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Locally Testable Codes Analogues to the Unique Games Conjecture Do Not Exist. Gillat Kol joint work with Ran Raz. Summary. The Unique Games Conjecture ( UGC ) is an important open problem in the study of PCP s It conjectures the existence of PCPs with special properties - PowerPoint PPT Presentation
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Gillat Koljoint work with Ran Raz
Locally Testable Codes Analogues to the
Unique Games Conjecture Do Not Exist
Summary• The Unique Games Conjecture (UGC) is an important
open problem in the study of PCPs• It conjectures the existence of PCPs with special
properties• Known PCP constructions are based on Locally
Testable Codes (LTCs) with analogues properties• We show that LTCs with properties analogues to the
UGC do not exist• Thus, show limitations of some of the current PCP
constructions techniques
The PCP Theorem
The PCP Theorem• A unbounded prover wants to convince a poly-time
verifier that SAT, by supplying a proof• The verifier wants to only read constant number of
symbols from the proof
• PCP Thm [BFL,FGLSS,AS,ALMSS ‘92]: This can be done!‐ Completeness: SAT proof accepted whp‐ Soundness: SAT proof rejected whp
The PCP Theorem
Probabilistically Checkable Proof p
i j
(2 queries)
1. Toss coins to get locations i and j2. Query pi and pj
3. Using pi and pj, decide if to accept
b q p myp wyr u t
Verifier
The Unique Games Conjecture
Why is the UGC Interesting?• Almost all hardness of approximation results rely on
the PCP Theorem• Yet, for many fundamental problems, optimal
hardness results are still not know• The UGC is a strengthening of the PCP Theorem
shown to imply many improved hardness resultsMax-Cut [MOO ‘05, KKMO ‘07], Vertex-Cover [KR ‘08], CSPs [Rag ‘08],…
Unique Tests• The UGC deals with verifiers V that read 2 locations
and only make unique tests:i,j queried by V permutation ij: s.t.
V accepts iff ij(pi) = pj
• That is, after reading location i, there exists a unique value for location j that makes V accept (and vice versa)
The Unique Games Conjecture
Unique Games Conjecture [Khot ‘02]:,s > 0 consts (const size depends on ,s) s.t. V checking proofs for “SAT” over by only performing unique tests Completeness 1-: SAT proof accepted wp ≥ 1- Soundness s: SAT proof accepted wp < s
Parallel Repetition Theorem [Raz ‘98]: Such a verifier exists when uniqueness is relaxed to projection
Locally Testable Codes
Error Correcting Codes• Hamming Distance:
‐ dist(u,w) = frac of coordinates u and w disagree on‐ agree(u,w) = frac of coordinates u and w agree on
• Error Correcting Code: C n
• Relative Distance: C has relative distance 1- if u w C, dist(u,w) ≥ 1-
equiv. agree(u,w) High relative distance Good error correcting ability
Locally Testable CodesLocally Testable Code: A code C with a tester (prob algo) that checks if a given word v is in C by only reading a constant number of locations Completeness 1-: vC accept wp ≥ 1- Soundness s: dist(v,C) > 1/3 accept wp < s
equiv. accept wp s uC, agree(u,v) 2/3
Low Soundness LTCs• Soundness (review): dist(v,C) > 1- = 1/3 accept wp < s• Observation: s cannot be lower than #queries
s is proportional to : Can only expect low accept prob (small s) for words that are far from the code (small )
• Soundness (generalized): Let s():(0,1)[0,1] be arbitrary (monotone) function
dist(v,C) > 1- accept wp < s() equiv. accept wp ≥ s() uC, agree(u,v)
PCPs and LTCs• Both PCP verifiers and LTC testers test if a given string is
“close” to being “good” (good = valid proof /codeword) by reading only a constant number of locations in it
• Known PCP constructions are based on LTCs with analogues properties
“LTCs Analogues to the UGC”?• (,,s)-LTC: , > 0, s:(0,1)[0,1]
Relative distance 1- (codewords agree frac) Completeness 1- (codewords accepted wp 1-) Soundness s() (dist > 1- accept wp < s())
• The UGC requires a low-error PCP with unique tests
• Uniqueness: A Unique LTC is an LTC with unique tests• Low-error: In known PCPs, the error originates from the
completeness, soundness, and distance of the LTC usedThus, we would have wanted:
> 0 const, LTC with , < and s() < for some
Our Results
Our Result
Theorem (Main):Let n, , s:(0,1)[0,1] be arbitrary (monotone)Assume s() 10-5 for some fixed Denote c1 = 10-102 and c2 = 1010||/
Let C n be an (,,s)-unique LTC. If , c1 then |C| c2
• I.e., fixing s fixes a const c1, s.t. and cannot both be smaller than c1, unless C is of const size
• Some Tightness: = {a, b, c, …}, C = {an, bn, cn, …}. C is a unique-LTC with ==0 (test: vi = vj), and |C|=||
Proof
Constraint Graphs• Proof by way of contradiction:
Let C be such a unique LTC with tester T • T can be viewed as a constraint graph G
‐ Vertex set = [n]‐ There exist an edge (i,j) if T may query locations (i,j) ‐ The edge (i,j) is associated with ij
• A word v satisfies the edge (i,j) if ij(vi) = vj
Step 1 (Main): Decompose GDecompose G to small connected components by removing only a small number of edges (obtain G*)• Each connected component of G* contains n vertices • G* contains 210-4e edges (e = #edges in G)
n vertices
210-4e edges
G*G
Step 2: Constructing a “Bad” Word• Set k 1/ constant• Partition the connected components of G* to k sets, each
containing n/k vertices (components of G* are small) • Let v* be “balanced” hybrid of any k different codewords
(|C| large), agreeing with each on one of the k parts of G*
G*
• v* is far from the code: ‐ v* is a hybrid of codewords‐ Codewords disagree on most coordinates (relative dist) ‐ v* cannot agree with either on many coordinates
• v* is accepted with non-negligible prob: ‐ On every component of G*, v* agrees with a codeword‐ On this component, v* only violates the edges violated
by the codeword‐ v* satisfies most of the edges in G* (Completeness)‐ v* satisfies many edges (G* contains many edges)
v* violates soundness!
v* Violates Soundness
Graph Decomposition(Main)
Decomposition (Review)
n vertices
210-4e edges
G*G
Decompose G to small connected components by removing only a small number of edges (obtain G*)• Each connected component of G* contains n vertices • G* contains 210-4e edges (e = #edges in G)
Decomposition Algo: First Attempt• Decomposition Algorithm:
Repeat‐ Select two new codewords u w‐ Disconnect A, the set of coordinates uand w agree on
• A is small: |A| n (relative distance)
• What about the numberof removed edges?
GA = {i: ui = wi}
How Many Edges Removed?
A = {i: ui = wi}
iijjG
• Observation: Each removed edge (i,j) violates either uor w• Proof: Assume iA, and (i,j) satisfied by both uand w.
Then, uj = ij (ui) = ij(wi) = wj jA • Conclusion: 2e edges were removed (Completeness)
Is 2 Good Enough? No!
We still may be removing too many edges:• |A| n /|| (assume C is a random code)
To decompose G, repeat || times
• Each iteration removes up to 2 frac of the edges Algo removes up to 2|| frac of the edges
• Recall that || may be much larger than 1/ All edges may be removed!
Cutting Down Expenses• Observation (review): Each removed edge violates uor w• Denote: Ev = set of edges violated by the word v
Ev∩A = edges in Ev with end-point in A
• Observation’: We only need to remove edges in Eu∩A and Ew∩A!
Assume A is a random set of size n, and G is regular• v, frac of the edges in Ev are in Ev∩A • Thus, each iteration removes 2 frac of the edges
But A = agree(u,w) Is Not Random• Fix u. Since there are many codewords, u cannot agree
with all on roughly the same set of coordinates• Thus, random selection of w yields “random enough” A
Most of the proof is devoted to showing that…
Thank You!