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Alphabet reduction controls explicit size and degree
Statement
Fix a finite alphabet with and let be a finite binary constraint graph over with edge records and maximum degree at most , where the degree of a vertex counts the incidence slots of its incident edge records and a loop therefore contributes two. Let be the alphabet reduction of Fixed-alphabet reduction with constant gap retention and let be its fixed output alphabet of symbols.
- Edges. , where with and for the absolute constant of Shared codeword blocks and edge acceptance circuits; both and depend only on .
- Vertices. , where and is the largest local gadget size; hence the vertex count is .
- Degree. Every vertex of has degree at most , a constant depending only on and .
- Uniformity. All relation tables of use the fixed alphabet , and is computable by a deterministic algorithm in time polynomial in the bit length of the explicit encoding of .
Facts & Assumptions
Given: The fixed alphabet , the input graph with edge records and maximum degree , and the map of Fixed-alphabet reduction with constant gap retention, applied to through the composition and the arity-six conversion.
The map is deterministic, binary, runs in polynomial time in the explicit input encoding, uses the fixed alphabet of size , and satisfies and for a constant depending only on . (Fixed-alphabet reduction with constant gap retention)
If , every edge of contributes exactly constraints of the composition , where over the positive local gadget sizes, so has constraints in total. If is edgeless then has no variables and no constraints. (Composition of an edge system with an assignment tester)
Each active vertex of has one shared block of coordinates, shared by all incident edge gadgets, and every non-named gadget variable has a private copy used by exactly one edge . (Composition of an edge system with an assignment tester)
The code length satisfies and each edge circuit has at most gates besides its formal input bits. (Shared codeword blocks and edge acceptance circuits)
A two-piece tester applied to a circuit with wires has a finite constraint list of at most constraints, each of arity at most six. (A two-piece constant-query PCP of proximity)
The conversion keeps one shared vertex per input variable of the Boolean system and adds one private tuple vertex per listed constraint, joining the tuple vertex to the constraint's variables by at most edge records in total for arities at most . (Bounded-arity Boolean constraints become binary graph constraints)
For each edge circuit, the local tester's total variable count is at most its constraint count ; this follows from the explicit table-variable and nine-family counts in step 1.3 of Fixed-alphabet reduction with constant gap retention. Hence the private auxiliary variables of one edge gadget are at most .
Proof
Given: Fix , , , the input graph , and the constants , , of the statement.
Put and , the arity-six conversion of . If , then has constraints by [F2], where each is the size of the local gadget of edge . Every edge circuit has at most wires by [F4], so each by [F5], and therefore divides . In particular , a constant depending only on .
If , then has no variables and no constraints by [F2], and the conversion of an empty system is an edgeless graph, so all three bounds in clauses 1--3 hold with and every vertex degree zero.
Assume . Each of the constraints of has arity at most six, so the conversion creates at most six edge records per constraint by [F6]. Hence , as asserted in clause 1.
The vertex set of consists of the vertices of together with one private tuple vertex per constraint of by [F6], so . The block coordinates account for at most coordinates, since each of the at most nonisolated vertices contributes coordinates by [F3]. By [F7], the edge-private auxiliary variables of one gadget number at most , giving . Therefore by step 1.1, proving clause 2.
Consider a vertex of that comes from a coordinate of a shared block of . By [F3] this coordinate appears in the gadgets of exactly the edge records incident to , and is incident to at most edge records by hypothesis. In one copy of the gadget of such an edge, the coordinate occurs in at most constraints, each of arity at most six, so at most times; after the uniform duplication of [F2] it occurs at most times in that gadget. Summing over the at most incident records gives .
A vertex of that comes from an edge-private auxiliary variable of belongs to the gadget of exactly one edge, so the same occurrence count gives degree at most ; a private tuple vertex created by the conversion has one edge record per variable occurrence of its constraint, hence degree at most six by [F6]. Since whenever , all these degrees are at most . This proves clause 3.
The determinism, polynomial running time and fixed output alphabet are inherited from the reduction by [F1]; the edge and vertex counts of clauses 1 and 2 are polynomial in by steps 2.1 and 2.2, and all relation tables are the constant-size tables of the -symbol conversion.
Clauses 1, 2, 3 and 4 hold: the edgeless case is step 1.2, the edge and vertex bounds are steps 2.1 and 2.2, the degree bound is steps 2.3 and 3.1, and uniformity is step 3.2. Every constant produced is a function of and alone, namely , and .
Remarks
The degree bound is what makes the iterated transformation self-contained: after one application the output has constant degree depending only on the fixed input alphabet and the input degree bound, so the next round can use the same reduction with the same constants. The count is a constant for fixed even though it is enormous, because each local tester has constant size once the alphabet is fixed. The proof is choice-free: the composition, the least common multiple and the conversion are deterministic finite constructions.
Depends on
Used by
Dependency tree · two levels
21 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Irit Dinur, The PCP Theorem by Gap Amplification, §5, Lemma 1.8 and its proof, printed pp. 17–19 (standard reference, not scraped)
- Sanjeev Arora and Boaz Barak, Computational Complexity: A Modern Approach, §18.5.2, Lemma 18.30 and its proof, printed pp. 378–379 (standard reference, not scraped)