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
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Limits of Real Functions
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Simple Field Extensions and the Construction of the Complex Numbers
- Splitting Fields
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
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
Regular multigraph and normalized adjacency
Definition
A finite -regular adjacency-slot multigraph on , with and integer , is a symmetric matrix with every row sum . counts slots from to ; diagonal entries count loop slots. Its normalized adjacency is . We use unless an unnormalized inner product is explicitly specified.
An adjacency list records the 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 slots; for a reverse-paired graph it induces the uniform distribution on its ordinary edges. Connectivity uses positive off-diagonal entries. When , the mean-zero subspace is .
Constant vector is a top eigenvector
Statement
For a finite -regular adjacency-slot multigraph, , every eigenvalue of lies in , and is invariant. The multiplicity of eigenvalue equals the number of connected components. For a connected graph, 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.
A finite -regular adjacency-slot multigraph on , with and integer , is a symmetric matrix with every row sum . counts slots from to ; diagonal entries count loop slots. Its normalized adjacency is . We use unless an unnormalized inner product is explicitly specified. An adjacency list records the 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 slots; for a reverse-paired graph it induces the uniform distribution on its ordinary edges. Connectivity uses positive off-diagonal entries. When , the mean-zero subspace is . (Regular multigraph and normalized adjacency).
Let be a finite-dimensional real inner product space and let be self-adjoint. Then has an orthonormal basis consisting of eigenvectors of . (Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis).
Proof
The row sums give ; symmetry gives . The real spectral theorem applies since is a symmetric endomorphism of a finite-dimensional real inner product space. If and is maximal and nonzero, then , proving the interval bound.
Expansion of the squares gives . 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.
Similarly . For a nonzero eigenvector all positive slots force . 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 has . At positive degree forces loops and , so no eigenvalue occurs and the mean-zero space is zero.
Spectral edge and vertex expansion
Definition
For the regular multigraph and spectral conventions in Constant vector is a top eigenvector, put . For order the eigenvalues , counting multiplicity, and put . Thus , which also controls negative eigenvalues.
Write and . Normalized edge expansion and external vertex expansion are For , put and leave 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 . Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in ; neighbor computation in time polynomial in is a stronger requirement.
Expander mixing lemma
Statement
For any subsets of a finite -regular adjacency-slot graph on vertices, let count ordered slots. Then Overlap and loop slots are allowed.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For the regular multigraph and spectral conventions in the stated convention, put . For order the eigenvalues , counting multiplicity, and put . Thus , which also controls negative eigenvalues. Write and . Normalized edge expansion and external vertex expansion are For , put and leave 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 . Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in ; neighbor computation in time polynomial in is a stronger requirement. (Spectral edge and vertex expansion).
For vectors in a real or complex inner product space, Equality holds if and only if and are linearly dependent, including the case in which either vector is zero. (Cauchy–Schwarz: , with equality exactly for linearly dependent vectors).
Proof
Set , , and . Both are mean zero and have normalized squared norms and . Since preserves constants and their orthogonal complement, . This counts loops and overlap exactly as specified.
Cauchy–Schwarz and the defining operator bound give . Multiply by . Empty or full sets give zero centered vectors and equality; at all sets are of that form. No division by a set size or by is made.
Cheeger indicator and positive part energy
Statement
Let and use normalized edge expansion and algebraic gap . Then . Moreover some sign of a nonzero mean-zero eigenvector has positive part supported on at most vertices and satisfying .
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For the regular multigraph and spectral conventions in the stated convention, put . For order the eigenvalues , counting multiplicity, and put . Thus , which also controls negative eigenvalues. Write and . Normalized edge expansion and external vertex expansion are For , put and leave 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 . Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in ; neighbor computation in time polynomial in is a stronger requirement. (Spectral edge and vertex expansion).
If is self-adjoint on a nonzero finite-dimensional real inner product space and its eigenvalues are ordered as then (The smallest and largest eigenvalues of a self-adjoint endomorphism are the minimum and maximum Rayleigh quotients).
Proof
On the nonzero invariant space , the Rayleigh quotient of is at least . For , the centered indicator has norm squared and energy . Hence . Minimize over the finite nonempty collection of such sets.
Choose a nonzero mean-zero eigenvector for . It has both positive and negative entries, so one sign has at most positive entries. Let for this sign. At a positive coordinate, since and is nonnegative; thus there. Multiply by , sum, and use elsewhere to obtain the energy bound. This also works when and when some coordinates of vanish.
Cheeger sweep and layer cake
Statement
For a finite -regular graph on vertices and a nonnegative supported on at most vertices, use the unnormalized inner product and energy . Then Also , and there exists a nonzero nonnegative function , supported on at most vertices, with : namely, the positive part of a suitable sign of a nonzero mean-zero 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.
Let and use normalized edge expansion and algebraic gap . Then . Moreover some sign of a nonzero mean-zero eigenvector has positive part supported on at most vertices and satisfying . (Cheeger indicator and positive part energy).
For vectors in a real or complex inner product space, Equality holds if and only if and are linearly dependent, including the case in which either vector is zero. (Cauchy–Schwarz: , with equality exactly for linearly dependent vectors).
Proof
Order coordinates and let . Put . Each difference of squared values telescopes across initial segments, so the middle numerator equals . Each cut is at least , and . For all sums are zero.
Factor and apply Cauchy–Schwarz with weights for . The first squared sum is ; the second is at most . Dividing by proves the upper estimate. Loop terms vanish in the difference sum and only reduce the nonloop degree sum.
Multiplying all normalized inner products by leaves the preceding lemma's inequalities unchanged; thus its positive-part and indicator estimates have exactly the stated unnormalized form. No division by or was used above, so zero functions and zero energy are included.
Cheeger inequalities for finite regular graphs
Statement
For a finite -regular adjacency-slot multigraph on vertices, Here is the algebraic gap; it is not replaced by .
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For a finite -regular graph on vertices and a nonnegative supported on at most vertices, use the unnormalized inner product and energy . Then Also , and there exists a nonzero nonnegative function , supported on at most vertices, with : namely, the positive part of a suitable sign of a nonzero mean-zero eigenvector. These are the indicator and positive-part conclusions of the preceding lemma in the unnormalized inner product. (Cheeger sweep and layer cake).
Proof
Take the nonzero positive part with and combine it with the sweep estimate. Since , this yields . The indicator estimate in the same cited result gives . Zero gap is permitted and forces .
For any nonempty eligible , each vertex of receives at least one and at most cut slots. Thus . The first inequality divided by and minimized gives . Apply the second to a set minimizing to obtain . The eligible collection is nonempty and finite because ; loops never cross its cuts.
Expander independent sets coloring and diameter
Statement
If is independent in a -regular adjacency-slot graph (meaning ), then . Thus a loopless graph with needs at least colors. For and its diameter is at most . A singleton has diameter zero.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For any subsets of a finite -regular adjacency-slot graph on vertices, let count ordered slots. Then Overlap and loop slots are allowed. (Expander mixing lemma).
For a finite -regular adjacency-slot multigraph on vertices, Here is the algebraic gap; it is not replaced by . (Cheeger inequalities for finite regular graphs).
Proof
For , mixing with gives . Cancel the positive and rearrange. For empty the bound holds directly. If there is no nonempty independent set. In a loopless graph every color class is independent, so summing their sizes gives the color bound when .
Every set of size at most has at least times its size in external neighbors, by the edge/vertex comparison. A ball therefore grows by a factor at least until it exceeds . With , a ball of radius 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 . Positive also excludes a separate component of size at most half. For use diameter zero without defining .
Margulis gabber galil graph
Definition
For integer , let . The Margulis–Gabber–Galil graph has the following eight slots at , with all arithmetic modulo : Multiplicities and fixed points are retained under Regular multigraph and normalized adjacency. Write and . Pair each forward affine map with its inverse as reverse ports, even when their destinations coincide.
Margulis family is constant degree and neighbor computable
Statement
The Margulis graph on is symmetric and -regular, with vertices, for every . One specified neighbor is computable in polynomial time in ; the whole adjacency list is computable in bit operations.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For integer , let . The Margulis–Gabber–Galil graph has the following eight slots at , with all arithmetic modulo : Multiplicities and fixed points are retained under the stated convention. Write and . Pair each forward affine map with its inverse as reverse ports, even when their destinations coincide. (Margulis gabber galil graph).
Proof
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.
A slot uses additions, subtraction, doubling, and reduction modulo of integers with bits. School arithmetic performs these in polynomial bit time. Enumerating the coordinate pairs and eight slots proves the total bound. For there is one vertex and eight loop slots; for repeated destinations remain distinct slots.
Finite torus fourier transform
Definition
On , , let and . Residue representatives do not affect these values. With inner product , define The norm is . At there is one character, the constant function one. The sign in the exponent is part of this convention.
Finite torus fourier orthogonality and affine change
Statement
For the normalized negative-exponent Fourier transform on , the characters are an orthonormal basis, and For every invertible matrix over , and ,
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
On , , let and . Residue representatives do not affect these values. With inner product , define The norm is . At there is one character, the constant function one. The sign in the exponent is part of this convention. (Finite torus fourier transform).
Proof
For , is if modulo , and otherwise is zero because multiplication by telescopes to . Applying this to each coordinate shows is one for and zero otherwise. At the one character has norm one directly.
There are orthonormal characters in the -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.
Substitute in the defining sum. The exponent becomes . Bijection of this substitution preserves the sum and its normalization, giving the positive phase in the displayed formula. It covers , constant and zero functions, and the singleton torus as well.
Fourier analysis of margulis adjacency
Statement
Let , modulo . Define the forward operator . For real mean-zero , put and Then , , , and the full Margulis adjacency satisfies .
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For the normalized negative-exponent Fourier transform on , the characters are an orthonormal basis, and For every invertible matrix over , and , (Finite torus fourier orthogonality and affine change).
The Margulis graph on is symmetric and -regular, with vertices, for every . One specified neighbor is computable in polynomial time in ; the whole adjacency list is computable in bit operations. (Margulis family is constant degree and neighbor computable).
For integer , the Margulis–Gabber–Galil graph has at the four slots , , , and the four inverse slots , , , , with multiplicities and fixed points retained. (Margulis gabber galil graph).
Proof
The Fourier identities give and the stated norm equality. Since , the transform of the first pair of summands in is ; the second pair gives . The phase uses .
Parseval's inner-product identity (obtained by expanding both functions in the orthonormal character basis) expresses as the coefficient inner product. Apply the triangle inequality and to obtain . The absolute cosines are independent of residue representatives.
The four slots defining 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 . Therefore , proving the last bound. For the singleton torus every mean-zero function vanishes and all displayed sums are zero.
Margulis diamond weight bound
Statement
For every integer and every nonnegative function on with , the quadratic expression in the Fourier reduction satisfies Consequently, for the forward/full adjacency operators and normalized transform in that reduction, for real mean-zero .
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
Let , modulo . Define the forward operator . For real mean-zero , put and Then , , , and the full Margulis adjacency satisfies . (Fourier analysis of margulis adjacency).
Proof
Represent each coordinate in . Define when both absolute coordinates of are at least those of , with one strict. Put if , if , and otherwise; then . Squaring gives . Apply this to each term of and reindex the inverse-shear terms; a shear leaves its corresponding cosine coordinate fixed. The coefficient at is bounded by , where .
Outside the open diamond , let and . They lie in with , so . Each weight is at most , giving coefficient at most . This includes the diamond boundary and the centered-coordinate endpoints.
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 with . The change strictly decreases its absolute value. For , the centered absolute value is , since . For , both and exceed by the same strict inequality. For , both and exceed , the latter since . Thus exactly three neighbors dominate and one is dominated, including when a coordinate wraps. The coefficient is at most .
If , then . Two neighbors preserve the pair of absolute coordinates, and the other two replace one coordinate by , because both and exceed . If , two neighbors fix the point and two change the zero coordinate to a nonzero centered residue of , since . In either case there are two weights and two weights , giving at most . The zero point contributes nothing because ; this also handles .
Every point is covered by the preceding cases. Summing the coefficient bounds proves the claim for . The Fourier reduction gives and Parseval gives , proving the stated consequence.
Margulis family has uniform spectral gap
Statement
For every the normalized Margulis adjacency has absolute nontrivial norm , hence algebraic gap at least . For the mean-zero space is zero and .
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For every integer and every nonnegative function on with , the quadratic expression in the Fourier reduction satisfies Consequently, for the forward/full adjacency operators and normalized transform in that reduction, for real mean-zero . (Margulis diamond weight bound).
Let be a finite-dimensional real inner product space and let be self-adjoint. Then has an orthonormal basis consisting of eigenvectors of . (Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis).
Proof
The diamond bound and its Fourier consequence give for every real mean-zero . Divide by the degree eight to get .
The mean-zero subspace is invariant and is real symmetric. In its orthonormal eigenbasis the preceding bound applied to each eigenvector gives , so the operator norm has that bound. This controls negative as well as positive eigenvalues; in particular . For use the zero-space norm convention directly.
Expander size adjustment and laziness
Statement
For every integer there is a polynomial-time constructible reverse-paired -regular multigraph on exactly vertices with For every satisfies . Every vertex has loops.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For every the normalized Margulis adjacency has absolute nontrivial norm , hence algebraic gap at least . For the mean-zero space is zero and . (Margulis family has uniform spectral gap).
For a finite -regular adjacency-slot multigraph on vertices, Here is the algebraic gap; it is not replaced by . (Cheeger inequalities for finite regular graphs).
Proof
For put . The Margulis graph has algebraic gap at least , so Cheeger's lower bound gives unnormalized cut ratio at least . Partition its vertices, in fixed lexicographic order, into nonempty consecutive fibers of size at most four. Such a partition exists since , by allocating one vertex per fiber and distributing the remainder up to the capacity four.
Sum adjacency entries across fibers to form the quotient, retaining internal slots on its diagonal. Every row has sum at most ; pad its diagonal to row sum . 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 crossing slots. Padding changes no cut, so the degree- graph has normalized .
Cheeger's other direction yields algebraic gap at least . Add diagonal slots at each vertex. The normalized matrix becomes , all its eigenvalues lie in , and its nontrivial norm is at most . Double all slots, giving degree with even diagonal and unchanged normalized matrix. Cuts double, giving the claimed unnormalized ratio.
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 slots with polynomial-length labels, in polynomial bit time. For output loop slots; its mean-zero norm is zero and all cut assertions are vacuous.
Explicit polynomial time constant degree expanders exist
Statement
There is a uniform polynomial-time algorithm producing, for each positive vertex count , a degree- expander with absolute nontrivial norm at most . The output has adjacency slots; its bit-time cost is polynomial in .
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For every integer there is a polynomial-time constructible reverse-paired -regular multigraph on exactly vertices with For every satisfies . Every vertex has loops. (Expander size adjustment and laziness).
Proof
Apply the all-size construction at the given . Its bound is independent of and is strictly smaller than one. For its unnormalized expansion is at least , so normalized expansion is at least .
The construction explicitly lists destinations at each of the 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.
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 , and for each edge a relation in a specified endpoint order. Reversing that order transposes the relation. Loops have two incidences and test . Relations are explicit Boolean tables.
For a labeling , is the fraction of ordinary edges satisfied. Put and , . 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 table entries and endpoint names of bits.
Constraint graph regularization
Definition
Use the degree- graphs of Expander size adjustment and laziness, whose unnormalized edge expansion is at least when . For a constraint graph as in Constraint graph and labeling value with , remove isolated vertices and replace each vertex of degree by a cloud of its incidence ports. Put 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 .
On the ports, add a copy of with tautological relations, and at each port add ordinary tautological loops, i.e. loop slots. Call this . Its degree is ; has degree . The alphabet is unchanged. For an edgeless input, output the empty graph with value one; positive-degree and nonempty-size claims about are restricted to . Fix an alphabet ordering for plurality tie breaking and for decoding removed isolated vertices.
Cloud plurality rounding
Statement
For any labeling of the cloud graph of a nonempty-edge constraint graph , decode each original vertex by its cloud's plurality label, using fixed tie breaking. Let count the ports disagreeing with that label, and let count violated internal equality and external edges. Then where is the decoded violation count in .
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
Use the degree- graphs of the stated convention, whose unnormalized edge expansion is at least when . For a constraint graph as in the stated convention with , remove isolated vertices and replace each vertex of degree by a cloud of its incidence ports. Put 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 . On the ports, add a copy of with tautological relations, and at each port add ordinary tautological loops, i.e. loop slots. Call this . Its degree is ; has degree . The alphabet is unchanged. For an edgeless input, output the empty graph with value one; positive-degree and nonempty-size claims about are restricted to . Fix an alphabet ordering for plurality tie breaking and for decoding removed isolated vertices. (Constraint graph regularization).
Proof
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 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 . Empty classes contribute nothing; a singleton cloud has no disagreeing port.
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 external constraints can newly fail. The constant labeling's external violations equal , including original loops whose two incidence ports now carry the same label. This proves , also when .
Regularization preserves value quantitatively
Statement
Let have ordinary edges, with the fixed nonempty alphabet and paired-loop convention. Its cloud graph is degree , has vertices and ordinary edges, and is constructible in polynomial time without changing the alphabet. Put and . Then For every labeling of , plurality decoding satisfies . For an edgeless input use the empty output convention and UNSAT zero.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For any labeling of the cloud graph of a nonempty-edge constraint graph , decode each original vertex by its cloud's plurality label, using fixed tie breaking. Let count the ports disagreeing with that label, and let count violated internal equality and external edges. Then where is the decoded violation count in . (Cloud plurality rounding).
Proof
Each original edge has two ports even if it is a loop. Every port has internal slots and one external slot, so there are vertices and 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 .
For an arbitrary output labeling, the rounding inequalities give . Divide by and use the output edge count to get the assignment-level inequality. Minimizing the output violation fraction then gives the lower bound with .
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 . If , both UNSAT values are zero by the stipulated empty-output convention, without these divisions.
Constraint expander overlay
Statement
For with edges, the full preprocessing graph has vertices, degree , and ordinary edges over the same alphabet. It has loops at every vertex and With and , For every port labeling , and . Construction and plurality decoding take polynomial time. The edgeless convention has UNSAT zero.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
Let have ordinary edges, with the fixed nonempty alphabet and paired-loop convention. Its cloud graph is degree , has vertices and ordinary edges, and is constructible in polynomial time without changing the alphabet. Put and . Then For every labeling of , plurality decoding satisfies . For an edgeless input use the empty output convention and UNSAT zero. (Regularization preserves value quantitatively).
Proof
The prescribed overlay adds slots and loop slots per vertex to the -regular cloud graph. Thus its normalized matrix is , and ordinary edge count is . For unit mean-zero , its Rayleigh quotient is at most and at least . The absolute value of the lower endpoint is no larger than the positive upper endpoint. The finite-dimensional symmetric spectral decomposition therefore gives .
All added relations are tautological, so for the same labeling the number of bad edges is unchanged while the denominator changes from to . 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.
Substitute the exact factor into the cloud decoder inequality to get . 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.
Graph power and walk constraint
Definition
For a -regular constraint graph with normalized adjacency under Constraint graph and labeling value, and an integer , the adjacency-slot graph power has one slot for every length- port walk. Its degree is , its adjacency is , and its transition matrix is : matrix multiplication counts walks with all multiplicities. Walk reversal supplies an inverse slot.
The predicate of a walk 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 tests, on at most 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.
Expander walk contraction
Statement
Fix a finite -regular adjacency-slot multigraph on vertices, with normalized adjacency , and put as in Spectral edge and vertex expansion.
A walk that at each step chooses one of the ports uniformly has transition matrix and stationary uniform law . For any initial probability vector and integer , using the ordinary Euclidean norm, For the factor is interpreted as one. For , the adjacency-slot power has nontrivial norm . Here total variation means half the distance.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
For the regular multigraph and spectral conventions in the stated convention, put . For order the eigenvalues , counting multiplicity, and put . Thus , which also controls negative eigenvalues. Write and . Normalized edge expansion and external vertex expansion are For , put and leave 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 . Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in ; neighbor computation in time polynomial in is a stronger requirement. (Spectral edge and vertex expansion).
For vectors in a real or complex inner product space, Equality holds if and only if and are linearly dependent, including the case in which either vector is zero. (Cauchy–Schwarz: , with equality exactly for linearly dependent vectors).
Proof
There are slots leading from to , so one-step transition probability is . Symmetry and row sums imply column sums one, hence stationarity of . Starting uniformly, all port walks of length have equal probability.
Since is mean zero, applying the operator norm bound times gives the Euclidean contraction (the common normalization of inner products cancels). Moreover . Cauchy–Schwarz bounds , giving the total variation assertion. At the norm inequality is equality before the last bound; at the difference is zero.
Matrix multiplication counts port walks, so normalized adjacency of the power is . On an orthonormal mean-zero eigenbasis its eigenvalues are ; for their largest absolute value is . The zero-dimensional case has both sides zero. The estimate allows and asserts convergence only when .
Expander walk restricted operator
Statement
Let have density in a finite regular graph and let project onto functions supported in . Then For a stationary length- walk, , its confinement probability is , where the inner product is unnormalized.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
A walk that at each step chooses one of the ports uniformly has transition matrix and stationary uniform law . For any initial probability vector and integer , using the ordinary Euclidean norm, For the factor is interpreted as one. For , the adjacency-slot power has nontrivial norm . Here total variation means half the distance. (Expander walk contraction).
Proof
For supported in , let be its constant projection. Finite-sum Cauchy–Schwarz gives . Write with orthogonal parts. Since fixes the constant part and contracts the other by , the Rayleigh form is at most and at least . The supported symmetric compression has an orthonormal eigenbasis, so its absolute norm has the asserted bound; outside that space it is zero.
Expand the matrix product: each factor deletes precisely the paths with a vertex outside , and each factor supplies its step probability. Summing endpoints with the factor gives uniform initial sampling. For the expression is ; if is empty it is zero and if it is one. These statements also cover .
Expander walk hits dense bad sets
Statement
Fix a finite -regular adjacency-slot multigraph on vertices, with normalized adjacency , and put .
Let and let be a fixed vertex set of density . For a walk begun from the uniform distribution and taking steps (thus sampling vertices), A zeroth power is interpreted as one even when its base is zero.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
Let have density in a finite regular graph and let project onto functions supported in . Then For a stationary length- walk, , its confinement probability is , where the inner product is unnormalized. (Expander walk restricted operator).
Proof
Apply the confinement identity to . The restricted norm is at most . Bounding the matrix power in the unnormalized inner product and using gives the first estimate. If is empty the probability is zero directly.
For , : the difference has value zero at zero and derivative . Here , so raising this inequality to the nonnegative integer gives the second bound. At the probability is ; at it is one and at zero.
Expander walk sampled and moving sets
Statement
For a stationary walk in a finite regular graph, take times , gaps , and fixed sets of densities . Then For the empty product is one, giving the exact probability .
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
Let have density in a finite regular graph and let project onto functions supported in . Then For a stationary length- walk, , its confinement probability is , where the inner product is unnormalized. (Expander walk restricted operator).
Proof
Let be the constant projection. On the mean-zero subspace has norm , and for . The rank-one operator has norm , by the norms of its two indicator vectors. The remaining term has norm at most , since projections are contractions. Thus . This is the same supported-operator framework as confinement.
Expand the finite path sum with the successive projections, using stationarity to start at 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 and . If this is simply ; an empty target makes the actual probability zero and the inequality remains valid. No independence of successive visits is used.
Expander walk bad edge return
Statement
Let be a nonempty set of nonloop ordinary edges of a reverse-paired -regular graph, and put . In a stationary walk, condition on some edge being in . For , the probability that the edge positions later belongs to is at most . Interpret .
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
A walk that at each step chooses one of the ports uniformly has transition matrix and stationary uniform law . For any initial probability vector and integer , using the ordinary Euclidean norm, For the factor is interpreted as one. For , the adjacency-slot power has nontrivial norm . Here total variation means half the distance. (Expander walk contraction).
Proof
The conditioned edge is uniform in and its orientation is uniform. Its terminal vertex therefore has law . The next-step probability of using from is . Also and , whence . This conditioning is legitimate because .
Between that terminal vertex and the later tested edge there are transitions, so the probability is . Its constant part is . For the other part, spectral contraction and Cauchy–Schwarz give absolute value at most ; here allows its constant component to be removed in that inner product. This proves the result, including adjacent edges . The nonloop condition ensures the stated two-endpoint count.
Expander walk hits bad edges
Statement
Let a stationary walk traverse edges of a reverse-paired regular graph with . For a fixed set of nonloop bad edges and , For this lower bound is zero.
Facts & Assumptions
Given: the objects and hypotheses in the statement above.
Let be a nonempty set of nonloop ordinary edges of a reverse-paired -regular graph, and put . In a stationary walk, condition on some edge being in . For , the probability that the edge positions later belongs to is at most . Interpret . (Expander walk bad edge return).
For vectors in a real or complex inner product space, Equality holds if and only if and are linearly dependent, including the case in which either vector is zero. (Cauchy–Schwarz: , with equality exactly for linearly dependent vectors).
Proof
For , let count bad edges. Stationarity gives . The return bound gives . Hence . For the pair sum is empty.
On the finite probability space, Cauchy–Schwarz applied to and the indicator of yields . Divide by the positive second-moment bound and cancel . If , then and the claimed bound is zero directly, without division.
Gap csp
Definition
Fix a finite nonempty alphabet and thresholds . Using the explicit binary constraint encoding of Constraint graph and labeling value, is the disjoint yes/no pair Inputs with are outside the promise. Malformed encodings are also outside it. For , distinguishes satisfiability from . This definition asserts no hardness theorem.
5 · Examples, counterexamples and false statements
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.
For the regular multigraph and spectral conventions in the stated convention, put . For order the eigenvalues , counting multiplicity, and put . Thus , which also controls negative eigenvalues. Write and . Normalized edge expansion and external vertex expansion are For , put and leave 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 . Polynomial-time constructibility means a uniform algorithm outputs the adjacency list in time polynomial in ; neighbor computation in time polynomial in is a stronger requirement. (Spectral edge and vertex expansion).
There is a uniform polynomial-time algorithm producing, for each positive vertex count , a degree- expander with absolute nontrivial norm at most . The output has adjacency slots; its bit-time cost is polynomial in . (Explicit polynomial time constant degree expanders exist).
Refutation
Let be the explicit degree- family with nontrivial norm at most . For , let be the permutation matrix interchanging vertices one and two and fixing the others. The two matrices and are symmetric degree- adjacency matrices. They differ in entry . On mean-zero vectors their normalized operator norms are at most , since both and have norm one and preserve constants.
Both families are expanders in the cut sense too: for any of size at most half, the centered indicator has energy at least , so its normalized cut ratio is at least . This calculation applies to either choice at each size.
Enumerate all graph-output programs with explicit polynomial-in- clocks. At stage use size and run the corresponding clocked program there. If its output, parsed as an adjacency matrix, equals , choose ; otherwise choose . Fix the singleton output arbitrarily to degree 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.
Sources
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §§2.1–2.3, pp19–21; Dinur §2.1, p8.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §2.3 spectral properties, pp20–21.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; Definitions2.1–2.3 and §2.3, pp19–21.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §2.4 Lemma2.5, p21.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §4.5.1 and beginning §4.5.2, pp40–42.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §4.5.2 Lemmas4.12–4.13, pp41–42.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §4.5 Theorem4.11 (also Theorem2.4), pp40–42.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §2.4 bullets after Lemma2.6, pp21–22; constants sharpened using centered indicators.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; Chapter8 Construction8.1, p69.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; Construction8.1, p69; explicit arithmetic cost analysis.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §8.1.1 Definition8.4 and torus examples, p70.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §8.1.1 Propositions8.5–8.6, pp70–71, corrected normalization.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §8.2 Theorems8.7–8.8, pp71–72; only the reduction, not the sharp constant.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §8.2 Proposition8.9 and complete weaker-bound proof, pp72–73.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; Theorem8.2 with fully proved weaker bound at end §8.2, pp69–73.
- Irit Dinur, The PCP theorem by gap amplification; §2.1 Lemma2.1 and Corollary2.4, pp8–9; HLW Construction8.1 and §4.5; explicit constants adapted.
- Irit Dinur, The PCP theorem by gap amplification; §2.1 Lemma2.1, p8, instantiated by preceding construction.
- Irit Dinur, The PCP theorem by gap amplification; §1.1 Definition1.1 and §1.2 Definition1.2, pp2–3.
- Irit Dinur, The PCP theorem by gap amplification; §4 Definitions4.1–4.2, PDF pages13–14, with explicit degree and loop conventions.
- Irit Dinur, The PCP theorem by gap amplification; §4 proof of Lemma4.1, PDF pages13–14.
- Irit Dinur, The PCP theorem by gap amplification; §4 Lemma4.1, PDF pages13–14.
- Irit Dinur, The PCP theorem by gap amplification; §4 Lemma4.2 and Corollary4.3, pp14–15.
- Irit Dinur, The PCP theorem by gap amplification; §1.2 Powering, pp4–5; underlying walk edges and original constraints.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §3.1 Definition3.1 and §3.1.1 Theorems3.2–3.3, Lemma3.4, pp25–26.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §3.2 Lemmas3.7–3.8, pp28–29; sharpened constant from the same projection proof.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §3.2 Theorem3.6 with the sharper Lemma3.8 estimate above, pp28–29.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §3.2 Theorems3.10–3.11, p29.
- Irit Dinur, The PCP theorem by gap amplification; §2.1 Proposition2.5 and its full proof, pp9–10.
- Irit Dinur, The PCP theorem by gap amplification; §2.1 Proposition2.5 and §2.2 Fact2.6, pp9–10, direct consequence.
- Irit Dinur, The PCP theorem by gap amplification; §1.1 Definition1.1 and Theorem1.2 formulation, pp2–3.
- Hoory–Linial–Wigderson, Expander Graphs and Their Applications, May 2006 draft; §2.1 Definition2.3, p19; Dinur §2.1 Lemma2.1 explicitly requires constructibility.