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Combinatorics·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
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Combinatorics·Captain: harry

Conway 99: structural constraints and graph reconstructionOpen Problem

Motivation

John Conway asked whether a graph on 99 vertices can have every edge in exactly one triangle and every nonedge in exactly one quadrilateral. He included this question among his five $1,000 problems and explicitly accepted either a construction or a proof of impossibility (Conway, 2017). This mission organizes structural constraints and graph reconstruction around the existing public formal statement, which remains open. It complements the existing Conway mission: both point to the same mathematical question, while this mission curates additional published intermediate theorems and the precise bridges still needed to use them.

The historical restriction most relevant here is Wilbrink's 1984 theorem that a hypothetical graph cannot be vertex-transitive (Wilbrink's paper). That result is already formalized on Prove2Me. The consolidated local Conway99 development also contains graph-owned point, finite-field, and rooted constraints. Its written and finite-check results have different verification levels; the platform-accepted lemmas cited below are the machine-checked part of that work.

Setting

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 each vertex has kkk neighbours, adjacent vertices have exactly λ\lambdaλ common neighbours, and distinct nonadjacent vertices have exactly μ\muμ common neighbours. Write GGG for a candidate graph and AAA for its integer adjacency matrix, whose (u,v)(u,v)(u,v) entry is 111 when uuu and vvv are adjacent and 000 otherwise. Let III be the identity matrix and JJJ the all-ones matrix. In this mission, the parameter tuple is (99,14,1,2)(99,14,1,2)(99,14,1,2).

The condition λ=1\lambda=1λ=1 makes each edge part of a unique triangle. Each vertex's 14 neighbours split into seven paired edges. The condition μ=2\mu=2μ=2 makes each nonadjacent pair have exactly two common neighbours. These facts lead to 231231231 triangles and the matrix equation A2+A=12I+2JA^2+A=12I+2JA2+A=12I+2J, both already proved on the platform (triangle count, matrix identity). Choosing one vertex as a root splits the remaining vertices into its 14 neighbours and 84 nonneighbours; the graph's actual adjacency blocks then satisfy further identities (rooted theorem). The word actual matters: arbitrary matrices satisfying some of these identities have not thereby been shown to arise from one graph.

Formalization targets

Goal

∃ (α:Type) (_:Fintype α) (G:SimpleGraph α) (_:DecidableRel G.Adj),IsSRGWith  G  99  14  1  2.\exists\,(\alpha:\mathrm{Type})\,(\_:\mathrm{Fintype}\,\alpha)\,(G:\mathrm{SimpleGraph}\,\alpha)\,(\_:\mathrm{DecidableRel}\,G.\mathrm{Adj}),\quad \mathrm{IsSRGWith}\;G\;99\;14\;1\;2.∃(α:Type)(_:Fintypeα)(G:SimpleGraphα)(_:DecidableRelG.Adj),IsSRGWithG991412.

This is the already published Conway99.conway_99 theorem, reused as the goal. A graph satisfying it proves existence; a proof of the negation of this full existential settles nonexistence. Neither outcome has been established in the archive.

Structural and reconstruction targets

The first layer collects necessary facts about every hypothetical GGG: rooted block equations, point-Gram identities, and finite-field identities. The accepted theorems are rooted blocks, point Gram, and finite fields. An independent binary cut-response rank theorem supplies reusable seven-vertex algebra; its statement does not assume a Conway graph. A selected twelve-owner contact theorem gives a further conclusion only when its chosen triangle, owner set, and regularity premises hold.

The reconstruction layer asks whether compatible local data can be obtained from, or assembled into, one graph satisfying all four strongly regular parameters. The public adjacency-matrix formulation and triangle line-system formulation are still open. A future coclique-completion equivalence concerns graphs with a specified independent 22-set; it cannot replace the unrestricted goal unless that set is shown to exist in every candidate. No unpublished or pending statement is presented as a completed milestone.

An accepted admitted-coclique spike theorem supplies a precise lattice membership identity when a 22-point set and its point-vector average are already admitted to one lattice. It does not produce that coclique, establish the needed lattice for every graph, or close the reconstruction gap. An accepted printed-census correction is retained as a reference to prevent the known erroneous expressions from being reused as graph counts; it is an audit of formulas, not a structural exclusion.

Significance

A construction would settle Conway's existence question with a checkable 99-vertex object. A nonexistence proof would explain why the local triangle and common-neighbour conditions cannot coexist globally, despite the standard parameter equations being consistent. Either result must address the whole graph, not one selected root, lattice, shell, or completion packet (Conway's question; formal goal).

The formalization already gives reusable graph-owned interfaces: block identities from an actual root, a point Gram representation, and modular restrictions. Those are machine-checked necessary conditions, while deeper rank, lattice, triangle-transport, and finite-support exclusions in the consolidated local development remain written or locally checked mathematics. This mission makes their status visible and provides a place to connect faithful formal versions as they are published. A conditional theorem remains useful, but its hypotheses must be derived from the candidate graph before it can narrow the unrestricted goal.

Difficulty

The adjacency equation and its eigenvalue multiplicities constrain every candidate but do not determine the binary off-diagonal entries of AAA. Local equations can have feasible matrix or incidence data even when those data have not been embedded in a common simple graph. Conversely, excluding a fixed support, one lattice choice, or a branch with an ordinary point leaves other configurations untouched. The consolidated local frontier records those coverage gaps, including exceptional-only configurations and unexcluded incidence systems. The central task is to close a graph-wide bridge or exhibit a graph, while retaining every hypothesis on narrower results.

Formalization scope

The goal uses Mathlib's SimpleGraph.IsSRGWith and quantifies over a finite vertex type, a graph, and decidable adjacency exactly as in the published statement. Supporting statements may use Fin 99 after an explicit relabelling, integer adjacency matrices, finite triangle sets, real Gram matrices, and finite-field reductions. They must derive data from one G, or mark any independent packet as an additional hypothesis. A theorem about selected owners, a prescribed coclique, a particular determinant or rank, or a saved finite support must keep that restriction visible; none may silently assert that every candidate supplies it.

Existing published definitions and theorems should be imported by their platform IDs. Contributions are welcome for exact bridges between graph, matrix, triangle, and coclique representations; proofs that a conditional packet follows from every candidate; faithful formalizations of written universal constraints; and a construction or unconditional contradiction. The mission's published milestones record actual theorem statements. Prospective bridge descriptions remain research targets until they have separately audited and published platform IDs.

Selected references

  • J. H. Conway, Five $1,000 Problems (Update 2017), OEIS attachment, 2017. PDF.
  • H. A. Wilbrink, On the (99,14,1,2) strongly regular graph, in Papers Dedicated to J. J. Seidel, EUT-Report 84-WSK-03, Technische Hogeschool Eindhoven, 1984, pp. 342–355. Institutional PDF; publication record.
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Combinatorics·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
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Computational GeometryDiscrete Geometry·Captain: hao jia

Uniform Obstacle Bounds for Planar Graphs (OPG-37357)Open Problem

Motivation

An obstacle representation turns a graph into a visibility system: vertices are points in the plane, and nonedges are blocked by polygonal obstacles. The obstacle number asks for the minimum number of obstacles needed. OPG-37357 records two different questions for planar graphs. The first asks whether one obstacle can ever be insufficient. The second asks whether some universal constant bounds the ordinary obstacle number of every planar graph.

The status of the two parts is different. Berman, Chappell, Faudree, Gimbel, Hartman, and Williams proved in 2017 that explicit planar graphs, including the icosahedron and their graphs X4X_4X4​ and X6X_6X6​, have ordinary obstacle number two. Thus the first question has a published positive answer. The universal-constant question remains the research target here. A separate invariant called planar or plane obstacle number requires a crossing-free visibility drawing; results for that invariant must not be substituted for the ordinary obstacle number used by this mission.

Setting

A finite simple graph GGG has a kkk-obstacle drawing when its vertices are placed injectively as points in R2\mathbb R^2R2 and there are kkk pairwise disjoint closed connected polygonal obstacles such that

uv∈E(G)⟺[p(u),p(v)] meets no obstacle.uv\in E(G) \quad\Longleftrightarrow\quad [p(u),p(v)]\text{ meets no obstacle}.uv∈E(G)⟺[p(u),p(v)] meets no obstacle.

Graph vertices lie outside every obstacle. The ordinary obstacle number obs⁡(G)\operatorname{obs}(G)obs(G) is the least such kkk. The drawing itself may contain crossings between visible graph edges; planarity is a property of the abstract input graph, not an extra constraint on the obstacle drawing.

The Lean model represents a polygonal obstacle as a connected finite union of closed filled triangles. This gives a compact polygonal region with exact real-coordinate segment incidence. Straight-line planarity of the abstract graph is represented separately.

Formalization targets

The two-part OPG record

The source records both

∃ finite planar G, obs⁡(G)>1\exists\text{ finite planar }G,\ \operatorname{obs}(G)>1∃ finite planar G, obs(G)>1

and

∃k∈N ∀ finite planar H, obs⁡(H)≤k.\exists k\in\mathbb N\ \forall\text{ finite planar }H, \ \operatorname{obs}(H)\le k.∃k∈N ∀ finite planar H, obs(H)≤k.

The first assertion is known in the literature and appears as a published-result milestone. The second is open and is therefore the mission's main theorem. Together they preserve the two-part source without presenting the whole record as unresolved.

Published first part

A milestone formalizes the stronger published statement

∃ finite planar G,obs⁡(G)≤2andobs⁡(G)≰1.\exists\text{ finite planar }G, \qquad \operatorname{obs}(G)\le2 \quad\text{and}\quad \operatorname{obs}(G)\not\le1.∃ finite planar G,obs(G)≤2andobs(G)≤1.

This captures ordinary obstacle number exactly two without hard-coding one graph before its adjacency data and lower-bound certificate are formalized.

Universal bound

The open milestone asks for a single natural number kkk, chosen before the graph, that works for every finite planar graph. The number of obstacle corners is not bounded by this theorem; only the number of connected polygonal obstacles is.

Significance

The published first part establishes that planarity alone does not force a one-obstacle representation. The second part asks whether planar graphs nevertheless have uniformly bounded visibility complexity. A positive answer would produce a common finite obstacle budget independent of graph order; a negative answer would require a family of planar graphs with unbounded ordinary obstacle number.

Formalization is especially useful because several nearby notions differ by one word but have different known bounds: ordinary versus plane obstacle number, arbitrary polygonal versus convex obstacles, and fixed-placement versus freely chosen drawings. The mission's definitions make those choices explicit and provide reusable segment-obstacle semantics for later geometric graph formalizations.

Difficulty

A finite combinatorial graph does not come with a canonical visibility drawing. Even when one starts with an arbitrary connected blocking set, replacing it by one bounded simple polygon requires compactness, component, incidence, and polygonal-neighborhood arguments. Conversely, lower bounds must quantify over every possible placement and obstacle, not merely refute a selected coordinate drawing.

Counting results for unrestricted graphs do not automatically preserve planarity. Bounds for planar obstacle number impose a crossing-free drawing and therefore answer a different question. The known two-obstacle examples close only the existential first part and give no universal kkk.

Formalization scope

All graph vertex types are finite. Obstacles are closed connected polygonal regions represented by finite triangle unions; they are pairwise disjoint and avoid graph vertices. Visibility uses the full closed segment, so tangency or boundary contact blocks a nonedge. The planarity witness is independent of the obstacle drawing. Empty and one-vertex graphs remain in the universal quantifier and should be handled without division or nonemptiness assumptions.

The repository's fixed-placement polygonization argument and finite arrangement code are candidate_only. They may motivate supporting lemmas, but they neither prove the unrestricted obstacle-drawing completeness theorem nor settle the universal bound. Contributions are welcome on exact geometry primitives, the published two-obstacle construction and lower bound, conversions between connected blockers and polygonal obstacles, and the universal root. A proof for the plane invariant, convex invariant, one fixed drawing, or a finite order cutoff must be labeled at that narrower scope.

Selected references

  • L. W. Berman, G. G. Chappell, J. R. Faudree, J. Gimbel, C. Hartman, and G. I. Williams, Graphs with Obstacle Number Greater than One, JGAA 21(6), 2017. https://doi.org/10.7155/jgaa.00452
  • J. Gimbel, P. Ossona de Mendez, and P. Valtr, Obstacle Numbers of Planar Graphs, Graph Drawing 2017. https://arxiv.org/abs/1706.06992
  • M. Balko, S. Chaplick, R. Ganian, S. Gupta, M. Hoffmann, P. Valtr, and A. Wolff, Bounding and Computing Obstacle Numbers of Graphs, SIAM Journal on Discrete Mathematics 38(2), 2024. https://arxiv.org/abs/2206.15414
  • Open Problem Garden / UnsolvedMath, OPG-37357. https://www.unsolvedmath.com/problems/OPG-37357
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CombinatoricsOptimization·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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Information TheoryQuantum Error Correction·Captain: mikedeng1

Quantum Expander Codes: On a Biregular Left-Right Expanding Graph with δA, δB < 1/6, the Greedy Generator Decoder Corrects Every Quantum Error of Weight Below min(γA·nA, γB·nB)/(3(1+ΔB))Research Paper

Motivation

A quantum LDPC code is a CSS code whose two check matrices HXH_XHX​ and HZH_ZHZ​ have rows and columns of bounded weight, so that every check measures only a few qubits. Such codes are the main candidates for fault-tolerant quantum memories, because their checks are local, but a code is only useful together with a decoder: an efficient procedure that recovers the error, up to stabilizers, from its syndrome. For classical LDPC codes built from expander graphs, Sipser and Spielman showed in 1996 that a simple bit-flipping algorithm corrects a constant fraction of the minimum distance in linear time (Sipser–Spielman 1996). In the quantum setting the obvious analogue fails, and before this work no family of quantum LDPC codes was known to admit an efficient decoder correcting a number of adversarial errors proportional to its minimum distance.

Timeline.

  • 1996: Sipser and Spielman, expander codes and their linear-time bit-flipping decoder.
  • 2009: Tillich and Zémor introduce the hypergraph product, giving quantum LDPC codes of positive rate and minimum distance of order n\sqrt nn​ (arXiv:0903.0566).
  • 2015: Leverrier, Tillich and Zémor take the hypergraph product of an expanding biregular graph with itself and prove that a greedy generator-flipping decoder corrects every error of weight up to a constant fraction of the minimum distance (arXiv:1504.00822, FOCS 2015). This mission formalizes that result.

Setting

Let G=(A∪B,E)G = (A \cup B, \mathcal E)G=(A∪B,E) be a finite bipartite graph, nA=∣A∣n_A = |A|nA​=∣A∣, nB=∣B∣n_B = |B|nB​=∣B∣, with nB≤nAn_B \le n_AnB​≤nA​, and write a∼ba \sim ba∼b for adjacency. GGG is (ΔA,ΔB)(\Delta_A, \Delta_B)(ΔA​,ΔB​)-biregular if every vertex of AAA has ΔA\Delta_AΔA​ neighbours and every vertex of BBB has ΔB\Delta_BΔB​. For S⊆AS \subseteq AS⊆A let Γ(S)\Gamma(S)Γ(S) be its neighbourhood. GGG is (γA,δA)(\gamma_A, \delta_A)(γA​,δA​)-left-expanding if ∣Γ(S)∣≥(1−δA)ΔA∣S∣|\Gamma(S)| \ge (1 - \delta_A)\Delta_A |S|∣Γ(S)∣≥(1−δA​)ΔA​∣S∣ for every S⊆AS \subseteq AS⊆A with ∣S∣≤γAnA|S| \le \gamma_A n_A∣S∣≤γA​nA​; right expansion is the same on the BBB side, and left-right expanding means both. Γu(S)\Gamma_u(S)Γu​(S) denotes the unique neighbours of SSS, adjacent to exactly one vertex of SSS.

Let HHH be the nB×nAn_B \times n_AnB​×nA​ binary biadjacency matrix of GGG. The quantum code QG\mathcal Q_GQG​ has n=nA2+nB2n = n_A^2 + n_B^2n=nA2​+nB2​ qubits, indexed by A2∪B2A^2 \cup B^2A2∪B2 (written αa\alpha aαa and bβb\betabβ), and check matrices

HX=(1nA⊗H, HT⊗1nB),HZ=(H⊗1nA, 1nB⊗HT),H_X = (\mathbb 1_{n_A} \otimes H,\ H^T \otimes \mathbb 1_{n_B}), \qquad H_Z = (H \otimes \mathbb 1_{n_A},\ \mathbb 1_{n_B} \otimes H^T),HX​=(1nA​​⊗H, HT⊗1nB​​),HZ​=(H⊗1nA​​, 1nB​​⊗HT),

with rows indexed by A×BA \times BA×B and B×AB \times AB×A. The row bababa of HZH_ZHZ​ has support the Z-generator gba={αa:α∼b}∪{bβ:β∼a}g_{ba} = \{\alpha a : \alpha \sim b\} \cup \{b\beta : \beta \sim a\}gba​={αa:α∼b}∪{bβ:β∼a}. An XXX error is e∈F2A2∪B2e \in \mathbb F_2^{A^2 \cup B^2}e∈F2A2∪B2​, its syndrome is σX(e)=HXeT\sigma_X(e) = H_X e^TσX​(e)=HX​eT, and CZ⊥\mathcal C_Z^\perpCZ⊥​ is the space of sums of Z-generators. The weight w(eX,eZ)w(e_X, e_Z)w(eX​,eZ​) of a quantum error counts the qubits where eXe_XeX​ or eZe_ZeZ​ is nonzero.

The decoder. On a syndrome sss, one iteration picks a vector e1e_1e1​ whose support lies in some generator gbag_{ba}gba​, such that s1=s+σX(e1)s_1 = s + \sigma_X(e_1)s1​=s+σX​(e1​) has ∣s1∣<∣s∣|s_1| < |s|∣s1​∣<∣s∣ and the ratio (∣s∣−∣s1∣)/∣e1∣(|s| - |s_1|)/|e_1|(∣s∣−∣s1​∣)/∣e1​∣ is maximal among all such vectors. It iterates on the current syndrome, fails if it gets stuck at a nonzero syndrome, and otherwise outputs the sum eOe_OeO​ of the flipped vectors. The ZZZ decoder is the same with HXH_XHX​ and HZH_ZHZ​ exchanged.

Formalization targets

Goal: Theorem 2

Assume GGG is (ΔA,ΔB)(\Delta_A, \Delta_B)(ΔA​,ΔB​)-biregular and (γA,δA,γB,δB)(\gamma_A, \delta_A, \gamma_B, \delta_B)(γA​,δA​,γB​,δB​)-left-right expanding with

δA<16,δB<16,\delta_A < \tfrac16, \qquad \delta_B < \tfrac16,δA​<61​,δB​<61​,

and let (eX,eZ)(e_X, e_Z)(eX​,eZ​) be a quantum error with

w(eX,eZ)<w0=13(1+ΔB)min⁡(γAnA,γBnB).w(e_X, e_Z) < w_0 = \frac{1}{3(1 + \Delta_B)} \min(\gamma_A n_A, \gamma_B n_B).w(eX​,eZ​)<w0​=3(1+ΔB​)1​min(γA​nA​,γB​nB​).

Then on every run, the XXX decoder on input σX(eX)\sigma_X(e_X)σX​(eX​) never outputs a failure and its output satisfies eO+eX∈CZ⊥e_O + e_X \in \mathcal C_Z^\perpeO​+eX​∈CZ⊥​. The ZZZ decoder on σZ(eZ)\sigma_Z(e_Z)σZ​(eZ​) behaves symmetrically, with eO+eZ∈CX⊥e_O + e_Z \in \mathcal C_X^\perpeO​+eZ​∈CX⊥​.

Milestones

In the paper's order of proof:

  • Lemma 3, ∣Γu(SA)∣≥(1−2δA)ΔA∣SA∣|\Gamma_u(S_A)| \ge (1 - 2\delta_A)\Delta_A |S_A|∣Γu​(SA​)∣≥(1−2δA​)ΔA​∣SA​∣;
  • Proposition 4, distance at least γAnA\gamma_A n_AγA​nA​ for the classical code;
  • the distance half of eq. (3), dQ≥min⁡(d,dT)d_Q \ge \min(d, d^T)dQ​≥min(d,dT), already posed on the platform;
  • Corollary 5, dQ≥min⁡(γAnA,γBnB)d_Q \ge \min(\gamma_A n_A, \gamma_B n_B)dQ​≥min(γA​nA​,γB​nB​);
  • the column-weight bound ∣σX(e)∣≤ΔB∣e∣|\sigma_X(e)| \le \Delta_B |e|∣σX​(e)∣≤ΔB​∣e∣;
  • Lemma 7, existence of a critical generator;
  • the displays (19), (22), (23), (24) of Appendix B;
  • Lemma 8, a step of ratio at least 1/31/31/3 inside a critical generator;
  • Lemma 10, the XXX half of the goal.

Significance

The result. Theorem 2 gives the first family of quantum LDPC codes with an efficient decoder that corrects a number of adversarial errors proportional to the minimum distance. Here that number is of order n\sqrt nn​. The same analysis yields a robustness property: low-weight errors have syndromes of proportional weight (Corollary 9 of the paper). Later work on quantum expander codes builds on this analysis, for example on their decoding under random noise (arXiv:1711.08351) and on fault tolerance with constant overhead (arXiv:1808.03821).

Formalizing it. The result is proved on paper. As far as is known, it has no machine-checked proof. The mission produces a formal statement of the decoder and its correctness, and it also produces reusable pieces:

  • unique-neighbour expansion;
  • the distance of expander codes;
  • the distance of hypergraph product codes, tied to an open item of the Tillich–Zémor mission;
  • a generic formalization of greedy syndrome-weight decoding.

Difficulty

The obvious quantum analogue of Sipser–Spielman flips single qubits that lower the syndrome weight. It fails because of degeneracy: an error and its sum with a generator have the same syndrome. A small error can therefore look locally like a large one, and no single qubit need be worth flipping. The paper's decoder instead flips arbitrary patterns inside one generator. The difficulty is to show that some pattern always lowers the syndrome weight by a constant fraction of its size. The proof needs a combinatorial structure, the critical generator, whose existence uses unique-neighbour expansion on both sides of the graph, and a case analysis on its six parts. A second difficulty is that the decoder minimizes the syndrome weight, not the error weight, so the intermediate error can grow. Bounding that growth is what limits the guarantee to w0w_0w0​ rather than the distance.

Formalization scope

  • The graph is a relation adj : A → B → Prop on finite types, and HHH = bipMatrix adj : Matrix B A (ZMod 2).
  • The published TillichZemor.HGP.Setting is reused. The paper's HXH_XHX​ is TillichZemor.HGP.hz H H and its HZH_ZHZ​ is hx H H; the X/Z names are swapped relative to the 2009 paper, and this is checked against eq. (5) in a sanity file.
  • Qubits are (A × A) ⊕ (B × B), weights are hammingNorm, and CZ⊥\mathcal C_Z^\perpCZ⊥​ is CodingTheory.dualCode of ker H_Z.
  • Expansion constants are real and cardinalities are compared in R\mathbb RR. Distance statements are per-vector, so no ∞\infty∞-valued minimum distance is cast to a real number.
  • The decoder is defined once (Candidate, Step, Reach, DecodesCorrectly), generic in the syndrome and generator matrices. Its ratio is computed in Q\mathbb QQ.
  • Reach contains every state reachable under any tie-breaking. Correctness requires, at every reachable state, a further step while the syndrome is nonzero, and eO+ee_O + eeO​+e in the row space of the generator matrix once it is zero. Termination is automatic, because each step lowers a natural number.

Additions and omissions.

  • The hypothesis ΔA≥1\Delta_A \ge 1ΔA​≥1 is added throughout. With ΔA=0\Delta_A = 0ΔA​=0 the expansion conditions are vacuous, H=0H = 0H=0, and every error is invisible.
  • In Lemma 8 the vector e1e_1e1​ is required to be nonzero. With e1=0e_1 = 0e1​=0 the printed inequality reads 0≥00 \ge 00≥0.
  • Lemma 8 is posed in its first assertion only. The critical generator is taken with respect to a minimum-weight representative of the coset, as in Appendix B.
  • The clauses "runs in time linear in the code length" and "letting ΔA\Delta_AΔA​, ΔB\Delta_BΔB​ be fixed and allowing nA,nBn_A, n_BnA​,nB​ to grow" are not formalized. The goal holds for every finite graph meeting the hypotheses.

Trivializing formalizations ruled out. The goal names the paper's own decoder, not "there exists a decoder", which a brute-force coset decoder would satisfy. It quantifies over every run, not a single favourable one. It asserts both never-failing and correctness, not partial correctness alone. It uses HXH_XHX​ = hz H H, not the swapped matrix.

Welcome contributions. Proofs of the counting milestones (Lemma 3, Proposition 4, the column-weight bound, (19), (22)–(24)) are independent of one another. The distance bound of Tillich–Zémor, open on the platform, is the deepest dependency. Unique-neighbour expansion and the hypergraph-product distance bound are useful beyond this mission.

Selected references

  • A. Leverrier, J.-P. Tillich, G. Zémor, Quantum expander codes, FOCS 2015; arXiv:1504.00822v1 (2015). https://arxiv.org/abs/1504.00822
  • J.-P. Tillich, G. Zémor, Quantum LDPC codes with positive rate and minimum distance proportional to the square root of the blocklength, IEEE Trans. Inf. Theory 60(2), 2014; arXiv:0903.0566. https://arxiv.org/abs/0903.0566
  • M. Sipser, D. A. Spielman, Expander codes, IEEE Trans. Inf. Theory 42(6), 1996. https://doi.org/10.1109/18.556667
  • O. Fawzi, A. Grospellier, A. Leverrier, Efficient decoding of random errors for quantum expander codes, STOC 2018; arXiv:1711.08351. https://arxiv.org/abs/1711.08351
  • O. Fawzi, A. Grospellier, A. Leverrier, Constant overhead quantum fault-tolerance with quantum expander codes, FOCS 2018; arXiv:1808.03821. https://arxiv.org/abs/1808.03821
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Combinatorics·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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Combinatorics·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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Dynamic ProgrammingOperations ResearchOptimization·Captain: mikedeng1

The Complexity of Markov Decision Processes III: The Optimal Discounted Cost of a Deterministic Process Is the Least Discounted Cost of a SigmaResearch Paper

Motivation

A Markov decision process with finitely many states and decisions, a discount factor β∈(0,1)\beta\in(0,1)β∈(0,1) and the objective of minimizing expected discounted cost is the basic model of sequential decision making in operations research and reinforcement learning. It is solvable in polynomial time by linear programming. Papadimitriou and Tsitsiklis (Math. Oper. Res. 12(3), 1987) asked the finer question of whether it can be solved fast in parallel, and showed a sharp split: the general (stochastic) problem is P-complete (their Theorem 1), so it is unlikely to admit a polylogarithmic parallel algorithm, while the deterministic special case, where every transition probability is 0 or 1, is in NC (their Theorem 4).

The deterministic case is a shortest-path problem with a twist. Every policy traces a walk in a graph, and the cost of the walk counts its ttt-th arc with weight βt\beta^tβt. The paper's argument identifies the optimal discounted cost with the cost of a finite combinatorial object, a sigma, and computes it from discounted min-plus matrix products. This mission formalizes that identity.

Setting

A deterministic stationary Markov decision process consists of a finite set SSS of nnn states, an initial state s0∈Ss_0\in Ss0​∈S, and for each state sss a finite nonempty set DsD_sDs​ of decisions. Decision i∈Dsi\in D_si∈Ds​ costs c(s,i)∈Rc(s,i)\in\mathbb Rc(s,i)∈R and leads with certainty to the state next(s,i)\mathrm{next}(s,i)next(s,i). Equivalently, it is a directed graph on SSS with an arc s→next(s,i)s\to\mathrm{next}(s,i)s→next(s,i) of weight c(s,i)c(s,i)c(s,i) for each decision; parallel arcs and loops are allowed.

A policy δ\deltaδ chooses a decision δ(s,t)∈Ds\delta(s,t)\in D_sδ(s,t)∈Ds​ for every state sss and time t=0,1,2,…t=0,1,2,\dotst=0,1,2,…; it is stationary if δ(s,t)\delta(s,t)δ(s,t) does not depend on ttt. From s0s_0s0​ it produces the states st+1=next(st,δ(st,t))s_{t+1}=\mathrm{next}(s_t,\delta(s_t,t))st+1​=next(st​,δ(st​,t)) and the discounted cost

Jβ(δ)=∑t=0∞βtc(st,δ(st,t)).J_\beta(\delta)=\sum_{t=0}^{\infty}\beta^t c\bigl(s_t,\delta(s_t,t)\bigr).Jβ​(δ)=t=0∑∞​βtc(st​,δ(st​,t)).

The optimal discounted cost is Jβ∗(s0)=inf⁡δJβ(δ)J^*_\beta(s_0)=\inf_\delta J_\beta(\delta)Jβ∗​(s0​)=infδ​Jβ​(δ), the infimum over all policies δ(s,t)\delta(s,t)δ(s,t).

A sigma is a walk w0=s0,w1,…,wNw_0=s_0,w_1,\dots,w_Nw0​=s0​,w1​,…,wN​ along decisions et∈Dwte_t\in D_{w_t}et​∈Dwt​​ whose nodes w0,…,wN−1w_0,\dots,w_{N-1}w0​,…,wN−1​ are distinct and whose last node repeats an earlier one, wN=wjw_N=w_jwN​=wj​ with j<Nj<Nj<N: the walk from s0s_0s0​ up to the first repetition of a node. It is a prefix of jjj arcs followed by a cycle of l=N−j≥1l=N-j\ge1l=N−j≥1 arcs. Its discounted cost cβ(P)c_\beta(P)cβ​(P) is the discounted cost of the infinite walk that follows it and repeats its cycle forever.

In the min-plus semiring (addition is min⁡\minmin, multiplication is +++, zero is +∞+\infty+∞), let AuvA_{uv}Auv​ be the least cost of a decision from uuu to vvv (+∞+\infty+∞ if none), let rArArA multiply every finite entry by rrr, and let

B0=I,Bk=A⊗βA⊗⋯⊗βk−1A,B_0=I,\qquad B_k=A\otimes\beta A\otimes\cdots\otimes\beta^{k-1}A ,B0​=I,Bk​=A⊗βA⊗⋯⊗βk−1A,

with III the min-plus identity. The entry (Bk)uv(B_k)_{uv}(Bk​)uv​ is the least discounted cost ∑t<kβtct\sum_{t<k}\beta^t c_t∑t<k​βtct​ of a kkk-arc walk from uuu to vvv.

Formalization targets

Goal

For 0<β<10<\beta<10<β<1,

Jβ∗(s0)=min⁡P sigmacβ(P)=min⁡{(Bk)s0u+βk(Bl)uu1−βl  :  u∈S, 0≤k≤n, 1≤l≤n, both entries finite},J^*_\beta(s_0)=\min_{P\text{ sigma}}c_\beta(P)=\min\Bigl\{(B_k)_{s_0u}+\frac{\beta^{k}(B_l)_{uu}}{1-\beta^{l}}\;:\;u\in S,\ 0\le k\le n,\ 1\le l\le n,\ \text{both entries finite}\Bigr\},Jβ∗​(s0​)=P sigmamin​cβ​(P)=min{(Bk​)s0​u​+1−βlβk(Bl​)uu​​:u∈S, 0≤k≤n, 1≤l≤n, both entries finite},

with both minima attained. This is the claim on pp. 446–447 on which Theorem 4 rests. The algebraic expression is the corrected form of the paper's (see Formalization scope).

Milestones

  1. The closed form of the cost of a sigma (p. 446): cβ(P)=∑t<jβtct+βj1−βl∑t<lβtcj+tc_\beta(P)=\sum_{t<j}\beta^tc_t+\frac{\beta^j}{1-\beta^l}\sum_{t<l}\beta^tc_{j+t}cβ​(P)=∑t<j​βtct​+1−βlβj​∑t<l​βtcj+t​.
  2. A stationary optimal policy exists (§2, p. 444; cited on p. 446). The general finite stochastic statement is on the platform as BertsekasDP.discounted_main_theorem (Proved), and the deterministic case is stated in this mission's model.
  3. The path of a stationary policy, cut at its first repeated node, is a sigma with the same discounted cost, and every sigma arises this way (p. 446).
  4. The products BjB_jBj​ are the cheapest discounted jjj-arc walks (p. 447).

Significance

The identity reduces an optimization over infinite objects, the time-dependent policies, to a minimum over finitely many closed-form expressions in matrix entries. Each BkB_kBk​ is a product of kkk min-plus matrices, and matrix products parallelize well; that is the source of the NC algorithm. The same reduction, with the discounting removed, underlies the minimum-mean-cycle characterization of the average-cost problem (mission II of this series), and the sigma structure reappears in the stationary finite-horizon analysis (mission IV).

On the formalization side, the result has been proved in the paper only as a short paragraph, which cites the existence of stationary optimal policies and leaves the algebra to the reader. Its printed formulas contain index slips (below). A machine-checked version fixes the exact constants. To our knowledge none of these statements has a formal proof. The existence of stationary optimal policies for finite discounted problems is formalized on the platform in Bertsekas's general stochastic setting.

Difficulty

The obvious argument says: an optimal policy may be taken stationary, a stationary deterministic policy produces an eventually periodic path, and an eventually periodic path is a sigma. The first step is the substantial one: the optimum is an infimum over all time-dependent policies δ(s,t)\delta(s,t)δ(s,t), and a non-stationary policy can follow any walk at all, so this step is a statement about the whole dynamic program, not a combinatorial fact about a single walk. The second part of the goal adds a matching argument in the other direction: the expression ranges over arbitrary kkk-arc walks and closed lll-walks, which need not be simple, so one must check that none of them beats the best sigma, and that the best sigma has k≤nk\le nk≤n and l≤nl\le nl≤n. Finally, the discount factors must be tracked exactly; the printed formulas are off by one power of β\betaβ in two places.

Formalization scope

The Lean development lives in the namespace MDPComplexity.Discounted, with one definition file and five theorems. Conventions committed to:

  • The model is stationary and deterministic. A DetMDP S has a finite state type, a nonempty finite decision type at each state (the paper says "a finite set DsD_sDs​"; nonemptiness is implicit since a policy must choose a decision), a successor map and real costs.
  • Policies are maps δ(s,t)\delta(s,t)δ(s,t), time is N\mathbb NN from 000, and the optimum optDisc is the infimum over all such policies. Defining it over stationary policies or over sigmas would make the goal true by definition. That shortcut is ruled out, and the stationarity step is part of the content.
  • Costs are tsums, which are junk (000) for non-summable series; every statement that sums costs assumes 0<β<10<\beta<10<β<1, under which the series converge absolutely.
  • A sigma is encoded as a lasso, wN=wjw_N=w_jwN​=wj​ with j<Nj<Nj<N. This includes j=0j=0j=0, a sigma whose cycle passes through s0s_0s0​, which the printed form (u0,…,uk,v1,…,vl,v1)(u_0,\dots,u_k,v_1,\dots,v_l,v_1)(u0​,…,uk​,v1​,…,vl​,v1​) omits but the definition "a path from u0u_0u0​ until the first repetition of a node" includes. The paper's kkk is j−1j-1j−1.
  • Corrected slips: (i) in the sigma display the cycle sum carries βj−1\beta^{j-1}βj−1, not βj\beta^jβj, for its jjj-th arc, and the indices are made consistent; (ii) the algorithm's expression is λ+βkμ/(1−βl)\lambda+\beta^k\mu/(1-\beta^l)λ+βkμ/(1−βl) with l≥1l\ge1l≥1, not λ+βk+1μ/(1+βl)\lambda+\beta^{k+1}\mu/(1+\beta^l)λ+βk+1μ/(1+βl) (one state with a loop of cost 111 has optimum 1/(1−β)1/(1-\beta)1/(1−β), which the printed form misses); (iii) B0B_0B0​, used but not defined, is the min-plus identity.
  • Matrix entries are in Tropical (WithTop ℝ), so +∞+\infty+∞ is ⊤ and the product is Mathlib's matrix product over the tropical semiring.

Not formalized: membership in NC, the processor count n4n^4n4, the log⁡2n+3log⁡n\log^2 n+3\log nlog2n+3logn parallel time, and the P-completeness of the general problem. Mathlib has no parallel complexity classes. Only the identity that makes the algorithm correct is a target.

Contributions welcome: proofs of any milestone. A bridge from DetMDP to BertsekasSSPModel (taking decisions as controls and 0/1 transition rows) would let milestone 2 be derived from the platform's proved Proposition 7.3.1. A direct proof of the deterministic case is also welcome. The lasso and walk lemmas are reusable for the other missions of the series.

Selected references

  • C. H. Papadimitriou and J. N. Tsitsiklis, The Complexity of Markov Decision Processes, Mathematics of Operations Research 12(3), 441–450, 1987. https://doi.org/10.1287/moor.12.3.441
  • D. P. Bertsekas, Dynamic Programming and Optimal Control, Vol. I, 3rd ed., Athena Scientific, 2005, §7.3 (discounted problems, Proposition 7.3.1).
  • M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 1994, Ch. 6 (existence of stationary optimal policies for discounted models). https://doi.org/10.1002/9780470316887
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Complexity TheoryOperations ResearchTheoretical Computer Science·Captain: mikedeng1

Complexity of Machine Scheduling Problems 6: DIRECTED HAMILTON PATH Reduces to No-Wait Flow Shop Makespan and Total Completion TimeResearch Paper

Motivation

In a no-wait flow shop every job passes through the machines M1,…,MmM_1,\dots,M_mM1​,…,Mm​ in the same order, and once it has started it may never wait between two machines. The constraint comes from processes in which the material changes state if it is left standing, such as hot metal rolling, chemical and food processing, and some pharmaceutical lines; see the survey of Hall and Sriskandarajah (doi:10.1287/opre.44.3.510). The question of how hard it is to schedule such a shop well is the question this mission formalizes.

Brucker, Lenstra and Rinnooy Kan settled it for the case in which the number of machines is part of the input. In their report Complexity of Machine Scheduling Problems (Mathematisch Centrum, 1975; journal version in Annals of Discrete Mathematics 1, 1977, doi:10.1016/S0167-5060(08)70743-X), Theorem 5 reduces DIRECTED HAMILTON PATH to the no-wait flow shop. Both minimizing the makespan Cmax⁡C_{\max}Cmax​ and minimizing the total completion time ∑Cj\sum C_j∑Cj​ are thereby NP-complete.

Timeline.

  • 1964: Gilmore and Gomory solve the two-machine no-wait flow shop with makespan in polynomial time (doi:10.1287/opre.12.5.655).
  • 1972: Wismer (doi:10.1287/opre.20.3.689) and Reddi and Ramamoorthy (doi:10.1057/jors.1972.52) show that no-wait makespan minimization is a travelling-salesman problem with arc weights computed from the processing times.
  • 1972: Karp proves DIRECTED HAMILTON CIRCUIT NP-complete (doi:10.1007/978-1-4684-2001-2_9).
  • 1975: Brucker, Lenstra and Rinnooy Kan reduce DIRECTED HAMILTON CIRCUIT to DIRECTED HAMILTON PATH (their Theorem 2(d)), and DIRECTED HAMILTON PATH to n∣m∣F,no wait∣Cmax⁡n|m|F,\textit{no wait}|C_{\max}n∣m∣F,no wait∣Cmax​ and to n∣m∣F,no wait,wj=1∣∑wjCjn|m|F,\textit{no wait},w_j=1|\sum w_jC_jn∣m∣F,no wait,wj​=1∣∑wj​Cj​ (Theorem 5).
  • 1984: Röck shows that the problem stays NP-hard for three machines (doi:10.1145/62.65).

Setting

There are nnn jobs J1,…,JnJ_1,\dots,J_nJ1​,…,Jn​ and mmm machines. Job JℓJ_\ellJℓ​ needs processing time pℓi∈Np_{\ell i}\in\mathbb Npℓi​∈N on machine MiM_iMi​. Write

qℓi=∑r=1ipℓr,qℓ0=0,q_{\ell i}=\sum_{r=1}^{i}p_{\ell r},\qquad q_{\ell 0}=0,qℓi​=r=1∑i​pℓr​,qℓ0​=0,

for the time job JℓJ_\ellJℓ​ spends on its first iii machines. Because a job never waits, a schedule is determined by the start times Bℓ∈NB_\ell\in\mathbb NBℓ​∈N. The operation of JℓJ_\ellJℓ​ on MiM_iMi​ occupies [Bℓ+qℓ,i−1, Bℓ+qℓi)[B_\ell+q_{\ell,i-1},\,B_\ell+q_{\ell i})[Bℓ​+qℓ,i−1​,Bℓ​+qℓi​), and job JℓJ_\ellJℓ​ completes at Cℓ=Bℓ+qℓmC_\ell=B_\ell+q_{\ell m}Cℓ​=Bℓ​+qℓm​. A schedule is feasible when no two distinct jobs occupy the same machine at the same time. The delay

cjk=max⁡1≤i≤m{qji−qk,i−1}c_{jk}=\max_{1\le i\le m}\{q_{ji}-q_{k,i-1}\}cjk​=1≤i≤mmax​{qji​−qk,i−1​}

is the least gap Bk−BjB_k-B_jBk​−Bj​ that lets JkJ_kJk​ follow JjJ_jJj​ on every machine.

A directed graph G=(V,A)G=(V,A)G=(V,A) on V={0,…,n−1}V=\{0,\dots,n-1\}V={0,…,n−1} has a Hamilton path if its vertices can be ordered σ(0),…,σ(n−1)\sigma(0),\dots,\sigma(n-1)σ(0),…,σ(n−1) with every (σ(i),σ(i+1))∈A(\sigma(i),\sigma(i+1))\in A(σ(i),σ(i+1))∈A.

From GGG the paper builds an instance with nnn jobs and m=n(n−1)+2m=n(n-1)+2m=n(n−1)+2 machines. Each ordered pair (j,k)(j,k)(j,k) of distinct jobs is assigned a middle machine ι(j,k)∈{2,…,m−1}\iota(j,k)\in\{2,\dots,m-1\}ι(j,k)∈{2,…,m−1}, and the partial sums qℓiq_{\ell i}qℓi​ are perturbed from iμi\muiμ by ±λ\pm\lambda±λ or ±(λ+1)\pm(\lambda+1)±(λ+1) on the machines ι(ℓ,⋅)\iota(\ell,\cdot)ι(ℓ,⋅) and just before the machines ι(⋅,ℓ)\iota(\cdot,\ell)ι(⋅,ℓ), depending on whether the pair is an arc. The parameters satisfy λ≥1\lambda\ge1λ≥1 and μ≥2λ+3\mu\ge2\lambda+3μ≥2λ+3.

Formalization targets

Goal (Theorem 5)

DIRECTED HAMILTON PATH ∝ n∣m∣F,no wait∣Cmax⁡andDIRECTED HAMILTON PATH ∝ n∣m∣F,no wait,wj=1∣∑wjCj,\text{DIRECTED HAMILTON PATH}\ \propto\ n|m|F,\textit{no wait}|C_{\max}\quad\text{and}\quad \text{DIRECTED HAMILTON PATH}\ \propto\ n|m|F,\textit{no wait},w_j=1|\textstyle\sum w_jC_j,DIRECTED HAMILTON PATH ∝ n∣m∣F,no wait∣Cmax​andDIRECTED HAMILTON PATH ∝ n∣m∣F,no wait,wj​=1∣∑wj​Cj​,

where ∝\propto∝ is polynomial-time many-one reducibility between the binary-coded recognition languages.

Milestones, in the order the proof uses them

  1. Eq. (9): cjkc_{jk}cjk​ is the least gap between BjB_jBj​ and BkB_kBk​ for which JkJ_kJk​ follows JjJ_jJj​ on every machine.
  2. The travelling-salesman reformulation: if all processing times are positive, a feasible schedule with Cmax⁡≤yC_{\max}\le yCmax​≤y (resp. ∑Cℓ≤y\sum C_\ell\le y∑Cℓ​≤y) exists iff some job order has path length (resp. summed completion times along the path) at most yyy.
  3. An admissible ordering ι\iotaι exists for every n≠2n\ne2n=2.
  4. All processing times of the construction are at least 111.
  5. The delays of the construction: cjk=μ+2λc_{jk}=\mu+2\lambdacjk​=μ+2λ if (j,k)∈A(j,k)\in A(j,k)∈A, and μ+2λ+2\mu+2\lambda+2μ+2λ+2 otherwise.
  6. Theorem 5(a), the equivalence: GGG has a Hamilton path   ⟺  \iff⟺ Cmax⁡≤(n−1)(μ+2λ)+mμC_{\max}\le(n-1)(\mu+2\lambda)+m\muCmax​≤(n−1)(μ+2λ)+mμ.
  7. Theorem 5(b), the equivalence: GGG has a Hamilton path   ⟺  \iff⟺ ∑jCj≤12n(n−1)(μ+2λ)+nmμ\sum_jC_j\le\tfrac12n(n-1)(\mu+2\lambda)+nm\mu∑j​Cj​≤21​n(n−1)(μ+2λ)+nmμ.
  8. Theorem 2(d), off the goal's path: G′G'G′ has a Hamilton circuit iff the graph obtained by splitting a vertex v′v'v′ into v′v'v′ and a new sink v′′v''v′′ has a Hamilton path.

Significance

The result. Theorem 5, together with the NP-completeness of DIRECTED HAMILTON PATH, places both no-wait criteria among the NP-complete problems once mmm is part of the input. The Gilmore–Gomory algorithm for two machines therefore cannot be extended to arbitrary mmm unless P = NP, and heuristics and exact exponential methods for no-wait shops are justified. The reduction also gives a structural fact of independent use: every directed graph can be realized, up to two arc-weight values, as the delay matrix of a no-wait flow shop.

Formalizing it. The result has been proved for fifty years; to our knowledge no machine-checked version exists. This mission produces a checked no-wait flow shop model, a checked travelling-salesman reformulation of it, the correctness of the paper's construction, and a polynomial-time reduction in an explicit Turing-machine model. It also records a gap in the printed proof: the ordering ι\iotaι the paper calls easy to construct does not exist for n=2n=2n=2.

Difficulty

Three steps are not routine.

  1. The passage from schedules to job orders assumes that the order in which jobs start is the order in which they visit every machine. With zero processing times this fails, so the reformulation needs the positivity milestone.
  2. Computing the delays requires a case analysis over all machines and all pairs of perturbations. The property of ι\iotaι is exactly what keeps the "+++" and "−-−" cases from colliding, and the constants λ≥1\lambda\ge1λ≥1, μ≥2λ+3\mu\ge2\lambda+3μ≥2λ+3 are tight enough that the comparison must be done carefully.
  3. The goal asks for an actual Turing machine with a polynomial step bound. It has to build ι\iotaι for every n≠2n\ne2n=2 and handle n≤2n\le2n≤2 separately. The equivalences alone do not give this.

Formalization scope

Jobs and machines are 000-based (Fin n, Fin m); cum p ℓ i is the paper's qℓiq_{\ell i}qℓi​, and the machine indices ι(j,k)\iota(j,k)ι(j,k) are the paper's 111-based indices. Start times are natural numbers, and release dates are 000. Section 3 computes times from processing orders on integer data, and every criterion is regular, so real start times would not change the yes-instances. A zero-length operation occupies the empty interval. Cmax⁡≤yC_{\max}\le yCmax​≤y is stated as Cℓ≤yC_\ell\le yCℓ​≤y for every job. The delays, the partial sums of the construction and its processing times are computed in Z\mathbb ZZ. The instance uses their conversion to N\mathbb NN, which is exact by milestone 4. The threshold of Theorem 5(b) is compared in Q\mathbb QQ, as printed.

The paper's loose phrases are made explicit as follows:

  • "scheduled directly after" means "precedes on every machine";
  • "equivalent to solving the TRAVELLING SALESMAN problem" means the two threshold equivalences of milestone 2, with the ∑Cj\sum C_j∑Cj​ version as the reading of "constructed as in (a)";
  • "such an ordering can easily be constructed" is stated for n≠2n\ne2n=2, the only case in which it is true.

Directed graphs are Boolean adjacency matrices. A graph with 000 or 111 vertices has a Hamilton path, and a one-vertex graph has a Hamilton circuit iff it has a loop.

The goal is CookPvsNP.PolyReducible from the published definition CookPvsNP_defs, with polynomial time measured on Cook's one-tape Turing machines. Instances are coded with the alphabet BSym and binary code encNats of the published ProjSchedTW.Complexity.Encoding (Neumann, Schwindt and Zimmermann). A graph is coded as nnn followed by its adjacency matrix; a flow-shop instance as nnn, mmm, the matrix (pℓi)(p_{\ell i})(pℓi​) and the threshold yyy.

Stating only the equivalences 6–7 and calling the result "reducible" would drop polynomiality; the goal therefore asserts PolyReducible. The target languages contain exactly the no-wait flow-shop instances, with no waiting allowed, so the reduction cannot land in a looser problem. The parameters λ,μ\lambda,\muλ,μ always carry λ≥1\lambda\ge1λ≥1, μ≥2λ+3\mu\ge2\lambda+3μ≥2λ+3.

Contributions are welcome at every level:

  • the general travelling-salesman reformulation, which is reusable for any no-wait flow-shop result;
  • the combinatorial existence of ι\iotaι;
  • the arithmetic of the construction;
  • Turing-machine infrastructure for computing arithmetic list transformations in polynomial time, which every reduction in this series needs.

Selected references

  • P. Brucker, J. K. Lenstra, A. H. G. Rinnooy Kan, Complexity of Machine Scheduling Problems, Mathematisch Centrum Report BW 43/75, Amsterdam, 1975; Annals of Discrete Mathematics 1 (1977) 343–362. doi:10.1016/S0167-5060(08)70743-X
  • R. M. Karp, Reducibility among combinatorial problems, in Complexity of Computer Computations, Plenum, 1972, 85–103. doi:10.1007/978-1-4684-2001-2_9
  • P. C. Gilmore, R. E. Gomory, Sequencing a one state-variable machine: a solvable case of the traveling salesman problem, Operations Research 12 (1964) 655–679. doi:10.1287/opre.12.5.655
  • D. A. Wismer, Solution of the flowshop-scheduling problem with no intermediate queues, Operations Research 20 (1972) 689–697. doi:10.1287/opre.20.3.689
  • S. S. Reddi, C. V. Ramamoorthy, On the flow-shop sequencing problem with no wait in process, Operational Research Quarterly 23 (1972) 323–331. doi:10.1057/jors.1972.52
  • H. Röck, The three-machine no-wait flow shop is NP-complete, Journal of the ACM 31 (1984) 336–345. doi:10.1145/62.65
  • N. G. Hall, C. Sriskandarajah, A survey of machine scheduling problems with blocking and no-wait in process, Operations Research 44 (1996) 510–525. doi:10.1287/opre.44.3.510
  • S. A. Cook, The complexity of theorem-proving procedures, STOC 1971, 151–158. doi:10.1145/800157.805047
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Dynamic ProgrammingOperations ResearchOptimization·Captain: mikedeng1

Send-and-Split Method for Minimum-Concave-Cost Network Flows II: Subproblem Minimum Costs Are the Greatest Solution of the Send-and-Split Equations, Unique if Circulations Cost Positively (Theorem 2)Research Paper

Motivation

Many network-design and production-planning problems ask for a cheapest way to route a commodity from supply points to demand points when the cost of an arc grows less than proportionally with the amount sent: setup charges, economies of scale, and fixed-plus-linear costs all give concave arc costs. Minimizing a concave function over the flow polytope is NP-hard in general, and the classical linear-cost machinery (potentials, negative-cycle tests) no longer applies. Erickson, Monma and Veinott (Math. Oper. Res. 12 (1987)) gave the send-and-split method, a dynamic program over subsets of the demand nodes that solves the uncapacitated problem in time polynomial in the number of nodes and arcs and exponential only in the number of demand nodes. It contains as special cases the Dreyfus–Wagner recursion for Steiner trees in graphs (Networks 1 (1971)), and the paper applies it to concave-cost production, inventory and network-design problems (§6).

Timeline. Zangwill (1968) solved minimum-concave-cost flows on special networks by dynamic programming. Dreyfus and Wagner (1971) gave the subset recursion for Steiner trees with positive arc lengths. Erickson, Monma and Veinott (1987) extended the subset recursion to arbitrary uncapacitated networks with additive concave arc costs and arbitrary real demands, proved that the minimum costs of the subproblems form the greatest solution of the resulting equations (Theorem 2), and showed it is the only solution when every simple circulation has positive cost.

Setting

A network consists of a directed graph G=(N,A)G = (N, A)G=(N,A) with nodes N={0,…,n−1}N = \{0, \dots, n-1\}N={0,…,n−1} and arcs AAA, ordered pairs of distinct nodes, together with a real demand vector r=(ri)r = (r_i)r=(ri​). A preflow is a nonnegative matrix x=(xij)x = (x_{ij})x=(xij​) supported on AAA; a flow for rrr is a preflow with inflow minus outflow equal to rir_iri​ at every node. Each arc has a cost function cijc_{ij}cij​, concave on [0,∞)[0, \infty)[0,∞) with cij(0)=0c_{ij}(0) = 0cij​(0)=0, and the cost of a preflow is c(x)=∑(i,j)∈Acij(xij)c(x) = \sum_{(i,j)\in A} c_{ij}(x_{ij})c(x)=∑(i,j)∈A​cij​(xij​). A minimum-cost flow is a flow whose cost is at most that of every flow. A simple circulation is a circulation equal to some θ>0\theta > 0θ>0 on the arcs of a simple directed circuit and 000 elsewhere.

The demand nodes are D={i:ri≠0}D = \{ i : r_i \neq 0 \}D={i:ri​=0}. For ∅⊂I⊆D\emptyset \subset I \subseteq D∅⊂I⊆D let rI=∑j∈Irjr_I = \sum_{j \in I} r_jrI​=∑j∈I​rj​. The subproblem i→Ii \to Ii→I keeps the demands of the nodes in III, sets all others to zero, and subtracts rIr_IrI​ at node iii; CiIC_{iI}CiI​ is its minimum cost, +∞+\infty+∞ when it has no flow. Let AI=AA_I = AAI​=A if rI>0r_I > 0rI​>0 and the reversed arcs if rI<0r_I < 0rI​<0, with sending cost cij(rI)c_{ij}(r_I)cij​(rI​), read as cji(−rI)c_{ji}(-r_I)cji​(−rI​) when rI<0r_I < 0rI​<0. The send-and-split equations for an array C′C'C′ are

CiI′=Cj,I∖{j}′ (j∈I)if rI=0,(1)C'_{iI} = C'_{j, I\setminus\{j\}} \ (j \in I) \quad \text{if } r_I = 0, \qquad (1)CiI′​=Cj,I∖{j}′​ (j∈I)if rI​=0,(1) CiI′=min⁡(i,j)∈AI[cij(rI)+CjI′]∧BiI′if rI≠0,(2)C'_{iI} = \min_{(i,j)\in A_I} \bigl[ c_{ij}(r_I) + C'_{jI} \bigr] \wedge B'_{iI} \quad \text{if } r_I \neq 0, \qquad (2)CiI′​=(i,j)∈AI​min​[cij​(rI​)+CjI′​]∧BiI′​if rI​=0,(2)

with BiI′=min⁡∅⊂J⊂I[CiJ′+Ci,I∖J′]B'_{iI} = \min_{\emptyset\subset J\subset I} [C'_{iJ} + C'_{i,I\setminus J}]BiI′​=min∅⊂J⊂I​[CiJ′​+Ci,I∖J′​] for ∣I∣>1|I| > 1∣I∣>1, and BiI′=0B'_{iI} = 0BiI′​=0 if I={i}I = \{i\}I={i}, +∞+\infty+∞ otherwise, for ∣I∣=1|I| = 1∣I∣=1 (3).

Formalization targets

Goal: Theorem 2 (Dynamic-Programming Equations)

If there is a minimum-cost flow for rrr, then

C solves (1)–(3),CjI′≤CjI  for every +∞-or-real solution C′,C \text{ solves (1)–(3)}, \qquad C'_{jI} \le C_{jI} \ \text{ for every } +\infty\text{-or-real solution } C',C solves (1)–(3),CjI′​≤CjI​  for every +∞-or-real solution C′,

for all j∈Nj \in Nj∈N and ∅⊂I⊆D\emptyset \subset I \subseteq D∅⊂I⊆D. If moreover every simple circulation has positive cost, then every +∞+\infty+∞-or-real solution equals CCC.

Milestones

In attack order: the subproblem alternative (a minimum-cost flow or no flow, p. 640); equation (1); inequalities (4), (5), (6) (p. 641); CiI=BiIC_{iI} = B_{iI}CiI​=BiI​ for i∈Ii \in Ii∈I (p. 641); CCC satisfies (1) and (2) (p. 642); and two generic statements about Bellman's equations (2)′ for minimum-cost chains in a graph with nonnegative, respectively positive, simple circuits: the minimum chain costs form the greatest solution and are nondecreasing in the arc costs, and with positive circuits the solution is unique (p. 642).

Significance

Theorem 2 is the correctness theorem of the send-and-split method: it says that solving equations (1)–(3) by increasing ∣I∣|I|∣I∣, with a shortest-chain computation for each III, yields the true subproblem minimum costs, in particular the optimum CiDC_{iD}CiD​ of the original problem. Its uniqueness clause identifies when the equations alone determine the answer, and the counterexample on p. 641 (zero costs on a strongly connected graph) shows that the positivity hypothesis cannot simply be dropped. The same structure underlies the Steiner-tree recursion.

The theorem is proved in the paper; to our knowledge none of it is formalized. This mission supplies Lean definitions of uncapacitated concave-cost flows with extended-real minimum costs and the send-and-split equations, and poses the correctness theorem as a proof goal. The two Bellman milestones are general facts about minimum-cost chains with +∞+\infty+∞-or-real values and are reusable well beyond this paper. Related items on the platform are credited, not reused: the Dreyfus–Wagner Steiner recursion DreyfusWagner.Steiner.steinerLength_recurrence and DreyfusWagner.Steiner.optimal_decomposition (the special case of undirected positive lengths), and BellmanRouting.PolicySpace.routing_equation_unique (uniqueness of the routing equation on a complete graph with positive real times).

Difficulty

The equations (2) mix two minima: sending the whole demand rIr_IrI​ across one arc, and splitting III at the current node. Showing that CCC satisfies them requires that some optimal flow of every subproblem has a tree-like structure, which in turn rests on the existence theory for concave-cost flows (an optimum is attained at an extreme flow whose support is a forest). Showing that CCC is the greatest solution requires an induction on ∣I∣|I|∣I∣ in which, for fixed III, equation (2) is a shortest-chain system whose arc costs to an auxiliary node depend on the solution at smaller sets; comparison then needs monotonicity of minimum chain costs in the arc costs, including arcs whose cost drops from +∞+\infty+∞. The naive attempt to prove uniqueness by iterating (2) fails without the positivity hypothesis, because zero-cost circuits admit spurious solutions.

Formalization scope

Nodes are Fin n; arcs are a Finset (Fin n × Fin n) with no loops; arc costs are functions R→R\mathbb{R} \to \mathbb{R}R→R assumed concave on Set.Ici 0 with cij(0)=0c_{ij}(0) = 0cij​(0)=0 at arcs (the paper's standing assumption, stated as a hypothesis). Minimum costs, sending costs, splitting terms and solutions take values in EReal; the empty infimum is +∞+\infty+∞, as on the page.

Explicit readings of the paper's phrases:

  • "there is a minimum-cost flow" means a flow for rrr whose cost is at most that of every flow for rrr;
  • "+∞+\infty+∞ or real-valued" means ≠−∞\neq -\infty=−∞, required on every ∅⊂I⊆D\emptyset \subset I \subseteq D∅⊂I⊆D;
  • "greatest solution" is two claims: CCC is a solution, and every solution is ≤C\le C≤C entrywise;
  • "each simple circulation has positive cost" quantifies over every simple circuit and every θ>0\theta > 0θ>0;
  • "CiIC_{iI}CiI​ is finite" is rendered as CiI=c(x)C_{iI} = c(x)CiI​=c(x) for a minimum-cost flow xxx of the subproblem;
  • "minimum-cost chain" is the infimum of walk costs, attained or +∞+\infty+∞; arc costs of +∞+\infty+∞ in (2)′ are missing arcs.

The definition of CiIC_{iI}CiI​ is the infimum over flows, never the equations themselves; a formalization defining CCC by (1)–(3), or omitting the ≠−∞\neq -\infty=−∞ condition on solutions, or dropping the clause that CCC is itself a solution, would make the theorem trivial or false and is ruled out. Arrays are compared only on admissible sets. The graph structure of (2)′ is the platform definition BertsekasSPGraph. Running-time bounds, the planar refinements of §4 (Theorem 3) and the capacitated reduction of §5 are out of scope. Proofs of any milestone, and a formalization of the paper's Theorem 1 that the proofs rely on, are welcome.

Selected references

  • R. E. Erickson, C. L. Monma, A. F. Veinott, Jr., Send-and-Split Method for Minimum-Concave-Cost Network Flows, Mathematics of Operations Research 12(4), 1987, 634–664. https://doi.org/10.1287/moor.12.4.634
  • S. E. Dreyfus, R. A. Wagner, The Steiner Problem in Graphs, Networks 1(3), 1971, 195–207. https://doi.org/10.1002/net.3230010302
  • R. Bellman, On a Routing Problem, Quarterly of Applied Mathematics 16(1), 1958, 87–90. https://doi.org/10.1090/qam/102435
  • W. M. Hirsch, A. J. Hoffman, Extreme Varieties, Concave Functions, and the Fixed Charge Problem, Communications on Pure and Applied Mathematics 14(3), 1961, 355–369. https://doi.org/10.1002/cpa.3160140312
  • W. I. Zangwill, Minimum Concave Cost Flows in Certain Networks, Management Science 14(7), 1968, 429–450. https://doi.org/10.1287/mnsc.14.7.429
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CombinatoricsOperations 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.
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Captain: Lucas

Hadwiger's ConjectureOpen Problem

Motivation

Hadwiger's conjecture (1943) asserts that for every integer t≥0t\ge 0t≥0, every graph with no Kt+1K_{t+1}Kt+1​ minor is ttt-colourable. It is a far-reaching strengthening of the four-colour theorem, and it is widely described as one of the central open problems of graph theory (Bollobás, Catlin and Erdős called it "one of the deepest unsolved problems in graph theory"). The interest is structural: the four-colour theorem concerns planar graphs, and Hadwiger's conjecture proposes that the only obstruction to ttt-colourability that matters is the presence of a complete graph Kt+1K_{t+1}Kt+1​ as a minor.

Timeline.

  • 1937 — Wagner shows that the case t=4t=4t=4 is equivalent to the four-colour theorem, via a clique-sum decomposition of graphs with no K5K_5K5​ minor.
  • 1943 — Hadwiger poses the conjecture and proves it for t≤3t\le 3t≤3 (graphs with no K4K_4K4​ minor have a vertex of degree at most two).
  • 1964 — Wagner proves that graphs with no Kt+1K_{t+1}Kt+1​ minor are 2t2^t2t-colourable.
  • 1967 — Mader proves that excluding any fixed minor forces a linear number of edges, and determines the exact extremal function for KtK_tKt​ minors when t≤7t\le 7t≤7.
  • 1976 — Appel and Haken prove the four-colour theorem, hence the case t=4t=4t=4.
  • 1982 — Duchet and Meyniel prove that every nnn-vertex graph has a KtK_tKt​ minor with t≥n/(2α(G)−1)t\ge n/(2\alpha(G)-1)t≥n/(2α(G)−1).
  • 1984 — Kostochka and Thomason independently show that graphs with no KtK_tKt​ minor have average degree O(tlog⁡t)O(t\sqrt{\log t})O(tlogt​), hence are O(tlog⁡t)O(t\sqrt{\log t})O(tlogt​)-colourable.
  • 1993 — Robertson, Seymour and Thomas prove the case t=5t=5t=5 (using the four-colour theorem).
  • 2023–2024 — Norin, Postle and Song, then Delcourt and Postle, improve the general bound to O(tlog⁡log⁡t)O(t\log\log t)O(tloglogt) colours.
  • (Date not recorded in the survey) Albar and Gonçalves prove that graphs with no K7K_7K7​ minor are 888-colourable and graphs with no K8K_8K8​ minor are 101010-colourable.

The case t=6t=6t=6 (graphs with no K7K_7K7​ minor are 666-colourable) is the first open case.

Setting

All graphs are finite and simple. A minor of a graph GGG is any graph obtained from a subgraph of GGG by contracting edges. Equivalently, a graph HHH on vertex set WWW is a minor of GGG if there are branch sets Bw⊆V(G)B_w\subseteq V(G)Bw​⊆V(G), w∈Ww\in Ww∈W, which are pairwise disjoint, each inducing a connected (nonempty) subgraph of GGG, and such that for every edge w1w2w_1w_2w1​w2​ of HHH some vertex of Bw1B_{w_1}Bw1​​ is adjacent to some vertex of Bw2B_{w_2}Bw2​​. GGG has a KtK_tKt​ minor if the complete graph KtK_tKt​ is a minor of GGG, i.e. GGG contains ttt pairwise disjoint connected vertex sets, every two joined by an edge.

A graph is ttt-colourable if its vertices can be coloured with ttt colours so that adjacent vertices receive different colours; χ(G)\chi(G)χ(G) is the least such ttt. Write HC(t)\mathrm{HC}(t)HC(t) for the statement "every graph with no Kt+1K_{t+1}Kt+1​ minor is ttt-colourable". A graph is kkk-degenerate if every nonempty set of vertices contains a vertex with at most kkk neighbours inside the set. The stability number α(G)\alpha(G)α(G) is the largest size of a set of pairwise non-adjacent vertices.

Formalization targets

Goal

∀t≥0:Kt+1⪯̸G ⟹ χ(G)≤tfor every finite graph G.\forall t\ge 0:\qquad K_{t+1}\not\preceq G\ \Longrightarrow\ \chi(G)\le t\qquad\text{for every finite graph } G.∀t≥0:Kt+1​⪯G ⟹ χ(G)≤tfor every finite graph G.

Proved special cases

HC(t) for t≤3,HC(4),HC(5).\mathrm{HC}(t)\ \text{for } t\le 3,\qquad \mathrm{HC}(4),\qquad \mathrm{HC}(5).HC(t) for t≤3,HC(4),HC(5).

Weaker colouring bounds

  • no Kt+1K_{t+1}Kt+1​ minor ⇒\Rightarrow⇒ χ(G)≤2t\chi(G)\le 2^tχ(G)≤2t (Wagner);
  • no KtK_tKt​ minor ⇒\Rightarrow⇒ χ(G)=O(tlog⁡t)\chi(G)=O(t\sqrt{\log t})χ(G)=O(tlogt​) (Kostochka, Thomason) and χ(G)=O(tlog⁡log⁡t)\chi(G)=O(t\log\log t)χ(G)=O(tloglogt) (Delcourt–Postle);
  • no K7K_7K7​ minor ⇒\Rightarrow⇒ χ≤8\chi\le 8χ≤8; no K8K_8K8​ minor ⇒\Rightarrow⇒ χ≤10\chi\le 10χ≤10 (Albar–Gonçalves).

Supporting extremal and structural results

  • non-null graphs with no K4K_4K4​ minor have a vertex of degree ≤2\le 2≤2;
  • kkk-degenerate graphs are (k+1)(k+1)(k+1)-colourable;
  • for every HHH there is ccc with ∣E(G)∣≤c∣V(G)∣|E(G)|\le c|V(G)|∣E(G)∣≤c∣V(G)∣ whenever H⪯̸GH\not\preceq GH⪯G (Mader);
  • the exact edge bounds n−1n-1n−1, 2n−32n-32n−3, 3n−63n-63n−6 for no K3K_3K3​, K4K_4K4​, K5K_5K5​ minor, and (t−2)n−(t−12)(t-2)n-\binom{t-1}{2}(t−2)n−(2t−1​) for no KtK_tKt​ minor, t≤7t\le 7t≤7 (Mader);
  • every nnn-vertex graph has a KtK_tKt​ minor with t≥n/(2α(G)−1)t\ge n/(2\alpha(G)-1)t≥n/(2α(G)−1) (Duchet–Meyniel);
  • a graph with no Kt+1K_{t+1}Kt+1​ minor has a ttt-colourable induced subgraph on at least half of its vertices.

Significance

A proof of the conjecture would give a structural explanation of the four-colour theorem that does not depend on planarity, and would settle the chromatic number of every minor-closed class defined by excluding a single complete graph. Partial results already drive the theory of graph minors: bounds on the average degree of KtK_tKt​-minor-free graphs are the standard input to colouring, and linear Hadwiger-type bounds are used in structural and algorithmic graph theory.

On the formal side, only the smallest cases have Lean proofs: the platform already contains proofs of the cases t≤2t\le 2t≤2 under a different encoding of minors (namespace Hadwiger), which may be reused after bridging the definitions. The cases t≤3t\le 3t≤3, Wagner's 2t2^t2t bound, the degeneracy lemma, the small extremal bounds and the Duchet–Meyniel theorem have elementary proofs and are realistic targets. The cases t=4,5t=4,5t=4,5 depend on the four-colour theorem, whose formal proof exists in Coq but not in Lean; formalizing them here requires either porting that proof or proving the reduction to it. The general conjecture is open.

Difficulty

The natural approach — contracting the colour classes of an optimal colouring — does not produce a minor, because colour classes are independent sets and contraction is only allowed along edges. Degeneracy arguments only give bounds of order tlog⁡tt\sqrt{\log t}tlogt​, since dense random graphs with no large clique minor have average degree of that order; closing the gap to ttt requires using large chromatic number itself, not just density. Already for t=4t=4t=4 the statement is equivalent to the four-colour theorem, so no short proof is expected for any t≥4t\ge 4t≥4.

Formalization scope

Graphs are SimpleGraph V on a finite vertex type V : Type. Minors are encoded by branch sets (IsMinor), complete minors by HasCompleteMinor G t (the complete graph on Fin t is a minor of G), colourability by Mathlib's SimpleGraph.Colorable, and edge counts by the cardinality of the edge set. HC(t)\mathrm{HC}(t)HC(t) is the definition HC t. Logarithms are natural logarithms; asymptotic bounds are stated with an explicit existential constant and a ceiling. The case t=0t=0t=0 is included; HasCompleteMinor G 0 holds for every graph, so no statement becomes vacuous through a degenerate minor definition.

Useful reusable infrastructure: minor models and their composition, contraction of connected sets, greedy colouring of degenerate graphs, and edge-counting for minor-free graphs. Contributions of intermediate lemmas along the milestones are welcome.

Selected references

  • P. Seymour, Hadwiger's conjecture, in: Open Problems in Mathematics, Springer, 2016 (survey; source of the milestone numbering).
  • Wikipedia, Hadwiger conjecture (graph theory). https://en.wikipedia.org/wiki/Hadwiger_conjecture_(graph_theory)
  • H. Hadwiger, Über eine Klassifikation der Streckenkomplexe, Vierteljschr. Naturforsch. Ges. Zürich 88 (1943).
  • K. Wagner, Über eine Eigenschaft der ebenen Komplexe, Math. Ann. 114 (1937).
  • N. Robertson, P. Seymour, R. Thomas, Hadwiger's conjecture for K6K_6K6​-free graphs, Combinatorica 13 (1993).
  • A. Kostochka, Lower bound of the Hadwiger number of graphs by their average degree, Combinatorica 4 (1984).
  • A. Thomason, An extremal function for contractions of graphs, Math. Proc. Cambridge Philos. Soc. 95 (1984).
  • M. Delcourt, L. Postle, Reducing linear Hadwiger's conjecture to coloring small graphs (2024).
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CombinatoricsLinear OptimizationOperations Research·Captain: mikedeng1

The Matroids with the Max-Flow Min-Cut Property: Binary Mengerian Clutters and the Q6 MinorResearch Paper

Motivation

Several classical theorems of combinatorial optimization say that a family of sets arising from a graph packs: the maximum number of pairwise disjoint members equals the minimum size of a set meeting every member. König's theorem on bipartite graphs, Menger's theorem, the max-flow min-cut theorem of Ford and Fulkerson, Edmonds' branching theorem and the Lucchesi–Younger theorem all have this form (Seymour 1977, (1.1)–(1.5)). In the capacitated version (weights on elements, integral flows) the max-flow min-cut theorem says more: the packing property survives every deletion and replication of elements. Clutters with this stronger property are called Mengerian. For each 000–111 matrix they are exactly the systems whose covering linear program and its dual have integral optima for every integral weight vector, which is why the notion matters to integer programming and polyhedral combinatorics.

Seymour's paper answers the question for the class of binary clutters, the clutters coming from binary matroids, which includes path collections, cut collections and odd-circuit collections of graphs. Earlier, Gallai's theorem implied that ports of regular matroids are Mengerian (Seymour 1977, p. 200); combined with Tutte's excluded-minor characterization of regular matroids, this showed that binary clutters without Q6Q_6Q6​ or b(Q6)b(Q_6)b(Q6​) minors are Mengerian. Seymour shows that the second excluded minor is unnecessary, so a single small clutter is the only obstruction.

Setting

All sets are finite. A clutter L\mathbf LL is a finite collection of finite sets, no member of which is contained in another; ∅\emptyset∅ and {∅}\{\emptyset\}{∅} are the two trivial clutters. Its ground set is E(L)=⋃A∈LAE(\mathbf L)=\bigcup_{A\in\mathbf L}AE(L)=⋃A∈L​A. The blocker b(L)b(\mathbf L)b(L) is the collection of minimal subsets of E(L)E(\mathbf L)E(L) that meet every member of L\mathbf LL, and τ(L)\tau(\mathbf L)τ(L) is the minimum cardinality of a member of b(L)b(\mathbf L)b(L).

L\mathbf LL is Mengerian if L={∅}\mathbf L=\{\emptyset\}L={∅}, or if for every weight map w:E(L)→Z+w:E(\mathbf L)\to\mathbb Z^+w:E(L)→Z+ there is an integral packing q:L→Z+q:\mathbf L\to\mathbb Z^+q:L→Z+ with ∑A∋xq(A)≤w(x)\sum_{A\ni x}q(A)\le w(x)∑A∋x​q(A)≤w(x) for each x∈E(L)x\in E(\mathbf L)x∈E(L) and

∑A∈Lq(A)=min⁡B∈b(L)∑x∈Bw(x).\sum_{A\in\mathbf L}q(A)=\min_{B\in b(\mathbf L)}\sum_{x\in B}w(x).A∈L∑​q(A)=B∈b(L)min​x∈B∑​w(x).

For a set ZZZ, the deletion is L∖Z={A∈L:A∩Z=∅}\mathbf L\setminus Z=\{A\in\mathbf L:A\cap Z=\emptyset\}L∖Z={A∈L:A∩Z=∅} and the contraction L/Z\mathbf L/ZL/Z is the collection of minimal members of {A−Z:A∈L}\{A-Z:A\in\mathbf L\}{A−Z:A∈L} (minimal, not minimal nonempty). A minor of L\mathbf LL is any clutter obtained by a finite sequence of deletions and contractions.

A clutter is binary if ∣A∩B∣|A\cap B|∣A∩B∣ is odd for all A∈LA\in\mathbf LA∈L and B∈b(L)B\in b(\mathbf L)B∈b(L); this is condition (3.2)(ii) of the paper, which is equivalent to being a port of a binary matroid. Finally

Q6={{1,3,5},{1,4,6},{2,3,6},{2,4,5}},Q_6=\{\{1,3,5\},\{1,4,6\},\{2,3,6\},\{2,4,5\}\},Q6​={{1,3,5},{1,4,6},{2,3,6},{2,4,5}},

the triangles of K4K_4K4​ with its edges labelled 1,…,61,\dots,61,…,6.

For the structure theory, a circuit of a binary clutter is a minimal nonempty C⊆E(L)C\subseteq E(\mathbf L)C⊆E(L) with ∣C∩B∣|C\cap B|∣C∩B∣ even for every B∈b(L)B\in b(\mathbf L)B∈b(L); xxx and yyy are parallel when {x,y}\{x,y\}{x,y} is a circuit, and the point ⟨x⟩\langle x\rangle⟨x⟩ is the parallel class of xxx. With mb(L)={B∈b(L):∣B∣=τ(L)}mb(\mathbf L)=\{B\in b(\mathbf L):|B|=\tau(\mathbf L)\}mb(L)={B∈b(L):∣B∣=τ(L)}, L\mathbf LL is critical if E(mb(L))=E(L)E(mb(\mathbf L))=E(\mathbf L)E(mb(L))=E(L). In a critical binary clutter, x→yx\to yx→y means that every member of mb(L)mb(\mathbf L)mb(L) containing xxx contains yyy while y∉⟨x⟩y\notin\langle x\rangley∈/⟨x⟩, and yyy is initial if no xxx has x→yx\to yx→y. MBC abbreviates "Mengerian binary clutter".

Formalization targets

Goal: Seymour's theorem (p. 209)

For every binary clutter L\mathbf LL,

L is Mengerian  ⟺  L has no minor isomorphic to Q6.\mathbf L\ \text{is Mengerian}\iff \mathbf L\ \text{has no minor isomorphic to } Q_6 .L is Mengerian⟺L has no minor isomorphic to Q6​.

Milestones

In the order the proof uses them:

  • (2.3) Every minor of a Mengerian clutter is Mengerian.
  • Section 1, p. 193. Q6Q_6Q6​ is not Mengerian. With (2.3) this is the "only if" direction.
  • (3.6)(i) Circuits of a binary clutter have at least two elements.
  • (3.6)(iii) If Z⊆E(L)Z\subseteq E(\mathbf L)Z⊆E(L) meets every member of b(L)b(\mathbf L)b(L) evenly, then ZZZ is a disjoint union of circuits. If it meets every member oddly, then ZZZ is a disjoint union of circuits and one member of L\mathbf LL.
  • (4.3) In a critical MBC, x→yx\to yx→y implies y↛xy\not\to xy→x.
  • (4.4) In a critical MBC, x→yx\to yx→y gives a circuit C∋x,yC\ni x,yC∋x,y with ∣C∣≥3|C|\ge3∣C∣≥3, z→yz\to yz→y for z∈C−{y}z\in C-\{y\}z∈C−{y}, and ∣B−(C−{y})∣≥τ(L)−1|B-(C-\{y\})|\ge\tau(\mathbf L)-1∣B−(C−{y})∣≥τ(L)−1 for B∈b(L)B\in b(\mathbf L)B∈b(L).
  • (4.5) In a critical MBC, a non-initial xxx lies on a circuit CCC with ∣C∣≥3|C|\ge3∣C∣≥3 whose other elements are initial and point to xxx, and ∣B∩(C−{x})∣≤1|B\cap(C-\{x\})|\le1∣B∩(C−{x})∣≤1 for B∈mb(L)B\in mb(\mathbf L)B∈mb(L).
  • (4.6) A nontrivial critical MBC has a member consisting of initial elements.
  • (5.1) A binary clutter with six elements x1,y1,x2,y2,x3,y3x_1,y_1,x_2,y_2,x_3,y_3x1​,y1​,x2​,y2​,x3​,y3​ whose only circuits are the three sets {xi,yi,xj,yj}\{x_i,y_i,x_j,y_j\}{xi​,yi​,xj​,yj​}, together with a member AAA that meets each pair {xi,yi}\{x_i,y_i\}{xi​,yi​} once and satisfies a minimality condition, has a Q6Q_6Q6​ minor.

Significance

The theorem is an excluded-minor characterization of the max-flow min-cut property. For binary clutters it decides exactly when the covering system Mx≥1Mx\ge1Mx≥1, x≥0x\ge0x≥0 has integral optimal primal and dual solutions for every integral cost vector, and it identifies Q6Q_6Q6​ as the single obstruction. Its matroid form (the Corollary, p. 220) states that for a matroid MMM the port Ω(M)\Omega(M)Ω(M) is Mengerian for every element Ω\OmegaΩ if and only if MMM is binary and has no F7∗F_7^*F7∗​ minor. Consequences discussed in the paper include the two-commodity setting of (3.5): the clutter of minimal edge sets joining sss to s′s's′ or ttt to t′t't′ is Mengerian exactly when the graph does not reduce to the configuration of its Figure 2. The theorem is also a basis for later work on ideal and Mengerian clutters, such as Cornuéjols' book Combinatorial Optimization: Packing and Covering (SIAM, 2001).

The result has been proved since 1977. To our knowledge no machine-checked proof exists. Mathlib at the pinned revision has matroids but no clutters, blockers, clutter minors, or matroids representable over GF(2). This mission builds that layer. The minor-closedness of the Mengerian property (2.3), the parity decomposition (3.6)(iii) and the structure theory of critical Mengerian binary clutters (4.3)–(4.6) are results in their own right and are useful beyond the main theorem.

Difficulty

The "only if" direction is short: minors of Mengerian clutters are Mengerian, and Q6Q_6Q6​ fails with unit weights. The "if" direction is, in the author's words, "very much harder". A natural first idea is to show directly, by LP duality, that the covering polyhedron of a Q6Q_6Q6​-free binary clutter is integral. This does not work: integrality of the polyhedron is the weak max-flow min-cut property, and Q6Q_6Q6​ itself has that property while not being Mengerian, so no argument that sees only fractional optima can separate the two cases. The paper's proof works with a minimal counterexample and derives the Q6Q_6Q6​ minor from the structure of critical Mengerian binary clutters in Section 4; its intermediate claims (5.2)–(5.39) hold only for that minimal counterexample, which is why they are not milestones here.

Formalization scope

Elements form a type α with decidable equality. A clutter is L : Finset (Finset α) with the clutter axiom as a hypothesis, E(L)E(\mathbf L)E(L) is the union of members, and deletion and contraction take an arbitrary finite set ZZZ. Weights www and packings qqq are N\mathbb NN-valued. The minimum in the Mengerian condition is expressed as "some B∈b(L)B\in b(\mathbf L)B∈b(L) of least weight has weight equal to the packing value", never as an infimum. {∅}\{\emptyset\}{∅} is Mengerian by the paper's convention, and τ({∅})\tau(\{\emptyset\})τ({∅}), which the paper leaves undefined, has the junk value 000 in Lean; every item reading τ\tauτ excludes {∅}\{\emptyset\}{∅} or is vacuous there. "Minor" is the reflexive–transitive closure of single deletions and contractions. "Has a Q6Q_6Q6​ minor" means that some minor equals the image of Q6Q_6Q6​ (on Fin 6, with the paper's labels shifted down by one) under an injective relabelling Fin 6 ↪ α. Binary clutters are defined by (3.2)(ii); the paper defines them as ports of binary matroids and quotes (3.2) [15, 28] for the equivalence, and Mathlib has no GF(2)-representable matroids at this revision. Circuits are defined intrinsically, which makes (3.6)(ii) hold by definition.

Four readings would change the theorem and are ruled out: real-valued packings qqq (the weak max-flow min-cut property, which Q6Q_6Q6​ has, so the goal would be false), a non-minimal blocker or one not restricted to E(L)E(\mathbf L)E(L), dropping the {∅}\{\emptyset\}{∅} exception, and reading "Q6Q_6Q6​ minor" as literal equality instead of isomorphism.

A complete development needs the blocker calculus ((2.1), (2.2), cited from [28] with proofs omitted), the parity theory of binary clutters, and the replication operation Lw\mathbf L_wLw​. The clutter layer (blocker, minors, Mengerian, binary, circuits) is reusable for later work on ideal clutters, Lehman's theorem and the Corollary's matroid form. Proofs of any milestone, of the helper facts b(b(L))=Lb(b(\mathbf L))=\mathbf Lb(b(L))=L, (2.1) and (2.2), and of the equivalences in (3.2) are welcome.

Selected references

  • P. D. Seymour, The Matroids with the Max-Flow Min-Cut Property, J. Combin. Theory Ser. B 23 (1977) 189–222. https://doi.org/10.1016/0095-8956(77)90031-4
  • J. Edmonds and D. R. Fulkerson, Bottleneck extrema, J. Combin. Theory 8 (1970) 299–306. https://doi.org/10.1016/S0021-9800(70)80083-7
  • L. R. Ford and D. R. Fulkerson, Maximal flow through a network, Canad. J. Math. 8 (1956) 399–404. https://doi.org/10.4153/CJM-1956-045-5
  • G. Cornuéjols, Combinatorial Optimization: Packing and Covering, CBMS-NSF Regional Conf. Ser. in Appl. Math. 74, SIAM, 2001. https://doi.org/10.1137/1.9780898717105
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CombinatoricsTheoretical Computer Science·Captain: mikedeng1

Sorting in c log n Parallel Steps: Sorting Networks of Logarithmic DepthResearch Paper

Motivation

A sorting network is a sorting procedure whose sequence of comparisons is fixed in advance, independently of the data. Its depth, the number of rounds of simultaneous comparisons on disjoint pairs, is the parallel running time. Sorting networks are used in parallel and hardware sorting, in switching networks, and in cryptography, where a data-independent (oblivious) sequence of operations is required. How small the depth can be as a function of the number of inputs nnn is a basic question of parallel computation.

Timeline:

  • 1968. Batcher's odd-even merge sort and bitonic sort give networks of depth O((log⁡n)2)O((\log n)^2)O((logn)2) and size O(n(log⁡n)2)O(n(\log n)^2)O(n(logn)2) (K. E. Batcher, Sorting networks and their applications, AFIPS Spring Joint Computer Conference, 1968). For nnn a power of two they remain the best explicit networks in practice.
  • 1973. Knuth's The Art of Computer Programming, Vol. 3, §5.3.4, surveys sorting networks. A simple counting argument gives the lower bound: every sorting network has depth at least log⁡2n\log_2 nlog2​n, since each output depends on at most 2depth2^{\text{depth}}2depth inputs.
  • 1983. Ajtai, Komlós and Szemerédi construct networks of depth O(log⁡n)O(\log n)O(logn) and size O(nlog⁡n)O(n\log n)O(nlogn) (Combinatorica 3 (1983) 1–19, doi:10.1007/BF02579338), matching the lower bound up to a constant. The constant is not computed in the paper and is known to be very large.
  • 1990. Paterson simplifies the construction and gives the first explicit, still very large, depth constant (M. S. Paterson, Improved sorting networks with O(log N) depth, Algorithmica 5 (1990) 75–92, doi:10.1007/BF01840378).
  • 2014. Goodrich gives Zig-zag sort, a simpler deterministic data-oblivious sorting algorithm with O(nlog⁡n)O(n\log n)O(nlogn) comparisons that avoids the AKS machinery but is not of logarithmic depth (arXiv:1403.2777).

Setting

There are nnn registers R1,…,RnR_1,\dots,R_nR1​,…,Rn​ holding elements of a linearly ordered set. An elementary step (a comparator) (i,j)(i,j)(i,j) with i≠ji\neq ji=j compares the contents of RiR_iRi​ and RjR_jRj​ and exchanges them if the content of RiR_iRi​ is larger. Afterwards RiR_iRi​ holds the minimum and RjR_jRj​ the maximum of the two, and every other register is unchanged. A parallel step is a set of comparators in which no register occurs twice, so it has at most n/2n/2n/2 comparators. A comparator network NNN is a finite sequence of parallel steps, fixed before the input is seen. Its depth depth⁡(N)\operatorname{depth}(N)depth(N) is the number of parallel steps and its size size⁡(N)\operatorname{size}(N)size(N) the total number of comparators. NNN sorts if for every input x=(x1,…,xn)x=(x_1,\dots,x_n)x=(x1​,…,xn​) the output N(x)N(x)N(x) satisfies N(x)1≤⋯≤N(x)nN(x)_1\le\cdots\le N(x)_nN(x)1​≤⋯≤N(x)n​.

The construction runs on the tree TTT of finite 000-111 sequences, whose levels are ordered lexicographically. A chain on level iii assigns to every node of that level a set of registers, with the sets pairwise disjoint and of a common size N(C)N(C)N(C). A ⟨k, ε⟩ expander on ⟨A, B⟩, for disjoint register sets AAA and BBB, is a bipartite graph between AAA and BBB of maximum degree kkk in which every nonempty X⊆AX\subseteq AX⊆A has more than (1−ε)ε−1min⁡{∣X∣,ε∣B∣}(1-\varepsilon)\varepsilon^{-1}\min\{|X|,\varepsilon|B|\}(1−ε)ε−1min{∣X∣,ε∣B∣} neighbours, and symmetrically for BBB. The Lean development uses the names ComparatorNetwork, compareExchange, IsChain, chainN, IsExpander, IsLowerSection for these objects.

Formalization targets

Goal: the AKS theorem (Abstract and §1, p. 1)

∃ c>0  ∀n≥2  ∃N:N sorts,depth⁡(N)≤clog⁡2n,size⁡(N)≤c nlog⁡2n.\exists\, c>0\ \ \forall n\ge 2\ \ \exists N:\quad N \text{ sorts},\qquad \operatorname{depth}(N)\le c\log_2 n,\qquad \operatorname{size}(N)\le c\,n\log_2 n .∃c>0  ∀n≥2  ∃N:N sorts,depth(N)≤clog2​n,size(N)≤cnlog2​n.

The constant is absolute and is not fixed. Any explicit value would be invalidated by the next improvement, and the paper gives none.

Milestones (the paper's numbered lemmas that hold as stated)

  • Lemma 3 (p. 6): for 0<ε<10<\varepsilon<10<ε<1 and c≥1c\ge1c≥1 there is k(ε,c)k(\varepsilon,c)k(ε,c) such that every pair of disjoint sets with 1/c≤∣A∣/∣B∣≤c1/c\le|A|/|B|\le c1/c≤∣A∣/∣B∣≤c carries a ⟨k,ε⟩\langle k,\varepsilon\rangle⟨k,ε⟩ expander.
  • Lemma 4 (p. 7): performing every comparator of such an expander once, in any order, from AAA to BBB leaves all but an ε\varepsilonε-fraction of any lower section SSS with ∣S∣≤∣A∣|S|\le|A|∣S∣≤∣A∣ in AAA, and symmetrically for upper sections in BBB:
∣S∖Cont(A)∣≤ε∣S∣.|S\setminus\mathrm{Cont}(A)|\le\varepsilon|S| .∣S∖Cont(A)∣≤ε∣S∣.
  • Lemma 1 (pp. 3–4): the splitting V(C,k)V(C,k)V(C,k) of a chain, which moves one register of each leaf set up the tree, produces chains with properties (1.1)–(1.5).
  • Lemma 2 (p. 4): chains W(C,k)W(C,k)W(C,k) with ak−1≤N(W(C,k))≤aka_k-1\le N(W(C,k))\le a_kak​−1≤N(W(C,k))≤ak​ exist under conditions (2a), (2.b).
  • Lemma 12(a) (p. 14): a violation of the order relation RGβR^\beta_GRGβ​ between two nodes of a level is witnessed by two consecutive nodes.

Significance

The result. The AKS theorem settles the asymptotic depth of sorting networks at Θ(log⁡n)\Theta(\log n)Θ(logn) and their size at Θ(nlog⁡n)\Theta(n\log n)Θ(nlogn). It gives an O(log⁡n)O(\log n)O(logn)-time sorting algorithm with nnn processors that performs only data-independent comparisons. It is the standard reference point for oblivious sorting in parallel algorithms, circuit complexity (sorting is in NC1\mathsf{NC}^1NC1 via comparators) and oblivious RAM constructions. Lemma 4, the ε-halver property of expander comparisons, is the component that later constructions (Paterson) reuse.

Formalizing it. The theorem has been proved since 1983. No Lean proof of the AKS theorem is known. Mathlib has no expander graphs in the ⟨k, ε⟩ sense and no sorting networks. The mission asks for a formal proof of the headline theorem by any route (the AKS construction or Paterson's variant), and for formal proofs of the paper's verified lemmas as reusable components. The expander lemma needs either an explicit family (Margulis; Gabber–Galil) or a probabilistic existence argument, both substantial on their own.

Difficulty

Every elementary argument stalls at depth O((log⁡n)2)O((\log n)^2)O((logn)2): recursive merging needs log⁡n\log nlogn merge rounds, and merging two sorted lists by a comparator network needs depth Ω(log⁡n)\Omega(\log n)Ω(logn). A depth of O(log⁡n)O(\log n)O(logn) therefore cannot come from exact merging. It must come from constant-depth approximate operations (ε-halvers, which require bounded-degree expanders) combined with a mechanism that corrects the errors they leave. In the paper this mechanism is a movement of registers up and down a binary tree, controlled by a family of constants chosen in a fixed order ("ε1≪q2≪1−g≪q1≪1/c1≪1\varepsilon_1\ll q_2\ll1-g\ll q_1\ll 1/c_1\ll1ε1​≪q2​≪1−g≪q1​≪1/c1​≪1", p. 2). The accounting that shows the misplaced elements decay geometrically is the hard part. Several intermediate lemmas of the paper are false as printed, so the paper's text is not a checklist to transcribe.

Formalization scope

Conventions committed to in Lean:

  • Registers are Fin n, and contents lie in an arbitrary linearly ordered type. A network is a List of layers, each a List (Fin n × Fin n) of comparators with distinct endpoints, and no register occurs twice in a layer. A comparator (i,j)(i,j)(i,j) puts the minimum into iii, and both directions i<ji<ji<j and i>ji>ji>j are allowed. Sorts means the output is monotone for every linearly ordered type and every input, not only for permutations.
  • log⁡2n\log_2 nlog2​n is Real.logb 2 n, and the goal is stated for n≥2n\ge2n≥2. The constant ccc is quantified before nnn.
  • Tree levels are Fin (2^i), with numeric order equal to lexicographic order. A chain is a Fin (2^i) → Finset R.
  • Definition 2.2 of the paper, read literally, requires ∣Γ∅∣>0|\Gamma_\emptyset|>0∣Γ∅​∣>0, which fails, so no graph would be an expander. The expansion inequalities are imposed on nonempty sets only, and the strict inequality is kept.

Trivializing formalizations are ruled out. The goal is not "for every nnn there is a network of depth O(log⁡n)O(\log n)O(logn)" with the constant chosen after nnn, which is true for trivial reasons. Layers without the disjointness condition would let a single layer contain a whole insertion sort. A bound on the number of comparisons alone, with unbounded depth, is a different and much older result; the goal states both the depth and the size bound.

The mission states the AKS theorem and the paper's lemmas that are correct as stated. It does not formalize the AKS algorithm itself (SαS^\alphaSα, PαP^\alphaPα, the operations CH1–CH4, IMP) or its intermediate Lemmas 5–11 and 13–15. Those depend on unspecified constants constrained only by "sufficiently small" chains, and Lemmas 5, 10 and 12(b) are false as printed. A solver may of course define the algorithm, with pinned constants, as part of a proof.

Contributions welcome: a library of comparator networks (composition, the 0-1 principle, depth of Batcher's networks), existence of bounded-degree bipartite expanders, the ε-halver lemma, and any complete proof of the goal. The network and expander definitions are independent of this paper and reusable.

Selected references

  • M. Ajtai, J. Komlós, E. Szemerédi, Sorting in c log n parallel steps, Combinatorica 3(1) (1983) 1–19. doi:10.1007/BF02579338
  • K. E. Batcher, Sorting networks and their applications, Proc. AFIPS Spring Joint Computer Conference 32 (1968) 307–314. doi:10.1145/1468075.1468121
  • D. E. Knuth, The Art of Computer Programming, Vol. 3: Sorting and Searching, Addison-Wesley, 1973, §5.3.4.
  • G. A. Margulis, Explicit constructions of concentrators, Problems of Information Transmission 9 (1973) 325–332.
  • O. Gabber, Z. Galil, Explicit constructions of linear-sized superconcentrators, J. Computer and System Sciences 22(3) (1981) 407–420. doi:10.1016/0022-0000(81)90040-4
  • M. S. Paterson, Improved sorting networks with O(log N) depth, Algorithmica 5 (1990) 75–92. doi:10.1007/BF01840378
  • M. T. Goodrich, Zig-zag sort: a simple deterministic data-oblivious sorting algorithm running in O(n log n) time, STOC 2014. arXiv:1403.2777
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Quantum Error CorrectionQuantum Information·Captain: mikedeng1

Decoding Quantum Tanner Codes: The Parallel Decoder Corrects Every Error of Weight Linear in the Code Length in Logarithmically Many RoundsResearch Paper

Why decoding good quantum LDPC codes matters

Quantum computers must correct errors faster than they accumulate, and quantum low-density parity-check (LDPC) codes are the leading candidates for doing so with little overhead: every check involves a bounded number of qubits, so syndromes can be measured by local circuits. In 2021–2022 the first asymptotically good quantum LDPC codes appeared, with dimension and minimum distance both linear in the code length nnn: the expander lifted product codes of Panteleev and Kalachev (arXiv:2111.03654) and the quantum Tanner codes of Leverrier and Zémor (arXiv:2202.13641). A code is only useful if errors can be corrected efficiently. Leverrier and Zémor (arXiv:2208.05537) give two decoders for quantum Tanner codes that correct every error of weight up to a constant fraction of nnn: a sequential one running in linear time, and a parallel decoder running in a logarithmic number of rounds of local updates. This mission is about the parallel decoder.

Timeline:

  • 2021, Panteleev–Kalachev: asymptotically good quantum LDPC codes and locally testable classical codes (arXiv:2111.03654).
  • 2022, Leverrier–Zémor: quantum Tanner codes, a simpler construction on left-right Cayley complexes (arXiv:2202.13641).
  • 2022, Gu–Pattison–Tang, Leverrier–Zémor and Dinur–Hsieh–Lin–Vidick: efficient decoders for linear-distance quantum LDPC codes (arXiv:2206.06557, arXiv:2206.07571, arXiv:2206.07750).
  • 2022, Leverrier–Zémor, Decoding quantum Tanner codes: sequential and parallel decoders up to a constant fraction of nnn, with a revised parallel analysis in v3 (arXiv:2208.05537v3).

The code and the decoder

Let GGG be a finite group and A=A−1A=A^{-1}A=A−1, B=B−1⊆GB=B^{-1}\subseteq GB=B−1⊆G with ∣A∣=∣B∣=Δ|A|=|B|=\Delta∣A∣=∣B∣=Δ. The quadripartite left-right Cayley complex has four copies of GGG as vertices, Vij=G×{ij}V_{ij}=G\times\{ij\}Vij​=G×{ij}, and one square {(g,00),(ag,01),(gb,10),(agb,11)}\{(g,00),(ag,01),(gb,10),(agb,11)\}{(g,00),(ag,01),(gb,10),(agb,11)} for every (g,a,b)∈G×A×B(g,a,b)\in G\times A\times B(g,a,b)∈G×A×B. The set of squares QQQ has n=∣G∣Δ2n=|G|\Delta^2n=∣G∣Δ2 elements, and every vertex vvv sees its QQQ-neighbourhood Q(v)Q(v)Q(v) as an A×BA\times BA×B array: columns are indexed by bbb, rows by aaa. The left Cayley graph Cay(G,A)\mathrm{Cay}(G,A)Cay(G,A) and the right Cayley graph Cay(G,B)\mathrm{Cay}(G,B)Cay(G,B) are assumed to be non-bipartite Ramanujan graphs: every adjacency eigenvalue other than ±Δ\pm\Delta±Δ has absolute value at most 2Δ−12\sqrt{\Delta-1}2Δ−1​.

Two local codes CA⊆F2AC_A\subseteq\mathbb F_2^ACA​⊆F2A​, CB⊆F2BC_B\subseteq\mathbb F_2^BCB​⊆F2B​ with dim⁡CA+dim⁡CB=Δ\dim C_A+\dim C_B=\DeltadimCA​+dimCB​=Δ are fixed. Write V0=V00∪V11V_0=V_{00}\cup V_{11}V0​=V00​∪V11​ and V1=V01∪V10V_1=V_{01}\cup V_{10}V1​=V01​∪V10​. The code C1⊆F2Q\mathcal C_1\subseteq\mathbb F_2^QC1​⊆F2Q​ consists of the vectors whose view on every Q(v)Q(v)Q(v), v∈V1v\in V_1v∈V1​, lies in the dual tensor code CA⊗F2B+F2A⊗CBC_A\otimes\mathbb F_2^B+\mathbb F_2^A\otimes C_BCA​⊗F2B​+F2A​⊗CB​: every local view is a sum of a word whose columns are in CAC_ACA​ and a word whose rows are in CBC_BCB​. C0\mathcal C_0C0​ is defined in the same way on V0V_0V0​ with CA⊥C_A^\perpCA⊥​, CB⊥C_B^\perpCB⊥​. The local codes and their duals have distance at least δΔ\delta\DeltaδΔ. Both dual tensor codes are κ\kappaκ-product-expanding: every codeword xxx splits as x=c+rx=c+rx=c+r with ∣x∣≥κΔ(∥c∥+∥r∥)|x|\ge\kappa\Delta(\|c\|+\|r\|)∣x∣≥κΔ(∥c∥+∥r∥), where ∥c∥\|c\|∥c∥ counts non-zero columns and ∥r∥\|r\|∥r∥ non-zero rows.

A σx\sigma_xσx​-type error e∈F2Qe\in\mathbb F_2^Qe∈F2Q​ is decoded in three phases. In the preprocessing phase every v∈V1v\in V_1v∈V1​ chooses a minimum-weight local error εv\varepsilon_vεv​ with the local syndrome of eee; the mismatch is Z=∑v∈V1εvZ=\sum_{v\in V_1}\varepsilon_vZ=∑v∈V1​​εv​. The mismatch decomposition writes Z=C^0+R^0+C^1+R^1Z=\hat C_0+\hat R_0+\hat C_1+\hat R_1Z=C^0​+R^0​+C^1​+R^1​, with C^j\hat C_jC^j​ a sum of CAC_ACA​-columns and R^i\hat R_iR^i​ a sum of CBC_BCB​-rows. Postprocessing outputs e^=∑v∈V10εv+C^0+R^1\hat e=\sum_{v\in V_{10}}\varepsilon_v+\hat C_0+\hat R_1e^=∑v∈V10​​εv​+C^0​+R^1​. The parallel decoder (Algorithm 3) runs rounds of four substeps, one for each of V00,V01,V10,V11V_{00},V_{01},V_{10},V_{11}V00​,V01​,V10​,V11​. In a substep, every vertex vvv of the current class simultaneously looks for a non-zero local dual tensor codeword xvx_vxv​ on Q(v)Q(v)Q(v) with ∣Z^∣−∣Z^+xv∣≥∣xv∣/2|\hat Z|-|\hat Z+x_v|\ge|x_v|/2∣Z^∣−∣Z^+xv​∣≥∣xv​∣/2, picks one of maximum weight, splits it as cv+rvc_v+r_vcv​+rv​ with ∥cv∥+∥rv∥\|c_v\|+\|r_v\|∥cv​∥+∥rv​∥ minimal, and updates C^j\hat C_jC^j​, R^i\hat R_iR^i​ and Z^\hat ZZ^.

Formalization targets

Goal: Theorem 20 (= Theorem 3)

Fix ε∈(0,1/6)\varepsilon\in(0,1/6)ε∈(0,1/6) and δ,κ>0\delta,\kappa>0δ,κ>0. For all Δ\DeltaΔ large enough, every error with

∣e∣<1212min⁡(ε316,κ4)δ2κ2nΔ|e|<\frac1{2^{12}}\min\Big(\frac{\varepsilon^3}{16},\frac\kappa4\Big)\delta^2\kappa^2\frac n\Delta∣e∣<2121​min(16ε3​,4κ​)δ2κ2Δn​

is corrected by every run of the parallel decoder. With θ=14(14−32ε)\theta=\frac14\big(\frac14-\frac32\varepsilon\big)θ=41​(41​−23​ε), each round satisfies ∣Z^k+1∣≤(1−θ)∣Z^k∣|\hat Z_{k+1}|\le(1-\theta)|\hat Z_k|∣Z^k+1​∣≤(1−θ)∣Z^k​∣, so Z^k=0\hat Z_k=0Z^k​=0 once (1−θ)k⋅4∣e∣<1(1-\theta)^k\cdot4|e|<1(1−θ)k⋅4∣e∣<1, and then e+e^e+\hat ee+e^ lies in the span C0⊥\mathcal C_0^\perpC0⊥​ of the σx\sigma_xσx​-generators.

Milestones

  • Lemma 4 (expander mixing) and Lemma 5: λ(G0□),λ(G1□)≤4Δ\lambda(\mathcal G^\square_0),\lambda(\mathcal G^\square_1)\le4\Deltaλ(G0□​),λ(G1□​)≤4Δ.
  • Lemmas 7, 10, 11, 8 and Theorem 9: the minimum distance, dmin⁡≥δ2κ2n/(1024Δ)d_{\min}\ge\delta^2\kappa^2n/(1024\Delta)dmin​≥δ2κ2n/(1024Δ).
  • Lemmas 14, 15, 16 and Theorem 12: if a minimum decomposition of a non-zero mismatch has few active vertices, some local codeword lowers ∣Z∣|Z|∣Z∣ by (1−ε)∣xv∣(1-\varepsilon)|x_v|(1−ε)∣xv​∣.
  • Lemma 17: ∣Z∣≤4∣e∣|Z|\le4|e|∣Z∣≤4∣e∣ and ∥Z∥≤4∣e∣/(κΔ)\|Z\|\le4|e|/(\kappa\Delta)∥Z∥≤4∣e∣/(κΔ).
  • Lemma 19: below 2−16ε3κ2δ2n/Δ2^{-16}\varepsilon^3\kappa^2\delta^2n/\Delta2−16ε3κ2δ2n/Δ, sequential decoding with parameter 1/21/21/2 never stalls, and switching to ε\varepsilonε still converges.
  • Lemma 18: if the ε\varepsilonε-sequential decoder converges, one parallel round removes at least θ∣Z∣\theta|Z|θ∣Z∣.

Significance

The theorem shows that asymptotically good quantum LDPC codes can be decoded against adversarial errors of linear weight by O(log⁡n)O(\log n)O(logn) rounds of local computation, each vertex acting only on its own QQQ-neighbourhood. This matters for fault tolerance, where decoding latency is a bottleneck. The milestones carry independent value. Theorem 9 is a linear minimum distance bound for quantum Tanner codes in the quadripartite setting. Theorem 12 is the local-to-global "weight reduction" principle on which both decoders rest. Lemmas 4–5 are spectral facts about Cayley graphs and square complexes that are reusable for any expander-based code.

The paper's results are proved, not open; none has been machine-checked. The sequential decoder's correctness (Theorem 13) is posed separately on the platform as an open goal in the mission Decoding quantum Tanner codes (Leverrier, Zemor), which shares this mission's published definitions; this mission does not restate it. Formalizing the present chain would give a complete, checked proof that a parallel decoder exists for good quantum LDPC codes, and would expose the constants. Two printed constants are corrected here (see the scope section), and sharper constants or simpler arguments are welcome.

Difficulty

The obvious argument is to bound the mismatch and its norm, apply Theorem 12 repeatedly, and let all vertices of a class flip at once. That argument fails because parallel flips are not sequential flips. A vertex's best local move against the mismatch at its turn need not be the move a converging sequential run would make, and a round could stall even though a sequential run never does. Lemma 18 resolves this with a virtual sequential run, a partition of its increments into "good" and "bad" ones (Claim 1), and the maximum-weight rule at every vertex. Without that rule the lemma is false. Theorem 12 itself is a counting contradiction. It uses robustness, the expander mixing lemma twice, and two notions of "exceptional" vertices, and it needs Δ\DeltaΔ large. Theorem 9 needs the coset-minimization of Lemma 7 before the same counting can start.

Formalization scope

The development builds on the published layers QAlgorithms_Defs_TensorCode and QAlgorithms_Defs_TannerDecoder. These define the complex, φv\varphi_vφv​, C0\mathcal C_0C0​, C1\mathcal C_1C1​, the σx\sigma_xσx​ span, minimum local corrections, the mismatch, Algorithm 2 and the output e^\hat ee^. The conventions are:

  • A vertex (g,ij)(g,ij)(g,ij) is (g, (i, j)) : G × (Bool × Bool), squares are G × A × B, and weights are hammingNorm cast to ℝ.
  • One new definitions file holds Algorithm 3 (admissible move, parallel choice, substep, round, run), decompositions and their norm, active, exceptional and ordinary vertices, the clustering sets TTT, stalled and converging sequential decoders, C1⊥\mathcal C_1^\perpC1⊥​, and the square graphs Gk□\mathcal G^\square_kGk□​.
  • "Δ\DeltaΔ large enough" is ∃Δ0 ∀Δ≥Δ0\exists\Delta_0\,\forall\Delta\ge\Delta_0∃Δ0​∀Δ≥Δ0​, with Δ0\Delta_0Δ0​ depending only on the constants fixed before it.
  • Only σx\sigma_xσx​-type errors are treated; the σz\sigma_zσz​ case is symmetric.
  • Non-bipartiteness of the Cayley graphs (the standing choice of p. 10) is assumed wherever spectral bounds are used.
  • Theorem 12 assumes Z≠0Z\ne0Z=0 and asks for a non-zero xvx_vxv​.

Corrected constants. Theorem 9 is stated with 102410241024 instead of the printed 256256256, because Lemma 8 and the proof's ∣x∣≥κΔ2∥x∥|x|\ge\frac{\kappa\Delta}2\|x\|∣x∣≥2κΔ​∥x∥ give 102410241024. The goal's error bound uses min⁡(ε3/16,κ/4)\min(\varepsilon^3/16,\kappa/4)min(ε3/16,κ/4) instead of min⁡(ε3/16,κ)\min(\varepsilon^3/16,\kappa)min(ε3/16,κ), which is what the corrected distance supports. Both corrected statements are weaker than the printed ones.

No trivialization. Every vertex must flip a maximum-weight admissible move whenever one exists, and runs range over all such choices. "Logarithmic time" is the per-round contraction factor, not mere eventual termination. Validity is membership of e+e^e+\hat ee+e^ in the σx\sigma_xσx​-generator span, with e^\hat ee^ the paper's postprocessing output. A formalization that lets vertices idle, drops the maximum-weight rule, or proves only eventual termination does not prove this theorem.

Needed and reusable infrastructure:

  • spectral graph theory for regular multigraphs (expander mixing, eigenvalues of commuting Cayley adjacencies);
  • product expansion and local minimality of decompositions;
  • the combinatorics of the left-right complex (shared rows and columns).

Contributions proving the graph lemmas or Lemma 18, whose proof is pure counting, are especially welcome.

Selected references

  • A. Leverrier, G. Zémor, Decoding quantum Tanner codes, IEEE Transactions on Information Theory; arXiv:2208.05537v3, 2022. https://arxiv.org/abs/2208.05537
  • A. Leverrier, G. Zémor, Quantum Tanner codes, FOCS 2022; arXiv:2202.13641. https://arxiv.org/abs/2202.13641
  • P. Panteleev, G. Kalachev, Asymptotically good quantum and locally testable classical LDPC codes, STOC 2022; arXiv:2111.03654. https://arxiv.org/abs/2111.03654
  • S. Gu, C. A. Pattison, E. Tang, An efficient decoder for a linear distance quantum LDPC code, STOC 2023; arXiv:2206.06557. https://arxiv.org/abs/2206.06557
  • A. Leverrier, G. Zémor, Efficient decoding up to a constant fraction of the code length for asymptotically good quantum codes, SODA 2023; arXiv:2206.07571. https://arxiv.org/abs/2206.07571
  • I. Dinur, M.-H. Hsieh, T.-C. Lin, T. Vidick, Good quantum LDPC codes with linear time decoders, arXiv:2206.07750, 2022. https://arxiv.org/abs/2206.07750
  • G. Kalachev, P. Panteleev, Two-sided robustly testable codes, arXiv:2206.09973, 2022. https://arxiv.org/abs/2206.09973
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Operations ResearchProbability·Captain: mikedeng1

A Dynamic Contagion Risk Model with Recovery Features: Fluid Limit for the Final Fraction of Failed Nodes in Threshold Contagion with Growth on the Configuration ModelResearch Paper

Motivation

Financial networks, supply chains and contact networks share a failure mode: a node fails when enough of its counterparties have failed, and its failure in turn weakens its own counterparties. Threshold contagion on random graphs is the standard mathematical model of this. Amini, Cont and Minca (Resilience to contagion in financial networks, Mathematical Finance 2016) proved that on a configuration-model network the final fraction of failed nodes concentrates, as the network grows, around an explicit value determined by the smallest fixed point of a one-dimensional map.

In that model thresholds are fixed. In practice institutions recover while a crisis unfolds: capital is raised, earnings accrue, and a node that survives long enough can absorb more losses than at the start. Amini, Minca and Sulem (A Dynamic Contagion Risk Model with Recovery Features, Mathematics of Operations Research 2022; the mission follows the authors' preprint of February 2021) add this recovery feature: every surviving node's threshold grows linearly in calendar time, at a rate proportional to its connectivity. The main result, Theorem 5, identifies the scaling limit of the cascade with growth. It is the basis of the paper's analysis of equilibrium connectivity choice (§3), where agents trade off the rewards of links against contagion risk.

Setting

A network has nnn nodes. Node iii has a connectivity λi∈N\lambda_i\in\mathbb Nλi​∈N (in-degree and out-degree both equal λi\lambda_iλi​) and an integer initial threshold θi∈N\theta_i\in\mathbb Nθi​∈N. The empirical type distribution is μλ,θ(n)=#{i:θi=θ,λi=λ}/n\mu^{(n)}_{\lambda,\theta}=\#\{i:\theta_i=\theta,\lambda_i=\lambda\}/nμλ,θ(n)​=#{i:θi​=θ,λi​=λ}/n and the mean connectivity is λˉ(n)=∑λ,θλμλ,θ(n)\bar\lambda^{(n)}=\sum_{\lambda,\theta}\lambda\mu^{(n)}_{\lambda,\theta}λˉ(n)=∑λ,θ​λμλ,θ(n)​. Two regularity conditions are assumed: (C1) μλ,θ(n)→μλ,θ\mu^{(n)}_{\lambda,\theta}\to\mu_{\lambda,\theta}μλ,θ(n)​→μλ,θ​ for a probability distribution μ\muμ on N2\mathbb N^2N2; (C2) λˉ(n)→λˉ=∑λμλ,θ∈(0,∞)\bar\lambda^{(n)}\to\bar\lambda=\sum\lambda\mu_{\lambda,\theta}\in(0,\infty)λˉ(n)→λˉ=∑λμλ,θ​∈(0,∞).

Each node owns λi\lambda_iλi​ out-going and λi\lambda_iλi​ in-coming half-edges. The directed configuration model matches out-half-edges to in-half-edges by a uniformly random bijection; self-loops and multiple links are allowed.

The cascade: at time 000 the nodes with θi=0\theta_i=0θi​=0 fail. Interactions happen at times Tk(n)T^{(n)}_kTk(n)​ whose gaps are i.i.d. exponential with rate nnn. At each interaction one unrevealed out-link of a failed node, chosen uniformly, is revealed; its counterparty jjj gains one failed link and fails if its count of failed links reaches its current threshold θj+αλjTk(n)/λˉ(n)\theta_j+\alpha\lambda_jT^{(n)}_k/\bar\lambda^{(n)}θj​+αλj​Tk(n)​/λˉ(n), where 0<α<10<\alpha<10<α<1 is the growth rate. Only surviving nodes grow. The cascade stops at kstop(n)k^{(n)}_{\rm stop}kstop(n)​, when no unrevealed failed link remains; ∣Df(n)∣|\mathcal D^{(n)}_f|∣Df(n)​∣ is the number of failed nodes and Sλ,θ(n)(kstop(n))S^{(n)}_{\lambda,\theta}(k^{(n)}_{\rm stop})Sλ,θ(n)​(kstop(n)​) the number of survivors of type (λ,θ)(\lambda,\theta)(λ,θ).

The limit objects: Pλ,θ,ℓ(π)P_{\lambda,\theta,\ell}(\pi)Pλ,θ,ℓ​(π) is the probability that the order statistics of ℓ\ellℓ i.i.d. uniform variables on [0,π][0,\pi][0,π] satisfy U(u)>(u−θ)/(αλ)U_{(u)}>(u-\theta)/(\alpha\lambda)U(u)​>(u−θ)/(αλ) for u=θ+1,…,ℓu=\theta+1,\dots,\ellu=θ+1,…,ℓ; Bλ,0α=1B^\alpha_{\lambda,0}=1Bλ,0α​=1 and, for θ≥1\theta\ge1θ≥1,

Bλ,θα(π)=1−∑ℓ=0min⁡{⌈θ+αλπ⌉−1,λ}(λℓ)πℓ(1−π)λ−ℓPλ,θ,ℓ(π);B^\alpha_{\lambda,\theta}(\pi)=1-\sum_{\ell=0}^{\min\{\lceil\theta+\alpha\lambda\pi\rceil-1,\lambda\}}\binom{\lambda}{\ell}\pi^\ell(1-\pi)^{\lambda-\ell}P_{\lambda,\theta,\ell}(\pi);Bλ,θα​(π)=1−ℓ=0∑min{⌈θ+αλπ⌉−1,λ}​(ℓλ​)πℓ(1−π)λ−ℓPλ,θ,ℓ​(π);

Jα(π)=∑λ,θλμλ,θBλ,θα(π)/λˉJ^\alpha(\pi)=\sum_{\lambda,\theta}\lambda\mu_{\lambda,\theta}B^\alpha_{\lambda,\theta}(\pi)/\bar\lambdaJα(π)=∑λ,θ​λμλ,θ​Bλ,θα​(π)/λˉ; and the relaxed fixed point π∗=min⁡{π∈[0,1]:Jα(π)≤π}\pi^*=\min\{\pi\in[0,1]:J^\alpha(\pi)\le\pi\}π∗=min{π∈[0,1]:Jα(π)≤π}.

Formalization targets

Goal: Theorem 5 (corrected)

With L=∑λ,θμλ,θBλ,θα(π∗)L=\sum_{\lambda,\theta}\mu_{\lambda,\theta}B^\alpha_{\lambda,\theta}(\pi^*)L=∑λ,θ​μλ,θ​Bλ,θα​(π∗): for every ε>0\varepsilon>0ε>0, with high probability ∣Df(n)∣/n≥L−ε|\mathcal D^{(n)}_f|/n\ge L-\varepsilon∣Df(n)​∣/n≥L−ε. (i) If π∗=1\pi^*=1π∗=1 and μ0,θ=0\mu_{0,\theta}=0μ0,θ​=0 for θ≥1\theta\ge1θ≥1, then ∣Df(n)∣/n→1|\mathcal D^{(n)}_f|/n\to1∣Df(n)​∣/n→1 in probability. (ii) If π∗<1\pi^*<1π∗<1 is a stable fixed point (Jα(π)<πJ^\alpha(\pi)<\piJα(π)<π just to the right of π∗\pi^*π∗), then

∣Df(n)∣n→pL,Sλ,θ(n)(kstop(n))n→pμλ,θ(1−Bλ,θα(π∗)).\frac{|\mathcal D^{(n)}_f|}{n}\xrightarrow{p}L,\qquad\frac{S^{(n)}_{\lambda,\theta}(k^{(n)}_{\rm stop})}{n}\xrightarrow{p}\mu_{\lambda,\theta}\big(1-B^\alpha_{\lambda,\theta}(\pi^*)\big).n∣Df(n)​∣​p​L,nSλ,θ(n)​(kstop(n)​)​p​μλ,θ​(1−Bλ,θα​(π∗)).

Milestones

  1. Lemma 16: a recursion in ℓ\ellℓ for Pλ,θ,ℓ(t)P_{\lambda,\theta,\ell}(t)Pλ,θ,ℓ​(t), for 0<α<10<\alpha<10<α<1 and θ<ℓ≤λ\theta<\ell\le\lambdaθ<ℓ≤λ, valid for t≥t^λ,θ,ℓ=(ℓ−θ)λˉ/(αλ)t\ge\hat t_{\lambda,\theta,\ell}=(\ell-\theta)\bar\lambda/(\alpha\lambda)t≥t^λ,θ,ℓ​=(ℓ−θ)λˉ/(αλ).
  2. Lemma 15: for 0<α<10<\alpha<10<α<1 and 0<θ≤λ0<\theta\le\lambda0<θ≤λ, the piecewise linear system (DE) for the rescaled numbers of surviving nodes with ℓ\ellℓ failed links has a unique continuous solution on [0,λˉ)[0,\bar\lambda)[0,λˉ), in closed form.
  3. (24)–(25): the limiting failed fraction of a class is μλ,θBλ,θα(t/λˉ)\mu_{\lambda,\theta}B^\alpha_{\lambda,\theta}(t/\bar\lambda)μλ,θ​Bλ,θα​(t/λˉ), and the limiting number of unrevealed failed links is λˉ(Jα(t/λˉ)−t/λˉ)\bar\lambda(J^\alpha(t/\bar\lambda)-t/\bar\lambda)λˉ(Jα(t/λˉ)−t/λˉ).
  4. (27)–(28): for k=γλˉ(n)n+o(n)k=\gamma\bar\lambda^{(n)}n+o(n)k=γλˉ(n)n+o(n), Tk(n)→γλˉT^{(n)}_k\to\gamma\bar\lambdaTk(n)​→γλˉ in probability.
  5. Remark 6: the minimum defining π∗\pi^*π∗ is attained.

Significance

Theorem 5 gives the final size of a cascade with recovery as an explicit function of the type distribution and the growth rate. The paper uses it to define each agent's expected reward as a function of its connectivity and to study the equilibrium of the resulting connectivity game (§3). Without the theorem, the equilibrium analysis would rest on the heuristic of §2.3 alone.

On the formal side, the platform has no random graph model of configuration type, no network contagion process and no fluid limit of a random cascade. This mission introduces all three. The result is proved on paper (by Wormald's differential-equation method); none of it is machine-checked. The work is to formalize the proof, and the milestones are self-contained pieces of it: an order-statistic identity, an explicit ODE solution, an algebraic identification of the limit, a concentration bound for sums of exponentials, and a fixed-point existence result.

Difficulty

The obvious approach is to view the cascade as a Markov chain in the counts of surviving nodes by type and number of failed links, and to apply Wormald's theorem directly. Two things break. First, with growth a node's survival depends on the whole history of its failed-link count, not on its current value, so the state space grows with time: new states become relevant at each growth time t^λ,θ,ℓ\hat t_{\lambda,\theta,\ell}t^λ,θ,ℓ​, and the fluid limit has to be established inductively on successive time intervals. Second, the cascade stops at a random time, near the first zero of the limiting number of unrevealed failed links. Showing that the stopping time concentrates requires the stability condition on π∗\pi^*π∗ and a truncation of the type distribution in the degree, since μ\muμ may have infinite support. The fixed point need not be a crossing point of a smooth map, so the identification of the stopping time is delicate even when the drift is known.

Formalization scope

  • Network. Half-edges of node iii are pairs (i,a)(i,a)(i,a) with a<λia<\lambda_ia<λi​; the matching is a uniformly random permutation of the half-edges. Each nnn has its own probability space: the matching, m=nλˉ(n)m=n\bar\lambda^{(n)}m=nλˉ(n) i.i.d. Exp(n)\mathrm{Exp}(n)Exp(n) gaps, and mmm i.i.d. uniform variables on [0,1)[0,1)[0,1) that choose the revealed link (uniform among the available ones given the past). The probability of an event averages over permutations the product law of gaps and choices. "With high probability" and "in probability" are stated with explicit ε\varepsilonε and limits of these probabilities.
  • Cascade. Computed step by step from the randomness and run for mmm steps; ∣Df(n)∣|\mathcal D^{(n)}_f|∣Df(n)​∣ and Sλ,θ(n)(kstop)S^{(n)}_{\lambda,\theta}(k_{\rm stop})Sλ,θ(n)​(kstop​) are read off the final state. A node fails when its failed links reach (not exceed) its threshold, as in every formula of the paper. Initially failed nodes are those with θi=0\theta_i=0θi​=0.
  • Limit objects. Order statistics are encoded by counting (U(u)>sU_{(u)}>sU(u)​>s iff fewer than uuu variables are ≤s\le s≤s), and probabilities as Lebesgue measures of subsets of [0,π]ℓ[0,\pi]^\ell[0,π]ℓ. Sums over types are tsums with summability hypotheses; μ\muμ is nonnegative with total mass 111.
  • Corrections, all labelled. (a) Bλ,0α:=1B^\alpha_{\lambda,0}:=1Bλ,0α​:=1; the printed formula gives a value below 111 for π>0\pi>0π>0, contradicting the paper's p. 9, and makes the theorem false. (b) Part (i) assumes no isolated nodes with a positive threshold. (c) Lemma 16 assumes t≥t^λ,θ,ℓt\ge\hat t_{\lambda,\theta,\ell}t≥t^λ,θ,ℓ​. (d) Lemma 15 is stated for 0<θ≤λ0<\theta\le\lambda0<θ≤λ, with uniqueness among continuous solutions, and reads the printed λˉ−u\bar\lambda-uλˉ−u as λˉ−t\bar\lambda-tλˉ−t. (e) (24) ends in μλ,θBλ,θα(t/λˉ)\mu_{\lambda,\theta}B^\alpha_{\lambda,\theta}(t/\bar\lambda)μλ,θ​Bλ,θα​(t/λˉ), not μλ,θ(1−Bλ,θα(t/λˉ))\mu_{\lambda,\theta}(1-B^\alpha_{\lambda,\theta}(t/\bar\lambda))μλ,θ​(1−Bλ,θα​(t/λˉ)). "Stable fixed point" means Jα(π)<πJ^\alpha(\pi)<\piJα(π)<π on some interval (π∗,π∗+η)(\pi^*,\pi^*+\eta)(π∗,π∗+η).
  • No trivialization. ∣Df(n)∣|\mathcal D^{(n)}_f|∣Df(n)​∣ and Sλ,θ(n)S^{(n)}_{\lambda,\theta}Sλ,θ(n)​ are computed from the random matching, the gaps and the revelation order, never from BαB^\alphaBα or JαJ^\alphaJα; π∗\pi^*π∗ is defined from μ\muμ and α\alphaα, not taken as a parameter.
  • Not included. Theorem 3 (the case α=0\alpha=0α=0, cited from earlier work), the equilibrium results of §3, Wormald's theorem (Lemma 17, an external result), the in/out-degree extension (Theorem 18) and the displays (32)–(41) of the inductive argument.
  • Infrastructure. A complete proof needs Wormald's differential-equation method for Markov chains with bounded increments, concentration of the configuration model, and the elementary facts in the milestones. A general Wormald theorem and the configuration model are reusable well beyond this mission; contributions of either, as separate theorems, are welcome.

Selected references

  • H. Amini, A. Minca, A. Sulem, A dynamic contagion risk model with recovery features, Mathematics of Operations Research, 2022 (preprint February 2021). https://doi.org/10.1287/moor.2021.1174
  • H. Amini, R. Cont, A. Minca, Resilience to contagion in financial networks, Mathematical Finance, 2016. https://doi.org/10.1111/mafi.12051
  • N. C. Wormald, Differential equations for random processes and random graphs, Annals of Applied Probability, 1995. https://doi.org/10.1214/aoap/1177004612
  • L. Warnke, On Wormald's differential equation method, arXiv:1905.08928, 2019. https://arxiv.org/abs/1905.08928
  • B. Bollobás, A probabilistic proof of an asymptotic formula for the number of labelled regular graphs, European Journal of Combinatorics, 1980. https://doi.org/10.1016/S0195-6698(80)80030-8
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EconomicsOperations ResearchProbability·Captain: mikedeng1

The Value of Price Discrimination in Large Random Networks: The Expected Fractional Regret of Uniform Pricing Vanishes in Erdős–Rényi Networks of Any DensityResearch Paper

Motivation

A seller whose customers influence one another faces a choice between two pricing policies. It can collect information about the social network and charge each customer a personalised price that reflects their position in it, or it can ignore the network and charge everyone one uniform price. Personalised pricing is never worse for the seller, but collecting network data is costly and raises privacy concerns. The question is how much profit the network information is worth.

For a fixed network the answer depends on its fine structure. Candogan, Bimpikis and Ozdaglar (Operations Research 2012) showed that with linear-quadratic utilities the optimal personalised prices are expressed through Bonacich centralities, so the gain from discrimination is a difference of discounted walk counts. Huang, Mani and Wang (SSRN 3368458; published in Management Science, 2021) study this gain, the value of price discrimination, in large random networks. Their main result for random networks, Theorem 5.1, says that in directed Erdős–Rényi networks the seller who prices uniformly loses only a vanishing fraction of the optimal profit, whatever the density of the network. This mission formalizes that theorem and the results its proof rests on.

Setting

There are nnn consumers. The network is an n×nn\times nn×n matrix GGG with non-negative entries, where GijG_{ij}Gij​ is the influence of consumer jjj on consumer iii. For a square matrix AAA, ∥A∥\|A\|∥A∥ is its spectral norm (the ℓ2\ell^2ℓ2 operator norm); 1\mathbf 11 is the all-ones vector, and 1TAk1\mathbf 1^{\mathsf T}A^k\mathbf 11TAk1 is the total weight of walks of length kkk in AAA.

Consumer iii facing price pip_ipi​ chooses a consumption level xix_ixi​ to maximise

ui=axi−xi2+4ρxi∑j≠iGij∥G+GT∥xj−pixi,u_i=ax_i-x_i^2+4\rho x_i\sum_{j\ne i}\frac{G_{ij}}{\|G+G^{\mathsf T}\|}x_j-p_ix_i,ui​=axi​−xi2​+4ρxi​j=i∑​∥G+GT∥Gij​​xj​−pi​xi​,

with a stand-alone utility a>0a>0a>0 and a network coefficient ρ∈(0,1)\rho\in(0,1)ρ∈(0,1). The resulting consumption equilibrium is (eq. (1))

x∗(p)=12(I−2ρ∥G+GT∥G)−1(a1−p).x^*(p)=\tfrac12\Big(I-\tfrac{2\rho}{\|G+G^{\mathsf T}\|}G\Big)^{-1}(a\mathbf 1-p).x∗(p)=21​(I−∥G+GT∥2ρ​G)−1(a1−p).

The seller has marginal cost c<ac<ac<a and profit π(p)=(p−c1)Tx∗(p)\pi(p)=(p-c\mathbf 1)^{\mathsf T}x^*(p)π(p)=(p−c1)Tx∗(p). The optimal profit is π∗=max⁡p∈Rnπ(p)\pi^*=\max_{p\in\mathbb R^n}\pi(p)π∗=maxp∈Rn​π(p); the optimal uniform price is p0=a+c21p_0=\frac{a+c}{2}\mathbf 1p0​=2a+c​1. The regret and fractional regret of uniform pricing are

R(p0)=π∗−π(p0),RF(p0)=1−π(p0)π∗.R(p_0)=\pi^*-\pi(p_0),\qquad R_F(p_0)=1-\frac{\pi(p_0)}{\pi^*}.R(p0​)=π∗−π(p0​),RF​(p0​)=1−π∗π(p0​)​.

A directed Erdős–Rényi network G(n,p)G(n,p)G(n,p) is the random 0/10/10/1 matrix whose off-diagonal entries are independent with Pr⁡[Gij=1]=p\Pr[G_{ij}=1]=pPr[Gij​=1]=p, and whose diagonal is zero. EG\mathbb E_GEG​ denotes expectation over it. The constants a,c,ρa,c,\rhoa,c,ρ are the same for every nnn.

Formalization targets

Goal: Theorem 5.1

For every sequence of densities (p(n))n∈N(p(n))_{n\in\mathbb N}(p(n))n∈N​ with values in [0,1][0,1][0,1],

EG(n,p(n))[R(p0)]=o(n)andEG(n,p(n))[RF(p0)]=o(1).\mathbb E_{G(n,p(n))}[R(p_0)]=o(n)\qquad\text{and}\qquad\mathbb E_{G(n,p(n))}[R_F(p_0)]=o(1).EG(n,p(n))​[R(p0​)]=o(n)andEG(n,p(n))​[RF​(p0​)]=o(1).

Both parts are stated. The goal fixes no rate and no density regime; the paper's Theorems 5.2–5.4 give exact rates in three regimes and are not part of this mission.

Milestones

  1. Eq. (3): π∗\pi^*π∗ is attained and equals 12(a−c2)2 1T(I−ρ∥G+GT∥(G+GT))−11\frac12(\frac{a-c}{2})^2\,\mathbf 1^{\mathsf T}(I-\frac{\rho}{\|G+G^{\mathsf T}\|}(G+G^{\mathsf T}))^{-1}\mathbf 121​(2a−c​)21T(I−∥G+GT∥ρ​(G+GT))−11, with its walk series.
  2. Eq. (4): p0p_0p0​ is the optimal uniform price, and π(p0)=12(a−c2)2 1T(I−2ρ∥G+GT∥G)−11\pi(p_0)=\frac12(\frac{a-c}{2})^2\,\mathbf 1^{\mathsf T}(I-\frac{2\rho}{\|G+G^{\mathsf T}\|}G)^{-1}\mathbf 1π(p0​)=21​(2a−c​)21T(I−∥G+GT∥2ρ​G)−11.
  3. Lemma 5.3 (for even ttt): 1T(G+GT)k1≤∥G+GT∥k−t1T(G+GT)t1\mathbf 1^{\mathsf T}(G+G^{\mathsf T})^k\mathbf 1\le\|G+G^{\mathsf T}\|^{k-t}\mathbf 1^{\mathsf T}(G+G^{\mathsf T})^t\mathbf 11T(G+GT)k1≤∥G+GT∥k−t1T(G+GT)t1 for k≥tk\ge tk≥t.
  4. Eq. (29): EG[R(p0)]≤12(a−c2)2 EG[ρ21−ρ∥G+GT∥−21T(G+GT)21]\mathbb E_G[R(p_0)]\le\frac12(\frac{a-c}{2})^2\,\mathbb E_G[\frac{\rho^2}{1-\rho}\|G+G^{\mathsf T}\|^{-2}\mathbf 1^{\mathsf T}(G+G^{\mathsf T})^2\mathbf 1]EG​[R(p0​)]≤21​(2a−c​)2EG​[1−ρρ2​∥G+GT∥−21T(G+GT)21].
  5. Appendix D.1, Case 1, and footnote 6: a non-empty binary network has ∥G+GT∥≥1\|G+G^{\mathsf T}\|\ge1∥G+GT∥≥1. In the footnote's words, "Therefore a non-empty network (with at lease one link) has spectral norm greater than or equal to 1."
  6. Lemma 5.2: EG[1T(G+GT)21]=2n(n−1)p(1−3p+2np)\mathbb E_G[\mathbf 1^{\mathsf T}(G+G^{\mathsf T})^2\mathbf 1]=2n(n-1)p(1-3p+2np)EG​[1T(G+GT)21]=2n(n−1)p(1−3p+2np).
  7. Lemma 5.5 (i): EG[1T(G+GT)k1]≥2kEG[1TGk1]\mathbb E_G[\mathbf 1^{\mathsf T}(G+G^{\mathsf T})^k\mathbf 1]\ge2^k\mathbb E_G[\mathbf 1^{\mathsf T}G^k\mathbf 1]EG​[1T(G+GT)k1]≥2kEG​[1TGk1] for all k≥0k\ge0k≥0.
  8. Lemma 5.5 (ii): for 1≤k<n1\le k<n1≤k<n and 1n−k+1≤p<1\frac1{n-k+1}\le p<1n−k+11​≤p<1, the gap in (i) is at most (k−1)2k−1P(n,k)pk−1+(k−1)k2kP(n,k−1)pk−2(k-1)2^{k-1}P(n,k)p^{k-1}+(k-1)^k2^kP(n,k-1)p^{k-2}(k−1)2k−1P(n,k)pk−1+(k−1)k2kP(n,k−1)pk−2, with P(n,k)=n!/(n−k)!P(n,k)=n!/(n-k)!P(n,k)=n!/(n−k)!.
  9. Eq. (12), Rayleigh step: ∥G+GT∥≥1T(G+GT)1/1T1\|G+G^{\mathsf T}\|\ge\mathbf 1^{\mathsf T}(G+G^{\mathsf T})\mathbf 1/\mathbf 1^{\mathsf T}\mathbf 1∥G+GT∥≥1T(G+GT)1/1T1.
  10. Eq. (9): for non-empty GGG and k>0k>0k>0, 0<1T((G+GT)/∥G+GT∥)k1≤n0<\mathbf 1^{\mathsf T}\big((G+G^{\mathsf T})/\|G+G^{\mathsf T}\|\big)^k\mathbf 1\le n0<1T((G+GT)/∥G+GT∥)k1≤n.
  11. Eq. (14): n2(a−c2)2≤π∗≤12(a−c2)2n1−ρ\frac n2(\frac{a-c}2)^2\le\pi^*\le\frac12(\frac{a-c}2)^2\frac n{1-\rho}2n​(2a−c​)2≤π∗≤21​(2a−c​)21−ρn​.

The paper calls the optimal uniform price p0p_0p0​ "under the assumption that c<ac<ac<a, according to [12]" (p. 7); milestone 2 states that optimality as part of eq. (4).

Significance

Theorem 5.1 says that in large random networks the network information has asymptotically no value to a monopolist: the uniform price p0p_0p0​ does not depend on the network, yet earns a fraction of the optimal profit tending to one, for sparse, critical and dense networks alike. This separates the deterministic picture, where some networks have a large value of discrimination (Theorem 4.1 of the paper characterises those with none), from the typical random one. It supplies a benchmark against which network-based pricing, and the cost of collecting network data, can be judged.

The paper proves the theorem by hand. No part of it, and no part of the model, has been machine-checked; the platform has no network pricing model and no walk-count results for directed random graphs. A complete development would make the closed forms (3) and (4), the spectral inequalities and the Erdős–Rényi walk moments reusable. The printed proof of Theorem 5.1 bounds expectations of ratios by almost-sure statements and splits the density sequence into "maximal subsequences"; a formal proof must replace these steps by quantitative ones. The printed proof of Lemma 5.5 (i) relies on a per-walk claim that fails (a closed walk 0→1→2→0→10\to1\to2\to0\to10→1→2→0→1 in G+GTG+G^{\mathsf T}G+GT can use four arcs of GGG while it visits three ordered pairs), although the statement itself was checked by exhaustive enumeration for small nnn; that lemma needs a new argument.

Difficulty

The regret is a difference of two discounted walk series. Bound (29) controls it by walks of length two and the spectral norm, which suffices when the network is very sparse, but in dense networks, where ∥G+GT∥≈2np\|G+G^{\mathsf T}\|\approx2np∥G+GT∥≈2np and 1T(G+GT)21≈4n3p2\mathbf 1^{\mathsf T}(G+G^{\mathsf T})^2\mathbf 1\approx4n^3p^21T(G+GT)21≈4n3p2, it only gives O(n)O(n)O(n). The dense case needs the cancellation between 1T(G+GT)k1\mathbf 1^{\mathsf T}(G+G^{\mathsf T})^k\mathbf 11T(G+GT)k1 and 2k1TGk12^k\mathbf 1^{\mathsf T}G^k\mathbf 12k1TGk1 for each kkk up to a slowly growing cutoff (Lemma 5.5), together with a lower bound on the spectral norm that holds with high probability. In the critical regime np=Θ(1)np=\Theta(1)np=Θ(1), neither bound applies directly, and one needs that ∥G+GT∥\|G+G^{\mathsf T}\|∥G+GT∥ grows with nnn in probability. The goal quantifies over all density sequences, so the argument must be uniform across these regimes; a proof for one regime does not suffice.

Formalization scope

All objects live in the namespace NetPriceDisc.Random and are defined in one file. Networks are Matrix (Fin n) (Fin n) ℝ; the spectral norm is Mathlib's ℓ2\ell^2ℓ2 operator norm (Matrix.Norms.L2Operator). The consumption equilibrium is formula (1) for every price vector, with no non-negativity constraint on consumption, as in the paper's definition of π(p)\pi(p)π(p). π∗\pi^*π∗ is defined as the supremum of π\piπ over Rn\mathbb R^nRn, and RRR, RFR_FRF​ from π∗\pi^*π∗ and π(p0)\pi(p_0)π(p0​); the closed forms (3) and (4) are theorems, not definitions, so the goal cannot be discharged by unfolding formulas. Lean's convention x/0=0x/0=0x/0=0 gives the empty network no externality, hence zero regret, which is the paper's own reading of the empty network (p. 12); the empty network keeps its probability (1−p)n(n−1)(1-p)^{n(n-1)}(1−p)n(n−1). The Erdős–Rényi expectation is the finite sum over arc sets SSS of off-diagonal pairs with weight p∣S∣(1−p)n(n−1)−∣S∣p^{|S|}(1-p)^{n(n-1)-|S|}p∣S∣(1−p)n(n−1)−∣S∣; no measure theory is used. The fractional regret's expectation is the expectation of the ratio. "Any sequence of densities" is a function N→[0,1]\mathbb N\to[0,1]N→[0,1], with no further restriction.

Two printed statements are corrected and disclosed: Lemma 5.3 is stated for even ttt (as printed it fails for odd ttt; for the star K1,3K_{1,3}K1,3​ with t=1t=1t=1, k=2k=2k=2 one gets 12>6312>6\sqrt312>63​), and Lemma 5.5 (ii) for k≥1k\ge1k≥1 (at k=0k=0k=0 it involves the undefined P(n,−1)P(n,-1)P(n,−1)). Only the deterministic Rayleigh-quotient part of eq. (12) is stated.

Useful infrastructure includes Neumann series and Rayleigh-quotient bounds for symmetric non-negative matrices, walk moments of directed Erdős–Rényi graphs, and maximum-degree or spectral lower bounds in probability. Proofs of any milestone, and alternative routes to the goal that avoid Lemma 5.5, are welcome.

Selected references

  • J. Huang, A. Mani, Z. Wang, The Value of Price Discrimination in Large Random Networks, SSRN 3368458 (EC '19 submission version); published as The Value of Price Discrimination in Large Social Networks, Management Science, 2021. https://ssrn.com/abstract=3368458, https://doi.org/10.1287/mnsc.2021.4108
  • O. Candogan, K. Bimpikis, A. Ozdaglar, Optimal Pricing in Networks with Externalities, Operations Research 60(4), 2012. https://doi.org/10.1287/opre.1120.1066
  • P. Bonacich, Power and Centrality: A Family of Measures, American Journal of Sociology 92(5), 1987. https://doi.org/10.1086/228631
  • M. Krivelevich, B. Sudakov, The Largest Eigenvalue of Sparse Random Graphs, Combinatorics, Probability and Computing 12(1), 2003. https://doi.org/10.1017/S0963548302005424
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Quantum Error CorrectionQuantum Information·Captain: mikedeng1

Quantum Tanner Codes 1: On a Ramanujan Left-Right Cayley Complex with Robust Dual Tensor Codes, the Quantum Tanner Code Has Rate at Least (1−2ρ)² and Distance at Least δn/(4Δ^{3/2+ε})Research Paper

Why quantum Tanner codes matter

An error-correcting code protects information by adding redundancy. A quantum code must also account for two kinds of errors, commonly called XXX and ZZZ errors. Quantum low-density parity-check codes use checks that each touch only a bounded number of qubits, while each qubit participates in only a bounded number of checks. Such locality matters when the number of qubits grows. For years it was difficult to make both the number of encoded qubits and the minimum distance proportional to the block length while retaining bounded-weight checks. Leverrier and Zémor's quantum Tanner construction gives a route through local codes placed on an expanding square complex; its main distance theorem is the target of this mission Leverrier and Zémor, 2022.

The construction combines two earlier ingredients. Left-right Cayley complexes provide the squares and expansion properties used by the code Dinur, Evra, Livne, Lubotzky, and Mozes, 2021. Robustness of a dual tensor code, including after puncturing coordinates, controls local words Panteleev and Kalachev, 2021. The paper states the local reduction property as Theorem 1 and derives the rate and distance bounds as Theorem 17. This mission formalizes that implication under the paper's stated component-code and expansion conditions; the separate random-code result that supplies robust examples belongs to the second mission of this series.

The square complex and its codes

Let GGG be a finite group and let A,B⊆GA,B\subseteq GA,B⊆G be inverse-closed sets, each of size Δ\DeltaΔ. The total no-conjugacy condition says ag≠gbag\ne gbag=gb for every a∈Aa\in Aa∈A, b∈Bb\in Bb∈B, and g∈Gg\in Gg∈G. Vertices are two copies of the group, V=G×{0,1}V=G\times\{0,1\}V=G×{0,1}. A square associated with v=(g,i)v=(g,i)v=(g,i) and (a,b)∈A×B(a,b)\in A\times B(a,b)∈A×B has vertices vvv, (ag,1−i)(ag,1-i)(ag,1−i), (gb,1−i)(gb,1-i)(gb,1−i), and (agb,i)(agb,i)(agb,i). The set of squares is QQQ, and a binary word on QQQ has length n=∣Q∣n=|Q|n=∣Q∣. At a vertex vvv, the incident squares are indexed by A×BA\times BA×B; this indexing makes the local view xvx_vxv​ of a word x∈F2Qx\in\mathbb F_2^Qx∈F2Q​ an A×BA\times BA×B matrix Leverrier and Zémor, §§3.2 and 4.1.

A binary linear code CAC_ACA​ has coordinates indexed by AAA, and CBC_BCB​ has coordinates indexed by BBB. Their tensor code CA⊗CBC_A\otimes C_BCA​⊗CB​ consists of matrices whose columns lie in CAC_ACA​ and whose rows lie in CBC_BCB​. The dual tensor code CA⊗F2B+F2A⊗CBC_A\otimes\mathbb F_2^B+\mathbb F_2^A\otimes C_BCA​⊗F2B​+F2A​⊗CB​ is the sum of the column and row codes. In Definition 5, it is www-robust when every member of weight at most www is supported on a union of few rows and columns, with the bounds determined by the two component minimum distances. Definition 8 requires this property after deleting up to ppp coordinates from each side. These are conditions on all relevant codewords and all permitted puncturings, including the unpunctured pair Leverrier and Zémor, pp. 15–16.

The two Tanner codes are C0=T(G0□,(CA⊗CB)⊥)\mathcal C_0=T(\mathcal G^\square_0,(C_A\otimes C_B)^\perp)C0​=T(G0□​,(CA​⊗CB​)⊥) and C1=T(G1□,(CA⊥⊗CB⊥)⊥)\mathcal C_1=T(\mathcal G^\square_1,(C_A^\perp\otimes C_B^\perp)^\perp)C1​=T(G1□​,(CA⊥​⊗CB⊥​)⊥). Here Gi□\mathcal G^\square_iGi□​ is the square-adjacency graph on the vertices in copy iii. Together they define the quantum CSS code Q=(C0,C1)\mathcal Q=(\mathcal C_0,\mathcal C_1)Q=(C0​,C1​). Its dimension counts encoded qubits. Its distance is the smaller of the minimum weights in C0∖C1⊥\mathcal C_0\setminus\mathcal C_1^\perpC0​∖C1⊥​ and C1∖C0⊥\mathcal C_1\setminus\mathcal C_0^\perpC1​∖C0⊥​ Leverrier and Zémor, pp. 14, 18.

Formalization targets

The goal is Theorem 17. Fix 0<ρ<1/20<\rho<1/20<ρ<1/2, 0<ε<1/20<\varepsilon<1/20<ε<1/2, 1/2+ε<γ<11/2+\varepsilon<\gamma<11/2+ε<γ<1, and δ>0\delta>0δ>0. For sufficiently large Δ\DeltaΔ, take a left-right Cayley complex satisfying total no-conjugacy and the paper's Ramanujan conditions. Require 0<dim⁡CA≤ρΔ0<\dim C_A\le\rho\Delta0<dimCA​≤ρΔ, dim⁡CB=Δ−dim⁡CA\dim C_B=\Delta-\dim C_AdimCB​=Δ−dimCA​, minimum distance at least δΔ\delta\DeltaδΔ for CA,CBC_A,C_BCA​,CB​ and both duals, and Δ3/2−ε/2\Delta^{3/2-\varepsilon/2}Δ3/2−ε/2 robustness with Δγ\Delta^\gammaΔγ resistance to puncturing for both relevant dual tensor codes. The target is

k≥(1−2ρ)2n,d≥δn4Δ3/2+ε.k\ge (1-2\rho)^2 n,\qquad d\ge \frac{\delta n}{4\Delta^{3/2+\varepsilon}}.k≥(1−2ρ)2n,d≥4Δ3/2+εδn​.

The milestones mirror the paper's route to this result. Lemma 4 bounds the nontrivial eigenvalues of the union and square graphs. Propositions 6 and 7 turn dual-tensor robustness into a decomposition and a 3/23/23/2 closeness bound. Theorem 1 supplies a weight-reducing local codeword for every sufficiently light nonzero Tanner-code word. Display (5) supplies the dimension lower bound. Theorem 17 combines the two distance directions with that rate estimate Leverrier and Zémor, pp. 12, 15–16, 18–23.

What the result supplies

The lower bound on kkk ensures a positive fraction of qubits can carry logical information when ρ<1/2\rho<1/2ρ<1/2. The lower bound on ddd rules out small undetectable logical errors. For a fixed allowable degree Δ\DeltaΔ, both bounds scale linearly with nnn. The paper also shows that random component codes can satisfy the robustness assumptions; that probabilistic result is a separate formalization target Leverrier and Zémor, Theorems 9 and 17.

A checked development would make explicit how the square indexing, duals, punctured codes, and CSS quotient fit together. The source proves the mathematical result; the statements here remain open Lean theorems. Reusable contributions include the tensor-code interface, spectral bounds for the graph construction, and lemmas connecting local views to the global Tanner codes. A proof of the existence of infinite Ramanujan complex families is outside this mission's stated target.

Where the proof is difficult

A light global codeword need not have a local view that is itself close to a tensor-code word. Some square-graph vertices have unusually large local weight, and puncturing is needed to isolate coordinates shared with those exceptional vertices. The paper's small-set expansion estimates and robustness conditions control those interactions. Simply averaging local weights does not yield the weight-reducing local codeword asserted by Theorem 1 Leverrier and Zémor, §4.2.

Formalization scope

Words are functions into ZMod 2, codes are submodules, and a matrix is a function on A×BA\times BA×B with AAA indexing rows. The square coordinates are four-element vertex sets. The construction uses V=G×Fin⁡(2)V=G\times\operatorname{Fin}(2)V=G×Fin(2) and counts nnn as the number of squares rather than building n=Δ2∣G∣/2n=\Delta^2|G|/2n=Δ2∣G∣/2 into a definition. The minimum distance of the zero code is infinity, and robustness uses inequalities involving that extended value. Powers of Δ\DeltaΔ and the parameters w,pw,pw,p are real; the paper uses integer w,pw,pw,p in Definition 8, while the theorem's power thresholds need real comparisons. The formal robustness predicate includes Definition 5's range 0≤w≤Δ20\le w\le\Delta^20≤w≤Δ2; the theorem thresholds satisfy it for sufficiently large Δ\DeltaΔ. These conventions are recorded with each item.

The goal quantifies over every finite complex satisfying inverse closure, total no-conjugacy, and Ramanujan conditions, with a uniform threshold Δ0\Delta_0Δ0​ chosen before the group and codes. This pins the complexes in the cited infinite family without formalizing their existence. In particular, the goal contains no auxiliary set or local reduction assumption that could make the distance bound immediate. The published CodingTheory and TillichZemor.HGP.Setting definitions supply dual codes and CSS dimension; the paper-specific setting supplies the square complex, local views, Tanner codes, and robustness. Contributions that keep these conventions and close any milestone theorem are welcome.

Selected references

  • A. Leverrier and G. Zémor, Quantum Tanner codes, arXiv preprint, 2022. arXiv:2202.13641v3.
  • I. Dinur, S. Evra, R. Livne, A. Lubotzky, and S. Mozes, Locally Testable Codes with constant rate, distance, and locality, arXiv preprint, 2021. arXiv:2111.04808.
  • P. Panteleev and G. Kalachev, Asymptotically good quantum and locally testable classical LDPC codes, arXiv preprint, 2021. arXiv:2111.03654.
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OptimizationQuantum Information·Captain: mikedeng1

A Quantum Approximate Optimization Algorithm II: On v-Regular Graphs the Variance of the Cut Value in the QAOA State Is at Most 2((v−1)^(2p+2)−1)/(v−2) Times the Number of EdgesResearch Paper

Motivation

The Quantum Approximate Optimization Algorithm (QAOA) of Farhi, Goldstone and Gutmann (arXiv:1411.4028, 2014) is a family of shallow quantum circuits for combinatorial optimization, indexed by a depth ppp and 2p2p2p real angles. It has become one of the standard candidate algorithms for near-term quantum hardware, and its MaxCut instance is the usual benchmark on which it is analysed.

The algorithm is run by preparing a quantum state, measuring it in the computational basis to obtain a bit string zzz, and evaluating the classical objective C(z)C(z)C(z). Its analysis in the paper bounds the mean FpF_pFp​ of C(z)C(z)C(z). A mean alone does not say what a single run returns. Section III of the paper supplies the missing piece for MaxCut on bounded-degree graphs: the variance of C(z)C(z)C(z) grows at most linearly in the number of edges, so the measured cut values are concentrated around FpF_pFp​ and a modest number of repetitions reproduces the mean. This mission formalizes that variance bound, equation (30) of the paper, together with the locality and counting facts it rests on.

Setting

Let GGG be a simple graph on vertices {0,…,n−1}\{0,\dots,n-1\}{0,…,n−1} with edge set {⟨jk⟩}\{\langle jk\rangle\}{⟨jk⟩} of size mmm, each edge listed once. Vertices are qubits; a computational basis state is a bit string z∈{0,1}nz\in\{0,1\}^nz∈{0,1}n.

  • The clause operator of an edge is C⟨jk⟩=12(−σjzσkz+1)C_{\langle jk\rangle}=\tfrac12(-\sigma^z_j\sigma^z_k+1)C⟨jk⟩​=21​(−σjz​σkz​+1). It is diagonal: C⟨jk⟩(z)=1C_{\langle jk\rangle}(z)=1C⟨jk⟩​(z)=1 if zj≠zkz_j\ne z_kzj​=zk​ and 000 otherwise.
  • The MaxCut objective is C=∑⟨jk⟩C⟨jk⟩C=\sum_{\langle jk\rangle}C_{\langle jk\rangle}C=∑⟨jk⟩​C⟨jk⟩​; C(z)C(z)C(z) is the number of edges cut by zzz.
  • The mixer is B=∑jσjxB=\sum_j\sigma^x_jB=∑j​σjx​, and U(C,γ)=e−iγCU(C,\gamma)=e^{-i\gamma C}U(C,γ)=e−iγC, U(B,β)=e−iβBU(B,\beta)=e^{-i\beta B}U(B,β)=e−iβB.
  • The initial state is ∣s⟩=2−n/2∑z∣z⟩|s\rangle=2^{-n/2}\sum_z|z\rangle∣s⟩=2−n/2∑z​∣z⟩.
  • For p≥1p\ge1p≥1 and angles γ=(γ1,…,γp)\boldsymbol\gamma=(\gamma_1,\dots,\gamma_p)γ=(γ1​,…,γp​), β=(β1,…,βp)\boldsymbol\beta=(\beta_1,\dots,\beta_p)β=(β1​,…,βp​), the QAOA state is
∣γ,β⟩=U(B,βp) U(C,γp)⋯U(B,β1) U(C,γ1) ∣s⟩.|\boldsymbol\gamma,\boldsymbol\beta\rangle=U(B,\beta_p)\,U(C,\gamma_p)\cdots U(B,\beta_1)\,U(C,\gamma_1)\,|s\rangle .∣γ,β⟩=U(B,βp​)U(C,γp​)⋯U(B,β1​)U(C,γ1​)∣s⟩.
  • Measuring it gives zzz with probability P(z)=∣⟨z∣γ,β⟩∣2P(z)=|\langle z|\boldsymbol\gamma,\boldsymbol\beta\rangle|^2P(z)=∣⟨z∣γ,β⟩∣2. The mean is Fp(γ,β)=⟨γ,β∣C∣γ,β⟩=∑zP(z)C(z)F_p(\boldsymbol\gamma,\boldsymbol\beta)=\langle\boldsymbol\gamma,\boldsymbol\beta|C|\boldsymbol\gamma,\boldsymbol\beta\rangle=\sum_zP(z)C(z)Fp​(γ,β)=⟨γ,β∣C∣γ,β⟩=∑z​P(z)C(z) and the variance is
⟨γ,β∣C2∣γ,β⟩−⟨γ,β∣C∣γ,β⟩2.\langle\boldsymbol\gamma,\boldsymbol\beta|C^2|\boldsymbol\gamma,\boldsymbol\beta\rangle-\langle\boldsymbol\gamma,\boldsymbol\beta|C|\boldsymbol\gamma,\boldsymbol\beta\rangle^2 .⟨γ,β∣C2∣γ,β⟩−⟨γ,β∣C∣γ,β⟩2.
  • The light cone (the paper's subgraph g(j,k)g(j,k)g(j,k)) of an edge is the set of qubits at graph distance at most ppp from jjj or from kkk.

In Lean these are edgeCut, cutValue, qaoaState, expectCut and edgeTerm in the shared FarhiQAOA.Cubic setting, and edgePairs, cutVariance, edgePairTerm, edgeCov and lightCone in FarhiQAOA.Concentration, all built on the published QAlgorithms definitions of qubit registers, measurement probabilities, the uniform superposition, the problem Hamiltonian and the QAOA unitary.

Formalization targets

Goal: the variance bound (30)

For a vvv-regular graph with v≥3v\ge3v≥3, every depth p≥1p\ge1p≥1 and all real angles,

⟨γ,β∣C2∣γ,β⟩−⟨γ,β∣C∣γ,β⟩2≤2[(v−1)2p+2−1(v−1)−1]⋅m.\langle\boldsymbol\gamma,\boldsymbol\beta|C^2|\boldsymbol\gamma,\boldsymbol\beta\rangle-\langle\boldsymbol\gamma,\boldsymbol\beta|C|\boldsymbol\gamma,\boldsymbol\beta\rangle^2\le2\left[\frac{(v-1)^{2p+2}-1}{(v-1)-1}\right]\cdot m .⟨γ,β∣C2∣γ,β⟩−⟨γ,β∣C∣γ,β⟩2≤2[(v−1)−1(v−1)2p+2−1​]⋅m.

The constant is the paper's, as printed.

Milestones

  1. (27)–(28). The variance is the double sum, over ordered pairs of edges, of the covariances ⟨C⟨jk⟩C⟨j′k′⟩⟩−⟨C⟨jk⟩⟩⟨C⟨j′k′⟩⟩\langle C_{\langle jk\rangle}C_{\langle j'k'\rangle}\rangle-\langle C_{\langle jk\rangle}\rangle\langle C_{\langle j'k'\rangle}\rangle⟨C⟨jk⟩​C⟨j′k′⟩​⟩−⟨C⟨jk⟩​⟩⟨C⟨j′k′⟩​⟩.
  2. Disjoint light cones. If g(j,k)g(j,k)g(j,k) and g(j′,k′)g(j',k')g(j′,k′) share no qubit, the covariance of the two edges is 000.
  3. (26). For maximum degree v≥3v\ge3v≥3, ∣g(j,k)∣≤2[((v−1)p+1−1)/((v−1)−1)]|g(j,k)|\le2\big[((v-1)^{p+1}-1)/((v-1)-1)\big]∣g(j,k)∣≤2[((v−1)p+1−1)/((v−1)−1)].
  4. (29). For maximum degree v≥3v\ge3v≥3, at most 2[((v−1)2p+2−1)/((v−1)−1)]2\big[((v-1)^{2p+2}-1)/((v-1)-1)\big]2[((v−1)2p+2−1)/((v−1)−1)] edges ⟨j′k′⟩\langle j'k'\rangle⟨j′k′⟩ have g(j′,k′)g(j',k')g(j′,k′) meeting g(j,k)g(j,k)g(j,k).
  5. Summand bound. Each covariance is at most 111 in absolute value.

Companions, printed in the paper for v=2v=2v=2: ∣g(j,k)∣≤2p+2|g(j,k)|\le2p+2∣g(j,k)∣≤2p+2 and at most 4p+44p+44p+4 meeting edges.

Significance

The result. For fixed vvv and ppp the bound makes the standard deviation of the cut value O(m)O(\sqrt m)O(m​), while the mean is of order mmm. The algorithm's output is therefore concentrated: a single measurement already returns a cut close to FpF_pFp​ in relative terms, and the paper uses this to argue that the sample mean of polynomially many runs estimates FpF_pFp​ within 111. The same locality structure, that each edge term depends only on a bounded neighbourhood, is what makes FpF_pFp​ classically computable for fixed ppp and underlies later analyses of QAOA on bounded-degree and random regular graphs.

Formalizing it. The bound is proved in the paper in a few lines; none of it is machine-checked. A formal proof has to make the paper's operator locality argument precise (the conjugated clause operator is supported on the light cone, and the initial state is a product state), connect it to the computational-basis probabilities, and carry out the graph counting with explicit constants. The resulting locality lemma for QAOA circuits on graphs is reusable for any statement about QAOA at fixed depth.

Difficulty

The algebra of (28) and the bound by 111 are routine. The substance is in two places.

The vanishing of a covariance requires showing that U†C⟨jk⟩UU^\dagger C_{\langle jk\rangle}UU†C⟨jk⟩​U, for the full depth-ppp QAOA unitary UUU, acts only on the qubits of g(j,k)g(j,k)g(j,k), and that for two operators acting on disjoint sets of qubits the expectation in the product state ∣s⟩|s\rangle∣s⟩ factorizes. Both are statements about operators on (C2)⊗n(\mathbb C^2)^{\otimes n}(C2)⊗n, whereas the objects in the statement are probabilities of computational basis states; the formal proof must pass between the two. Matrix exponentials of commuting sums have to be split into commuting factors, which in Lean means working with NormedSpace.exp on matrices indexed by bit strings.

The counting (26) and (29) is a ball-growth bound in a graph of bounded degree. The paper's count, via (26) with ppp replaced by 2p+12p+12p+1, bounds vertices, while (29) counts edges; the passage between the two, and the extended graph distance on disconnected graphs, must be made exact with the printed constant.

Formalization scope

  • Qubits are indexed by Fin n, bit strings are Fin n → Bool with true the bit 111, and states live in EuclideanSpace ℂ (Fin n → Bool).
  • All expectations are sums ∑zP(z) f(z)\sum_zP(z)\,f(z)∑z​P(z)f(z) over the computational basis. This equals the operator expectation in the paper because every operator involved is diagonal.
  • Each edge is counted once (pairs j<kj<kj<k), in mmm, in CCC, in the double sum of (28) and in the count (29).
  • Graph distance is the extended distance SimpleGraph.edist (+∞+\infty+∞ between components).
  • The goal is stated for vvv-regular graphs with v≥3v\ge3v≥3, the domain of the printed formula. The milestones (26) and (29) assume only maximum degree ≤v\le v≤v, as printed there.
  • The angles range over all of Rp\mathbb R^pRp. The paper restricts them to [0,2π][0,2\pi][0,2π] and [0,π][0,\pi][0,π] by periodicity; the bound does not depend on that restriction.
  • The QAOA state is the state itself, not an arbitrary vector: normalisation of the measurement probabilities follows from unitarity and is part of what must be proved.

A variance defined with two different states, or a probability vector that is not normalised, would make the goal vacuous or false; the definitions use the single state ∣γ,β⟩|\boldsymbol\gamma,\boldsymbol\beta\rangle∣γ,β⟩ throughout.

Out of scope: the O(m)O(\sqrt m)O(m​) standard-deviation reading and the sampling claim after (30), which carry no explicit constant; the measurement-and-repetition procedure, running time, circuit depth and the classical search for the angles.

Welcome contributions: a general light-cone lemma for conjugation by products of exponentials of local Hamiltonians on qubits; factorization of product-state expectations over disjoint qubit sets; ball-size bounds in graphs of bounded degree.

Selected references

  • E. Farhi, J. Goldstone, S. Gutmann, A Quantum Approximate Optimization Algorithm, arXiv preprint, 2014. https://arxiv.org/abs/1411.4028 (v1; Section III, pp. 6–7)
  • E. Farhi, J. Goldstone, S. Gutmann, M. Sipser, Quantum Computation by Adiabatic Evolution, arXiv preprint, 2000. https://arxiv.org/abs/quant-ph/0001106
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CombinatoricsProbabilityStatistics·Captain: mikedeng1

Partial Recovery in the Graph Alignment Problem II: Under Explicit Sparsity Conditions with nqs Above a Constant, Some Algorithm Recovers a Fraction α of the AlignmentResearch Paper

Motivation

Graph alignment asks for the correspondence between the vertex sets of two graphs that are noisy copies of each other. It underlies the de-anonymisation of social networks, where an anonymised graph is matched to a public one (Narayanan, Shmatikov 2009), the comparison of protein–protein interaction networks across species, and matching problems in computer vision. The standard random model for studying when alignment is possible is the correlated Erdős–Rényi model (Pedarsani, Grossglauser 2011).

In that model, exact recovery of the hidden correspondence and almost exact recovery (all but a vanishing fraction of vertices) have sharp thresholds: exact recovery by Cullina, Kiyavash 2016 and 2017; almost exact recovery when nqs=ω(1)nqs = \omega(1)nqs=ω(1) by Cullina, Kiyavash, Mittal and Poor (arXiv:1809.03553). Hall and Massoulié (arXiv:2007.00533v5, Operations Research 2023) study partial recovery, the recovery of a constant fraction α\alphaα of the correspondence, in the sparse regime nqs=Θ(1)nqs = \Theta(1)nqs=Θ(1), where almost exact recovery is impossible. This mission formalizes their possibility result, Theorem 2.3. Its companion mission (Partial Recovery in the Graph Alignment Problem I) formalizes the impossibility result, Theorem 2.1.

Setting

Fix n≥1n \ge 1n≥1 and parameters q,sq, sq,s with 0≤q<s≤10 \le q < s \le 10≤q<s≤1. A parent graph on {1,…,n}\{1, \dots, n\}{1,…,n} includes each edge independently with probability q/sq/sq/s. Two copies are made, and each copy keeps every edge independently with probability sss. The resulting graphs GAG_AGA​ and GB′G_{B'}GB′​ have adjacency matrices AAA and B′B'B′. For each unordered pair {i,j}\{i, j\}{i,j} of distinct vertices, (Aij,Bij′)(A_{ij}, B'_{ij})(Aij​,Bij′​) has the law PPP of display (3):

p00=1−2q+qs,p01=p10=q(1−s),p11=qs,p_{00} = 1 - 2q + qs, \qquad p_{01} = p_{10} = q(1-s), \qquad p_{11} = qs,p00​=1−2q+qs,p01​=p10​=q(1−s),p11​=qs,

independently over the (n2)\binom n2(2n​) pairs. The covariance of AijA_{ij}Aij​ and Bij′B'_{ij}Bij′​ is σ2=q(s−q)\sigma^2 = q(s-q)σ2=q(s−q) (display (2)), which is positive when q>0q > 0q>0.

A planted permutation π∗\pi^*π∗ relabels GB′G_{B'}GB′​ into the observed graph GBG_BGB​: Bπ∗(i)π∗(j)=Bij′B_{\pi^*(i)\pi^*(j)} = B'_{ij}Bπ∗(i)π∗(j)​=Bij′​. An algorithm sees (A,B)(A, B)(A,B) and returns a permutation π^\hat\piπ^. Its quality is the overlap

β(π^,π∗)=1n∑i=1n1{π^(i)=π∗(i)}.\beta(\hat\pi, \pi^*) = \frac1n \sum_{i=1}^n \mathbf 1\{\hat\pi(i) = \pi^*(i)\}.β(π^,π∗)=n1​i=1∑n​1{π^(i)=π∗(i)}.

For 0<α<10 < \alpha < 10<α<1, a permutation π\piπ is good (Definition 2) if at least n(1+α)/2n(1+\alpha)/2n(1+α)/2 vertices iii have degree at least nqs/2nqs/2nqs/2 in the intersection of GAG_AGA​ with GBG_BGB​ relabelled by π\piπ:

∑i=1n1{∑j≠iAijBπ(i)π(j)≥nqs2}≥n⋅1+α2.\sum_{i=1}^n \mathbf 1\Big\{\sum_{j \ne i} A_{ij} B_{\pi(i)\pi(j)} \ge \frac{nqs}{2}\Big\} \ge n\cdot\frac{1+\alpha}{2}.i=1∑n​1{j=i∑​Aij​Bπ(i)π(j)​≥2nqs​}≥n⋅21+α​.

In Lean, these objects are pLaw, Config, cfgProb, prob, A, B', B, overlap, good and Cond6 in GraphAlignment.Possible.

Formalization targets

Goal: Theorem 2.3 (pp. 8–9)

Let 0<α<10 < \alpha < 10<α<1 and let q=qnq = q_nq=qn​, s=sns = s_ns=sn​ be sequences. Suppose there exist β,γ>0\beta, \gamma > 0β,γ>0 such that, for all large nnn,

nqs≥max⁡{20, 84log⁡21−α, 16min⁡(γ,β)(1−α)},s>8q1−α,2q(1−s2)s≤n−β,qs≤n−2γ.(6)nqs \ge \max\Big\{20,\ 84\log\frac{2}{1-\alpha},\ \frac{16}{\min(\gamma,\beta)(1-\alpha)}\Big\}, \quad s > \frac{8q}{1-\alpha}, \quad \frac{2q(1-s^2)}{s} \le n^{-\beta}, \quad qs \le n^{-2\gamma}. \tag{6}nqs≥max{20, 84log1−α2​, min(γ,β)(1−α)16​},s>1−α8q​,s2q(1−s2)​≤n−β,qs≤n−2γ.(6)

Then some algorithm returns π^\hat\piπ^ with

P[β(π^,π∗)≥α]→1(n→∞).\mathbb P\big[\beta(\hat\pi, \pi^*) \ge \alpha\big] \to 1 \qquad (n \to \infty).P[β(π^,π∗)≥α]→1(n→∞).

Milestones

  1. Lemma B.3: Mn=αnM−βIα−β+βnM−αIβ−αM^n = \alpha^n \frac{M - \beta I}{\alpha - \beta} + \beta^n \frac{M - \alpha I}{\beta - \alpha}Mn=αnα−βM−βI​+βnβ−αM−αI​ for a 2×22 \times 22×2 matrix with distinct eigenvalues α,β\alpha, \betaα,β.
  2. Lemma B.2: for n+1n+1n+1 independent pairs with law PPP and W=A0B1+⋯+AnB0W = A_0B_1 + \dots + A_nB_0W=A0​B1​+⋯+An​B0​,
E[etW]=(T+T2−4D2)n+1+(T−T2−4D2)n+1,T=p11(et−1)+1, D=σ2(et−1).\mathbb E[e^{tW}] = \Big(\tfrac{T + \sqrt{T^2 - 4D}}{2}\Big)^{n+1} + \Big(\tfrac{T - \sqrt{T^2 - 4D}}{2}\Big)^{n+1}, \quad T = p_{11}(e^t - 1) + 1,\ D = \sigma^2(e^t - 1).E[etW]=(2T+T2−4D​​)n+1+(2T−T2−4D​​)n+1,T=p11​(et−1)+1, D=σ2(et−1).
  1. Lemma B.1: (αk+βk)n/k≤(α2+β2)n/2(\alpha^k + \beta^k)^{n/k} \le (\alpha^2 + \beta^2)^{n/2}(αk+βk)n/k≤(α2+β2)n/2 for 2≤k≤n2 \le k \le n2≤k≤n and α,β>0\alpha, \beta > 0α,β>0.
  2. Lemma 4.3: min⁡z>0z−τeq2(z2−1)+q1(z−1)≤ζτ\min_{z > 0} z^{-\tau} e^{q_2(z^2-1) + q_1(z-1)} \le \zeta^\tauminz>0​z−τeq2​(z2−1)+q1​(z−1)≤ζτ, with ζ=max⁡{2eq1/τ, 4e(q2/τ)1/2}\zeta = \max\{\sqrt2 e q_1/\tau,\ 4e(q_2/\tau)^{1/2}\}ζ=max{2​eq1​/τ, 4e(q2​/τ)1/2}, and the minimizer satisfies z∗2=(τ−q1z∗)/(2q2)≤τ/(2q2)z^{*2} = (\tau - q_1 z^*)/(2q_2) \le \tau/(2q_2)z∗2=(τ−q1​z∗)/(2q2​)≤τ/(2q2​).
  3. Display (10): for a permutation ppp with a fraction ϵ\epsilonϵ of fixed points, ∣S21∣≤n(1−ϵ)|S^1_2| \le n(1-\epsilon)∣S21​∣≤n(1−ϵ) and ∣Sˉ22∣≤(n2)−(nϵ2)≤n22(1−ϵ2)|\bar S^2_2| \le \binom n2 - \binom{n\epsilon}2 \le \frac{n^2}2(1-\epsilon^2)∣Sˉ22​∣≤(2n​)−(2nϵ​)≤2n2​(1−ϵ2).
  4. Lemma A.2, in particular: P(Po(2λ/3)≥λ/2−1)≥0.7\mathbb P(\mathrm{Po}(2\lambda/3) \ge \lambda/2 - 1) \ge 0.7P(Po(2λ/3)≥λ/2−1)≥0.7 for λ≥20\lambda \ge 20λ≥20.
  5. Proposition 4.2: under (6), π∗\pi^*π∗ is good with probability tending to 1.
  6. Proposition 4.4: under (6), P(∀π, [π good⇒β(π,π∗)>α])→1\mathbb P(\forall \pi,\ [\pi \text{ good} \Rightarrow \beta(\pi, \pi^*) > \alpha]) \to 1P(∀π, [π good⇒β(π,π∗)>α])→1.

Significance

The result. Theorem 2.3 shows that partial alignment is information-theoretically achievable in the sparse regime nqs=Θ(1)nqs = \Theta(1)nqs=Θ(1), provided nqsnqsnqs is above an explicit constant depending on α\alphaα. Together with Theorem 2.1, it locates partial recovery between a necessary condition stated through the divergence D(P∥Q)D(P\|Q)D(P∥Q) and a sufficient condition stated through nqsnqsnqs. It complements the almost-exact-recovery results for nqs=ω(1)nqs = \omega(1)nqs=ω(1), which do not apply when nqsnqsnqs stays bounded. The moment generating function computation of Lemma B.2 and the inequality of Lemma B.1 were reused for the sharp exact-recovery threshold by Wu, Xu, Yu (p. 9 of the source).

Formalizing it. The theorem is proved on paper; no part of it is machine-checked. The paper's route to Proposition 4.2 applies a kkk-core theorem for G(n,λ/n)G(n, \lambda/n)G(n,λ/n) with λ\lambdaλ fixed and kkk an integer, at λ=nqs\lambda = nqsλ=nqs and k=nqs/2k = nqs/2k=nqs/2, which vary with nnn and need not be integers. The statement of Proposition 4.2 remains true, but a formal proof needs a different argument. Proposition 4.4's union bound contains two repairable gaps: an infimum taken over z>0z > 0z>0 instead of z>1z > 1z>1, and the bound n!≤nlog⁡nn! \le n^{\log n}n!≤nlogn. A formal development would settle the theorem under exactly the conditions (6).

Difficulty

The natural first step is to compute P(π good)\mathbb P(\pi \text{ good})P(π good) for a fixed wrong permutation π\piπ and take a union bound over all n!n!n! permutations. The difficulty is that for π≠π∗\pi \ne \pi^*π=π∗ the terms AijBp(i)p(j)′A_{ij} B'_{p(i)p(j)}Aij​Bp(i)p(j)′​ are not independent: an edge variable enters several terms along the cycles that ppp induces on vertex pairs. A Chernoff bound therefore needs the moment generating function of a sum over each cycle of pairs. That is a transfer-matrix computation, and the per-cycle bounds must then be recombined uniformly in the cycle lengths. The resulting exponent must beat log⁡n!≈nlog⁡n\log n! \approx n \log nlogn!≈nlogn. This is where the sparsity conditions 2q(1−s2)/s≤n−β2q(1-s^2)/s \le n^{-\beta}2q(1−s2)/s≤n−β and qs≤n−2γqs \le n^{-2\gamma}qs≤n−2γ, and the constant 16/(min⁡(γ,β)(1−α))16/(\min(\gamma,\beta)(1-\alpha))16/(min(γ,β)(1−α)), enter. Proposition 4.2 is a concentration statement for the number of vertices of degree at least nqs/2nqs/2nqs/2 in the intersection graph, an Erdős–Rényi graph G(n,qs)G(n, qs)G(n,qs), when nqsnqsnqs is only a large constant.

Formalization scope

Everything is finite. Vertices are Fin n and permutations are Equiv.Perm (Fin n). A configuration assigns (Aij,Bij′)∈(A_{ij}, B'_{ij}) \in(Aij​,Bij′​)∈ Bool × Bool to each non-diagonal element of Sym2 (Fin n), and probabilities are finite sums of the product weights. They are not taken against a free measure, so the model is a genuine probability space whenever 0≤q<s≤10 \le q < s \le 10≤q<s≤1. The standing assumptions 0≤qn<sn≤10 \le q_n < s_n \le 10≤qn​<sn​≤1 for every nnn (p. 5 and the running assumption s>qs > qs>q on p. 7) are hypotheses of the asymptotic statements. The paper's remark "we assume s<1s < 1s<1" (p. 6) is used by neither result and is not imposed. Logarithms are natural, "→1\to 1→1" is a limit along n→∞n \to \inftyn→∞, and the thresholds nqs/2nqs/2nqs/2 and n(1+α)/2n(1+\alpha)/2n(1+α)/2 are real numbers, not floored.

In the goal, an algorithm is a family of maps from the observed pair (A,B)(A, B)(A,B) to a permutation. It is chosen before the planted permutations and may depend on nnn, qnq_nqn​, sns_nsn​, α\alphaα, but never on π∗\pi^*π∗. This rules out the trivial algorithm "output π∗\pi^*π∗". The theorem does not say how π∗\pi^*π∗ is drawn, so the limit is required for every sequence of planted permutations, which covers the uniform case. "Exponential time" is not formalized.

Three statements are posed in a corrected reading, each disclosed in its Formalization Note. In Lemma 4.3, τ,q2>0\tau, q_2 > 0τ,q2​>0 and z>0z > 0z>0: the paper allows τ=q2=0\tau = q_2 = 0τ=q2​=0 and z=0z = 0z=0, where ζ\zetaζ and (13) are undefined, and Lean's convention 0−τ=00^{-\tau} = 00−τ=0 would make the bound trivial. Display (10) is stated for an arbitrary permutation ppp. Only the "in particular" clause of Lemma A.2 is stated, because the paper's proof of the general normal-approximation bound does not hold as written. Lemma 4.1 (a kkk-core theorem of Pittel, Spencer and Wormald) is not posed.

A complete development needs: Chernoff bounds for finite sums of dependent Bernoulli variables via the transfer-matrix formula, binomial and Poisson tail estimates, and the cycle decomposition of the action of a permutation on unordered pairs. Lemmas B.1, B.3 and 4.3 are general-purpose and reusable. Proofs of any milestone, and alternative proofs of Proposition 4.2 that avoid kkk-cores, are welcome.

The source is the arXiv preprint v5, whose appendices contain the proofs that the main text defers to an electronic companion.

Selected references

  • G. Hall, L. Massoulié, Partial Recovery in the Graph Alignment Problem, Operations Research, 2023; preprint arXiv:2007.00533v5, 2022. https://arxiv.org/abs/2007.00533
  • D. Cullina, N. Kiyavash, P. Mittal, H. V. Poor, Partial Recovery of Erdős–Rényi Graph Alignment via k-Core Alignment, arXiv preprint, 2018. https://arxiv.org/abs/1809.03553
  • D. Cullina, N. Kiyavash, Exact Alignment Recovery for Correlated Erdős–Rényi Graphs, arXiv preprint, 2017. https://arxiv.org/abs/1711.06783
  • D. Cullina, N. Kiyavash, Improved Achievability and Converse Bounds for Erdős–Rényi Graph Matching, SIGMETRICS, 2016. https://arxiv.org/abs/1602.01042
  • B. Pittel, J. Spencer, N. Wormald, Sudden Emergence of a Giant k-Core in a Random Graph, J. Combin. Theory Ser. B, 1996. https://doi.org/10.1006/jctb.1996.0036
  • Y. Wu, J. Xu, S. H. Yu, Settling the Sharp Reconstruction Thresholds of Random Graph Matching, arXiv preprint, 2021. https://arxiv.org/abs/2102.00082
  • A. Narayanan, V. Shmatikov, De-anonymizing Social Networks, IEEE Symposium on Security and Privacy, 2009. https://doi.org/10.1109/SP.2009.22
  • P. Pedarsani, M. Grossglauser, On the Privacy of Anonymized Networks, KDD, 2011. https://doi.org/10.1145/2020408.2020596
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OptimizationQuantum Information·Captain: mikedeng1

A Quantum Approximate Optimization Algorithm I: At p = 1 the QAOA Expected Cut Is at Least 0.6924 Times the Maximum Cut on Every Connected 3-Regular GraphResearch Paper

Motivation

The quantum approximate optimization algorithm (QAOA) of Farhi, Goldstone and Gutmann (arXiv:1411.4028, 2014) is a family of shallow quantum circuits, indexed by an integer depth p≥1p \ge 1p≥1, that prepare states whose computational-basis measurements are biased toward good solutions of a combinatorial optimization problem. It became the standard candidate for optimization on near-term quantum hardware, and its performance on MaxCut is the benchmark against which later analyses and experiments are measured.

The original paper proves one concrete performance guarantee: at depth p=1p = 1p=1, on 3-regular graphs, the algorithm achieves an approximation ratio of at least 0.69240.69240.6924. Short timeline:

  • 2014, Farhi–Goldstone–Gutmann: the p=1p = 1p=1 bound 0.69240.69240.6924 on 3-regular graphs, by reducing the expected cut to three subgraph functions and a numerical minimization (arXiv:1411.4028).
  • 2018, Wang, Hadfield, Jiang and Rieffel: closed-form expressions for the p=1p = 1p=1 MaxCut expectation on arbitrary graphs (arXiv:1706.02998, Phys. Rev. A 97, 022304).
  • Classically, Goemans and Williamson's semidefinite rounding achieves 0.8780.8780.878 on all graphs (J. ACM 42, 1995), so the p=1p = 1p=1 guarantee is, as the paper itself notes, below the best classical algorithms.

Setting

A graph GGG has vertex set {0,…,n−1}\{0, \dots, n-1\}{0,…,n−1}, and 3-regular means every vertex has exactly three neighbours. Each vertex carries a qubit; a computational basis state is a bit string z=z0⋯zn−1z = z_0 \cdots z_{n-1}z=z0​⋯zn−1​. For an edge ⟨jk⟩\langle jk\rangle⟨jk⟩ the clause C⟨jk⟩(z)C_{\langle jk\rangle}(z)C⟨jk⟩​(z) is 111 if zj≠zkz_j \neq z_kzj​=zk​ and 000 otherwise, and the cut objective is

C(z)=∑⟨jk⟩C⟨jk⟩(z),C(z) = \sum_{\langle jk\rangle} C_{\langle jk\rangle}(z),C(z)=⟨jk⟩∑​C⟨jk⟩​(z),

the number of edges cut by the bipartition zzz defines. As an operator, CCC is diagonal in the computational basis.

Let B=∑jσjxB = \sum_j \sigma^x_jB=∑j​σjx​ and let ∣s⟩=2−n/2∑z∣z⟩|s\rangle = 2^{-n/2}\sum_z |z\rangle∣s⟩=2−n/2∑z​∣z⟩. For angles γ,β∈R\gamma, \beta \in \mathbb Rγ,β∈R, the depth-one QAOA state and its expected cut are

∣γ,β⟩=e−iβB e−iγC ∣s⟩,F1(γ,β)=⟨γ,β∣ C ∣γ,β⟩=∑z∣⟨z∣γ,β⟩∣2 C(z).|\gamma,\beta\rangle = e^{-i\beta B}\, e^{-i\gamma C}\, |s\rangle, \qquad F_1(\gamma,\beta) = \langle\gamma,\beta|\,C\,|\gamma,\beta\rangle = \sum_z |\langle z|\gamma,\beta\rangle|^2\, C(z).∣γ,β⟩=e−iβBe−iγC∣s⟩,F1​(γ,β)=⟨γ,β∣C∣γ,β⟩=z∑​∣⟨z∣γ,β⟩∣2C(z).

The angles may be restricted to γ∈[0,2π]\gamma \in [0, 2\pi]γ∈[0,2π], β∈[0,π]\beta \in [0, \pi]β∈[0,π] by periodicity.

Two kinds of small subgraphs govern the analysis. A crossed square is a set of four vertices spanning exactly five edges (a 4-cycle with one diagonal). An isolated triangle is a triangle whose three outgoing edges end on three distinct vertices. SSS and TTT denote their numbers in GGG. Finally, for an edge ⟨jk⟩\langle jk\rangle⟨jk⟩ the subgraph type is the shape of the edge together with its adjacent edges; in a 3-regular graph there are three types g4,g5,g6g_4, g_5, g_6g4​,g5​,g6​, according as jjj and kkk have two, one or no common neighbours. Each type ggg defines a function fg(γ,β)f_g(\gamma, \beta)fg​(γ,β), the expectation of the clause of the central edge in the depth-one QAOA state of the subgraph ggg alone.

Formalization targets

Goal: the depth-one ratio on cubic graphs

For every connected 3-regular graph GGG there are angles γ∈[0,2π]\gamma \in [0, 2\pi]γ∈[0,2π], β∈[0,π]\beta \in [0, \pi]β∈[0,π] with

F1(γ,β)  ≥  0.6924⋅C(z)for every bit string z,F_1(\gamma, \beta) \;\ge\; 0.6924 \cdot C(z) \qquad \text{for every bit string } z,F1​(γ,β)≥0.6924⋅C(z)for every bit string z,

that is, M1=max⁡γ,βF1≥0.6924⋅max⁡zC(z)M_1 = \max_{\gamma,\beta} F_1 \ge 0.6924 \cdot \max_z C(z)M1​=maxγ,β​F1​≥0.6924⋅maxz​C(z). The constant is exactly the paper's 0.69240.69240.6924, and K4K_4K4​ is included.

Milestones

The paper's argument passes through the following claims, each a milestone:

  1. At p=1p = 1p=1 each edge term of a 3-regular graph equals fg4f_{g_4}fg4​​, fg5f_{g_5}fg5​​ or fg6f_{g_6}fg6​​ according to its subgraph type (p. 5).
  2. In a connected 3-regular graph other than K4K_4K4​, there are SSS edges of type g4g_4g4​ and 4S+3T4S + 3T4S+3T of type g5g_5g5​ (pp. 8–9).
  3. Equation (32): F1=Sfg4+(4S+3T)fg5+(3n/2−5S−3T)fg6F_1 = S f_{g_4} + (4S + 3T) f_{g_5} + (3n/2 - 5S - 3T) f_{g_6}F1​=Sfg4​​+(4S+3T)fg5​​+(3n/2−5S−3T)fg6​​ (p. 9).
  4. 3T+4S≤n3T + 4S \le n3T+4S≤n (p. 8).
  5. Every cut has at most 3n/2−S−T3n/2 - S - T3n/2−S−T edges (p. 9).
  6. For s,t≥0s, t \ge 0s,t≥0 with 4s+3t≤14s + 3t \le 14s+3t≤1, the scaled ratio (35) M1(1,s,t)/(3/2−s−t)M_1(1, s, t)/(3/2 - s - t)M1​(1,s,t)/(3/2−s−t) is at least 0.69240.69240.6924 (p. 9).
  7. On K4K_4K4​ the depth-one ratio exceeds 0.69240.69240.6924 (p. 8).

Significance

The result is the first rigorous worst-case guarantee for the QAOA, and the method behind it (the expected objective is a sum of local terms, each depending only on a bounded neighbourhood of an edge) is the template for every later analysis of fixed-depth QAOA on bounded-degree graphs. It shows that a depth-one circuit, of depth independent of nnn, achieves a constant approximation ratio uniformly over an infinite class of graphs.

The paper's proof is complete apart from one numerical step: the minimization of (35) is reported as a numerical evaluation. No machine-checked version of the result exists. A formal proof requires an exact evaluation of the three subgraph functions at suitable angles, an exact combinatorial count of edge types, and a certified inequality in place of the numerical minimization. The margin is small: the exact minimum is 12+133≈0.692450\tfrac12 + \tfrac{1}{3\sqrt 3} \approx 0.69245021​+33​1​≈0.692450, about 5⋅10−55 \cdot 10^{-5}5⋅10−5 above the paper's constant.

Difficulty

The light-cone reduction (milestone 1) is the conceptual step: one must show that conjugating a two-qubit clause by e−iγCe^{-i\gamma C}e−iγC and e−iβBe^{-i\beta B}e−iβB leaves an operator that involves only the qubits adjacent to the edge, and that the remaining qubits contribute a factor of one. In matrix form on 2n2^n2n-dimensional space this is a statement about tensor-product structure that the definitions do not make visible. The combinatorial count (milestones 2–4) needs a careful case analysis of how triangles sit in a cubic graph, including the exceptional graph K4K_4K4​, where the count fails. The final inequality (milestone 6) has a margin of order 10−410^{-4}10−4, so crude bounds on the trigonometric functions fgf_{g}fg​ do not suffice.

Formalization scope

  • Representation. Bit strings are Qubits n = Fin n → Bool from the published QAlgorithms layer, with true the bit 111. States are vectors in EuclideanSpace ℂ (Qubits n); ∣s⟩|s\rangle∣s⟩, CCC as a diagonal operator, BBB and the QAOA unitary are the published definitions uniformState, problemHamiltonian, qaoaMixer, qaoaUnitary. The latter takes mixer angles before phase angles.
  • Conventions. Graphs are SimpleGraph (Fin n) with decidable adjacency; "3-regular" is IsRegularOfDegree 3. The cut counts each edge once. F1F_1F1​ is defined as ∑zPr⁡[z] C(z)\sum_z \Pr[z]\,C(z)∑z​Pr[z]C(z), which equals ⟨γ,β∣C∣γ,β⟩\langle\gamma,\beta|C|\gamma,\beta\rangle⟨γ,β∣C∣γ,β⟩ because CCC is diagonal. The constant 0.69240.69240.6924 is the exact rational 1731/25001731/25001731/2500.
  • Added hypotheses. Milestones 2 and 3 assume GGG connected and n≠4n \ne 4n=4, which excludes K4K_4K4​, the case the paper sets aside on p. 8. No other hypothesis is added.
  • Out of scope. The goal concerns the expected cut at good angles, which is what the paper's analysis bounds. Measuring the state, repeating, and the claim that a cut of that size is then found with high probability are not formalized; nor are running time, circuit depth, or the search for optimal angles.
  • Ruled-out trivializations. A cut objective counting each edge twice, or an unnormalized state, would leave the ratio statement formally intact but about the wrong objects; the setting counts each edge once and uses the normalized uniform superposition. The goal quantifies over all bit strings, so it cannot be satisfied by bounding a particular cut.
  • Contributions welcome. Reusable lemmas on the light-cone locality of conjugated Pauli operators, closed forms of the depth-one MaxCut expectation, and counting lemmas for triangles in cubic graphs all serve beyond this mission.

Selected references

  • E. Farhi, J. Goldstone, S. Gutmann, A Quantum Approximate Optimization Algorithm, arXiv preprint, 2014. https://arxiv.org/abs/1411.4028
  • Z. Wang, S. Hadfield, Z. Jiang, E. G. Rieffel, Quantum approximate optimization algorithm for MaxCut: A fermionic view, Phys. Rev. A 97, 022304, 2018. https://arxiv.org/abs/1706.02998
  • M. X. Goemans, D. P. Williamson, Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming, J. ACM 42(6), 1995. https://doi.org/10.1145/227683.227684
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Control TheoryDynamical SystemsLinear algebra·Captain: mikedeng1

Synchronization via Pinning Control on General Complex Networks II: Componentwise LMIs on the Strongly Connected Components Globally Synchronize a Pinned Network with a Directed Spanning TreeResearch Paper

Motivation

Networks of identical dynamical systems coupled along a directed graph model power grids, sensor and robot swarms, neural populations and coupled oscillators. A basic control question is how to steer every node of such a network onto a prescribed reference trajectory by acting directly on only a few nodes. This is pinning control: feedback is injected at a small set of pinned vertices, and the coupling has to carry the reference signal to the rest of the network.

For undirected or strongly connected networks, criteria for pinning synchronization go back to Wang and Chen (2002) and Chen, Liu and Lu (2007), and the first mission of this series treats the strongly connected case of Yu, Chen, Lü and Kurths (2013). Many networks of interest are not strongly connected: information flows down a hierarchy of groups, and the minimal requirement for the reference to reach every node is that the network, with the reference added as a virtual leader, contains a directed spanning tree rooted at the leader. Section 4 of that paper gives a criterion for this general case that splits into one condition per strongly connected component. This mission formalizes that criterion, Theorem 4.5, with the lemmas its proof uses and three corollaries.

Setting

There are NNN vertices with states xi(t)∈Rnx_i(t)\in\mathbb R^nxi​(t)∈Rn. The coupling configuration matrix G∈RN×NG\in\mathbb R^{N\times N}G∈RN×N has Gij≥0G_{ij}\ge0Gij​≥0 for i≠ji\ne ji=j, where Gij>0G_{ij}>0Gij​>0 records a connection from vertex jjj to vertex iii, and zero row sums, Gii=−∑j≠iGijG_{ii}=-\sum_{j\ne i}G_{ij}Gii​=−∑j=i​Gij​. Given a function f:Rn×R+→Rnf:\mathbb R^n\times\mathbb R_+\to\mathbb R^nf:Rn×R+​→Rn, a coupling strength ccc, an inner coupling matrix Γ∈Rn×n\Gamma\in\mathbb R^{n\times n}Γ∈Rn×n and pinning gains di≥0d_i\ge0di​≥0 (vertex iii is pinned when di>0d_i>0di​>0), the controlled network is

x˙i=f(xi,t)+c∑j=1NGijΓxj−c di Γ(xi−s),i=1,…,N,\dot x_i=f(x_i,t)+c\sum_{j=1}^N G_{ij}\Gamma x_j-c\,d_i\,\Gamma(x_i-s),\qquad i=1,\dots,N,x˙i​=f(xi​,t)+cj=1∑N​Gij​Γxj​−cdi​Γ(xi​−s),i=1,…,N,

where the reference sss solves s˙=f(s,t)\dot s=f(s,t)s˙=f(s,t). The network is globally synchronized when xi(t)−s(t)→0x_i(t)-s(t)\to0xi​(t)−s(t)→0 for every vertex and every pair of solutions.

The nonlinearity is controlled by Assumption 1: there is a matrix KKK with (x−y)T(f(x,t)−f(y,t))≤(x−y)TKΓ(x−y)(x-y)^{\mathsf T}(f(x,t)-f(y,t))\le(x-y)^{\mathsf T}K\Gamma(x-y)(x−y)T(f(x,t)−f(y,t))≤(x−y)TKΓ(x−y) for all x,yx,yx,y and t≥0t\ge0t≥0. Throughout §4 the paper also assumes that KKK and Γ\GammaΓ commute, that Γ\GammaΓ is positive definite, and puts θ=λmax⁡((K+KT)/2)≥0\theta=\lambda_{\max}((K+K^{\mathsf T})/2)\ge0θ=λmax​((K+KT)/2)≥0.

The vertices split into strongly connected components V1,…,VMV_1,\dots,V_MV1​,…,VM​. In the Frobenius normal form the components are ordered so that no edge goes from a later component to an earlier one, which makes GGG block lower triangular. For each component jjj the mission uses three matrices:

  • G~j\widetilde G_jGj​, the diagonal block of G−diag(d)G-\mathrm{diag}(d)G−diag(d) on VjV_jVj​;
  • AjA_jAj​, the within-component part of GGG with zero row sums;
  • Π~j=diag(Πj)\widetilde\Pi_j=\mathrm{diag}(\Pi_j)Πj​=diag(Πj​), where Πj\Pi_jΠj​ is a positive vector with ΠjTAj=0\Pi_j^{\mathsf T}A_j=0ΠjT​Aj​=0.

In Lean, component membership is a map blk : Fin N → Fin M, and Fin M carries the Frobenius order.

Formalization targets

Goal: Theorem 4.5

If, for every component jjj,

2θΠ~j+c(Π~jG~j+G~jTΠ~j)<0,(4.8)2\theta\widetilde\Pi_j+c\bigl(\widetilde\Pi_j\widetilde G_j+\widetilde G_j^{\mathsf T}\widetilde\Pi_j\bigr)<0, \tag{4.8}2θΠj​+c(Πj​Gj​+GjT​Πj​)<0,(4.8)

then the controlled network with a directed spanning tree is globally synchronized.

Milestones

  1. The θ bound (p. 1402). Commutation and Γ>0\Gamma>0Γ>0 turn Assumption 1 into (x−y)T(f(x,t)−f(y,t))≤θ(x−y)TΓ(x−y)(x-y)^{\mathsf T}(f(x,t)-f(y,t))\le\theta(x-y)^{\mathsf T}\Gamma(x-y)(x−y)T(f(x,t)−f(y,t))≤θ(x−y)TΓ(x−y).
  2. Lemma 2.13. The strict Schur complement test for a symmetric block matrix.
  3. Lemma 4.4. If Π~jG~j+G~jTΠ~j<0\widetilde\Pi_j\widetilde G_j+\widetilde G_j^{\mathsf T}\widetilde\Pi_j<0Πj​Gj​+GjT​Πj​<0 on every diagonal block of a block lower-triangular G‾\overline GG, then positive block weights Δj\Delta_jΔj​ exist with ΔΠ~G‾+G‾TΠ~Δ<0\Delta\widetilde\Pi\overline G+\overline G^{\mathsf T}\widetilde\Pi\Delta<0ΔΠG+GTΠΔ<0.
  4. (4.10). Along solutions, V=∑jΔje~jTΠ~je~jV=\sum_j\Delta_j\widetilde e_j^{\mathsf T}\widetilde\Pi_j\widetilde e_jV=∑j​Δj​ejT​Πj​ej​ satisfies V˙≤eT{[ΔΠ~(cG‾+θIN)+(cG‾+θIN)TΠ~Δ]⊗Γ}e\dot V\le e^{\mathsf T}\{[\Delta\widetilde\Pi(c\overline G+\theta I_N)+(c\overline G+\theta I_N)^{\mathsf T}\widetilde\Pi\Delta]\otimes\Gamma\}eV˙≤eT{[ΔΠ(cG+θIN​)+(cG+θIN​)TΠΔ]⊗Γ}e.

Companions

  • Corollary 4.7: the same conclusion under θΠ~j+c(A‾j−Π~jCj−Π~jD‾j)<0\theta\widetilde\Pi_j+c(\overline A_j-\widetilde\Pi_jC_j-\widetilde\Pi_j\overline D_j)<0θΠj​+c(Aj​−Πj​Cj​−Πj​Dj​)<0, which separates the outer in-degrees CjC_jCj​ from the pinning gains D‾j\overline D_jDj​.
  • Corollary 4.8 (i): an explicit coupling-strength threshold under Assumption 2.
  • Corollary 4.9: partial synchronization of the first components.

Significance

Theorem 4.5 replaces a single Nn×NnNn\times NnNn×Nn matrix inequality for the whole network with MMM inequalities, one of the size of each component. In practice the test runs component by component down the Frobenius order: decide which components need pinning, choose gains for each one separately, and get synchronization of the whole network. Corollary 4.7 makes this explicit in terms of the outer in-degrees: a component with no incoming edges from earlier components has to be pinned (Remark 4), while a component that receives enough weight from upstream needs no control. These conditions underlie the pinning scheme algorithm of §5 of the paper.

On the formal side, as far as a search of the platform and of Mathlib shows, none of these results is machine-checked. Mathlib has the positive semidefinite Schur complement and irreducible nonnegative matrices, but not the strict Schur complement, block-diagonal Lyapunov scaling, or Lyapunov arguments for networked ODEs with Kronecker-structured couplings. The mission produces, beyond the paper's theorem:

  • a reusable strict Schur complement lemma;
  • a block-triangular diagonal-stability lemma;
  • a template for Lyapunov estimates on coupled networks in Lean.

Difficulty

The obvious argument is to take one Lyapunov function weighted by a positive left null vector of the whole coupling matrix, as in the strongly connected case. Here it fails: the coupling matrix of a network that is not strongly connected has no positive left null vector, and condition (4.8) is local to each component. The central step is the passage from blockwise to global negative definiteness (Lemma 4.4). Condition (4.8) says nothing about the off-diagonal blocks that couple a component to earlier ones, and a block lower-triangular matrix whose symmetrized diagonal blocks are negative definite need not have a negative definite symmetric part; the component weights Δj\Delta_jΔj​ have to be found so that those couplings are dominated, which is a quantitative statement about Schur complements. Turning the resulting matrix inequality into convergence of the trajectories then needs a differential inequality on [0,∞)[0,\infty)[0,∞), a Kronecker-product definiteness argument with Γ\GammaΓ, and the commutation of KKK and Γ\GammaΓ to reach the θ bound.

Formalization scope

States are Fin n → ℝ and matrices are indexed by Fin N, Fin n, or the subtype of one component's vertices. Solutions are functions on ℝ that satisfy their ODE on [0,∞)[0,\infty)[0,∞) through HasDerivWithinAt … (Set.Ici 0). "For any initial conditions" means for every pair of solutions (s,x)(s,x)(s,x); existence of solutions is not claimed. Convergence is componentwise, which agrees with every norm on Rn\mathbb R^nRn. "Positive definite" is Mathlib's Matrix.PosDef, which includes symmetry, and X<0X<0X<0 is (-X).PosDef. λmax⁡\lambda_{\max}λmax​ is the least Rayleigh bound, IsLamMax, never a supremum of a possibly empty set.

The vertices are not permuted into Frobenius form. The hypotheses say instead that:

  • every component is nonempty;
  • every component is strongly connected inside itself;
  • every edge between components points forward in the order;
  • every vertex is reachable from a pinned vertex.

The weights Πj\Pi_jΠj​ are tied to the paper's object by ΠjTAj=0\Pi_j^{\mathsf T}A_j=0ΠjT​Aj​=0. The standing assumptions of p. 1402 (Assumption 1, KΓ=ΓKK\Gamma=\Gamma KKΓ=ΓK, θ=λmax⁡((K+KT)/2)≥0\theta=\lambda_{\max}((K+K^{\mathsf T})/2)\ge0θ=λmax​((K+KT)/2)≥0, Γ>0\Gamma>0Γ>0) are hypotheses of every goal-level statement. The goal does not assume the weights Δj\Delta_jΔj​, the bound (4.10), or definiteness of a whole-network matrix; it assumes only (4.8) on each component, so it cannot be trivialized by assuming its own conclusion. For n=0n=0n=0 the λmax⁡\lambda_{\max}λmax​ hypothesis cannot be met, so that degenerate dimension is excluded.

The pages contain three misprints. (4.10) has "−c-c−c" in its third line and "++++++" in its last; the last line, with a single +++, is formalized. Corollary 4.9 ranges "i=1,…,Ni=1,\dots,Ni=1,…,N" where the component range is meant.

A complete development needs a Gronwall-type decay lemma on [0,∞)[0,\infty)[0,∞), the strict Schur complement, definiteness of Kronecker products, and the square root of a positive definite matrix commuting with matrices that commute with it. Each of these is reusable well beyond this mission, and contributions to any of them are welcome, as are proofs of the milestones in any order.

Selected references

  • W. Yu, G. Chen, J. Lü, J. Kurths, Synchronization via pinning control on general complex networks, SIAM J. Control Optim. 51(2) (2013), 1395–1416. https://doi.org/10.1137/100781699
  • X. F. Wang, G. Chen, Pinning control of scale-free dynamical networks, Physica A 310 (2002), 521–531. https://doi.org/10.1016/S0378-4371(02)00772-0
  • T. Chen, X. Liu, W. Lu, Pinning complex networks by a single controller, IEEE Trans. Circuits Syst. I 54(6) (2007), 1317–1326. https://doi.org/10.1109/TCSI.2007.895383
  • S. Boyd, L. El Ghaoui, E. Feron, V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, SIAM, 1994. https://doi.org/10.1137/1.9781611970777
  • R. A. Brualdi, H. J. Ryser, Combinatorial Matrix Theory, Cambridge University Press, 1991. https://doi.org/10.1017/CBO9781107325708
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CombinatoricsProbability·Captain: mikedeng1

Algorithms for #BIS-Hard Problems on Expander Graphs 4: On Almost Every Δ-Regular Bipartite Graph with Δ ≥ Cq² log² q, Proper q-Colorings Reduce to Kotecký–Preiss Polymer ModelsResearch Paper

Motivation

Counting the proper qqq-colorings of a graph, and sampling one uniformly at random, are basic problems of approximate counting with a long history in statistical physics, where proper colorings are the zero-temperature antiferromagnetic Potts model. On bipartite graphs these problems are tied to #BIS, the problem of approximately counting independent sets in a bipartite graph, whose complexity is unresolved. Galanis, Štefankovič, Vigoda and Yang showed that approximately counting proper qqq-colorings is #BIS-hard on bipartite graphs of maximum degree Δ\DeltaΔ whenever q≤Δ/(2log⁡Δ)q \le \Delta/(2\log\Delta)q≤Δ/(2logΔ) (SIAM J. Comput. 45(6), 2016). A positive result in that regime therefore has to use structure beyond bipartiteness and bounded degree.

Jenssen, Keevash and Perkins (SIAM J. Comput. 49(4) 2020) use expansion. Their Theorem 5 says that for Δ≥Cq2log⁡2q\Delta \ge Cq^2\log^2 qΔ≥Cq2log2q there is an FPTAS and a polynomial-time sampler for proper qqq-colorings on almost every Δ\DeltaΔ-regular bipartite graph. This answered Conjecture 1 of the authors' SODA 2019 extended abstract. Liao, Lin, Lu and Mao (APPROX/RANDOM 2019) independently proved a similar result under a slightly stronger condition on Δ\DeltaΔ. The organizing idea, that most colorings of a strongly expanding bipartite graph follow one of a few patterns, comes from the work of Peled and Spinka on rigidity of colorings of Zd\mathbb Z^dZd (arXiv:1808.03597).

This mission is the fourth of a series formalizing the four main results of that paper; it covers Theorem 5.

Setting

Fix integers q≥3q \ge 3q≥3, Δ≥3\Delta \ge 3Δ≥3 and m≥1m \ge 1m≥1. The vertex set is V=O⊔EV = \mathcal O \sqcup \mathcal EV=O⊔E, two sides of mmm vertices each (so n=2mn = 2mn=2m). Gbip(2m,Δ)\mathcal G^{\mathrm{bip}}(2m,\Delta)Gbip(2m,Δ) is the finite set of labelled simple graphs on VVV whose edges all join O\mathcal OO to E\mathcal EE and in which every vertex has degree Δ\DeltaΔ. A property holds for almost every Δ\DeltaΔ-regular bipartite graph if the fraction of graphs in Gbip(2m,Δ)\mathcal G^{\mathrm{bip}}(2m,\Delta)Gbip(2m,Δ) having it tends to 111 as m→∞m \to \inftym→∞.

For S⊆VS \subseteq VS⊆V the vertex boundary ∂S\partial S∂S is the set of vertices outside SSS with a neighbour in SSS, and S+=S∪∂SS^+ = S \cup \partial SS+=S∪∂S. The graph is a bipartite (σ,ρ)(\sigma,\rho)(σ,ρ)-expander if ∣∂S∣≥ρ∣S∣|\partial S| \ge \rho|S|∣∂S∣≥ρ∣S∣ for every SSS inside one side with ∣S∣≤σm|S| \le \sigma m∣S∣≤σm.

A proper qqq-coloring is a map f:V→[q]f : V \to [q]f:V→[q] with f(u)≠f(v)f(u) \ne f(v)f(u)=f(v) on every edge; Xq(G)\mathcal X_q(G)Xq​(G) is their set and ZG(q)=∣Xq(G)∣Z_G(q) = |\mathcal X_q(G)|ZG​(q)=∣Xq​(G)∣. A pattern is a pair (A,B)(A,B)(A,B) of disjoint sets with A∪B=[q]A \cup B = [q]A∪B=[q]; there are 2q2^q2q of them. A coloring agrees with (A,B)(A,B)(A,B) at vvv if v∈Ov \in \mathcal Ov∈O and f(v)∈Af(v) \in Af(v)∈A, or v∈Ev \in \mathcal Ev∈E and f(v)∈Bf(v) \in Bf(v)∈B. For S⊆VS \subseteq VS⊆V, χA,B(S)\chi_{A,B}(S)χA,B​(S) is the set of proper colorings that disagree with (A,B)(A,B)(A,B) at every vertex of SSS and agree everywhere else.

G3G^3G3 joins two vertices at GGG-distance at most 333. A set is little if ∣S∣≤4qlog⁡ΔΔm|S| \le 4q\frac{\log\Delta}{\Delta}m∣S∣≤4qΔlogΔ​m. A polymer is a little set that is connected in G3G^3G3; C(G)\mathcal C(G)C(G) is the set of polymers, and two polymers are compatible if their GGG-distance exceeds 333. For a pattern (A,B)(A,B)(A,B), the weight and the polymer partition function are

wA,B(γ)=∣χA,B(γ)∣∣A∣m∣B∣m,ΞA,B(G)=∑Γ∏γ∈ΓwA,B(γ),w_{A,B}(\gamma) = \frac{|\chi_{A,B}(\gamma)|}{|A|^m|B|^m}, \qquad \Xi_{A,B}(G) = \sum_{\Gamma} \prod_{\gamma\in\Gamma} w_{A,B}(\gamma),wA,B​(γ)=∣A∣m∣B∣m∣χA,B​(γ)∣​,ΞA,B​(G)=Γ∑​γ∈Γ∏​wA,B​(γ),

the sum over all families Γ⊆C(G)\Gamma \subseteq \mathcal C(G)Γ⊆C(G) of pairwise compatible polymers, the empty family included. Finally Z^\hat ZZ^ is an ε\varepsilonε-relative approximation to ZZZ if e−εZ^≤Z≤eεZ^e^{-\varepsilon}\hat Z \le Z \le e^{\varepsilon}\hat Ze−εZ^≤Z≤eεZ^.

Formalization targets

Goal: the deterministic core of Theorem 5

There is an absolute constant C>0C > 0C>0 such that for all q≥3q \ge 3q≥3 and Δ≥Cq2log⁡2q\Delta \ge Cq^2\log^2 qΔ≥Cq2log2q, almost every Δ\DeltaΔ-regular bipartite graph GGG satisfies

e−εZ~G(q)≤ZG(q)≤eεZ~G(q),Z~G(q)=∑(A,B)∣A∣m∣B∣m ΞA,B(G),ε=e−m/(4q),e^{-\varepsilon}\tilde Z_G(q) \le Z_G(q) \le e^{\varepsilon}\tilde Z_G(q), \qquad \tilde Z_G(q) = \sum_{(A,B)} |A|^m|B|^m\,\Xi_{A,B}(G), \quad \varepsilon = e^{-m/(4q)},e−εZ~G​(q)≤ZG​(q)≤eεZ~G​(q),Z~G​(q)=(A,B)∑​∣A∣m∣B∣mΞA,B​(G),ε=e−m/(4q),

and, for every pattern (A,B)(A,B)(A,B) and every polymer γ∈C(G)\gamma \in \mathcal C(G)γ∈C(G), the Kotecký–Preiss condition

∑γ′∈C(G): d(γ′,γ)≤3wA,B(γ′) e∣γ′∣+g(γ′)≤∣γ∣,g(γ)=Δ10q2log⁡Δ∣γ∣.\sum_{\gamma' \in \mathcal C(G):\ d(\gamma',\gamma)\le 3} w_{A,B}(\gamma')\,e^{|\gamma'| + g(\gamma')} \le |\gamma|, \qquad g(\gamma) = \frac{\Delta}{10q^2\log\Delta}|\gamma|.γ′∈C(G): d(γ′,γ)≤3∑​wA,B​(γ′)e∣γ′∣+g(γ′)≤∣γ∣,g(γ)=10q2logΔΔ​∣γ∣.

The constant CCC is left existential, as in the paper.

Milestones

In the order of the proof:

  1. Lemma 23, the expansion of almost every regular bipartite graph;
  2. Lemma 27, the bound on ∣χA,B(S)∣|\chi_{A,B}(S)|∣χA,B​(S)∣;
  3. Lemma 28, the covering of Xq(G)\mathcal X_q(G)Xq​(G) by χA,B(S)\chi_{A,B}(S)χA,B​(S) with SSS little;
  4. the product formula after (13);
  5. Claim 30, the size bound for sparse sets;
  6. the binomial estimate, an existing platform theorem;
  7. Lemma 29, the reduction;
  8. the weight bound of §5.1;
  9. the per-vertex estimate (15).

Significance

The goal is the whole mathematical content of Theorem 5. The paper's algorithm (its Theorem 8, adapted from Helmuth, Perkins and Regts, Algorithmic Pirogov–Sinai theory) takes exactly these two facts as input. Given the Kotecký–Preiss condition, the cluster expansion of each log⁡ΞA,B(G)\log\Xi_{A,B}(G)logΞA,B​(G) converges and truncating it gives the estimator; the reduction transfers the estimate to ZG(q)Z_G(q)ZG​(q). With the goal proved, the remaining distance to the FPTAS is algorithmic bookkeeping, not combinatorics.

The result has been proved since 2019 and is not formalized anywhere. A formal proof would add a machine-checked polymer-model reduction for a spin system with exponentially many ground states, a reusable model of uniformly random regular bipartite graphs, and a checked instance of the "expansion implies few ground states" argument used throughout this literature.

Difficulty

The obvious first idea is a one-ground-state expansion, as for the hard-core model, where configurations are close to "all even" or "all odd". For colorings there are 2q2^q2q patterns. A coloring can lie close to several of them, so the reduction must show that the sets χA,B(S)\chi_{A,B}(S)χA,B​(S) with SSS little overlap only negligibly. That needs an inclusion–exclusion step whose error is exponentially small in m/qm/qm/q. A second difficulty is the size of polymers: little sets are qqq times larger than the sets of the hard-core analysis. Claim 30 is what keeps the union of compatible polymers from becoming a large set that the expansion no longer controls. Finally, the constants must be uniform in qqq. The single condition Δ≥Cq2log⁡2q\Delta \ge Cq^2\log^2 qΔ≥Cq2log2q has to cover the expansion lemma, the reduction and the Kotecký–Preiss estimate at once.

Formalization scope

The FPTAS and the sampling algorithm are not formalized. Mathlib has no machine model in which "polynomial time" could be stated, and the running times carry unquantified constants. The goal is the mathematics the algorithm rests on: the reduction and the Kotecký–Preiss condition, with the paper's constants e−m/(4q)e^{-m/(4q)}e−m/(4q), 4qlog⁡Δ/Δ4q\log\Delta/\Delta4qlogΔ/Δ and Δ/(10q2log⁡Δ)\Delta/(10q^2\log\Delta)Δ/(10q2logΔ). Neither half may be dropped. Replacing e−m/(4q)e^{-m/(4q)}e−m/(4q) by an existential error, or summing the Kotecký–Preiss condition over compatible rather than incompatible polymers, would make the goal trivial or wrong.

Conventions committed to in Lean:

  • Vertices are Fin m ⊕ Fin m, and Gbip(2m,Δ)\mathcal G^{\mathrm{bip}}(2m,\Delta)Gbip(2m,Δ) is the set of labelled Δ\DeltaΔ-regular graphs with edges only between the two fixed sides. The paper does not fix a bipartition; this fixed-sides model is a disclosed choice, matching §5's ∣O∣=∣E∣=m|\mathcal O| = |\mathcal E| = m∣O∣=∣E∣=m.
  • Distances are Mathlib's extended distance edist, so vertices in different components are at distance ∞\infty∞ and never incompatible.
  • A pattern is encoded by A⊆[q]A \subseteq [q]A⊆[q] with B=[q]∖AB = [q]\setminus AB=[q]∖A. Patterns with an empty side are kept: their weights are 000 under Lean's x/0=0x/0 = 0x/0=0, so they contribute 000 to Z~G(q)\tilde Z_G(q)Z~G​(q), as on the page for large mmm.
  • log is the natural logarithm; the binomial estimate is used in nats.

Added hypotheses:

  • A,B≠∅A, B \ne \emptysetA,B=∅ in Lemma 27 and in the product formula, which divide by ∣A∣|A|∣A∣ and ∣B∣|B|∣B∣;
  • Δ≥3\Delta \ge 3Δ≥3 in Lemma 28 and Claim 30, the paper's standing convention (§1.3);
  • m≥1m \ge 1m≥1 in Lemma 29, which fails on the empty vertex set.

The goal needs none of these, since its limit is in mmm and its constant absorbs Δ\DeltaΔ.

A complete development needs a counting theory of random regular bipartite graphs (for Lemma 23, through Bassalygo's expansion theorem), counts of connected sets in bounded-degree graphs, and inclusion–exclusion over patterns. The random-graph model and the polymer-model definitions are reusable across this series and beyond. Proofs of any milestone, and alternative routes to Lemma 23, are welcome.

Selected references

  • M. Jenssen, P. Keevash and W. Perkins, Algorithms for #BIS-hard problems on expander graphs, SIAM J. Comput. 49(4) (2020) 681–710. https://doi.org/10.1137/19M1286669
  • A. Galanis, D. Štefankovič, E. Vigoda and L. Yang, Ferromagnetic Potts model: refined #BIS-hardness and related results, SIAM J. Comput. 45(6) (2016) 2004–2065.
  • R. Peled and Y. Spinka, Rigidity of proper colorings of Zd\mathbb Z^dZd, arXiv:1808.03597 (2018). https://arxiv.org/abs/1808.03597
  • C. Liao, J. Lin, P. Lu and Z. Mao, Counting independent sets and colorings on random regular bipartite graphs, APPROX/RANDOM 2019, Schloss Dagstuhl–Leibniz-Zentrum für Informatik (2019).
  • T. Helmuth, W. Perkins and G. Regts, Algorithmic Pirogov–Sinai theory, Probab. Theory Related Fields (2020); extended abstract at STOC 2019.
  • R. Kotecký and D. Preiss, Cluster expansion for abstract polymer models, Comm. Math. Phys. 103(3) (1986) 491–498.
  • L. A. Bassalygo, Asymptotically optimal switching circuits, Problems Inform. Transmission 17(3) (1981) 206–211.
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Mathematical PhysicsProbability·Captain: mikedeng1

Algorithms for #BIS-Hard Problems on Expander Graphs 3: On Almost Every Δ-Regular Bipartite Graph, the Hard-Core Model at λ > 50 log²Δ/Δ Reduces to Two Kotecký–Preiss Polymer ModelsResearch Paper

Motivation

The hard-core model assigns weight to independent vertex sets of a graph. Its partition function counts those sets when the activity is one and weights larger sets more heavily when the activity increases. Counting independent sets on bipartite graphs is a standard benchmark for approximate counting: the general problem is difficult, while random regular graphs have additional expansion that can make approximation possible. Jenssen, Keevash and Perkins prove an approximation and sampling theorem for almost every sufficiently high degree regular bipartite graph when the activity is above an explicit threshold Jenssen–Keevash–Perkins, Theorem 2.

The question is specific to a random graph model. It does not assert the same conclusion for every regular bipartite graph. The authors use a criterion attributed to Bassalygo to show that almost every graph in the model has the expansion needed by their argument Jenssen–Keevash–Perkins, Theorem 22 and Lemma 23. Liao, Lin, Lu and Mao independently obtained related counting results under somewhat stronger parameter conditions Liao–Lin–Lu–Mao, 2019.

Setting

Let GGG be a simple graph with two labelled sides O\mathcal OO and E\mathcal EE, each containing mmm vertices, and let every vertex have degree Δ\DeltaΔ. An independent set III contains no adjacent pair. For activity λ>0\lambda>0λ>0, the hard-core partition function is

ZG(λ)=∑I independentλ∣I∣.Z_G(\lambda)=\sum_{I\text{ independent}}\lambda^{|I|}.ZG​(λ)=I independent∑​λ∣I∣.

For a vertex set SSS, its vertex boundary ∂S\partial S∂S consists of vertices outside SSS with a neighbor in SSS. The graph is a bipartite (σ,ρ)(\sigma,\rho)(σ,ρ)-expander when every subset of either side with size at most σm\sigma mσm has boundary of size at least ρ\rhoρ times its size. The expansion parameters used here are σ=4log⁡Δ/Δ\sigma=4\log\Delta/\Deltaσ=4logΔ/Δ and ρ=Δ/(4log⁡Δ)−1/2\rho=\Delta/(4\log\Delta)-1/2ρ=Δ/(4logΔ)−1/2. A set is tiny when its size is at most σm\sigma mσm Jenssen–Keevash–Perkins, Definition 21 and §4.4.

The graph G2G^2G2 joins distinct vertices whose distance in GGG is at most two. On either side, a polymer is a nonempty, tiny, G2G^2G2-connected vertex set. Two polymers on the same side are compatible if every pair of their vertices is more than distance two apart in GGG. A polymer γ\gammaγ has weight wγ=λ∣γ∣/(1+λ)∣∂γ∣w_\gamma=\lambda^{|\gamma|}/(1+\lambda)^{|\partial\gamma|}wγ​=λ∣γ∣/(1+λ)∣∂γ∣. Summing products of weights over mutually compatible polymer families gives the partition functions ΞE(G)\Xi^{\mathcal E}(G)ΞE(G) and ΞO(G)\Xi^{\mathcal O}(G)ΞO(G) Jenssen–Keevash–Perkins, §§4.1, 4.4.

“Almost every” means that, for fixed Δ\DeltaΔ, the fraction of labelled Δ\DeltaΔ-regular bipartite graphs satisfying a property tends to one as mmm tends to infinity. The random graph is chosen uniformly from graphs on the two fixed sides Jenssen–Keevash–Perkins, p. 3.

Formalization targets

The first target is the authors' expansion premise: for all sufficiently large Δ\DeltaΔ, almost every graph is a bipartite (4log⁡Δ/Δ,Δ/(4log⁡Δ)−1/2)(4\log\Delta/\Delta,\Delta/(4\log\Delta)-1/2)(4logΔ/Δ,Δ/(4logΔ)−1/2)-expander. The main target combines two deterministic claims on almost every such graph. For every λ>50log⁡2Δ/Δ\lambda>50\log^2\Delta/\Deltaλ>50log2Δ/Δ, write n=2mn=2mn=2m and

Z^G(λ)=(1+λ)m(ΞE(G)+ΞO(G)),ε0=(1+λ)−n/4.\widehat Z_G(\lambda)=(1+\lambda)^m\bigl(\Xi^{\mathcal E}(G)+\Xi^{\mathcal O}(G)\bigr),\qquad \varepsilon_0=(1+\lambda)^{-n/4}.ZG​(λ)=(1+λ)m(ΞE(G)+ΞO(G)),ε0​=(1+λ)−n/4.

First, Z^G\widehat Z_GZG​ is an ε0\varepsilon_0ε0​-relative approximation in the paper's exponential convention:

e−ε0Z^G(λ)≤ZG(λ)≤eε0Z^G(λ).e^{-\varepsilon_0}\widehat Z_G(\lambda)\le Z_G(\lambda)\le e^{\varepsilon_0}\widehat Z_G(\lambda).e−ε0​ZG​(λ)≤ZG​(λ)≤eε0​ZG​(λ).

Second, each of the two polymer models obeys the Kotecký–Preiss condition with decay g(γ)=∣γ∣Δlog⁡(1+λ)/(10log⁡Δ)g(\gamma)=|\gamma|\Delta\log(1+\lambda)/(10\log\Delta)g(γ)=∣γ∣Δlog(1+λ)/(10logΔ):

∑γ′ incompatible with γwγ′e∣γ′∣+g(γ′)≤∣γ∣.\sum_{\gamma'\text{ incompatible with }\gamma}w_{\gamma'}e^{|\gamma'|+g(\gamma')}\le |\gamma|.γ′ incompatible with γ∑​wγ′​e∣γ′∣+g(γ′)≤∣γ∣.

The same graph event must support every activity above the threshold; the activity quantifier is inside the limiting probability. The intermediate targets are Bassalygo's entropy criterion, the high probability expansion lemma, the tiny-side lemma for independent sets, the approximation lemma at its weaker activity threshold, a published binomial-entropy estimate, and the per-vertex polymer sum bound Jenssen–Keevash–Perkins, Theorem 22, Lemmas 23–25 and §4.5.

Significance

The reduction expresses a weighted independent-set count through two finite polymer partition functions whose weights decay under the same expansion condition. The KP bound supplies the convergence condition used by the paper's general polymer approximation theorem. Together these are the mathematical inputs to its algorithmic Theorem 2 Jenssen–Keevash–Perkins, Theorems 2, 8.

A formal proof would make the expansion assumptions, distance convention, graph probability limit, and error normalization explicit. The target is a formalization of a published result, not an open conjecture. The proposal contains statements and definitions; their proofs remain to be supplied. The binomial estimate is already machine checked on Prove2Me and is included by reference.

Difficulty

An independent set can occupy vertices on both sides of a bipartite graph. Merely taking one side as the dominant phase double counts sets that are sparse on both sides. The tiny-side lemma and the polymer decomposition identify and control that overlap. The random graph step also requires an expansion estimate strong enough to apply to every small subset of either side, rather than only to a typical subset Jenssen–Keevash–Perkins, §§4.4–4.5.

Formalization scope

Lean uses finite simple graphs on Fin(m)⊕Fin(m)\mathrm{Fin}(m)\oplus\mathrm{Fin}(m)Fin(m)⊕Fin(m), with the left and right summands serving as the odd and even sides. This fixes a labelled bipartition for the uniform random graph model. Distances use extended natural graph distance, so vertices in different components have infinite distance. Polymer families include the empty family, and graph connectivity excludes the empty polymer. Sizes are natural numbers; thresholds involving logarithms are real inequalities. Binary entropy in the cited expansion theorem is measured in bits, while Mathlib's binary entropy is converted from natural units. The approximation uses exponential relative error exactly as in Definition 10 of the paper.

The FPTAS, sampling algorithm, and running-time assertion are outside this mission because Mathlib has no matching machine and cost model. The goal formalizes both the partition-function reduction and its KP condition, the mathematical inputs consumed by the paper's algorithm theorem. It quantifies over all activities above the threshold within the almost-every event, preventing a separate favorable graph set for each activity. The fixed-sides model and the explicit domain of Bassalygo's entropy formula are disclosed representation choices. The tiny-side milestone (Lemma 24) carries a graph-size threshold depending on the degree: as printed, for every graph size, it fails on small dense graphs (the complete bipartite graph minus disjoint complete blocks), and the main result uses it only as the graph size grows. Reusable contributions include finite graph expansion lemmas, polymer partition-function identities, and bounds on connected vertex sets.

Selected references

  • M. Jenssen, P. Keevash and W. Perkins, Algorithms for #BIS-hard problems on expander graphs, SIAM Journal on Computing 49(4), 2020, 681–710. DOI.
  • L. Bassalygo, Asymptotically optimal switching circuits, Problems of Information Transmission 17(3), 1981, 206–211. Publisher record.
  • C. Liao, J. Lin, P. Lu and Z. Mao, Counting independent sets and colorings on random regular bipartite graphs, APPROX/RANDOM, 2019. DOI.
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