Alphabeta Math
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How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Expander Graphs and Constraint Graphs

1 · Prerequisites

2 · Summary

Normalized adjacency connects graph expansion with spectral estimates and walk probabilities. An elementary finite Fourier argument constructs constant-degree expanders at every positive size. Equality clouds and tautological overlays then regularize binary constraint graphs with explicit violation and decoding bounds.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Regular multigraph and normalized adjacency

Definition

A finite d-regular adjacency-slot multigraph on V={1,,n}, with n1 and integer d>0, is a symmetric matrix AZ0n×n with every row sum d. Auv counts slots from u to v; diagonal entries count loop slots. Its normalized adjacency is M=A/d. We use f,g=n1vf(v)g(v) unless an unnormalized inner product is explicitly specified.

An adjacency list records the d destinations per vertex, including repetitions. An ordinary undirected edge has two incidence slots paired by reversal; an ordinary loop has two slots at the same vertex. Such a representation requires even diagonal entries. Any symmetric integer matrix can be converted to it by doubling every slot. Uniform directed-slot sampling chooses one of the nd slots; for a reverse-paired graph it induces the uniform distribution on its nd/2 ordinary edges. Connectivity uses positive off-diagonal entries. When n=1, the mean-zero subspace is {0}.

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Constant vector is a top eigenvector

Statement

For a finite d-regular adjacency-slot multigraph, M1=1, every eigenvalue of M lies in [1,1], and 1 is invariant. The multiplicity of eigenvalue 1 equals the number of connected components. For a connected graph, 1 is an eigenvalue if and only if its positive slots join opposite parts of a bipartition, so in particular it has no loop slots.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

A finite d-regular adjacency-slot multigraph on V={1,,n}, with n1 and integer d>0, is a symmetric matrix AZ0n×n with every row sum d. Auv counts slots from u to v; diagonal entries count loop slots. Its normalized adjacency is M=A/d. We use f,g=n1vf(v)g(v) unless an unnormalized inner product is explicitly specified. An adjacency list records the d destinations per vertex, including repetitions. An ordinary undirected edge has two incidence slots paired by reversal; an ordinary loop has two slots at the same vertex. Such a representation requires even diagonal entries. Any symmetric integer matrix can be converted to it by doubling every slot. Uniform directed-slot sampling chooses one of the nd slots; for a reverse-paired graph it induces the uniform distribution on its nd/2 ordinary edges. Connectivity uses positive off-diagonal entries. When n=1, the mean-zero subspace is {0}. (Regular multigraph and normalized adjacency).

[F2]

Let V be a finite-dimensional real inner product space and let T:VV be self-adjoint. Then V has an orthonormal basis consisting of eigenvectors of T. (Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis).

Proof

1.1

The row sums give M1=1; symmetry gives Mf,1=f,1. The real spectral theorem applies since M is a symmetric endomorphism of a finite-dimensional real inner product space. If Mf=μf and f(v) is maximal and nonzero, then μf(v)uMvuf(u)f(v), proving the interval bound.

F1F2
2.1

Expansion of the squares gives f,(IM)f=(2nd)1u,vAuv(f(u)f(v))2. Its zero vectors are exactly functions constant on each connected component. Thus the eigenspace for eigenvalue one has dimension the component count; the spectral theorem identifies this with multiplicity.

step 1.1algebra
3.1

Similarly f,(I+M)f=(2nd)1u,vAuv(f(u)+f(v))2. For a nonzero 1 eigenvector all positive slots force f(v)=f(u). Connectivity propagates one nonzero absolute value to every vertex, excludes loops, and partitions the vertices by sign. Conversely on such a bipartition the vector taking values +1,1 has Mf=f. At n=1 positive degree forces loops and M=[1], so no 1 eigenvalue occurs and the mean-zero space is zero.

step 1.1algebra
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Spectral edge and vertex expansion

Definition

For the regular multigraph and spectral conventions in Constant vector is a top eigenvector, put α=M1. For n2 order the eigenvalues 1=μ1μ2μn, counting multiplicity, and put γ=1μ2. Thus α=maxj2μj, which also controls negative eigenvalues.

Write cut(S)=uS,vSAuv and VS={vS:Auv>0 for some uS}. Normalized edge expansion and external vertex expansion are h=min0<Sn/2cut(S)dS,hV=min0<Sn/2VSS. For n=1, put α=0 and leave μ2,γ,h,hV undefined; cut-expansion assertions are vacuous. A bounded-degree family is an expander family when its normalized edge expansion has a positive uniform lower bound for n2. Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in n; neighbor computation in time polynomial in logn is a stronger requirement.

TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Expander mixing lemma

Statement

For any subsets S,T of a finite d-regular adjacency-slot graph on n1 vertices, let e(S,T)=uS,vTAuv count ordered slots. Then e(S,T)dSTnαdS(1S/n)T(1T/n). Overlap and loop slots are allowed.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For the regular multigraph and spectral conventions in the stated convention, put α=M1. For n2 order the eigenvalues 1=μ1μ2μn, counting multiplicity, and put γ=1μ2. Thus α=maxj2μj, which also controls negative eigenvalues. Write cut(S)=uS,vSAuv and VS={vS:Auv>0 for some uS}. Normalized edge expansion and external vertex expansion are h=min0<Sn/2cut(S)dS,hV=min0<Sn/2VSS. For n=1, put α=0 and leave μ2,γ,h,hV undefined; cut-expansion assertions are vacuous. A bounded-degree family is an expander family when its normalized edge expansion has a positive uniform lower bound for n2. Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in n; neighbor computation in time polynomial in logn is a stronger requirement. (Spectral edge and vertex expansion).

[F2]

For vectors u,v in a real or complex inner product space, u,vuv. Equality holds if and only if u and v are linearly dependent, including the case in which either vector is zero. (Cauchy–Schwarz: u,vuv, with equality exactly for linearly dependent vectors).

Proof

1.1

Set s=S/n, t=T/n, f=1Ss1 and g=1Tt1. Both are mean zero and have normalized squared norms s(1s) and t(1t). Since M preserves constants and their orthogonal complement, e(S,T)/(nd)st=f,Mg. This counts loops and overlap exactly as specified.

F1
2.1

Cauchy–Schwarz and the defining operator bound give f,Mgαs(1s)t(1t). Multiply by nd. Empty or full sets give zero centered vectors and equality; at n=1 all sets are of that form. No division by a set size or by α is made.

F2step 1.1
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Cheeger indicator and positive part energy

Statement

Let n2 and use normalized edge expansion h and algebraic gap γ=1μ2. Then γ2h. Moreover some sign of a nonzero mean-zero μ2 eigenvector has positive part f0 supported on at most n/2 vertices and satisfying f,(IM)fγf2.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For the regular multigraph and spectral conventions in the stated convention, put α=M1. For n2 order the eigenvalues 1=μ1μ2μn, counting multiplicity, and put γ=1μ2. Thus α=maxj2μj, which also controls negative eigenvalues. Write cut(S)=uS,vSAuv and VS={vS:Auv>0 for some uS}. Normalized edge expansion and external vertex expansion are h=min0<Sn/2cut(S)dS,hV=min0<Sn/2VSS. For n=1, put α=0 and leave μ2,γ,h,hV undefined; cut-expansion assertions are vacuous. A bounded-degree family is an expander family when its normalized edge expansion has a positive uniform lower bound for n2. Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in n; neighbor computation in time polynomial in logn is a stronger requirement. (Spectral edge and vertex expansion).

[F2]

If T is self-adjoint on a nonzero finite-dimensional real inner product space and its eigenvalues are ordered as λ1λn, then λ1=maxv0RT(v)andλn=minv0RT(v). (The smallest and largest eigenvalues of a self-adjoint endomorphism are the minimum and maximum Rayleigh quotients).

Proof

1.1

On the nonzero invariant space 1, the Rayleigh quotient of IM is at least γ. For 0<Sn/2, the centered indicator has norm squared S(1S/n)/n and energy cut(S)/(nd). Hence γcut(S)/(dS(1S/n))2cut(S)/(dS). Minimize over the finite nonempty collection of such sets.

F1F2
2.1

Choose a nonzero mean-zero eigenvector g for μ2. It has both positive and negative entries, so one sign has at most n/2 positive entries. Let f=max(g,0) for this sign. At a positive coordinate, MfMg since fg and M is nonnegative; thus (IM)f(IM)g=γg there. Multiply by f, sum, and use f=0 elsewhere to obtain the energy bound. This also works when γ=0 and when some coordinates of g vanish.

step 1.1algebra
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Cheeger sweep and layer cake

Statement

For a finite d-regular graph on n2 vertices and a nonnegative f supported on at most n/2 vertices, use the unnormalized inner product f,g0=vf(v)g(v) and energy E(f)=f,(IM)f0. Then hf021du<vAuvf(u)2f(v)22E(f)f02. Also γ2h, and there exists a nonzero nonnegative function g, supported on at most n/2 vertices, with E(g)γg02: namely, the positive part of a suitable sign of a nonzero mean-zero μ2 eigenvector. These are the indicator and positive-part conclusions of the preceding lemma in the unnormalized inner product.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

Let n2 and use normalized edge expansion h and algebraic gap γ=1μ2. Then γ2h. Moreover some sign of a nonzero mean-zero μ2 eigenvector has positive part f0 supported on at most n/2 vertices and satisfying f,(IM)fγf2. (Cheeger indicator and positive part energy).

[F2]

For vectors u,v in a real or complex inner product space, u,vuv. Equality holds if and only if u and v are linearly dependent, including the case in which either vector is zero. (Cauchy–Schwarz: u,vuv, with equality exactly for linearly dependent vectors).

Proof

1.1

Order coordinates f1fn0 and let a=suppfn/2. Put fa+1=0. Each difference of squared values telescopes across initial segments, so the middle numerator equals i=1a(fi2fi+12)cut({1,,i}). Each cut is at least dhi, and i=1ai(fi2fi+12)=ifi2. For a=0 all sums are zero.

givenalgebra
2.1

Factor f(u)2f(v)2=f(u)f(v)f(u)+f(v) and apply Cauchy–Schwarz with weights Auv for u<v. The first squared sum is dE(f); the second is at most 2u<vAuv(f(u)2+f(v)2)2df02. Dividing by d proves the upper estimate. Loop terms vanish in the difference sum and only reduce the nonloop degree sum.

F2step 1.1
3.1

Multiplying all normalized inner products by n leaves the preceding lemma's inequalities unchanged; thus its positive-part and indicator estimates have exactly the stated unnormalized form. No division by E(f) or f0 was used above, so zero functions and zero energy are included.

F1step 2.1
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Cheeger inequalities for finite regular graphs

Statement

For a finite d-regular adjacency-slot multigraph on n2 vertices, γ2h2γ,hhVdh. Here γ=1μ2 is the algebraic gap; it is not replaced by 1α.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For a finite d-regular graph on n2 vertices and a nonnegative f supported on at most n/2 vertices, use the unnormalized inner product f,g0=vf(v)g(v) and energy E(f)=f,(IM)f0. Then hf021du<vAuvf(u)2f(v)22E(f)f02. Also γ2h, and there exists a nonzero nonnegative function g, supported on at most n/2 vertices, with E(g)γg02: namely, the positive part of a suitable sign of a nonzero mean-zero μ2 eigenvector. These are the indicator and positive-part conclusions of the preceding lemma in the unnormalized inner product. (Cheeger sweep and layer cake).

Proof

1.1

Take the nonzero positive part with E(f)γf02 and combine it with the sweep estimate. Since f02>0, this yields h2γ. The indicator estimate in the same cited result gives γ/2h. Zero gap is permitted and forces h=0.

F1
2.1

For any nonempty eligible S, each vertex of VS receives at least one and at most d cut slots. Thus cut(S)/dVScut(S). The first inequality divided by S and minimized gives hhV. Apply the second to a set minimizing h to obtain hVdh. The eligible collection is nonempty and finite because n2; loops never cross its cuts.

step 1.1algebra
CorollaryStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Expander independent sets coloring and diameter

Statement

If S is independent in a d-regular adjacency-slot graph (meaning e(S,S)=0), then Sαn/(1+α). Thus a loopless graph with α>0 needs at least (1+α)/α colors. For n2 and h>0 its diameter is at most 2logn/log(1+h)+2. A singleton has diameter zero.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For any subsets S,T of a finite d-regular adjacency-slot graph on n1 vertices, let e(S,T)=uS,vTAuv count ordered slots. Then e(S,T)dSTnαdS(1S/n)T(1T/n). Overlap and loop slots are allowed. (Expander mixing lemma).

[F2]

For a finite d-regular adjacency-slot multigraph on n2 vertices, γ2h2γ,hhVdh. Here γ=1μ2 is the algebraic gap; it is not replaced by 1α. (Cheeger inequalities for finite regular graphs).

Proof

1.1

For S, mixing with S=T gives dS2/nαdS(1S/n). Cancel the positive dS and rearrange. For empty S the bound holds directly. If α=0 there is no nonempty independent set. In a loopless graph every color class is independent, so summing their sizes gives the color bound when α>0.

F1
2.1

Every set of size at most n/2 has at least h times its size in external neighbors, by the edge/vertex comparison. A ball therefore grows by a factor at least 1+h until it exceeds n/2. With R=logn/log(1+h)+1, a ball of radius R must exceed half the graph; otherwise successive growth from its initial single vertex contradicts its size bound. Two such balls intersect, giving distance at most 2R. Positive h also excludes a separate component of size at most half. For n=1 use diameter zero without defining h.

F2algebra
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Margulis gabber galil graph

Definition

For integer m1, let V=(Z/mZ)2. The Margulis–Gabber–Galil graph has the following eight slots at (x,y), with all arithmetic modulo m: (x+2y,y), (x+2y+1,y), (x2y,y), (x2y1,y), (x,y+2x), (x,y+2x+1), (x,y2x), (x,y2x1). Multiplicities and fixed points are retained under Regular multigraph and normalized adjacency. Write T1(x,y)=(x+2y,y) and T2(x,y)=(x,y+2x). Pair each forward affine map with its inverse as reverse ports, even when their destinations coincide.

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Margulis family is constant degree and neighbor computable

Statement

The Margulis graph on (Z/mZ)2 is symmetric and 8-regular, with m2 vertices, for every m1. One specified neighbor is computable in polynomial time in log(m+2); the whole adjacency list is computable in O(m2poly(log(m+2))) bit operations.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For integer m1, let V=(Z/mZ)2. The Margulis–Gabber–Galil graph has the following eight slots at (x,y), with all arithmetic modulo m: (x+2y,y), (x+2y+1,y), (x2y,y), (x2y1,y), (x,y+2x), (x,y+2x+1), (x,y2x), (x,y2x1). Multiplicities and fixed points are retained under the stated convention. Write T1(x,y)=(x+2y,y) and T2(x,y)=(x,y+2x). Pair each forward affine map with its inverse as reverse ports, even when their destinations coincide. (Margulis gabber galil graph).

Proof

1.1

Each of the four forward affine maps in the definition is a bijection: subtracting its shear and its optional unit shift gives the listed inverse. Pairing a map with its inverse makes the adjacency symmetric. Exactly eight slots are retained at every vertex, regardless of coincidences.

F1
2.1

A slot uses additions, subtraction, doubling, and reduction modulo m of integers with O(log(m+2)) bits. School arithmetic performs these in polynomial bit time. Enumerating the m2 coordinate pairs and eight slots proves the total bound. For m=1 there is one vertex and eight loop slots; for m=2 repeated destinations remain distinct slots.

step 1.1algebra
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Finite torus fourier transform

Definition

On V=(Z/mZ)2, m1, let ω=e2πi/m and χb(x)=ωb1x1+b2x2. Residue representatives do not affect these values. With inner product f,g=m2xf(x)g(x), define f^(b)=f,χb=m2xf(x)ωbx. The norm is f2=m2xf(x)2. At m=1 there is one character, the constant function one. The sign in the exponent is part of this convention.

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Finite torus fourier orthogonality and affine change

Statement

For the normalized negative-exponent Fourier transform on (Z/mZ)2, the characters are an orthonormal basis, and f=bf^(b)χb,f2=bf^(b)2,xf(x)=0    f^(0)=0. For every invertible matrix T over Z/mZ, and g(x)=f(Tx+a), g^(y)=ωyT1af^(TTy).

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

On V=(Z/mZ)2, m1, let ω=e2πi/m and χb(x)=ωb1x1+b2x2. Residue representatives do not affect these values. With inner product f,g=m2xf(x)g(x), define f^(b)=f,χb=m2xf(x)ωbx. The norm is f2=m2xf(x)2. At m=1 there is one character, the constant function one. The sign in the exponent is part of this convention. (Finite torus fourier transform).

Proof

1.1

For m>1, j=0m1ωcj is m if c=0 modulo m, and otherwise is zero because multiplication by 1ωc0 telescopes to 1ωcm=0. Applying this to each coordinate shows χb,χc is one for b=c and zero otherwise. At m=1 the one character has norm one directly.

F1
2.1

There are m2 orthonormal characters in the m2-dimensional function space, hence they form a basis: linear independence follows by taking inner products, and an independent list of that length spans by elementary elimination. Expansion in this basis gives inversion and, on taking its squared norm, Parseval. The zero coefficient is exactly the normalized sum, establishing both directions of the mean-zero criterion.

step 1.1algebra
3.1

Substitute u=Tx+a in the defining sum. The exponent becomes yT1(ua)=(TTy)u+yT1a. Bijection of this substitution preserves the sum and its normalization, giving the positive phase in the displayed formula. It covers a=0, constant and zero functions, and the singleton torus as well.

F1step 2.1algebra
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Fourier analysis of margulis adjacency

Statement

Let T1(x,y)=(x+2y,y), T2(x,y)=(x,y+2x) modulo m. Define the forward operator Kf(x)=f(T1x)+f(T1x+e1)+f(T2x)+f(T2x+e2). For real mean-zero f, put g=f^ and Q(g)=z2g(z)[g(T21z)cos(πz1/m)+g(T11z)cos(πz2/m)]. Then g(0)=0, f2=zg(z)2, f,KfQ(g), and the full Margulis adjacency A satisfies f,Af2Q(g).

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For the normalized negative-exponent Fourier transform on (Z/mZ)2, the characters are an orthonormal basis, and f=bf^(b)χb,f2=bf^(b)2,xf(x)=0    f^(0)=0. For every invertible matrix T over Z/mZ, and g(x)=f(Tx+a), g^(y)=ωyT1af^(TTy). (Finite torus fourier orthogonality and affine change).

[F2]

The Margulis graph on (Z/mZ)2 is symmetric and 8-regular, with m2 vertices, for every m1. One specified neighbor is computable in polynomial time in log(m+2); the whole adjacency list is computable in O(m2poly(log(m+2))) bit operations. (Margulis family is constant degree and neighbor computable).

[F3]

For integer m1, the Margulis–Gabber–Galil graph has at (x,y) the four slots (x+2y,y), (x+2y+1,y), (x,y+2x), (x,y+2x+1) and the four inverse slots (x2y,y), (x2y1,y), (x,y2x), (x,y2x1), with multiplicities and fixed points retained. (Margulis gabber galil graph).

Proof

1.1

The Fourier identities give g(0)=0 and the stated norm equality. Since T1T=T2, the transform of the first pair of summands in Kf is (1+ωz1)f^(T21z); the second pair gives (1+ωz2)f^(T11z). The phase uses Tj1ej=ej.

F1
2.1

Parseval's inner-product identity (obtained by expanding both functions in the orthonormal character basis) expresses f,Kf as the coefficient inner product. Apply the triangle inequality and 1+e2iθ=2cosθ to obtain Q(g). The absolute cosines are independent of residue representatives.

step 1.1algebra
3.1

The four slots defining K are exactly the forward slots in the Margulis construction, and the other four are their inverses. Inverse permutations are the adjoints of the forward permutation operators under uniform counting, so A=K+K. Therefore f,Af=2Ref,Kf, proving the last bound. For the singleton torus every mean-zero function vanishes and all displayed sums are zero.

F3F2step 2.1
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Margulis diamond weight bound

Statement

For every integer m1 and every nonnegative function g on (Z/mZ)2 with g(0)=0, the quadratic expression Q in the Fourier reduction satisfies Q(g)7320zg(z)2. Consequently, for the forward/full adjacency operators and normalized transform in that reduction, f,Af(73/10)f2 for real mean-zero f.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

Let T1(x,y)=(x+2y,y), T2(x,y)=(x,y+2x) modulo m. Define the forward operator Kf(x)=f(T1x)+f(T1x+e1)+f(T2x)+f(T2x+e2). For real mean-zero f, put g=f^ and Q(g)=z2g(z)[g(T21z)cos(πz1/m)+g(T11z)cos(πz2/m)]. Then g(0)=0, f2=zg(z)2, f,KfQ(g), and the full Margulis adjacency A satisfies f,Af2Q(g). (Fourier analysis of margulis adjacency).

Proof

1.1

Represent each coordinate in [m/2,m/2). Define zu when both absolute coordinates of z are at least those of u, with one strict. Put w(z,u)=5/4 if zu, 4/5 if uz, and 1 otherwise; then w(z,u)w(u,z)=1. Squaring wab/w gives 2abwa2+w1b2. Apply this to each term of Q and reindex the inverse-shear terms; a shear leaves its corresponding cosine coordinate fixed. The coefficient at g(z)2 is bounded by c1(z)[w(z,T2z)+w(z,T21z)]+c2(z)[w(z,T1z)+w(z,T11z)], where cj(z)=cos(πzj/m).

F1algebra
2.1

Outside the open diamond z1+z2<m/2, let a=πz1/m and b=πz2/m. They lie in [0,π/2] with a+bπ/2, so cosa+cosbcosa+sina2. Each weight is at most 5/4, giving coefficient at most 52/2<73/20. This includes the diamond boundary and the centered-coordinate endpoints.

step 1.1algebra
2.2

Inside the diamond and away from zero, sign changes and coordinate interchange permute the four shear neighbors and preserve their absolute-coordinate order. First suppose the absolute coordinates are a>b>0 with a+b<m/2. The change aa2b strictly decreases its absolute value. For a+2b, the centered absolute value is min(a+2b,ma2b)>a, since m>2a+2b. For b+2a, both b+2a and mb2a exceed b by the same strict inequality. For b2a, both 2ab and m2a+b exceed b, the latter since 2a<m. Thus exactly three neighbors dominate and one is dominated, including when a coordinate wraps. The coefficient is at most 3(4/5)+5/4=73/20.

step 1.1algebra
2.3

If a=b>0, then a<m/4. Two neighbors preserve the pair of absolute coordinates, and the other two replace one coordinate by 3amodm>a, because both 3a and m3a exceed a. If a>0,b=0, two neighbors fix the point and two change the zero coordinate to a nonzero centered residue of 2a, since 0<2a<m. In either case there are two weights 1 and two weights 4/5, giving at most 18/5<73/20. The zero point contributes nothing because g(0)=0; this also handles m=1.

step 1.1algebra
3.1

Every point is covered by the preceding cases. Summing the coefficient bounds proves the claim for Q. The Fourier reduction gives f,Af2Q(f^) and Parseval gives f^2=f2, proving the stated consequence.

F1step 2.1step 2.2step 2.3
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Margulis family has uniform spectral gap

Statement

For every m2 the normalized Margulis adjacency has absolute nontrivial norm α73/80, hence algebraic gap at least 7/80. For m=1 the mean-zero space is zero and α=0.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For every integer m1 and every nonnegative function g on (Z/mZ)2 with g(0)=0, the quadratic expression Q in the Fourier reduction satisfies Q(g)7320zg(z)2. Consequently, for the forward/full adjacency operators and normalized transform in that reduction, f,Af(73/10)f2 for real mean-zero f. (Margulis diamond weight bound).

[F2]

Let V be a finite-dimensional real inner product space and let T:VV be self-adjoint. Then V has an orthonormal basis consisting of eigenvectors of T. (Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis).

Proof

1.1

The diamond bound and its Fourier consequence give f,Af(73/10)f2 for every real mean-zero f. Divide by the degree eight to get f,Mf(73/80)f2.

F1
2.1

The mean-zero subspace is invariant and M is real symmetric. In its orthonormal eigenbasis the preceding bound applied to each eigenvector gives μ73/80, so the operator norm has that bound. This controls negative as well as positive eigenvalues; in particular 1μ27/80. For m=1 use the zero-space norm convention directly.

F2step 1.1
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Expander size adjustment and laziness

Statement

For every integer N1 there is a polynomial-time constructible reverse-paired 128-regular multigraph HN on exactly N vertices with α(HN)ρ0:=1491638400<1. For N2 every S satisfies cut(S)(7/10)min(S,NS). Every vertex has loops.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For every m2 the normalized Margulis adjacency has absolute nontrivial norm α73/80, hence algebraic gap at least 7/80. For m=1 the mean-zero space is zero and α=0. (Margulis family has uniform spectral gap).

[F2]

For a finite d-regular adjacency-slot multigraph on n2 vertices, γ2h2γ,hhVdh. Here γ=1μ2 is the algebraic gap; it is not replaced by 1α. (Cheeger inequalities for finite regular graphs).

Proof

1.1

For N2 put m=N. The Margulis graph has algebraic gap at least 7/80, so Cheeger's lower bound gives unnormalized cut ratio at least 8(7/80)/2=7/20. Partition its m2 vertices, in fixed lexicographic order, into N nonempty consecutive fibers of size at most four. Such a partition exists since Nm24N, by allocating one vertex per fiber and distributing the remainder up to the capacity four.

F1F2
2.1

Sum adjacency entries across fibers to form the quotient, retaining internal slots on its diagonal. Every row has sum at most 32; pad its diagonal to row sum 32. For a quotient cut, the two lifts each have at least as many vertices as their respective sets of fibers. The old cut lower bound therefore yields at least (7/20)min(S,NS) crossing slots. Padding changes no cut, so the degree-32 graph has normalized h7/640.

step 1.1algebra
3.1

Cheeger's other direction yields algebraic gap at least h2/249/819200. Add 32 diagonal slots at each vertex. The normalized matrix becomes (I+M)/2, all its eigenvalues lie in [0,1], and its nontrivial norm is at most 149/1638400. Double all slots, giving degree 128 with even diagonal and unchanged normalized matrix. Cuts double, giving the claimed 7/10 unnormalized ratio.

F2step 2.1
4.1

Symmetry and even diagonal allow explicit reverse pairing: match opposite off-diagonal slots, and pair diagonal slots in order. At least the added loops remain at every vertex. Integer square-root search, fiber allocation, summation, and padding operate on O(N) slots with polynomial-length labels, in polynomial bit time. For N=1 output 128 loop slots; its mean-zero norm is zero and all cut assertions are vacuous.

step 3.1algebra
CorollaryStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Explicit polynomial time constant degree expanders exist

Statement

There is a uniform polynomial-time algorithm producing, for each positive vertex count N, a degree-128 expander HN with absolute nontrivial norm at most 149/1638400. The output has 128N adjacency slots; its bit-time cost is polynomial in N.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For every integer N1 there is a polynomial-time constructible reverse-paired 128-regular multigraph HN on exactly N vertices with α(HN)ρ0:=1491638400<1. For N2 every S satisfies cut(S)(7/10)min(S,NS). Every vertex has loops. (Expander size adjustment and laziness).

Proof

1.1

Apply the all-size construction at the given N. Its bound is independent of N and is strictly smaller than one. For N2 its unnormalized expansion is at least 7/10, so normalized expansion is at least 7/1280.

F1
2.1

The construction explicitly lists 128 destinations at each of the N vertices and runs in polynomial bit time. The singleton output has only loops and satisfies the zero-space spectral convention. Endpoint names require logarithmically many bits, so the adjacency-slot count alone is not a claim of linear bit time.

F1step 1.1
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Constraint graph and labeling value

Definition

A binary constraint graph consists of a finite ordinary undirected multigraph, with paired incidence slots as in Regular multigraph and normalized adjacency, a finite nonempty alphabet Σ, and for each edge a relation ReΣ2 in a specified endpoint order. Reversing that order transposes the relation. Loops have two incidences and test Re(a,a). Relations are explicit Boolean tables.

For a labeling σ:VΣ, valσ(G) is the fraction of ordinary edges satisfied. Put val(G)=maxσvalσ(G) and UNSATσ(G)=1valσ(G), UNSAT(G)=minσUNSATσ(G). An edgeless graph has value one. Isolated vertices may be removed without changing value. Fractions computed using directed slots agree with edge fractions. Duplicating each ordinary edge preserves them. An explicit representation uses O(V+EΣ2) table entries and endpoint names of O(log(V+2)) bits.

DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Constraint graph regularization

Definition

Use the degree-128 graphs Hr of Expander size adjustment and laziness, whose unnormalized edge expansion is at least h0=7/10 when r2. For a constraint graph G as in Constraint graph and labeling value with E, remove isolated vertices and replace each vertex of degree r by a cloud of its r incidence ports. Put Hr inside that cloud, with equality on every edge. Keep one external edge for every original edge, joining its two designated ports and carrying its original relation. A loop's two ports are distinct. Call the resulting graph G1.

On the 2E ports, add a copy of H2E with tautological relations, and at each port add 65 ordinary tautological loops, i.e. 130 loop slots. Call this G2. Its degree is 129+128+130=387; G1 has degree 129. The alphabet is unchanged. For an edgeless input, output the empty graph with value one; positive-degree and nonempty-size claims about G1,G2 are restricted to E. Fix an alphabet ordering for plurality tie breaking and for decoding removed isolated vertices.

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Cloud plurality rounding

Statement

For any labeling of the cloud graph G1 of a nonempty-edge constraint graph G, decode each original vertex by its cloud's plurality label, using fixed tie breaking. Let S count the ports disagreeing with that label, and let Uint,Uext count violated internal equality and external edges. Then Uinth02S,UGUext+S,h0=7/10, where UG is the decoded violation count in G.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

Use the degree-128 graphs Hr of the stated convention, whose unnormalized edge expansion is at least h0=7/10 when r2. For a constraint graph G as in the stated convention with E, remove isolated vertices and replace each vertex of degree r by a cloud of its r incidence ports. Put Hr inside that cloud, with equality on every edge. Keep one external edge for every original edge, joining its two designated ports and carrying its original relation. A loop's two ports are distinct. Call the resulting graph G1. On the 2E ports, add a copy of H2E with tautological relations, and at each port add 65 ordinary tautological loops, i.e. 130 loop slots. Call this G2. Its degree is 129+128+130=387; G1 has degree 129. The alphabet is unchanged. For an edgeless input, output the empty graph with value one; positive-degree and nonempty-size claims about G1,G2 are restricted to E. Fix an alphabet ordering for plurality tie breaking and for decoding removed isolated vertices. (Constraint graph regularization).

Proof

1.1

In a cloud, every label class other than the chosen largest class has size at most half the cloud: a class larger than half would be the unique largest. Its outgoing boundary therefore has at least h0 times its size in edges. Each such edge violates equality, and summing over these classes counts any edge at most twice. Sum also over clouds to obtain 2Uinth0S. Empty classes contribute nothing; a singleton cloud has no disagreeing port.

F1
2.1

Compare the labeling with the labeling constant at its decoded label on each cloud. Every originally violated constraint whose external copy was satisfied must have a changed port at one endpoint. Each changed port is incident to exactly one external edge, so at most S external constraints can newly fail. The constant labeling's external violations equal UG, including original loops whose two incidence ports now carry the same label. This proves UGUext+S, also when S=0.

step 1.1algebra
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Regularization preserves value quantitatively

Statement

Let G have m=E>0 ordinary edges, with the fixed nonempty alphabet and paired-loop convention. Its cloud graph G1 is degree 129, has 2m vertices and 129m ordinary edges, and is constructible in polynomial time without changing the alphabet. Put K=max(1,2/h0)=20/7 and c=1/(129K). Then cUNSAT(G)UNSAT(G1)UNSAT(G)/129. For every labeling τ of G1, plurality decoding Dτ satisfies UNSATDτ(G)129KUNSATτ(G1). For an edgeless input use the empty output convention and UNSAT zero.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For any labeling of the cloud graph G1 of a nonempty-edge constraint graph G, decode each original vertex by its cloud's plurality label, using fixed tie breaking. Let S count the ports disagreeing with that label, and let Uint,Uext count violated internal equality and external edges. Then Uinth02S,UGUext+S,h0=7/10, where UG is the decoded violation count in G. (Cloud plurality rounding).

Proof

1.1

Each original edge has two ports even if it is a loop. Every port has 128 internal slots and one external slot, so there are 2m vertices and 129(2m)/2=129m edges. Listing each all-size cloud expander and copying the original relation tables takes polynomial time in the explicit input size; the sum of polynomial cloud costs is polynomial since their total size is 2m.

F1
2.1

For an arbitrary output labeling, the rounding inequalities give UGUext+(2/h0)UintK(Uext+Uint). Divide by m and use the 129m output edge count to get the assignment-level inequality. Minimizing the output violation fraction then gives the lower bound with c.

F1step 1.1
3.1

An optimal original labeling exists because the alphabet and vertex set are finite nonempty (the empty vertex set has its one labeling). Extend it constantly on each cloud. No equality edge fails and exactly the original bad external edges fail, giving the upper bound after division by 129m. If m=0, both UNSAT values are zero by the stipulated empty-output convention, without these divisions.

step 1.1algebra
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Constraint expander overlay

Statement

For G with m>0 edges, the full preprocessing graph G2 has 2m vertices, degree 387, and 387m ordinary edges over the same alphabet. It has loops at every vertex and α(G2)ρ2:=259+128ρ0387<1. With K=20/7 and c=1/(129K), 129c387UNSAT(G)UNSAT(G2)UNSAT(G)387. For every port labeling τ, UNSATτ(G2)=(129/387)UNSATτ(G1) and UNSATDτ(G)387KUNSATτ(G2). Construction and plurality decoding take polynomial time. The edgeless convention has UNSAT zero.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

Let G have m=E>0 ordinary edges, with the fixed nonempty alphabet and paired-loop convention. Its cloud graph G1 is degree 129, has 2m vertices and 129m ordinary edges, and is constructible in polynomial time without changing the alphabet. Put K=max(1,2/h0)=20/7 and c=1/(129K). Then cUNSAT(G)UNSAT(G1)UNSAT(G)/129. For every labeling τ of G1, plurality decoding Dτ satisfies UNSATDτ(G)129KUNSATτ(G1). For an edgeless input use the empty output convention and UNSAT zero. (Regularization preserves value quantitatively).

Proof

1.1

The prescribed overlay adds 128 slots and 130 loop slots per vertex to the 129-regular cloud graph. Thus its normalized matrix is (129M1+128MH+130I)/387, and ordinary edge count is 387m. For unit mean-zero f, its Rayleigh quotient is at most (129+128ρ0+130)/387 and at least (129128ρ0+130)/387. The absolute value of the lower endpoint is no larger than the positive upper endpoint. The finite-dimensional symmetric spectral decomposition therefore gives αρ2<1.

F1algebra
2.1

All added relations are tautological, so for the same labeling the number of bad edges is unchanged while the denominator changes from 129m to 387m. This proves the exact assignment-level factor, hence also its equality after minimizing over the unchanged set of labelings. Combine with the cloud bounds to obtain the displayed two inequalities.

F1step 1.1
3.1

Substitute the exact factor into the cloud decoder inequality to get UNSATDτ(G)387KUNSATτ(G2). Counting label frequencies in each finite cloud implements fixed plurality tie breaking in polynomial time; isolated original vertices get the first alphabet symbol. The overlay generator and relation copying are polynomial. For no original edges use the stipulated empty graph instead of a positive-degree assertion.

F1step 2.1
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Graph power and walk constraint

Definition

For a d-regular constraint graph with normalized adjacency M under Constraint graph and labeling value, and an integer t1, the adjacency-slot graph power has one slot for every length-t port walk. Its degree is dt, its adjacency is At, and its transition matrix is Mt: matrix multiplication counts walks with all multiplicities. Walk reversal supplies an inverse slot.

The predicate of a walk (v0,e1,v1,,et,vt) tests every original edge relation on the labels of its incident vertex occurrences. Repeated occurrences of one vertex use the same label. Thus it is a conjunction of t tests, on at most t+1 original vertices; it is not in general a binary predicate on endpoint letters. A fixed labeling satisfies the walk predicate exactly when none of the traversed edges is violated.

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Expander walk contraction

Statement

Fix a finite d-regular adjacency-slot multigraph on n1 vertices, with normalized adjacency M, and put α=M1 as in Spectral edge and vertex expansion.

A walk that at each step chooses one of the d ports uniformly has transition matrix M and stationary uniform law u=1/n. For any initial probability vector p and integer t0, using the ordinary Euclidean norm, Mtpu2αtpu2,TV(Mtp,u)n2αt. For t=0 the factor α0 is interpreted as one. For t1, the adjacency-slot power has nontrivial norm αt. Here total variation means half the 1 distance.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For the regular multigraph and spectral conventions in the stated convention, put α=M1. For n2 order the eigenvalues 1=μ1μ2μn, counting multiplicity, and put γ=1μ2. Thus α=maxj2μj, which also controls negative eigenvalues. Write cut(S)=uS,vSAuv and VS={vS:Auv>0 for some uS}. Normalized edge expansion and external vertex expansion are h=min0<Sn/2cut(S)dS,hV=min0<Sn/2VSS. For n=1, put α=0 and leave μ2,γ,h,hV undefined; cut-expansion assertions are vacuous. A bounded-degree family is an expander family when its normalized edge expansion has a positive uniform lower bound for n2. Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in n; neighbor computation in time polynomial in logn is a stronger requirement. (Spectral edge and vertex expansion).

[F2]

For vectors u,v in a real or complex inner product space, u,vuv. Equality holds if and only if u and v are linearly dependent, including the case in which either vector is zero. (Cauchy–Schwarz: u,vuv, with equality exactly for linearly dependent vectors).

Proof

1.1

There are Avw slots leading from v to w, so one-step transition probability is Mvw. Symmetry and row sums imply column sums one, hence stationarity of u. Starting uniformly, all ndt port walks of length t have equal probability.

F1
1.2

Since pu is mean zero, applying the operator norm bound t times gives the Euclidean contraction (the common normalization of inner products cancels). Moreover pu22=pv21/n1. Cauchy–Schwarz bounds q1nq2, giving the total variation assertion. At t=0 the norm inequality is equality before the last bound; at n=1 the difference is zero.

F1F2
2.1

Matrix multiplication counts port walks, so normalized adjacency of the power is Mt. On an orthonormal mean-zero eigenbasis its eigenvalues are μjt; for t1 their largest absolute value is αt. The zero-dimensional case has both sides zero. The estimate allows α=1 and asserts convergence only when α<1.

step 1.2algebra
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Expander walk restricted operator

Statement

Let S have density β=S/n in a finite regular graph and let PS project onto functions supported in S. Then PSMPSα+(1α)β. For a stationary length-t walk, t0, its confinement probability is n11S,(PSMPS)t1S0, where the inner product is unnormalized.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

A walk that at each step chooses one of the d ports uniformly has transition matrix M and stationary uniform law u=1/n. For any initial probability vector p and integer t0, using the ordinary Euclidean norm, Mtpu2αtpu2,TV(Mtp,u)n2αt. For t=0 the factor α0 is interpreted as one. For t1, the adjacency-slot power has nontrivial norm αt. Here total variation means half the 1 distance. (Expander walk contraction).

Proof

1.1

For f supported in S, let Jf be its constant projection. Finite-sum Cauchy–Schwarz gives Jf2βf2. Write f=Jf+f0 with orthogonal parts. Since M fixes the constant part and contracts the other by α, the Rayleigh form is at most Jf2+αf02[α+(1α)β]f2 and at least αf2. The supported symmetric compression has an orthonormal eigenbasis, so its absolute norm has the asserted bound; outside that space it is zero.

F1algebra
2.1

Expand the matrix product: each factor PS deletes precisely the paths with a vertex outside S, and each M factor supplies its step probability. Summing endpoints with the factor 1/n gives uniform initial sampling. For t=0 the expression is S/n; if S is empty it is zero and if S=V it is one. These statements also cover n=1.

F1step 1.1
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Expander walk hits dense bad sets

Statement

Fix a finite d-regular adjacency-slot multigraph on n1 vertices, with normalized adjacency M, and put α=M1.

Let α<1 and let B be a fixed vertex set of density δ[0,1]. For a walk begun from the uniform distribution and taking t0 steps (thus sampling t+1 vertices), Pr[no visit to B](1δ)[1(1α)δ]t(1δ)e(1α)δt. A zeroth power is interpreted as one even when its base is zero.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

Let S have density β=S/n in a finite regular graph and let PS project onto functions supported in S. Then PSMPSα+(1α)β. For a stationary length-t walk, t0, its confinement probability is n11S,(PSMPS)t1S0, where the inner product is unnormalized. (Expander walk restricted operator).

Proof

1.1

Apply the confinement identity to S=VB. The restricted norm is at most q=α+(1α)(1δ)=1(1α)δ. Bounding the matrix power in the unnormalized inner product and using 1S02=n(1δ) gives the first estimate. If S is empty the probability is zero directly.

F1
2.1

For x0, 1xex: the difference has value zero at zero and derivative 1ex0. Here x=(1α)δ[0,1], so raising this inequality to the nonnegative integer t gives the second bound. At t=0 the probability is 1δ; at δ=0 it is one and at δ=1 zero.

step 1.1algebra
PropositionStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Expander walk sampled and moving sets

Statement

For a stationary walk in a finite regular graph, take times 0t0<<tr, gaps gi=titi11, and fixed sets Si of densities βi. Then Pr[XtiSi for all 0ir]β0βri=1r(βi1βi+αgi). For r=0 the empty product is one, giving the exact probability β0.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

Let S have density β=S/n in a finite regular graph and let PS project onto functions supported in S. Then PSMPSα+(1α)β. For a stationary length-t walk, t0, its confinement probability is n11S,(PSMPS)t1S0, where the inner product is unnormalized. (Expander walk restricted operator).

Proof

1.1

Let J be the constant projection. On the mean-zero subspace MJ has norm α, and Mg=J+(MJ)g for g1. The rank-one operator PTJPS has norm βTβS, by the norms of its two indicator vectors. The remaining term has norm at most αg, since projections are contractions. Thus PTMgPSβTβS+αg. This is the same supported-operator framework as confinement.

F1algebra
2.1

Expand the finite path sum with the successive projections, using stationarity to start at t0 uniformly. It is the normalized inner product of the endpoint indicators with the product of the intermediate restricted operators. Bound each operator by the first step and the endpoint norms by β0 and βr. If r=0 this is simply β0; an empty target makes the actual probability zero and the inequality remains valid. No independence of successive visits is used.

step 1.1algebra
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Expander walk bad edge return

Statement

Let F be a nonempty set of nonloop ordinary edges of a reverse-paired d-regular graph, and put ε=F/E. In a stationary walk, condition on some edge being in F. For i1, the probability that the edge i positions later belongs to F is at most ε+αi1. Interpret α0=1.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

A walk that at each step chooses one of the d ports uniformly has transition matrix M and stationary uniform law u=1/n. For any initial probability vector p and integer t0, using the ordinary Euclidean norm, Mtpu2αtpu2,TV(Mtp,u)n2αt. For t=0 the factor α0 is interpreted as one. For t1, the adjacency-slot power has nontrivial norm αt. Here total variation means half the 1 distance. (Expander walk contraction).

Proof

1.1

The conditioned edge is uniform in F and its orientation is uniform. Its terminal vertex therefore has law xv=degF(v)/(2F). The next-step probability of using F from v is yv=degF(v)/d=(2F/d)xv. Also maxxvd/(2F) and xv=1, whence x22d/(2F). This conditioning is legitimate because F.

F1algebra
2.1

Between that terminal vertex and the later tested edge there are i1 transitions, so the probability is yTMi1x. Its constant part is yTu=2F/(dn)=ε. For the other part, spectral contraction and Cauchy–Schwarz give absolute value at most (2F/d)xu22αi1αi1; here y=(2F/d)x allows its constant component to be removed in that inner product. This proves the result, including adjacent edges i=1. The nonloop condition ensures the stated two-endpoint count.

F1step 1.1algebra
PropositionStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Expander walk hits bad edges

Statement

Let a stationary walk traverse t1 edges of a reverse-paired regular graph with α<1. For a fixed set F of nonloop bad edges and ε=F/E, Pr[at least one bad edge]tεtε+1+2/(1α). For F= this lower bound is zero.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

Let F be a nonempty set of nonloop ordinary edges of a reverse-paired d-regular graph, and put ε=F/E. In a stationary walk, condition on some edge being in F. For i1, the probability that the edge i positions later belongs to F is at most ε+αi1. Interpret α0=1. (Expander walk bad edge return).

[F2]

For vectors u,v in a real or complex inner product space, u,vuv. Equality holds if and only if u and v are linearly dependent, including the case in which either vector is zero. (Cauchy–Schwarz: u,vuv, with equality exactly for linearly dependent vectors).

Proof

1.1

For ε>0, let X=j=1tIj count bad edges. Stationarity gives EX=tε. The return bound gives E(IjIj+i)ε(ε+αi1). Hence EX2tε+t(t1)ε2+2tεi=1t1αi1tε[1+tε+2/(1α)]. For t=1 the pair sum is empty.

F1
2.1

On the finite probability space, Cauchy–Schwarz applied to X and the indicator of X>0 yields (EX)2EX2Pr[X>0]. Divide by the positive second-moment bound and cancel tε. If ε=0, then X=0 and the claimed bound is zero directly, without division.

F2step 1.1
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Gap csp

Definition

Fix a finite nonempty alphabet and thresholds 0s<c1. Using the explicit binary constraint encoding of Constraint graph and labeling value, GapCSP(c,s) is the disjoint yes/no pair Y={G:val(G)c},N={G:val(G)s}. Inputs with s<val(G)<c are outside the promise. Malformed encodings are also outside it. For 0<ε1, GapCSP(1,1ε) distinguishes satisfiability from UNSAT(G)ε. This definition asserts no hardness theorem.

5 · Examples, counterexamples and false statements

False statementConstruction: AI-adaptedVerification: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-07Open item page →

Nonconstructive expanders suffice for uniform reductions

Statement

False statement: every choice of one bounded-degree expander on each positive vertex count automatically supplies a polynomial-time uniform adjacency generator for the chosen family.

Facts & Assumptions

Given: the objects and hypotheses in the statement above.

[F1]

For the regular multigraph and spectral conventions in the stated convention, put α=M1. For n2 order the eigenvalues 1=μ1μ2μn, counting multiplicity, and put γ=1μ2. Thus α=maxj2μj, which also controls negative eigenvalues. Write cut(S)=uS,vSAuv and VS={vS:Auv>0 for some uS}. Normalized edge expansion and external vertex expansion are h=min0<Sn/2cut(S)dS,hV=min0<Sn/2VSS. For n=1, put α=0 and leave μ2,γ,h,hV undefined; cut-expansion assertions are vacuous. A bounded-degree family is an expander family when its normalized edge expansion has a positive uniform lower bound for n2. Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in n; neighbor computation in time polynomial in logn is a stronger requirement. (Spectral edge and vertex expansion).

[F2]

There is a uniform polynomial-time algorithm producing, for each positive vertex count N, a degree-128 expander HN with absolute nontrivial norm at most 149/1638400. The output has 128N adjacency slots; its bit-time cost is polynomial in N. (Explicit polynomial time constant degree expanders exist).

Refutation

1.1

Let HN be the explicit degree-128 family with nontrivial norm at most ρ0<1. For N2, let PN be the permutation matrix interchanging vertices one and two and fixing the others. The two matrices CN0=A(HN)+128I and CN1=A(HN)+128PN are symmetric degree-256 adjacency matrices. They differ in entry (1,2). On mean-zero vectors their normalized operator norms are at most η=(ρ0+1)/2<1, since both I and PN have norm one and preserve constants.

F2algebra
2.1

Both families are expanders in the cut sense too: for any S of size at most half, the centered indicator has energy at least (1η)1S(S/N)12, so its normalized cut ratio is at least (1η)(1S/N)(1η)/2. This calculation applies to either choice at each size.

F1step 1.1
3.1

Enumerate all graph-output programs with explicit polynomial-in-N clocks. At stage i0 use size N=i+2 and run the corresponding clocked program there. If its output, parsed as an adjacency matrix, equals CN0, choose CN1; otherwise choose CN0. Fix the singleton output arbitrarily to degree 256 loops. Every selected graph expands with the same constants, yet every polynomial-time generator differs from the chosen graph at its assigned size, even if it uses a different ordering of adjacency slots. Thus the chosen family has no polynomial-time uniform generator.

step 1.1step 2.1algebra

Sources