updates Vt+1=Vt(y^t)V_{t+1} = V_t^{(\hat y_t)}Vt+1​=Vt(y^​t​)​ using its own prediction instead of the label. Weighted Majority over ddd experts with costs zt∈[0,1]dz_t \in [0,1]^dzt​∈[0,1]d keeps weights wt[i]∝exp⁡(−η∑s<tzs[i])w_t[i] \propto \exp(-\eta \sum_{s<t} z_s[i])wt​[i]∝exp(−η∑s<t​zs​[i]) and pays ⟨wt,zt⟩\langle w_t, z_t\rangle⟨wt​,zt​⟩.

Formalization targets

Goal: Theorem 3.6, upper bound

For every class HHH with L=Ldim(H)<∞L = \mathrm{Ldim}(H) < \inftyL=Ldim(H)<∞ and every horizon T≥LT \ge LT≥L there is an online learner with pt∈[0,1]p_t \in [0,1]pt​∈[0,1] such that for every h∈Hh \in Hh∈H and every sequence of TTT examples

∑t=1T∣pt−yt∣−∑t=1T∣h(xt)−yt∣≤2Lln⁡(eT/L) T.\sum_{t=1}^T |p_t - y_t| - \sum_{t=1}^T |h(x_t) - y_t| \le 2\sqrt{L \ln(eT/L)\, T}.t=1∑T​∣pt​−yt​∣−t=1∑T​∣h(xt​)−yt​∣≤2Lln(eT/L)T​.

The paper states O(Ldim(H)ln⁡(T) T)O(\sqrt{\mathrm{Ldim}(H)\ln(T)\,T})O(Ldim(H)ln(T)T​); the constant 222 and the factor ln⁡(eT/L)\ln(eT/L)ln(eT/L) are what its proof gives.

Milestones

  • Lemma 3.3: SOA makes at most Ldim(H)\mathrm{Ldim}(H)Ldim(H) mistakes in the realizable case (already proved on the platform).
  • Lemma 3.7: for every h∈Hh \in Hh∈H and instance sequence some Expert(i1,…,iL)(i_1,\dots,i_L)(i1​,…,iL​) with L≤Ldim(H)L \le \mathrm{Ldim}(H)L≤Ldim(H) predicts h(xt)h(x_t)h(xt​) on every round.
  • Corollary 3.8: some such expert makes at most min⁡h∈H∑t∣h(xt)−yt∣\min_{h \in H}\sum_t |h(x_t) - y_t|minh∈H​∑t​∣h(xt​)−yt​∣ mistakes.
  • Equation (3.1): ∑L=0Ldim(H)(TL)≤(eT/Ldim(H))Ldim(H)\sum_{L=0}^{\mathrm{Ldim}(H)} \binom{T}{L} \le (eT/\mathrm{Ldim}(H))^{\mathrm{Ldim}(H)}∑L=0Ldim(H)​(LT​)≤(eT/Ldim(H))Ldim(H).
  • Theorem 3.1: Weighted Majority has regret at most log⁡(d)/η+ηT\log(d)/\eta + \eta Tlog(d)/η+ηT, and 2log⁡(d) T2\sqrt{\log(d)\,T}2log(d)T​ for η=log⁡(d)/T\eta = \sqrt{\log(d)/T}η=log(d)/T​.

Significance

The theorem shows that finiteness of one combinatorial parameter suffices for sublinear agnostic regret, for classes that may be infinite and need not have any parametric structure. With the lower bound it characterizes agnostic online learnability, parallel to the role of the VC dimension in PAC learning; the gap between the two dimensions (thresholds on [0,1][0,1][0,1] have VC dimension 111 and infinite Littlestone dimension) separates online from batch learnability. The expert construction, which simulates a realizable-case learner on guessed mistake rounds, is a reusable reduction from agnostic to realizable online learning.

On the formal side, the realizable theory (Littlestone dimension, SOA and its mistake bound) is already on the platform as published definitions and theorems from the textbook Understanding Machine Learning; the agnostic step, the expert construction, the bound on the number of experts and the Weighted Majority bound with bounded costs are not. To our knowledge no proof assistant library contains a machine-checked proof of the agnostic upper bound.

Difficulty

The obvious approach, running Weighted Majority with one expert per hypothesis, fails for infinite HHH and gives log⁡∣H∣\log|H|log∣H∣ instead of Ldim(H)\mathrm{Ldim}(H)Ldim(H) for finite HHH. The class must be replaced by a finite family that reproduces every h∈Hh \in Hh∈H on the given instance sequence while having only poly(T)Ldim(H)\mathrm{poly}(T)^{\mathrm{Ldim}(H)}poly(T)Ldim(H) members. The experts in the family never see labels, so the argument that each hhh is reproduced must track two runs, the expert's and SOA's on labels generated by hhh, and show their version spaces coincide round by round. This depends on the tie-breaking rule and on how an empty side Vt(r)V_t^{(r)}Vt(r)​ is ranked, details the informal argument leaves implicit. The bound on the number of experts is a Sauer-type binomial estimate that holds only in the range 1≤Ldim(H)≤T1 \le \mathrm{Ldim}(H) \le T1≤Ldim(H)≤T.

Formalization scope

The development reuses the published definitions UnderstandingML_Online (online algorithms as maps from the history and the current instance to a prediction, history, mistakes, cumLoss, cumLossHyp, ldim, ldimBot, soa, wmWeights) and the proved theorem UnderstandingML.soa_mistake_bound (Lemma 3.3). Sequences are indexed by Fin T and rounds from 000; the paper's indices 1≤i1<⋯<iL≤T1 \le i_1 < \dots < i_L \le T1≤i1​<⋯<iL​≤T become a set I⊆{0,…,T−1}I \subseteq \{0,\dots,T-1\}I⊆{0,…,T−1}. A learner of type OnlineAlgR X cannot read the current label, so the existential goal cannot be satisfied by a learner that peeks at yty_tyt​; the goal also requires pt∈[0,1]p_t \in [0,1]pt​∈[0,1]. Logarithms are natural.

Committed conventions:

  • The O(·) of Theorem 3.6 is instantiated as 2Lln⁡(eT/L) T2\sqrt{L\ln(eT/L)\,T}2Lln(eT/L)T​ under T≥LT \ge LT≥L, where L=Ldim(H)L = \mathrm{Ldim}(H)L=Ldim(H) is a natural number. At L=0L = 0L=0 the bound reads regret ≤0\le 0≤0.
  • Expert(i1,…,iL)(i_1,\dots,i_L)(i1​,…,iL​) breaks ties toward 000, as its box on p. 166 says; the SOA box (p. 163) breaks ties toward 111. Lemma 3.7 holds with the expert's rule. An empty Vt(r)V_t^{(r)}Vt(r)​ ranks below every nonempty class (ldimBot).
  • Theorem 3.1's bound 1 is stated for every η>0\eta > 0η>0, bound 2 for η∈(0,1)\eta \in (0,1)η∈(0,1), the tuned bound for every d≥1d \ge 1d≥1, T≥0T \ge 0T≥0; minima over experts and over HHH are stated as "for every expert", "for every hhh".
  • Equation (3.1) is stated with 1≤L≤T1 \le L \le T1≤L≤T, together with the count of index sets.

Excluded: the lower bound Ω(Ldim(H) T)\Omega(\sqrt{\mathrm{Ldim}(H)\,T})Ω(Ldim(H)T​) (the survey cites it to Ben-David, Pál and Shalev-Shwartz), Theorem 3.5 (VC dimension at most Littlestone dimension), Corollary 3.4. Lemma 3.2 and the Halving bound are already on the platform.

Welcome contributions: proofs of the milestones, a proof of SOA's mistake bound with ties toward 000, and a reusable version of the Weighted Majority bound for general finite expert types.

Selected references

  • S. Shalev-Shwartz, Online Learning and Online Convex Optimization, Foundations and Trends in Machine Learning 4(2), 2011, 107–194. https://doi.org/10.1561/2200000018
  • S. Ben-David, D. Pál, S. Shalev-Shwartz, Agnostic Online Learning, COLT 2009. https://www.cs.mcgill.ca/~colt2009/papers/032.pdf
  • N. Littlestone, Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm, Machine Learning 2, 1988, 285–318. https://doi.org/10.1007/BF00116827
  • N. Littlestone, M. K. Warmuth, The Weighted Majority Algorithm, Information and Computation 108(2), 1994, 212–261. https://doi.org/10.1006/inco.1994.1009
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Number Theory·Captain: marwahaha

Quasipolynomial Bounds for Arithmetic ProgressionsOpen Problem

Motivation

A divergent reciprocal sum is a sparse largeness condition on integers. The mission asks whether it still forces additive structure of every finite length. The source manuscript presents the surrounding research claim.

Setting

For A⊆ℕ, reciprocalTerm(A,n) is 1/n when n∈A and zero otherwise, using Lean’s total real inverse at n=0. A nonconstant arithmetic progression has terms a+i·d with positive integer d.

Formalization target

Prove

∑n∈A1n is not summable⟹∀k∈N, ∃a∈N, d∈N>0,a+id∈A (i<k).\sum_{n\in A}\frac1n\text{ is not summable}\quad\Longrightarrow\quad\forall k\in\mathbb N,\ \exists a\in\mathbb N,\ d\in\mathbb N_{>0},\quad a+id\in A\ (i<k).n∈A∑​n1​ is not summable⟹∀k∈N, ∃a∈N, d∈N>0​,a+id∈A (i<k).

The starting term a is a natural number and is not separately required positive; the step d is strictly positive.

The selected formal target is OAI.Erdos3.manuscriptReciprocalProgressionTheorem.

Significance and status

The selected endpoint is the reciprocal-sum progression theorem. The manuscript’s quantitative bounds for progression-free subsets motivate it but are not supplied as separate published targets. The published target is currently Open; the manuscript's argument and a checked Lean proof are separate deliverables.

Difficulty

Positive density arguments alone do not cover all sets with divergent reciprocal sums. The hypothesis permits sets whose density tends to zero.

Formalization scope

The formal hypothesis is ¬Summable of a real sequence and ranges over all natural-number sets. All natural lengths k are included, including the degenerate zero and one cases. The n=0 summand equals zero and does not affect divergence. No quantitative constants are asserted here.

Selected references

  • OpenAI, Quasipolynomial Bounds for Arithmetic Progressions, preprint, 2026. Pinned manuscript.
  • OpenAI, accompanying formal statements, commit adc7f1241b42. Goal source.
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Operations ResearchProbability·Captain: mikedeng1

Concentration of Measure and Isoperimetric Inequalities in Product Spaces XI: The Random Assignment Problem — Fluctuations of the Optimal Cost of Order (log N)²/(√N log log N)Textbook

Motivation

The assignment problem asks for a one-to-one matching of NNN agents to NNN tasks that minimizes the total cost. It is one of the basic problems of combinatorial optimization and is solved in polynomial time by the Hungarian method. Its random version, in which the N2N^2N2 costs are independent random variables, is a standard model for the typical behavior of optimization problems on random inputs, and it has been studied in probability, operations research and statistical physics.

Timeline:

  • Walkup (1979) showed that the expected optimal cost E(LN)E(L_N)E(LN​) of the random assignment problem with independent uniform [0,1][0,1][0,1] costs stays bounded as N→∞N \to \inftyN→∞ (doi:10.1137/0208036).
  • Karp (1987) proved E(LN)≤2E(L_N) \le 2E(LN​)≤2.
  • Talagrand (1995), in the article this mission formalizes, bounded the fluctuations of LNL_NLN​ around its median by K(log⁡N)2/(Nlog⁡log⁡N)K(\log N)^2/(\sqrt N \log\log N)K(logN)2/(N​loglogN), as a showcase of his concentration inequality for product measures (doi:10.1007/BF02699376).
  • Aldous (2001) proved the limit E(LN)→π2/6E(L_N) \to \pi^2/6E(LN​)→π2/6, the value predicted by Mézard and Parisi (doi:10.1002/rsa.1015).

This mission is Chapter 10 of Talagrand's article.

Setting

Fix NNN and two sets III and JJJ of cardinal NNN. An assignment is a one-to-one map τ\tauτ from III onto JJJ. Given a cost matrix a=(ai,j)i∈I,j∈Ja = (a_{i,j})_{i \in I, j \in J}a=(ai,j​)i∈I,j∈J​, the cost of τ\tauτ is ∑i∈Iai,τ(i)\sum_{i \in I} a_{i,\tau(i)}∑i∈I​ai,τ(i)​. In the random problem the costs are Xi,jX_{i,j}Xi,j​, independent and uniformly distributed on [0,1][0,1][0,1], and the optimal cost is the random variable

LN=min⁡{∑i∈IXi,τ(i)  ;  τ assignment}.L_N = \min\Big\{ \sum_{i \in I} X_{i,\tau(i)} \;;\; \tau \text{ assignment} \Big\}.LN​=min{i∈I∑​Xi,τ(i)​;τ assignment}.

A median of LNL_NLN​ is any real MMM with P(LN≤M)≥1/2P(L_N \le M) \ge 1/2P(LN​≤M)≥1/2 and P(LN≥M)≥1/2P(L_N \ge M) \ge 1/2P(LN​≥M)≥1/2.

The argument uses digraphs. A digraph is a subset D⊆I×JD \subseteq I \times JD⊆I×J, and for S⊆IS \subseteq IS⊆I the set of neighbors is D(S)={j∈J; ∃i∈S, (i,j)∈D}D(S) = \{ j \in J ;\ \exists i \in S,\ (i,j) \in D \}D(S)={j∈J; ∃i∈S, (i,j)∈D}. For α≥2\alpha \ge 2α≥2, DDD is α\alphaα-expanding if for every S⊆IS \subseteq IS⊆I

card⁡S≤N2⇒card⁡D(S)≥min⁡(αcard⁡S,N2),card⁡S≥N2⇒card⁡D(S)≥N−1α(N−card⁡S).\operatorname{card} S \le \tfrac N2 \Rightarrow \operatorname{card} D(S) \ge \min\big(\alpha \operatorname{card} S, \tfrac N2\big), \qquad \operatorname{card} S \ge \tfrac N2 \Rightarrow \operatorname{card} D(S) \ge N - \tfrac 1\alpha (N - \operatorname{card} S).cardS≤2N​⇒cardD(S)≥min(αcardS,2N​),cardS≥2N​⇒cardD(S)≥N−α1​(N−cardS).

For u>0u > 0u>0 the threshold digraph is Du={(i,j); Xi,j≤2uN−1log⁡N}D_u = \{(i,j) ;\ X_{i,j} \le 2uN^{-1}\log N\}Du​={(i,j); Xi,j​≤2uN−1logN}: it keeps only the cheap edges.

Formalization targets

Goal: Theorem 10.5

There is a universal constant KKK such that for N≥3N \ge 3N≥3, every median MMM of LNL_NLN​ and every t≥0t \ge 0t≥0,

t≤log⁡N⇒P(∣LN−M∣≥Kt(log⁡N)2Nlog⁡log⁡N)≤2e−t2,t \le \sqrt{\log N} \Rightarrow P\Big(|L_N - M| \ge \frac{K t (\log N)^2}{\sqrt N \log\log N}\Big) \le 2 e^{-t^2},t≤logN​⇒P(∣LN​−M∣≥N​loglogNKt(logN)2​)≤2e−t2, t≥log⁡N⇒P(∣LN−M∣≥Kt3log⁡NNlog⁡t2)≤2e−t2.t \ge \sqrt{\log N} \Rightarrow P\Big(|L_N - M| \ge \frac{K t^3 \log N}{\sqrt N \log t^2}\Big) \le 2 e^{-t^2}.t≥logN​⇒P(∣LN​−M∣≥N​logt2Kt3logN​)≤2e−t2.

The constant KKK is left unspecified, as in the paper.

Milestones, in attack order

  1. Lemma 10.1 (deterministic): in an α\alphaα-expanding digraph with αm≥N/2\alpha^m \ge N/2αm≥N/2, every i∈Ii \in Ii∈I lies on a cycle i=i1,…,in+1=ii = i_1, \dots, i_{n+1} = ii=i1​,…,in+1​=i of distinct points, n≤2mn \le 2mn≤2m, with (iℓ,τ(iℓ+1))∈D(i_\ell, \tau(i_{\ell+1})) \in D(iℓ​,τ(iℓ+1​))∈D.
  2. Corollary 10.2 (deterministic in the costs): if DuD_uDu​ is α\alphaα-expanding and αm≥N/2\alpha^m \ge N/2αm≥N/2, every optimal assignment satisfies Xi,τ(i)≤4muN−1log⁡NX_{i,\tau(i)} \le 4muN^{-1}\log NXi,τ(i)​≤4muN−1logN.
  3. Lemma 10.4: for independent events of probability ppp, fewer than δpN\delta p NδpN occur with probability at most exp⁡(−Np/K(δ))\exp(-Np/K(\delta))exp(−Np/K(δ)).
  4. Proposition 10.3: for a universal KKK and u>Ku > Ku>K with ulog⁡N≤Nu \log N \le NulogN≤N, DuD_uDu​ is ulog⁡Nu\log NulogN-expanding with probability at least 1−N−u/K1 - N^{-u/K}1−N−u/K.

Significance

The expected value of LNL_NLN​ is of the same order as a single cost, yet LNL_NLN​ is a function of N2N^2N2 independent variables. A generic bounded-difference bound in all N2N^2N2 coordinates gives no useful concentration at this scale. Theorem 10.5 shows that the fluctuations are at most of order (log⁡N)2/(Nlog⁡log⁡N)(\log N)^2/(\sqrt N \log\log N)(logN)2/(N​loglogN), and by the remark after the theorem, so is the standard deviation of LNL_NLN​. The chapter is a worked example of how a concentration inequality in product spaces is combined with a combinatorial localization argument, the reduction to small costs, and that pattern recurs in the study of random combinatorial optimization problems.

Status: the theorem and its lemmas are proved in the paper. As far as a search of the platform shows, none of them has a machine-checked proof. The two posed statements of Theorem 4.1.1 on the platform (finite-alphabet form) are the concentration inequality the proof calls on; formalizing this chapter adds the combinatorics of expanding digraphs, the random-graph estimate Proposition 10.3 and the final assembly.

Difficulty

The naive approach applies a concentration inequality directly to LNL_NLN​ as a function of the N2N^2N2 costs, each of which can change LNL_NLN​ by up to 111. That loses all control, because the variance proxy grows with the number of coordinates. The central step is to show that, with high probability, an optimal assignment only uses costs of order N−1(log⁡N)2N^{-1}(\log N)^2N−1(logN)2. This requires the cycle structure of Lemma 10.1, and it requires showing that the sparse random digraph DuD_uDu​ has expansion uniformly over all subsets SSS of III, whose number is exponential in NNN. Proposition 10.3 has to beat that union bound, and its proof covers only one of the two expansion conditions in detail; the condition (10.2) for large sets is "left to the reader". The final step combines the truncation with Theorem 4.1.1 and a careful choice of parameters for the two ranges of ttt.

Formalization scope

The Lean development lives in the namespace TalagrandConc.Assignment and commits to the following conventions:

  • I=J=I = J = I=J= Fin N, assignments are Equiv.Perm (Fin N), and LNL_NLN​ is optCost, a Finset.inf' over all permutations (no junk value).
  • The probability space is the cost matrices Fin N × Fin N → ℝ with costLaw N, the product of Lebesgue measure restricted to [0,1][0,1][0,1]. The coordinates are the Xi,jX_{i,j}Xi,j​.
  • Cardinalities of subsets are Set.ncard on the finite type Fin N. α\alphaα-expansion includes α≥2\alpha \ge 2α≥2 as part of the definition.
  • Probabilities are in ℝ≥0∞, and 1−N−u/K1 - N^{-u/K}1−N−u/K is truncated subtraction there.
  • Universal constants are existentially quantified before every object they are uniform over. K(δ)K(\delta)K(δ) in Lemma 10.4 is chosen after δ\deltaδ and before everything else.
  • Implicit side conditions of the paper are stated: m≥1m \ge 1m≥1 in Lemma 10.1 and Corollary 10.2, n≥1n \ge 1n≥1 for the cycle, N≥1N \ge 1N≥1 in Proposition 10.3, t≥0t \ge 0t≥0 in Theorem 10.5.

A cycle of length n=0n = 0n=0 would satisfy Lemma 10.1 vacuously. The formal statement therefore requires n≥1n \ge 1n≥1, which rules out that trivializing reading. Similarly, Theorem 10.5 fixes one constant KKK for all NNN; a KKK depending on NNN would make the bound empty.

Infrastructure: the product of uniform laws on a finite index set, iterated expansion arguments for bipartite digraphs, a lower-tail Chernoff bound for independent events, and the union bound over subsets are reusable beyond this mission. Theorem 4.1.1 of the paper is posed elsewhere on the platform and is not restated here. The bound (8.1.1) used in Step 2 of the proof belongs to another chapter of the series. Contributions of proofs of any milestone, or of intermediate displayed estimates such as (10.5), (10.6) and (10.9), are welcome.

Selected references

  • M. Talagrand, Concentration of measure and isoperimetric inequalities in product spaces, Publ. Math. IHÉS 81 (1995), 73–205. doi:10.1007/BF02699376
  • D. W. Walkup, On the expected value of a random assignment problem, SIAM J. Comput. 8 (1979), 440–442. doi:10.1137/0208036
  • R. M. Karp, An upper bound on the expected cost of an optimal assignment, in Discrete Algorithms and Complexity, Academic Press (1987), 1–4.
  • J. M. Steele, Probability Theory and Combinatorial Optimization, SIAM (1997). doi:10.1137/1.9781611970029
  • D. J. Aldous, The ζ(2) limit in the random assignment problem, Random Structures Algorithms 18 (2001), 381–418. doi:10.1002/rsa.1015
7 thms3 active usersReviewed
Graph TheoryOperations Research·Captain: mikedeng1

An Introduction to Timetabling 1: Every Class–Teacher Requirement Matrix Has a Timetable over Any p Days with Every Daily Load and Pair Count Between ⌊r/p⌋ and ⌈r/p⌉Research Paper

Motivation

School and university timetabling is one of the oldest applications of combinatorial optimization. In his invited review An introduction to timetabling (European Journal of Operational Research, 1985), D. de Werra opens with the class–teacher model: classes meet teachers for a prescribed number of one-period lectures, and the lectures must be placed into periods or days so that no class and no teacher is overloaded. Every richer timetabling model of the review (preassignments, unavailabilities, course scheduling) is built on top of this one, and the review keeps returning to its graph-theoretic reading as an edge-colouring problem in a bipartite multigraph.

The model comes in three levels of demand. The daily problem asks for a clash-free timetable. The weekly problem asks for an assignment of lectures to days within daily load limits. The balanced weekly problem asks, in addition, that the load of every class and every teacher be spread as evenly as possible over the week, and that the lectures of each class–teacher pair be spread evenly too. The last one is the practical requirement that a teacher should not have all four lectures with one class on Monday.

Timeline (as cited in the review).

  • 1916. D. König proves that a bipartite multigraph of maximum degree Δ\DeltaΔ can be edge-coloured with Δ\DeltaΔ colours. The review states it as Proposition 2.1, citing Berge's Graphes [1].
  • 1975. D. de Werra, "A few remarks on chromatic scheduling" (reference [37] of the review), gives the balanced version: for every ppp the edges of a bipartite multigraph can be ppp-coloured so that every node and every bundle of parallel edges is balanced. This is Proposition 2.3.
  • 1978. D. de Werra, "Some comments on a note about timetabling" (INFOR, reference [38]), gives the weekly version with daily load limits, Proposition 2.2.
  • 1985. The review collects the three results in matrix form as problems CT1, CT2 and CT3 and sketches a network-flow construction for CT3, citing Krarup [19].

Setting

There are mmm classes c1,…,cmc_1,\dots,c_mc1​,…,cm​ and nnn teachers t1,…,tnt_1,\dots,t_nt1​,…,tn​. The requirement matrix R=(rij)R=(r_{ij})R=(rij​) is an m×nm\times nm×n matrix of nonnegative integers: rijr_{ij}rij​ is the number of lectures class cic_ici​ must have with teacher tjt_jtj​. Write ri⋅=∑jrijr_{i\cdot}=\sum_j r_{ij}ri⋅​=∑j​rij​ for the total load of class cic_ici​ and r⋅j=∑irijr_{\cdot j}=\sum_i r_{ij}r⋅j​=∑i​rij​ for that of teacher tjt_jtj​. For nonnegative integers sss and p≥1p\ge1p≥1, ⌊s/p⌋\lfloor s/p\rfloor⌊s/p⌋ and ⌈s/p⌉\lceil s/p\rceil⌈s/p⌉ are the floor and ceiling of the rational s/ps/ps/p.

A schedule over ppp periods or days is an array x=(xijk)x=(x_{ijk})x=(xijk​) of nonnegative integers (i≤mi\le mi≤m, j≤nj\le nj≤n, k≤pk\le pk≤p) that places every lecture exactly once:

∑k=1pxijk=rijfor all i,j.(1)\sum_{k=1}^p x_{ijk}=r_{ij}\quad\text{for all } i,j. \tag{1}k=1∑p​xijk​=rij​for all i,j.(1)
  • CT1 asks in addition that xijk∈{0,1}x_{ijk}\in\{0,1\}xijk​∈{0,1} and that ∑jxijk≤1\sum_j x_{ijk}\le1∑j​xijk​≤1, ∑ixijk≤1\sum_i x_{ijk}\le1∑i​xijk​≤1: no class and no teacher has two lectures in one period.
  • CT2, given positive integers aia_iai​, bjb_jbj​, asks that ∑jxijk≤ai\sum_j x_{ijk}\le a_i∑j​xijk​≤ai​ and ∑ixijk≤bj\sum_i x_{ijk}\le b_j∑i​xijk​≤bj​ on every day kkk.
  • CT3 asks that on every day kkk
⌊ri⋅p⌋≤∑jxijk≤⌈ri⋅p⌉,⌊r⋅jp⌋≤∑ixijk≤⌈r⋅jp⌉,⌊rijp⌋≤xijk≤⌈rijp⌉.(8–10)\Big\lfloor \tfrac{r_{i\cdot}}{p}\Big\rfloor\le\sum_j x_{ijk}\le\Big\lceil \tfrac{r_{i\cdot}}{p}\Big\rceil,\quad \Big\lfloor \tfrac{r_{\cdot j}}{p}\Big\rfloor\le\sum_i x_{ijk}\le\Big\lceil \tfrac{r_{\cdot j}}{p}\Big\rceil,\quad \Big\lfloor \tfrac{r_{ij}}{p}\Big\rfloor\le x_{ijk}\le\Big\lceil \tfrac{r_{ij}}{p}\Big\rceil. \tag{8–10}⌊pri⋅​​⌋≤j∑​xijk​≤⌈pri⋅​​⌉,⌊pr⋅j​​⌋≤i∑​xijk​≤⌈pr⋅j​​⌉,⌊prij​​⌋≤xijk​≤⌈prij​​⌉.(8–10)

In the Lean development these are IsCT1 R p x, IsCT2 R p a b x and IsCT3 R p x, with the floor and ceiling written lo s p and hi s p and the totals rowSum R i, colSum R j.

Formalization targets

Goal: Proposition 2.3

∀ R∈Nm×n, ∀ p≥1:∃ x,  x solves CT3 for R over p days.\forall\,R\in\mathbb N^{m\times n},\ \forall\,p\ge1:\quad \exists\,x,\ \ x \text{ solves CT3 for } R \text{ over } p \text{ days}.∀R∈Nm×n, ∀p≥1:∃x,  x solves CT3 for R over p days.

The statement fixes no constant: it asserts that perfectly balanced weekly timetables always exist.

Milestones

  1. One balanced day (flow sketch, p. 153): for p≥1p\ge1p≥1 there is a matrix yyy satisfying the bounds (8), (9), (10) at once.
  2. The remaining days stay balanced (same sketch): if p≥2p\ge2p≥2 and ⌊s/p⌋≤y≤⌈s/p⌉\lfloor s/p\rfloor\le y\le\lceil s/p\rceil⌊s/p⌋≤y≤⌈s/p⌉, then ⌊s/p⌋≤⌊(s−y)/(p−1)⌋\lfloor s/p\rfloor\le\lfloor (s-y)/(p-1)\rfloor⌊s/p⌋≤⌊(s−y)/(p−1)⌋ and ⌈(s−y)/(p−1)⌉≤⌈s/p⌉\lceil (s-y)/(p-1)\rceil\le\lceil s/p\rceil⌈(s−y)/(p−1)⌉≤⌈s/p⌉.
  3. Proposition 2.1 (König): CT1 is solvable iff r⋅j≤pr_{\cdot j}\le pr⋅j​≤p and ri⋅≤pr_{i\cdot}\le pri⋅​≤p for all i,ji,ji,j.
  4. Proposition 2.2: CT2 is solvable iff r⋅j≤p bjr_{\cdot j}\le p\,b_jr⋅j​≤pbj​ and ri⋅≤p air_{i\cdot}\le p\,a_iri⋅​≤pai​.
  5. Minimum number of days: CT2 is solvable over ppp days iff p≥max⁡(max⁡j⌈r⋅j/bj⌉,max⁡i⌈ri⋅/ai⌉)p\ge\max\big(\max_j\lceil r_{\cdot j}/b_j\rceil,\max_i\lceil r_{i\cdot}/a_i\rceil\big)p≥max(maxj​⌈r⋅j​/bj​⌉,maxi​⌈ri⋅​/ai​⌉).
  6. Figure 1: the printed timetable for R=(124302)R=\begin{pmatrix}1&2&4\\3&0&2\end{pmatrix}R=(13​20​42​), p=3p=3p=3, solves CT3.

Significance

The result. Proposition 2.3 says that the balancing requirement costs nothing: whatever the requirement matrix, the week can be organised so that each class and each teacher has an essentially constant daily load and each class–teacher pair is spread evenly. It contains König's edge-colouring theorem (take ppp at least the maximum degree, so every daily load is at most one) and the weekly Proposition 2.2 as special cases. Combined with Proposition 2.1 applied to each day, it reduces weekly timetabling to independent daily problems, which is the decomposition the review recommends ("one may start by solving the weekly problem … and then solve separately the resulting daily problems", p. 154). In graph language it is the existence of equitable edge colourings of bipartite multigraphs, a standard tool in scheduling and in the theory of edge colourings.

Formalizing it. All results of this mission are classical and proved in the literature; none of them is formalized on the platform. Mathlib contains Hall's marriage theorem but not König's edge-colouring theorem for bipartite multigraphs, and not balanced or equitable colourings. A complete development gives a machine-checked König edge-colouring theorem in matrix form, the matrix-rounding step with simultaneous row, column and entry bounds, and the balanced-colouring theorem itself.

Difficulty

Each family of constraints alone is easy: spreading a single number rijr_{ij}rij​ evenly over ppp days, or a single row total, is a matter of division with remainder. The difficulty is that one array must satisfy (1), (8), (9) and (10) simultaneously, so that the per-entry roundings must add up to correct roundings of every row total and every column total on every day. Rounding each entry independently breaks the row and column constraints, and greedy day-by-day choices can leave a remainder that no longer fits the bounds. The crux is integrality: the rational average rij/pr_{ij}/prij​/p satisfies every bound on every day, but the problem asks for integers, and an integral one-day slice must round all entries, all row totals and all column totals consistently.

Formalization scope

Classes, teachers and days are Fin m, Fin n, Fin p; RRR and xxx are natural-number valued, which is the paper's "xijk≥0x_{ijk}\ge0xijk​≥0 integer". Constraint (4) of CT1 is written xijk≤1x_{ijk}\le1xijk​≤1. Floors and ceilings are Nat.floor and Nat.ceil of the rational quotient. In (8) and (9) they are applied to the row and column totals, never summed entrywise. The minimum number of days uses Finset.sup, whose empty value is 000, and is stated as an equivalence ("solvable over ppp days iff p≥pmin⁡p\ge p_{\min}p≥pmin​") rather than as an infimum.

Disclosed choices:

  • The goal and the one-day milestone assume p≥1p\ge1p≥1; the paper's "for any ppp" means any number of days, and for p=0p=0p=0 Lean's division returns 000.
  • The remaining-days milestone assumes p≥2p\ge2p≥2 so that p−1p-1p−1 days remain.
  • Propositions 2.1, 2.2 and the minimum-days statement hold for every ppp, including p=0p=0p=0, and assume nothing more than the page: ai,bja_i,b_jai​,bj​ positive.
  • The edge-colouring and network-flow readings of the paper are stated in prose only; the formal statements are in matrix form, which is how the propositions are stated.

The formalization does not admit a trivial reading: CT3 requires all four constraint families at once, (1) is an equality, and the sanity checks show both that the Figure 1 data satisfy CT3 and that CT1 is not satisfiable for R=(2)R=(2)R=(2) and p=1p=1p=1.

Useful infrastructure, reusable beyond this mission: integral flows with lower bounds, or total unimodularity of bipartite incidence matrices; König's edge-colouring theorem for bipartite multigraphs; floor and ceiling arithmetic for natural-number quotients. Proofs of any milestone, alternative proofs of the goal, and a sorry-free proof of the Figure 1 instance are all welcome.

Selected references

  • D. de Werra, An introduction to timetabling, European Journal of Operational Research 19 (1985) 151–162. https://doi.org/10.1016/0377-2217(85)90167-5
  • D. König, Über Graphen und ihre Anwendung auf Determinantentheorie und Mengenlehre, Mathematische Annalen 77 (1916) 453–465. https://doi.org/10.1007/BF01456961
  • C. Berge, Graphes, Gauthier-Villars, Paris, 1983 (reference [1] of the review).
  • D. de Werra, A few remarks on chromatic scheduling, in B. Roy (ed.), Combinatorial Programming: Methods and Applications, Reidel, Dordrecht, 1975, 337–342.
  • D. de Werra, Some comments on a note about timetabling, INFOR 16 (1978) 90–92.
  • J. Krarup, Chromatic optimisation: Limitations, objectives, uses, references, European Journal of Operational Research 11 (1982) 1–19.
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Discrete GeometryLinear Optimization·Captain: mikedeng1

The d-Step Conjecture for Polyhedra of Dimension d < 6 1: The Bounded 5-Step Conjecture Holds, Every 5-Polytope with 10 Facets Has Diameter at Most 5, i.e. Δb(5, 10) = 5Research Paper

Motivation

The simplex method and every other edge-following linear programming algorithm moves from vertex to vertex of the feasible region along its edges. The number of steps such an algorithm needs in the worst case is at least the combinatorial diameter of the feasible polyhedron: the largest distance, counted in edges, between two of its vertices. Klee and Walkup introduced the two functions that measure this worst case, Δ(d,n)\Delta(d,n)Δ(d,n) for polyhedra and Δb(d,n)\Delta_b(d,n)Δb​(d,n) for bounded polyhedra of dimension ddd with nnn facets, and wrote that Δ(d,n)\Delta(d,n)Δ(d,n) "represents, in a sense, the number of iterations required to solve the 'worst' linear program of nnn inequalities in ddd variables using the 'best' edge-following algorithm" (Klee and Walkup 1967, p. 54).

The Hirsch conjecture, reported by Dantzig in 1963, asserted Δ(d,n)≤n−d\Delta(d,n)\le n-dΔ(d,n)≤n−d. Its diagonal cases Δ(d,2d)≤d\Delta(d,2d)\le dΔ(d,2d)≤d and Δb(d,2d)≤d\Delta_b(d,2d)\le dΔb​(d,2d)≤d are the general and the bounded ddd-step conjectures.

Timeline.

  • 1957–1963: W. M. Hirsch's conjecture, reported in Dantzig's Linear Programming and Extensions (1963, pp. 160, 168), with the claim that it holds for m≤4m\le 4m≤4.
  • Before 1967: Klee determines Δ(3,n)=n−3\Delta(3,n)=n-3Δ(3,n)=n−3 and Δb(3,n)=⌊2n/3⌋−1\Delta_b(3,n)=\lfloor 2n/3\rfloor-1Δb​(3,n)=⌊2n/3⌋−1 and settles Δb(d,n)\Delta_b(d,n)Δb​(d,n) for n≤d+4n\le d+4n≤d+4 (Canad. J. Math. 16 and the papers cited as [9], [12], [13] by Klee and Walkup, p. 55).
  • 1967: Klee and Walkup (Acta Math. 117) prove Δb(5,10)=5\Delta_b(5,10)=5Δb​(5,10)=5, show that Δb(d,2d)≤d\Delta_b(d,2d)\le dΔb​(d,2d)≤d for all ddd is equivalent to the bounded Hirsch conjecture, and disprove the unbounded conjecture with Δ(4,8)=5\Delta(4,8)=5Δ(4,8)=5.
  • 2012: Santos disproves the bounded Hirsch conjecture with a 43-dimensional polytope with 86 facets (Ann. Math. 176). The bounded ddd-step conjecture therefore fails for some ddd, and the smallest failing ddd is unknown; it is at least 6.

Setting

A polyhedron in Rd\mathbb R^dRd is a nonempty set P={x:⟨ai,x⟩≤bi, i=1,…,n}P=\{x:\langle a_i,x\rangle\le b_i,\ i=1,\dots,n\}P={x:⟨ai​,x⟩≤bi​, i=1,…,n}; a polytope is a bounded polyhedron. PPP is a ddd-polyhedron with nnn facets when it has nonempty interior and none of the nnn inequalities is redundant; then the rows are in bijection with the facets, and row iii is tight at vvv exactly when vvv lies on the iii-th facet. The vertices of PPP are its extreme points. Two vertices u≠vu\ne vu=v span an edge when the segment [u,v][u,v][u,v] is an extreme subset of PPP. A path of length lll is a sequence of lll edges [x0,x1],…,[xl−1,xl][x_0,x_1],\dots,[x_{l-1},x_l][x0​,x1​],…,[xl−1​,xl​], and δP(x,y)\delta_P(x,y)δP​(x,y) is the least length of a path from xxx to yyy. Then

Δb(d,n)=max⁡{δP(x,y):P a d-polytope with n facets, x,y vertices of P}.\Delta_b(d,n)=\max\{\delta_P(x,y): P\text{ a }d\text{-polytope with }n\text{ facets},\ x,y\text{ vertices of }P\}.Δb​(d,n)=max{δP​(x,y):P a d-polytope with n facets, x,y vertices of P}.

PPP is simple when every vertex lies on exactly ddd facets. A face is PPP itself or the intersection of PPP with a supporting hyperplane, never ∅\emptyset∅; its dimension is that of its affine hull. A (k1,…,kr)(k_1,\dots,k_r)(k1​,…,kr​)-path from xxx to yyy is a sequence of faces K1,…,KrK_1,\dots,K_rK1​,…,Kr​ with x∈K1x\in K_1x∈K1​, y∈Kry\in K_ry∈Kr​, dim⁡Ki=ki\dim K_i=k_idimKi​=ki​ and Ki∩Ki+1≠∅K_i\cap K_{i+1}\ne\emptysetKi​∩Ki+1​=∅. A ddd-dimensional Dantzig figure (P,x,y)(P,x,y)(P,x,y) is a ddd-polyhedron PPP with 2d2d2d facets and two vertices x,yx,yx,y such that exactly ddd facets are incident to xxx and the other ddd to yyy. A ddd-polytope QQQ has property A when it has between 2d2d2d and 2d+22d+22d+2 facets and the following holds: if its facets are split into two classes X,Y\mathfrak X,\mathfrak YX,Y of at most d+1d+1d+1 facets each, and both sets XXX, YYY of vertices all of whose facets lie in X\mathfrak XX, respectively Y\mathfrak YY, are nonempty, then some vertex of XXX is joined to some vertex of YYY by a path of length at most ddd.

Formalization targets

Goal: 4.3 THEOREM

Δb(5,10)=5.\Delta_b(5,10)=5.Δb​(5,10)=5.

In two halves: every 555-polytope with 101010 facets satisfies δP(x,y)≤5\delta_P(x,y)\le 5δP​(x,y)≤5 for all vertices x,yx,yx,y, and some 555-polytope with 101010 facets has two vertices with δP(x,y)>4\delta_P(x,y)>4δP​(x,y)>4.

Milestones, in the order the proof uses them

  1. 2.8 THEOREM (bounded part): Δb(d,n)\Delta_b(d,n)Δb​(d,n) is attained by two vertices of a simple ddd-polytope with nnn facets, and for n≥2dn\ge 2dn≥2d by two vertices on no common facet. For n=2dn=2dn=2d this is a bounded simple Dantzig figure.
  2. 3.4 PROPOSITION (a): every simple Dantzig figure admits a (k1,1,k3)(k_1,1,k_3)(k1​,1,k3​)-path for positive k1+1+k3=dk_1+1+k_3=dk1​+1+k3​=d.
  3. 3.1 PROPOSITION: (a) facial paths in all ddd-dimensional simple Dantzig figures, (b) facet-avoiding facial paths in simple ddd-polytopes with 2d+12d+12d+1 facets, and (c) facet-avoiding (1,k1,…,kr)(1,k_1,\dots,k_r)(1,k1​,…,kr​)-paths in bounded (d+1)(d+1)(d+1)-dimensional figures are equivalent.
  4. 3.4 PROPOSITION (b): every bounded simple Dantzig figure admits a (1,k2,1,k4)(1,k_2,1,k_4)(1,k2​,1,k4​)-path for positive 1+k2+1+k4=d1+k_2+1+k_4=d1+k2​+1+k4​=d.
  5. Remark after 3.4: adjacent terms of a facial path in a simple figure merge into their sum.
  6. Remark after 3.4, p. 68: bounded simple Dantzig figures of dimension d≥2d\ge 2d≥2 admit (1,d−2,1)(1,d-2,1)(1,d−2,1)-paths.
  7. 4.1 PROPOSITION: if the middle face QQQ of a (1,d−2,1)(1,d-2,1)(1,d−2,1)-path ([x,x′],Q,[y′,y])([x,x'],Q,[y',y])([x,x′],Q,[y′,y]) has property A, then δP(x,y)≤d\delta_P(x,y)\le dδP​(x,y)≤d.
  8. 4.2 LEMMA: every simple 333-polytope with 666, 777 or 888 facets has property A.

Significance

The result. By the paper's reductions, Δb(d,2d)≤d\Delta_b(d,2d)\le dΔb​(d,2d)≤d for a given ddd is equivalent to the bounded Hirsch bound Δb(d′,d′+d)≤d\Delta_b(d',d'+d)\le dΔb​(d′,d′+d)≤d for every d′d'd′. So 4.3 gives Δb(d,n)≤n−d\Delta_b(d,n)\le n-dΔb​(d,n)≤n−d for all n≤d+5n\le d+5n≤d+5, which is the column n−d=5n-d=5n−d=5 of the paper's Fig. 2. Since Santos's counterexample, the smallest ddd with Δb(d,2d)>d\Delta_b(d,2d)>dΔb​(d,2d)>d is a well-defined open number. This theorem was the first to show it is at least 666; later computer-assisted work treated further small cases, including the bounded 666-step case (Bremner and Schewe 2011). Santos's construction starts from exactly the reduction to Dantzig figures (the "strong ddd-step theorem" for spindles) that this paper uses.

Formalizing it. The result has been proved since 1967; no machine-checked proof of Δb(5,10)=5\Delta_b(5,10)=5Δb​(5,10)=5 exists. The platform has the Hirsch bound in dimension ≤3\le 3≤3 (Hirsch.dimension_three_bound, Hirsch.klee_three_dimensional_bound), the Klee–Walkup wedge reduction at the level of inequality descriptions (Hirsch.balanced_hpoly_transfer), products (Hirsch.diamLE_prod) and Santos's counterexample. The mission adds the facial-path calculus of §3 and the property-A argument of §4. Both are reusable in any later formal treatment of small ddd-step cases.

Difficulty

The obvious approach, a direct case analysis of 555-polytopes with 101010 facets, is out of reach: there are far too many combinatorial types, and nothing bounds the search. The paper first reduces to simple polytopes, and the reduction itself (2.8) rests on a perturbation lemma (2.6) whose proof is deferred to a separate paper by Walkup: simplicity must be gained without shrinking any distance. The facial-path results then pass between bounded and unbounded figures through truncation and the wedge (3.1), and a statement about all unbounded ddd-dimensional figures is what yields one about bounded (d+1)(d+1)(d+1)-dimensional ones. Finally the (d−2)(d-2)(d−2)-face QQQ must be shown to have property A. That property fails for some simple polytopes of classes (4,10)(4,10)(4,10) and (5,11)(5,11)(5,11) (p. 69), so 4.2 depends on the dimension being 333 and on the facet count being at most 888. Its proof uses Euler's relation and Balinski's theorem that the graph of a 333-polytope is 333-connected (posed on the platform as Balinski61.Connectivity.disjoint_paths_between_vertices).

Formalization scope

Polyhedra are Hirsch.Hpoly a b with a : Fin n → EuclideanSpace ℝ (Fin d); edges and walks are Hirsch.Adj and Hirsch.Reach from the published Hirsch_model and Hirsch_walk. The conventions are:

  • "ddd-polyhedron with nnn facets" is a facet presentation: nonempty interior and no redundant row. The rows are exactly the facets; a statement over arbitrary inequality systems would count redundant rows as facets and is not the paper's.
  • δP(x,y)≤L\delta_P(x,y)\le LδP​(x,y)≤L is Hirsch.Reach P L x y, a walk of LLL steps that may stand still. Δb(5,10)=5\Delta_b(5,10)=5Δb​(5,10)=5 is an upper bound for every polytope plus a witness, never a supremum of reals. A version proving only the upper bound, or only the lower bound, is not the goal.
  • Simple means pointed with exactly ddd tight rows at every vertex. On a facet presentation this is equivalent to the paper's "exactly ddd edges at each vertex".
  • Faces are nonempty sets {x∈P:⟨ai,x⟩=bi, i∈I}\{x\in P:\langle a_i,x\rangle=b_i,\ i\in I\}{x∈P:⟨ai​,x⟩=bi​, i∈I}, and dimension is the dimension of the affine span. A Dantzig figure has Fin (2 * d) rows.
  • Property A is defined for a bounded face QQQ of dimension mmm: its facets are the (m−1)(m-1)(m−1)-faces of PPP inside QQQ, and the path runs in QQQ. This keeps 4.1, where QQQ is a (d−2)(d-2)(d−2)-face of a polytope in Rd\mathbb R^dRd, and 4.2, where Q=PQ=PQ=P, under one notion. Facet counts use Set.encard.
  • Explicit readings: 2.8's "Δb\Delta_bΔb​ can be realized as the distance … of a simple polytope" is stated polytope by polytope (δP(u,v)≤δP′(x,y)\delta_P(u,v)\le\delta_{P'}(x,y)δP​(u,v)≤δP′​(x,y)). Only the Δb\Delta_bΔb​ sentences of 2.8 are stated. 3.4's joint hypothesis on k1,…,k4k_1,\dots,k_4k1​,…,k4​ is split by part. The merging remark carries the needed hypothesis ki+ki+1≤dk_i+k_{i+1}\le dki​+ki+1​≤d. The printed "3.5 implies" on p. 67 is read as 3.4.

The paper cites two inputs without proving them: Lemma 2.6 (Walkup's simple perturbation) and Balinski's theorem. A complete development needs face lattices of H-polyhedra, wedges and truncations, and the planar-graph arguments of 4.2. The face and facial-path layer is reusable for the companion mission on Δ(4,8)=5\Delta(4,8)=5Δ(4,8)=5 and for other diameter problems. Contributions are welcome at every milestone, including proofs of 2.6.

Selected references

  • V. Klee and D. W. Walkup, The d-step conjecture for polyhedra of dimension d < 6, Acta Mathematica 117 (1967), 53–78. https://doi.org/10.1007/BF02395040
  • G. B. Dantzig, Linear Programming and Extensions, Princeton University Press, 1963.
  • V. Klee, Diameters of polyhedral graphs, Canadian Journal of Mathematics 16 (1964), 602–614. https://doi.org/10.4153/CJM-1964-058-5
  • M. L. Balinski, On the graph structure of convex polyhedra in n-space, Pacific Journal of Mathematics 11 (1961), 431–434. https://doi.org/10.2140/pjm.1961.11.431
  • F. Santos, A counterexample to the Hirsch conjecture, Annals of Mathematics 176 (2012), 383–412. https://doi.org/10.4007/annals.2012.176.1.7
  • D. Bremner and L. Schewe, Edge-graph diameter bounds for convex polytopes with few facets, Experimental Mathematics 20 (2011), 229–237. https://doi.org/10.1080/10586458.2011.564966
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Operations ResearchTheoretical Computer Science·Captain: mikedeng1

One-Processor Scheduling with Symmetric Earliness and Tardiness Penalties 1: Minimizing the Total Discrepancy from Preferred Times Is NP-CompleteResearch Paper

Motivation

Scheduling with earliness and tardiness penalties asks for schedules in which a job finishing early is as undesirable as a job finishing late. The model fits production planned for just-in-time delivery, where finished goods held before their due date cost money, and sequences of experiments tied to fixed external events. It departs from classical scheduling, in which finishing early is never penalized.

Garey, Tarjan and Wilfong (Math. Oper. Res. 13 (1988) 330–348) study the symmetric version on one processor: each task has a preferred starting time, and the penalty is the absolute deviation from it. Their §2.1 settles the complexity of the most natural objective, the sum of these deviations, by proving it NP-complete. The result explains why the rest of their paper, and much of the later literature, turns to special cases that can be solved efficiently: a fixed task order, equal task lengths, or the maximum deviation instead of the sum.

A short history of the model:

  • Kanet (1981) minimized total absolute deviation from a common due date that is large enough not to constrain the schedule, by a sorting rule. The special case in the middle of §2.1 is the midtime version of this problem.
  • Garey, Tarjan and Wilfong (1988) proved the problem with arbitrary preferred times NP-complete (THEOREM 1, this mission), and gave an O(Nlog⁡N)O(N\log N)O(NlogN) algorithm for a fixed task order.
  • Hall, Kubiak and Sethi (1991) proved that the common-due-date problem becomes NP-hard when the due date is restrictive. The reduction of THEOREM 1 already uses a common preferred midtime that is not large, together with one extra task that pins the right end.

Setting

There are NNN tasks T1,…,TNT_1,\dots,T_NT1​,…,TN​. Task TiT_iTi​ has a length lil_ili​ and a preferred midtime MiM_iMi​. A schedule SSS assigns every task a starting time si≥0s_i\ge0si​≥0 such that no two tasks overlap on the single processor: for i≠ji\ne ji=j, either si+li≤sjs_i+l_i\le s_jsi​+li​≤sj​ or sj+lj≤sis_j+l_j\le s_isj​+lj​≤si​. Idle time is allowed. The actual midtime of TiT_iTi​ is mi(S)=si+li/2m_i(S)=s_i+l_i/2mi​(S)=si​+li​/2, and the total discrepancy of SSS is

cost(S)=∑i=1N∣mi(S)−Mi∣.\mathrm{cost}(S)=\sum_{i=1}^N |m_i(S)-M_i| .cost(S)=i=1∑N​∣mi​(S)−Mi​∣.

The paper works with midtimes because the special case below is then symmetric. Preferred midtimes and preferred starting times aia_iai​ are interchangeable through ai=Mi−li/2a_i=M_i-l_i/2ai​=Mi​−li​/2.

Total discrepancy (the decision problem). Given N,k∈Z+N,k\in\mathbb Z^+N,k∈Z+ and Mi,li∈Z+M_i,l_i\in\mathbb Z^+Mi​,li​∈Z+, is there a schedule with cost(S)≤k\mathrm{cost}(S)\le kcost(S)≤k?

Even-odd partition. Given positive integers x1<x2<⋯<x2nx_1<x_2<\dots<x_{2n}x1​<x2​<⋯<x2n​, can they be split into two sets of equal sum so that each set contains exactly one of x2i−1,x2ix_{2i-1},x_{2i}x2i−1​,x2i​ for every iii?

The special case. For tasks T0,…,T2nT_0,\dots,T_{2n}T0​,…,T2n​ with 0<l0<l1<⋯<l2n0<l_0<l_1<\dots<l_{2n}0<l0​<l1​<⋯<l2n​ and one common preferred midtime M>∑iliM>\sum_i l_iM>∑i​li​, write A(S)={Ti:mi(S)<M}A(S)=\{T_i: m_i(S)<M\}A(S)={Ti​:mi​(S)<M} and B(S)={Ti:mi(S)>M}B(S)=\{T_i:m_i(S)>M\}B(S)={Ti​:mi​(S)>M}. A schedule is ordered if, on each side of MMM, shorter tasks lie nearer to MMM. The bracket [An,…,A1,T0@M,B1,…,Bn][A_n,\dots,A_1,T_0@M,B_1,\dots,B_n][An​,…,A1​,T0​@M,B1​,…,Bn​] is the schedule that puts T0T_0T0​ at midtime MMM and packs the listed tasks against it in the listed order.

Formalization targets

Goal: THEOREM 1

Partition is NP-complete ⟹ Total discrepancy is NP-complete.\text{Partition is NP-complete}\ \Longrightarrow\ \text{Total discrepancy is NP-complete.}Partition is NP-complete ⟹ Total discrepancy is NP-complete.

The hypothesis is the one result the paper imports (Garey and Johnson, 1979). The conclusion includes membership in NP and polynomial-time many-one reductions from every NP language, with Turing machines as the model of computation.

Milestones, in attack order

  1. Even-odd partition is in NP; the instance x1=1x_1=1x1​=1, x2i=x2i−1+yix_{2i}=x_{2i-1}+y_ix2i​=x2i−1​+yi​, x2i+1=x2i+1x_{2i+1}=x_{2i}+1x2i+1​=x2i​+1 built from a Partition instance YYY is a yes-instance if and only if YYY is; and LEMMA 1, even-odd partition is NP-complete.
  2. The special case: a minimum cost schedule has no gaps and is ordered; LEMMA 2 (some mi(S)=Mm_i(S)=Mmi​(S)=M), LEMMA 3 (∣A(S)∣=∣B(S)∣|A(S)|=|B(S)|∣A(S)∣=∣B(S)∣), LEMMA 4 (m0(S)=Mm_0(S)=Mm0​(S)=M), LEMMA 5 (swapping AiA_iAi​ and BiB_iBi​ in a bracket keeps the cost), LEMMA 6 ({Ai,Bi}={T2i,T2i−1}\{A_i,B_i\}=\{T_{2i},T_{2i-1}\}{Ai​,Bi​}={T2i​,T2i−1​} in a minimum cost bracket), and the minimum cost
k=∑i=1n(l2i+l2i−1)(n−i+12)+l0 n.k=\sum_{i=1}^n(l_{2i}+l_{2i-1})\left(n-i+\tfrac12\right)+l_0\,n .k=i=1∑n​(l2i​+l2i−1​)(n−i+21​)+l0​n.
  1. At the midtime M=12∑i=02nliM=\frac12\sum_{i=0}^{2n}l_iM=21​∑i=02n​li​ used in the reduction, every schedule of T0,…,T2nT_0,\dots,T_{2n}T0​,…,T2n​ costs at least kkk; equality forces the ordered, gap-free form in item 2.
  2. The reduction: the instance D with l0=x1−1l_0=x_1-1l0​=x1​−1, li=xil_i=x_ili​=xi​, l2n+1=2l_{2n+1}=2l2n+1​=2, Mj=M=∑i≤2nli/2M_j=M=\sum_{i\le 2n}l_i/2Mj​=M=∑i≤2n​li​/2, M2n+1=2M+1M_{2n+1}=2M+1M2n+1​=2M+1 has a schedule of cost at most kkk if and only if XXX has an even-odd partition. Total discrepancy is in NP.

Significance

THEOREM 1 is the hardness boundary for one-processor scheduling with symmetric earliness–tardiness penalties and arbitrary preferred times. It is the reason exact algorithms for this objective are enumerative, and the reason polynomial results are sought under extra structure, such as the fixed-order algorithm of the same paper. The special-case lemmas characterize every optimal schedule for a common, unrestrictive midtime, not just one of them: shortest task centered at MMM, the iii-th pair of lengths in the iii-th positions on either side, either member on either side. This characterization holds independently of the reduction.

The result has been proved since 1988, and no machine-checked version of it is known to us. A formalization adds three things. First, the parts the paper calls "straightforward" or "a simple exercise", membership of both problems in NP. Second, the two places where the published argument is imprecise. The instance D has half-integer midtimes and threshold although the decision problem asks for integers, so a correct reduction must rescale. And the special-case lemmas are proved for a large midtime but applied with M=∑ili/2M=\sum_i l_i/2M=∑i​li​/2, so they must be restated for every MMM. Third, a reusable formal treatment of absolute-deviation scheduling objectives and of reductions between number problems written in binary.

Difficulty

The obvious argument fails at the step from the special case to the instance D. The lemmas on pp. 333–336 assume the common midtime is large, so that the constraint si≥0s_i\ge0si​≥0 never binds. In D the midtime is M=∑i=02nli/2M=\sum_{i=0}^{2n}l_i/2M=∑i=02n​li​/2, exactly half the total length, and the reduction works because the constraint binds. The tasks before MMM must fit in [0,M−l0/2][0,M-l_0/2][0,M−l0​/2], and the extra task T2n+1T_{2n+1}T2n+1​ must sit at [2M,2M+2][2M,2M+2][2M,2M+2]. Together these force the two sides to have equal total length. A proof that only cites the large-MMM lemmas proves nothing about D. Conversely, dropping si≥0s_i\ge 0si​≥0 makes the reduction false: put the shorter element of every pair after MMM.

The other obstacle is the complexity bookkeeping. NP-completeness here means Turing machines, binary codes, and polynomial bounds, and a full proof must compute D from the code of XXX, double the times to clear the half-integers, and certify membership in NP for a problem whose schedules have real starting times. The certificate cannot be the real starting times themselves.

Formalization scope

  • Everything is real-valued except the instance codes. Schedules have real starting times si≥0s_i\ge0si​≥0, and integer data are cast to R\mathbb RR. Restricting to integer starting times would be a different problem and is not what is stated.
  • Nonnegative starting times are a standing assumption. The paper uses them ("scheduled between 0 and M−l0/2M-l_0/2M−l0​/2", p. 336) without writing them into the model. Nonoverlap is "one task finishes before the other starts", the reading of "intersect only at their endpoints".
  • Tasks are indexed by Fin N from 000. In the special case TiT_iTi​ is index iii, and the paper's T2i−1,T2iT_{2i-1},T_{2i}T2i−1​,T2i​ (1≤i≤n1\le i\le n1≤i≤n) are the indices 2k+1,2k+22k+1,2k+22k+1,2k+2 for k=i−1k=i-1k=i−1.
  • Languages use the published CookPvsNP_defs (Cook's one-tape Turing machines, NP, NPComplete) and the published ProjSchedTW.Complexity.Encoding (four-letter alphabet and binary codes). This mission defines positivePartitionLang by restricting the imported equal-sum predicate to nonempty lists of positive integers, as on p. 333. Only codes of well-formed instances belong to the even-odd and total discrepancy languages.
  • The goal is not the combinatorial equivalence of milestone 4. It states NP-completeness, so it contains the polynomial-time computation of the reduction and membership in NP. Taking NPComplete evenOddLang as the hypothesis instead would drop LEMMA 1 and is not what is asked.
  • Running times of the algorithms of §2.2–§2.4 are out of scope for this mission, as are all results after §2.1.

Contributions welcome: proofs of any milestone, and reusable lemmas on Turing-machine computability of arithmetic on binary codes, which the two membership results and both reductions need.

Selected references

  • M. R. Garey, R. E. Tarjan, G. T. Wilfong, One-Processor Scheduling with Symmetric Earliness and Tardiness Penalties, Mathematics of Operations Research 13(2):330–348, 1988. https://doi.org/10.1287/moor.13.2.330
  • M. R. Garey, D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman, 1979.
  • J. J. Kanet, Minimizing the average deviation of job completion times about a common due date, Naval Research Logistics Quarterly 28(4):643–651, 1981. https://doi.org/10.1002/nav.3800280411
  • N. G. Hall, W. Kubiak, S. P. Sethi, Earliness–tardiness scheduling problems, II: Deviation of completion times about a restrictive common due date, Operations Research 39(5):847–856, 1991. https://doi.org/10.1287/opre.39.5.847
  • S. Cook, The P versus NP problem, Clay Mathematics Institute Millennium Problems, 2000. https://www.claymath.org/wp-content/uploads/2022/06/pvsnp.pdf
19 thms3 active usersReviewed
Discrete GeometryLinear Optimization·Captain: mikedeng1

A Quasi-Polynomial Bound for the Diameter of Graphs of Polyhedra: Every Polyhedron Given by n Inequalities in ℝ^d, Bounded or Not, Has Graph Diameter at Most n^(log₂ d + 2)Research Paper

Motivation

The simplex method solves a linear program by walking along the edges of the feasible polyhedron, from vertex to vertex. However cleverly a pivot rule chooses its steps, it cannot reach the optimum in fewer steps than the graph distance from the starting vertex. The largest possible graph diameter of a polyhedron therefore bounds from below the worst-case number of pivots of every pivot rule, and whether that diameter is polynomial in the size of the problem is one of the basic open questions of linear optimization (Klee–Kleinschmidt 1987).

Timeline.

  • 1957: Hirsch conjectures that a ddd-dimensional polyhedron with nnn facets has graph diameter at most n−dn-dn−d (reported in Dantzig, Linear Programming and Extensions, 1963).
  • 1967: Klee and Walkup show the conjecture false for unbounded polyhedra, with the lower bound n−d+[d/5]n-d+[d/5]n−d+[d/5] for n≥2dn\ge2dn≥2d (Acta Math. 1967).
  • 1967–1970: Barnette proves Δ(d,n)≤n3d−3\Delta(d,n)\le n3^{d-3}Δ(d,n)≤n3d−3, and Larman improves it to n2d−3n2^{d-3}n2d−3 (Proc. LMS 1970); both are linear in nnn but exponential in ddd.
  • 1990–1992: Kalai proves a subexponential bound, and then Kalai and Kleitman prove the quasi-polynomial bound nlog⁡d+2n^{\log d+2}nlogd+2 that is the subject of this mission (Bull. AMS 1992, arXiv:math/9204233).
  • 2012: Santos disproves the Hirsch conjecture for bounded polytopes (Ann. Math. 2012). Whether a polynomial bound holds remains open; the best general upper bounds known are refinements of the exponent of the Kalai–Kleitman bound.

Setting

A polyhedron in Rd\mathbb R^dRd is the solution set of finitely many linear inequalities,

P={x∈Rd: ⟨ai,x⟩≤bi, i=1,…,n},ai∈Rd, bi∈R.P=\{x\in\mathbb R^d:\ \langle a_i,x\rangle\le b_i,\ i=1,\dots,n\},\qquad a_i\in\mathbb R^d,\ b_i\in\mathbb R .P={x∈Rd: ⟨ai​,x⟩≤bi​, i=1,…,n},ai​∈Rd, bi​∈R.

It may be bounded or unbounded, and empty. Its vertices are its extreme points. Two distinct vertices u,vu,vu,v are adjacent when the segment [u,v][u,v][u,v] is an edge of PPP, a one-dimensional face; unbounded one-dimensional faces (rays) are not edges. The graph G(P)G(P)G(P) consists of these vertices and edges, and its diameter δ(P)\delta(P)δ(P) is the largest graph distance between two vertices. Kalai and Kleitman write Δ(d,n)\Delta(d,n)Δ(d,n) for the largest diameter of the graph of a ddd-dimensional polyhedron with nnn facets.

For a set FFF of row indices, the relaxation QFQ_FQF​ is the polyhedron obtained from PPP by ignoring the inequalities with index outside FFF. A walk of BBB steps is a sequence w0,…,wBw_0,\dots,w_Bw0​,…,wB​ of points in which each step stays put or crosses an edge; "diameter at most BBB" means that any two vertices are joined by such a walk, which includes connectivity of the graph.

Formalization targets

Goal: Theorem 1

Δ(d,n)≤nlog⁡d+2.\Delta(d,n)\le n^{\log d+2}.Δ(d,n)≤nlogd+2.

Stated for polyhedra given by inequalities: for all d,nd,nd,n and all a∈(Rd)na\in(\mathbb R^d)^na∈(Rd)n, b∈Rnb\in\mathbb R^nb∈Rn, the graph of PPP is connected and has diameter at most ⌊nlog⁡2d+2⌋\lfloor n^{\log_2 d+2}\rfloor⌊nlog2​d+2⌋. No nonemptiness, boundedness or full-dimensionality is assumed.

Milestones, in the order the proof uses them

  1. If a vertex ω\omegaω of PPP satisfies with equality only rows in FFF, then ω\omegaω is a vertex of QFQ_FQF​ (published).
  2. If an edge of QFQ_FQF​ starts at a point of PPP and is not an edge of PPP, then it meets the hyperplane of an ignored row at a vertex of PPP adjacent to its start, or at its start (published).
  3. The claim kv≤Δ(d,[n/2])k_v\le\Delta(d,[n/2])kv​≤Δ(d,[n/2]): if every vertex within distance kkk of vvv lies only on rows of FFF, and G(QF)G(Q_F)G(QF​) has diameter at most BBB, then every vertex within distance kkk of vvv is within distance BBB of vvv.
  4. The facet step: any two vertices of a face {x∈P:⟨ai,x⟩=bi}\{x\in P:\langle a_i,x\rangle=b_i\}{x∈P:⟨ai​,x⟩=bi​} with ai≠0a_i\ne0ai​=0 are joined, inside that face, by a walk of at most Δ(d−1,n−1)\Delta(d-1,n-1)Δ(d−1,n−1) edges.
  5. The recursion Δ(d,n)≤Δ(d−1,n−1)+2Δ(d,[n/2])+2\Delta(d,n)\le\Delta(d-1,n-1)+2\Delta(d,[n/2])+2Δ(d,n)≤Δ(d−1,n−1)+2Δ(d,[n/2])+2, for polyhedra with nonzero rows.
  6. The arithmetic: N(d,n)=⌊nlog⁡2d+2⌋N(d,n)=\lfloor n^{\log_2 d+2}\rfloorN(d,n)=⌊nlog2​d+2⌋ satisfies N(d−1,n−1)+2N(d,⌊n/2⌋)+2≤N(d,n)N(d-1,n-1)+2N(d,\lfloor n/2\rfloor)+2\le N(d,n)N(d−1,n−1)+2N(d,⌊n/2⌋)+2≤N(d,n) for d≥2d\ge2d≥2, n≥d+1n\ge d+1n≥d+1.

Significance

The bound is polynomial in nnn for each fixed ddd, and nO(log⁡d)n^{O(\log d)}nO(logd) overall. Before it, every bound was exponential in ddd or subexponential in nnn. It remains, up to improvements of the exponent, the best known upper bound on the diameter of polyhedra, and thus the best known limit on how badly an optimal pivot rule must behave. Its proof is also the template for the later abstractions of the problem (connected layer families, subset partition graphs) in which the polynomial Hirsch question is studied.

The bounded case is already formalized on the platform: Hirsch.kalai_kleitman_bound states the theorem for nonempty bounded polytopes and is Proved, together with the bounded forms of the recursion (Hirsch.kalai_kleitman_recursion), of the facet step (Hirsch.facet_reduction, Hirsch.facet_walk) and of the zero-row reduction (Hirsch.diamLE_of_nonzero_rows), and the one-step relaxation lemma Hirsch.relaxation_no_new_neighbours. Kalai and Kleitman state Theorem 1 for all polyhedra: their introduction contrasts unbounded polyhedra (where Hirsch fails) with bounded ones. This mission formalizes the theorem as the paper states it, including unbounded polyhedra, where the bounded proofs do not apply as written. The combinatorial abstraction of the same bound (EHRR10.KalaiKleitman.diameter_le_kalai_kleitman, for the Eisenbrand–Hähnle–Razborov–Rothvoß model) is a different object and is not reused.

Difficulty

The bounded argument relies on compactness: faces are polytopes, every relaxation used in the induction is again a polytope, and the facet of a polytope is a polytope of one dimension less. The relaxation QFQ_FQF​ contains PPP and is typically unbounded even when PPP is bounded, and for an unbounded polyhedron a face on a facet hyperplane can be unbounded or have no vertices at all. Each step must therefore hold for general polyhedra: that a shortest walk in G(QF)G(Q_F)G(QF​) between two vertices of the ball uses only edges of PPP, and that the graph of a face, identified with a polyhedron in Rd−1\mathbb R^{d-1}Rd−1, sits inside G(P)G(P)G(P). The "maximal positive number kvk_vkv​" of the paper need not exist, and the proof must handle the two cases (no radius qualifies; every radius qualifies) separately. Zero rows are allowed in the goal and must be removed before the recursion applies, since a zero row with bi=0b_i=0bi​=0 is tight everywhere.

Formalization scope

Lean represents Rd\mathbb R^dRd as EuclideanSpace ℝ (Fin d) and the polyhedron as Hirsch.Hpoly a b with a : Fin n → ℝ^d, b : Fin n → ℝ, from the published definition Hirsch_model, which also supplies adjacency Hirsch.Adj (the segment is an extreme subset) and the diameter predicate Hirsch.DiamLE. The mission commits to these explicit readings:

  • "Δ(d,n)≤N\Delta(d,n)\le NΔ(d,n)≤N" is ∀ a b, DiamLE (Hpoly a b) N over nnn inequalities in Rd\mathbb R^dRd. This is equivalent to the facet formulation, since a ddd-dimensional polyhedron is cut out by its facet inequalities and a polyhedron given by nnn inequalities in Rd\mathbb R^dRd has dimension at most ddd and at most nnn facets.
  • "log" is log⁡2\log_2log2​ (Real.logb 2), the power is the real power, and the integer bound is its floor ⌊·⌋₊, which is equivalent because distances are natural numbers.
  • "[n/2][n/2][n/2]" is ⌊n/2⌋\lfloor n/2\rfloor⌊n/2⌋ (ℕ division); "at most n/2n/2n/2 facets" is a row set of cardinality at most ⌊n/2⌋\lfloor n/2\rfloor⌊n/2⌋.
  • An ignored row is replaced by the vacuous inequality ⟨0,x⟩≤1\langle0,x\rangle\le1⟨0,x⟩≤1, which keeps the index type Fin n.
  • Walks have exactly BBB steps with stationary steps allowed, so "BBB steps" means "at most BBB edges".
  • The paper's kvk_vkv​ is not defined; milestone 3 quantifies over every radius with the ball property.
  • Milestones 4 and 5 assume nonzero rows; the goal does not.

A statement with a boundedness or nonemptiness hypothesis, with the graph diameter measured by a distance function whose value on unreachable pairs is 000, or with the bound read with Nat.log or the natural logarithm, is not the target. Contributions welcome: proofs of the general-polyhedron facet step and of milestone 3, a general zero-row reduction, the arithmetic of milestone 6, and the base cases d≤1d\le1d≤1 and n≤dn\le dn≤d.

Selected references

  • G. Kalai and D. J. Kleitman, A quasi-polynomial bound for the diameter of graphs of polyhedra, Bull. Amer. Math. Soc. 26 (1992), 315–316. https://doi.org/10.1090/S0273-0979-1992-00285-9 (arXiv:math/9204233)
  • V. Klee and D. W. Walkup, The d-step conjecture for polyhedra of dimension d < 6, Acta Math. 117 (1967), 53–78. https://doi.org/10.1007/BF02395040
  • V. Klee and P. Kleinschmidt, The d-step conjecture and its relatives, Math. Oper. Res. 12 (1987), 718–755. https://doi.org/10.1287/moor.12.4.718
  • D. G. Larman, Paths on polytopes, Proc. London Math. Soc. (3) 20 (1970), 161–178. https://doi.org/10.1112/plms/s3-20.1.161
  • F. Santos, A counterexample to the Hirsch conjecture, Ann. of Math. 176 (2012), 383–412. https://doi.org/10.4007/annals.2012.176.1.7
9 thms3 active usersReviewed
Graph TheoryOperations Research·Captain: mikedeng1

The Strong Perfect Graph Theorem I: A Graph Is Perfect If and Only If It Is BergeResearch Paper

Motivation

A perfect graph is one whose coloring problem has a particularly sharp answer on every induced subgraph: the fewest colors needed is exactly the size of its largest clique. This makes a local obstruction, a clique, certify the optimum number of colors throughout the graph. Claude Berge proposed in 1961 that perfection could be recognized by the absence of two kinds of induced odd cycles, one in the graph and one in its complement. The equivalence became known as the strong perfect graph conjecture. Chudnovsky, Robertson, Seymour, and Thomas proved it in their 2006 paper, which also proves a structural decomposition of the graphs under study. The paper connects this question to graph coloring, Shannon capacity, and linear and integer programming. Chudnovsky et al., pp. 51–54

The earlier complement theorem was proved by Lovász in 1972 and appears as Theorem 1.1 in the paper. The strong conjecture remained unresolved for roughly four decades; the authors' Theorem 1.2 settles it. Their proof places a second result, Theorem 1.3, beside the equivalence: a graph with no forbidden odd hole or antihole must either belong to a basic class or admit one of several specified decompositions. The graph classes and decompositions are therefore part of the statement of the route to the main result, not merely vocabulary for a proof. Chudnovsky et al., pp. 52–56

Setting

All graphs here are finite and simple. The complement G‾\overline GG has the same vertices as GGG, and two distinct vertices are adjacent in G‾\overline GG exactly when they are not adjacent in GGG. For a vertex set XXX, the notation G∣XG|XG∣X means the induced subgraph on XXX. A clique is a set of pairwise adjacent vertices. Its largest possible size in a graph HHH is ω(H)\omega(H)ω(H), and χ(H)\chi(H)χ(H) is the minimum number of colors in a proper vertex coloring of HHH.

A hole is an induced cycle of length at least four. An antihole of GGG is a hole in G‾\overline GG. A graph is Berge if every hole and antihole has even length. Thus a perfect graph requires χ(G∣X)=ω(G∣X)\chi(G|X)=\omega(G|X)χ(G∣X)=ω(G∣X) for every X⊆V(G)X\subseteq V(G)X⊆V(G), while a Berge graph satisfies a restriction on induced cycles in both GGG and G‾\overline GG. “Induced” matters: a cycle with a chord is not a hole. Chudnovsky et al., pp. 51–52

For the structural milestones, a basic graph is a bipartite graph, the complement of one, a line graph of a bipartite graph, the complement of such a line graph, or a double split graph. The latter consists of paired vertices ai,bia_i,b_iai​,bi​ and cj,djc_j,d_jcj​,dj​ with the within-pair and between-pair adjacencies specified on pp. 52–53. A proper 2-join partitions the vertices into two sides with two prescribed complete cross-edge blocks, connected-component conditions on both sides, and a special odd-path condition. A proper homogeneous pair is a pair of vertex sets whose outside vertices split into four nonempty adjacency classes. A balanced skew partition has one side disconnected and the other disconnected in the complement, together with parity restrictions on induced paths and antipaths. Chudnovsky et al., pp. 52–54

Formalization targets

Theorem 1.2: perfection and the Berge property

The goal is the exact equivalence for every finite simple graph:

G is perfect⟺G is Berge.G\text{ is perfect}\quad\Longleftrightarrow\quad G\text{ is Berge}.G is perfect⟺G is Berge.

No order bound, chosen graph class, or decomposition hypothesis is attached to the goal. Chudnovsky et al., p. 52, 1.2

Structural and reduction milestones

Theorem 1.1 says that GGG perfect implies G‾\overline GG perfect. Theorem 1.5 says that a minimum imperfect graph, a Berge nonperfect graph with the smallest vertex count among all such graphs, cannot admit a balanced skew partition. Theorem 13.5 says that a recalcitrant graph—a Berge graph with the listed line-graph, double-split, 2-join, homogeneous-pair, and balanced-skew outcomes absent—has GGG or G‾\overline GG bipartite. Theorem 1.3 states the decomposition conclusion:

G Berge⟹G basic ∨ G or G‾ has a proper 2-join ∨ G has a proper homogeneous pair ∨ G has a balanced skew partition.G\text{ Berge}\Longrightarrow G\text{ basic}\ \lor\ G\text{ or }\overline G\text{ has a proper 2-join}\ \lor\ G\text{ has a proper homogeneous pair}\ \lor\ G\text{ has a balanced skew partition}.G Berge⟹G basic ∨ G or G has a proper 2-join ∨ G has a proper homogeneous pair ∨ G has a balanced skew partition.

The milestone order records the two reduction results, the later structural capstone, and the decomposition statement it yields. The paper's other section results that establish 13.5 are posed in the remaining missions of this series. Chudnovsky et al., pp. 52, 54–55, 154

Significance

Theorem 1.2 gives a forbidden-induced-subgraph characterization of perfect graphs. Its cycle condition is intrinsic to the graph and its complement; its coloring condition quantifies over every induced subgraph. Together with Theorem 1.3, it ties a numerical property of colorings to explicit graph structures and separations. The complement theorem and the exclusion of decompositions for a minimum imperfect graph explain why the structural alternatives have the strength needed for the equivalence. Chudnovsky et al., pp. 52–56

The mathematical theorem was proved in the cited paper. This mission poses its statements in Lean and seeks machine-checked proofs; the draft theorem declarations are open targets. The definition layer is useful beyond this mission: induced holes, Berge graphs, perfection, balanced skew partitions, and the decomposition predicates can support the later missions without changing what each source statement means. No machine-checked proof of these draft targets is claimed here.

Difficulty

The forward implication can be tested on induced odd cycles, but that observation does not settle the converse. Excluding odd holes and antiholes does not give an immediate coloring of an arbitrary induced subgraph. The paper instead establishes a detailed account of what a Berge graph can look like when it is not in a basic class. The delicate point in turning this account into Theorem 1.2 is that each decomposition outcome must be incompatible with a minimum imperfect graph. Ordinary skew partitions are too broad for that role; the balanced parity conditions are part of the statement. The structural conclusion 13.5 collects restrictions established across many later sections, so formalizing its prerequisites is a substantial graph-theoretic task. Chudnovsky et al., pp. 52–56, 154

Formalization scope

Lean uses SimpleGraph V with finite vertices, decidable vertex equality, and the Mathlib complement, induced subgraph, chromatic number, clique number, bipartiteness, and line graph. A hole is a list in cyclic order whose adjacency relation agrees exactly with the cycle edges; a path is likewise listed in one orientation with exactly its consecutive edges. Antiholes and antipaths use the complement graph. The empty vertex set is connected, matching p. 54. The double split partition is encoded by an equivalence from the four indexed parts to the whole vertex type, making disjointness and coverage explicit. A minimum imperfect graph is globally minimal by vertex count, among all finite Berge graphs.

No hypothesis beyond the paper's finite, simple graph convention is added to the goal or numbered milestones. In particular, “Berge” includes holes in both GGG and G‾\overline GG; “perfect” ranges over every induced subgraph; and the complement occurs only in those decomposition outcomes where the paper places it. A non-induced cycle or a missing complement condition would make the formal target different. The definitions and Lean proofs of the graph classes, their boundary cases, and the structural milestones are welcome contributions. Later missions pose the paper's intervening numbered results rather than duplicating them here.

Selected references

  • Maria Chudnovsky, Neil Robertson, Paul Seymour, and Robin Thomas, The strong perfect graph theorem, Annals of Mathematics 164 (2006), 51–229. DOI 10.4007/annals.2006.164.51.
16 thms3 active usersReviewed
Operations ResearchProbabilityTheoretical Computer Science·Captain: mikedeng1

A Polylogarithmic-Competitive Algorithm for the k-Server Problem: Randomized k-Server Is O(log² k · log³ n · log log n)-Competitive on Every n-Point MetricResearch Paper

Motivation

The k-server problem (Manasse, McGeoch and Sleator, 1990) is the central problem of online computation: kkk servers sit on points of a metric space, requests arrive one at a time at points of the space, and each request must be served by moving a server to it, at a cost equal to the distance travelled. An online algorithm decides without knowing future requests; its quality is its competitive ratio, the worst-case ratio between its cost and the cost of an optimal offline schedule. Paging (caching) is the special case of a uniform metric, and weighted paging the case of a weighted star.

Timeline of the upper bounds for general metrics:

  • 1990: Manasse, McGeoch and Sleator prove that every deterministic algorithm has ratio at least kkk and conjecture that kkk is achievable.
  • 1991: Fiat, Rabani and Ravid give the first ratio depending on kkk only (exponential in kkk).
  • 1995: Koutsoupias and Papadimitriou prove that the work function algorithm is (2k−1)(2k-1)(2k−1)-competitive.
  • For randomized algorithms against an oblivious adversary, the conjectured answer is O(log⁡k)O(\log k)O(logk), achieved for paging (Fiat et al., 1991), but until 2011 nothing better than the deterministic 2k−12k-12k−1 was known for general metrics, even when the ratio may depend on the number of points nnn.
  • 2011: Bansal, Buchbinder, Mądry and Naor give the first polylogarithmic bound, O(log⁡2klog⁡3nlog⁡log⁡n)O(\log^2 k\log^3 n\log\log n)O(log2klog3nloglogn) (arXiv:1110.1580; J. ACM 62(5), 2015, DOI 10.1145/2783434), the result of this mission.

Setting

Let (M,dist)(M,\mathrm{dist})(M,dist) be a finite metric space with nnn points and kkk a number of servers. A configuration C:{1,…,k}→MC:\{1,\dots,k\}\to MC:{1,…,k}→M places server iii at C(i)C(i)C(i). A deterministic online algorithm maps each prefix of the request sequence to a configuration that has a server at the last request; its cost on a sequence ρ\rhoρ is the total distance travelled. OPT(C0,ρ)\mathrm{OPT}(C_0,\rho)OPT(C0​,ρ) is the least cost of any schedule serving ρ\rhoρ from the initial configuration C0C_0C0​. A randomized algorithm is a probability distribution over deterministic online algorithms, all starting at C0C_0C0​; it is ccc-competitive if there is a constant aaa such that its expected cost on every request sequence ρ\rhoρ is at most c⋅OPT(C0,ρ)+ac\cdot\mathrm{OPT}(C_0,\rho)+ac⋅OPT(C0​,ρ)+a.

The paper works with three auxiliary objects. A σ-HST is a rooted tree whose leaves are the points, in which all edges from a node to its children have one common length, equal to 1/σ1/\sigma1/σ times the length of the edge above that node; the distance between two leaves is the length of the tree path. A weighted σ-HST only requires that the edge above a non-root internal node be at least σ\sigmaσ times each edge below it. In the fractional k-server problem on a tree, the state is a vector xxx of server probabilities on the leaves with 0≤xi≤10\le x_i\le10≤xi​≤1 and ∑ixi=k\sum_i x_i=k∑i​xi​=k, a request at leaf iii forces xi=1x_i=1xi​=1, and moving from xxx to x′x'x′ costs ∑vW(v) ∣xv′−xv∣\sum_v W(v)\,|x'_v-x_v|∑v​W(v)∣xv′​−xv​∣, where xvx_vxv​ is the mass below node vvv and W(v)W(v)W(v) the length of the edge above vvv. In the allocation problem on a weighted star with weights wiw_iwi​, requests carry a location iti^tit, a monotone cost vector ht(0)≥⋯≥ht(k)≥0h^t(0)\ge\dots\ge h^t(k)\ge0ht(0)≥⋯≥ht(k)≥0 (the cost of serving with jjj servers there) and a server quota κ(t)≤k\kappa(t)\le kκ(t)≤k.

Formalization targets

Goal: Theorem 1

There is a universal constant C>0C>0C>0 such that for all k≥2k\ge2k≥2, every metric space MMM with n≥3n\ge3n≥3 points and every initial configuration C0C_0C0​, some randomized online algorithm starting at C0C_0C0​ is

C log⁡2k log⁡3n log⁡log⁡n-competitive.C\,\log^2 k\,\log^3 n\,\log\log n\text{-competitive.}Clog2klog3nloglogn-competitive.

Milestones

In the order the proof uses them:

  1. Claim 15: the fix-stage inequality behind the allocation algorithm's analysis.
  2. Theorem 5: for every 0<ε≤10<\varepsilon\le10<ε≤1, a fractional allocation algorithm whose hit cost is at most (1+ε)(Opt+wmax⁡g(κ))+a(1+\varepsilon)(\mathrm{Opt}+w_{\max}g(\kappa))+a(1+ε)(Opt+wmax​g(κ))+a and whose movement cost is at most O(log⁡(k/ε))(Opt+wmax⁡g(κ))+aO(\log(k/\varepsilon))(\mathrm{Opt}+w_{\max}g(\kappa))+aO(log(k/ε))(Opt+wmax​g(κ))+a, where g(κ)=∑t∣κ(t)−κ(t−1)∣g(\kappa)=\sum_t|\kappa(t)-\kappa(t-1)|g(κ)=∑t​∣κ(t)−κ(t−1)∣.
  3. Theorem 6: given such allocation algorithms, an O(ℓlog⁡(kℓ))O(\ell\log(k\ell))O(ℓlog(kℓ))-competitive fractional k-server algorithm on every weighted σ-HST of depth ℓ\ellℓ with σ=Ω(ℓlog⁡(kℓ))\sigma=\Omega(\ell\log(k\ell))σ=Ω(ℓlog(kℓ)).
  4. Theorem 8: every σ-HST with nnn leaves becomes a weighted σ-HST of depth O(log⁡n)O(\log n)O(logn) on the same leaves, with distances distorted by at most 2σ/(σ−1)2\sigma/(\sigma-1)2σ/(σ−1).
  5. Lemma 25 and Theorem 24: on a σ-HST with σ>5\sigma>5σ>5, randomized states consistent with a changing fractional state can be maintained online at cost O(ct)O(c_t)O(ct​) per step.
  6. Theorem 7: on a σ-HST with σ>5\sigma>5σ>5, a ccc-competitive fractional algorithm yields an O(c)O(c)O(c)-competitive randomized one.

Significance

The theorem broke the exponential gap between the Ω(log⁡k)\Omega(\log k)Ω(logk) lower bound and the 2k−12k-12k−1 upper bound for randomized k-server, and showed that randomization helps on every finite metric, not only on uniform or specially structured ones. Its two-level method (a fractional algorithm on trees driven by per-node allocation problems, followed by an online rounding) became the template for later work, including the O(log⁡2k)O(\log^2 k)O(log2k) bound on HSTs of Bubeck, Cohen, Lee, Lee and Mądry (STOC 2018) and Lee's O(log⁡6k)O(\log^6 k)O(log6k) bound on general metrics (FOCS 2018).

The result is proved, in this paper. As far as is known it has no machine-checked proof. Formalizing it means formalizing the analysis of an online algorithm driven by a continuous-time process, a potential-function argument with exact constants, a tree contraction with a distortion bound, and an online randomized rounding against a transportation cost. The allocation, HST and rounding statements are reusable for other online problems on trees (metrical task systems, weighted paging).

Difficulty

For a deterministic or randomized algorithm on a tree, the natural recursion splits the servers of each node among its children. Coté, Meyerson and Poplawski showed that this works if each node solves an allocation problem with a strong guarantee: hit cost within a factor 1+ε1+\varepsilon1+ε of optimal. Integral allocation algorithms cannot achieve this; the integrality gap example of the paper (p. 8) gives a factor Ω(k)\Omega(k)Ω(k). The fractional relaxation avoids the gap, but then the rounding step must keep a randomized state consistent with a fractional state at constant-factor cost, and the HSTs obtained from general metrics have depth growing with the aspect ratio, which a depth-dependent ratio cannot afford. Each of the three reductions (allocation to fractional k-server, deep HST to shallow weighted HST, fractional to randomized) loses only polylogarithmic or constant factors, and the main theorem needs all three at once.

Formalization scope

The k-server model, randomized algorithms and competitiveness are the published definitions KServer_model and KServer_randomized; competitiveness carries an additive constant fixed before the request sequence. Trees are finite rooted trees with a parent map, a depth function and positive edge lengths; points of the k-server problem are the leaves, and the theorems take an arbitrary finite metric space together with a bijection to the leaves and the hypothesis that the distance equals the tree distance. Fractional k-server states have exactly kkk units of mass, each leaf at most 111, and fractional algorithms are measured against the integral offline optimum. The allocation optimum is the integral optimum; cost vectors are finite, non-negative and non-increasing; the diameter of the star is wmax⁡=max⁡iwiw_{\max}=\max_i w_iwmax​=maxi​wi​. The cost of changing a randomized state is the transportation cost over couplings, with minimum-matching cost between configurations. Every O(⋅)O(\cdot)O(⋅) is an explicit constant quantified before the instance, except that in Theorems 7 and 24 and Lemma 25 it may depend on σ\sigmaσ.

Formalizations that make the targets trivial are excluded: the fractional state must place a full server on every request and stay in [0,1][0,1][0,1], the benchmark is the integral optimum (not the algorithm's own or the fractional cost), and no constant may depend on kkk, nnn, the metric or the tree, since otherwise Theorem 1 would follow from the 2k−12k-12k−1 bound.

The proof of Theorem 1 also uses the embedding of Fakcharoenphol, Rao and Talwar [18] of a finite metric into a distribution over σ-HSTs with expected distortion O(σlog⁡σn)O(\sigma\log_\sigma n)O(σlogσ​n). It is an external ingredient, not a result of this paper, and is not a milestone; contributions formalizing it (or Bartal's earlier embedding) are welcome, as are formalizations of the integral optimum's properties on trees (Lemmas 21–22 of the paper), which are not stated here.

Selected references

  • N. Bansal, N. Buchbinder, A. Mądry, J. Naor, A Polylogarithmic-Competitive Algorithm for the k-Server Problem, arXiv:1110.1580v1, 2011; J. ACM 62(5), 2015. https://arxiv.org/abs/1110.1580, https://doi.org/10.1145/2783434
  • M. Manasse, L. McGeoch, D. Sleator, Competitive algorithms for server problems, J. Algorithms 11, 1990. https://doi.org/10.1016/0196-6774(90)90003-W
  • E. Koutsoupias, C. Papadimitriou, On the k-server conjecture, J. ACM 42(5), 1995. https://doi.org/10.1145/210118.210128
  • A. Fiat, R. Karp, M. Luby, L. McGeoch, D. Sleator, N. Young, Competitive paging algorithms, J. Algorithms 12, 1991. https://doi.org/10.1016/0196-6774(91)90041-V
  • J. Fakcharoenphol, S. Rao, K. Talwar, A tight bound on approximating arbitrary metrics by tree metrics, J. Comput. Syst. Sci. 69(3), 2004. https://doi.org/10.1016/j.jcss.2004.04.011
  • A. Coté, A. Meyerson, L. Poplawski, Randomized k-server on hierarchical binary trees, STOC 2008. https://doi.org/10.1145/1374376.1374474
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