Umans Complexity Theory Lectures Lecture 6b: Formula Lower Bounds: -Best known formula lower bound for any NP function -Formula lower bound Ω(n 3-o(1)

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Presentation transcript:

Umans Complexity Theory Lectures Lecture 6b: Formula Lower Bounds: -Best known formula lower bound for any NP function -Formula lower bound Ω(n 3-o(1) ) on Andreev function

Andreev function best formula lower bound for language in NP: Theorem (Andreev, Hastad ‘93): the Andreev function requires ( , ,  )- formulas of size at least Ω(n 3-o(1) ). 2

Andreev function A(x,y) of 2n bits selector Output y i n-bit string y XOR... log n copies; n/log n bits each A : {0,1} 2n  {0,1} 3 n bit x input n bit y input

Random restrictions key idea: given function f:{0,1} n  {0,1} restrict by ρ to get f ρ –ρ sets some variables to 0/1, others remain free R(n, єn) = set of restrictions that leave єn variables free for 0 < є < 1 Definition: L(f) = smallest ( , ,  ) formula computing f (measured as leaf-size) 4

Random restrictions observation: E ρ  R(n, єn) [L(f ρ )] ≤ єL(f) –each leaf survives with probability є => Expected number of leafs after random restriction R(n, єn) is єL(f) may shrink more… –propogate constants 5

Hastad’s shrinkage result Lemma (Hastad 93): for all f E ρ  R(n, єn) [L(f ρ )] ≤ O(є 2-o(1) L(f)) Proof of theorem: –Recall (from counting argument for Formula Lower Bounds): –there exists a hard function of log n variables h : {0,1} log n  {0,1} for which L(h) > n/2loglog n. –hardwire truth table of that hard function h into y to get A * (x) from Andreev function A(x,y) –Let є = 2(log n)(ln log n)/n and m =єn –apply random restriction R(n, m) to A * (x) 6

The lower bound Proof of theorem (continued): –probability given XOR is killed by restriction is probability that we “miss it” m times: (1 – (n/log n)/n) m ≤ (1 – 1/log n) m ≤ (1/e) 2ln log n ≤ 1/log 2 n –probability even one of XORs is killed by restriction is at most: log n(1/log 2 n) = 1/log n < ½. 7

The lower bound –(1): probability even one of XORs is killed by restriction is at most: log n(1/log 2 n) = 1/log n < ½. –(2): by Markov probability distribution tail bound ( Prob(L > k) < E(L)/k ) for k=2: Pr[ L(A * ρ ) > 2 E ρ  R(n, m) [L(A * ρ )] ] < ½. –Conclude: for some restriction ρ all XORs survive, and L(A * ρ ) ≤ 2 E ρ  R(n, m) [L(A * ρ )] 8

The lower bound Proof of theorem (continued): –if all XORs survive, can restrict formula further to compute hard function h may need to add  ’s L(h) = n /2loglogn ≤ L(A * ρ ) ≤ 2E ρ  R(n, m) [L(A * ρ )] ≤ O((m/n) 2-o(1) L(A * )) ≤ O( ((log n)(ln log n)/ n ) 2-o(1) L(A * ) ) –Solving for L(A * ), this implies theorem: Ω(n 3-o(1) ) ≤ L(A * ) ≤ L(A). 9