Non-trivial derandomization implies weak lower bounds: An (almost) elementary proof Roei Tell August 15, 2017

A derandomization algorithm for a circuit class C is a deterministic algorithm that gets as input a circuit C ∈ C , and distinguishes between the case that the acceptance probability of C is one and the case that the acceptance probability of C is less than half. (Indeed, in this text we focus on derandomization of circuits with one-sided error.) Ryan Williams’ fundamental result [Wil13] asserts that for many circuit classes C , any non-trivial derandomization of C implies weak lower bounds for C . In Williams’ original result, “weak lower bounds” means that C does not contain the (large) class N E X P . The current text presents an alternative proof of the result, in which “weak lower bounds” means that C does not contain the (also large) class E N P . 1 The alternative proof is folklore, and is partially sketched in [BV14] and somewhat implicit in [Wil13]. The advantages of this alternative proof are that it applies to more circuit classes C , and that it is short and almost completely self-contained. In fact, the only non-elementary part in the proof is a black-box use of a known PCP construction. As in the original result, to obtain lower bounds for a class C of circuits of some fixed size and depth, one needs a non-trivial derandomization algorithm for a class Cb of circuits of polynomially-larger size and of somewhat larger depth. The exact overhead in the proof (i.e., the difference between C and Cb) is flexible, since it depends on various technical details. Since these details are of secondary importance, I will first state the main theorem slightly informally, without fully defining what Cb is with respect to C , and elaborate on this point immediately after the proof. Theorem 1 (non-trivial derandomization implies weak lower bounds; informal). Let C be a class of circuits of size less than 20.99·n that is closed under any fixing of some of the input bits, and let Cb be a class of circuits that are “slightly larger” than circuits in C . Assume that there exists a deterministic algorithm D that, when given as input a circuit C ∈ Cb over n input bits, distinguishes in time 2n /nω (1) between the case that C accepts all of its inputs and the case that C rejects most of its inputs. Then, there exists a function f ∈ E N P such that f ∈ / C. far as I know, the two results are incomparable, since I don’t know of any containment E N P ⊆ N E X P or N E X P ⊆ E N P . Note that both classes are contained in E X P N P . 1 As

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Proof. Assume towards a contradiction that E N P ⊆ C . Fixing an arbitrary set L ∈ NTI ME(2n ), we will construct a non-deterministic machine that decides L in time 2n /nω (1) . This will contradict the non-deterministic time hierarchy. Towards describing the machine, first fix a PCP system for L with the following properties: The verifier in this system, denoted V, is given an input x ∈ {0, 1}n , n0

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randomness r ∈ {0, 1}n , and a proof-oracle π x ∈ {0, 1}2 for x, where n0 = n + O(log(n)); the verifier issues poly(n) queries to π x , verifies the answers to the queries in time poly(n), and has perfect completeness and soundness 1/2. A PCP construction with such properties appears in [BGH+ 05], which builds on [BS05]. Observe that if we denote N = 2n (such that L ∈ NTI ME( N )), then the proof length is O˜ ( N ), and the number of queries and running time of V are polylogarithmic in N. The strategy of the machine is as follows. Assume, for a moment, that for any x ∈ L there exists a proof π x for x that has a concise representation by a circuit from C . Specifically, assume that there exists a circuit Px ∈ C such that for i ∈ [n0 ] it holds that Px (i ) is the ith bit of some (fixed) proof π x for x. Since circuits in C are of size at most 20.99·n , the representation length of Px is much smaller than the length of proofs in the PCP 0 system (i.e., 2n ). Then, given input x ∈ {0, 1}n , the machine can non-deterministically guess the circuit Px , and construct a circuit Cx,Px ∈ Cb such that Cx,Px (r ) = V Px ( x, r ); that is, the circuit Cx,Px implements the verification procedure of V, while resolving oracle queries using copies of the circuit Px . Observe that if x ∈ L, then for some guess of Px it holds that Cx,Px has acceptance probability one, and if x ∈ / L, then for any guess of Px it holds that Cx,Px has acceptance probability at most 1/2. The machine can distinguish 0 between these two cases in time 2n /(n0 )ω (1) = 2n /nω (1) using the algorithm D from the theorem’s hypothesis. The key observation is that if E N P ⊆ C , then for any x ∈ L there indeed exists a concise representation of a proof π x for x. To see this, consider the function Π that gets 0 as input x and a location i ∈ [2n ], and outputs the ith bit of the lexicographically-first correct proof for x, if such a proof exists. Note that the function Π is in E N P , since the lexicographically-first proof for x can be constructed in its entirety by an E N P machine, bit-by-bit. 2 Therefore, relying on the hypothesis that E N P ⊆ C , there exists a circuit P ∈ C such that P( x, i ) = Π( x, i ). By hard-wiring x into P, there exists a circuit Px ∈ C such that Px (i ) = Π( x, i ). In the theorem’s hypothesis, we assumed that the algorithm D works when given a circuit from a class Cb of circuits that we informally referred to as “slightly larger” than circuits in C . What we will actually rely on is that the algorithm D works when given the circuit Cx,Px . To conclude, let us bound the running-time of the non-deterministic machine. Since the verifier V runs in time poly(n), and circuits in C are of size at most 20.99·n , the size of Cx,Px is at most poly(n) · 20.99·n = 2(1−Ω(1))·n . Therefore, Cx,Px can be constructed in time 2(1−Ω(1))·n . The running time is thus dominated by the running 0 time of the algorithm D, which is at most 2n /(n0 )ω (1) = 2n /nω (1) . 0

the E N P algorithm works in 2n iterations: In the ith iteration, the algorithm extends the prefix of length i − 1 of the lexicographically-first proof for x by a single bit, using the N P oracle to decide whether or not the prefix can be extended by zero. 2 Specifically,

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Let me now be more specific with respect to the definition of the circuit class Cb. The exact requirements from Cb depend on the specific PCP system that we use in the proof; that is, fixing a suitable PCP system with a verifier V, the class Cb can be defined as the class of circuits that, when given input r, implement the verification procedure of V on some input x with randomness r, while resolving each query to the proof oracle by a sub-circuit from C . Subsequent research indeed focused on constructing PCP systems with extremely efficient verifiers; see [BV14]. The only property of E N P that is used in the proof is that the function Π is in E N P (which allowed us to deduce that for any x ∈ L there exists a concise representation of a proof π x for x by a C -circuit). Thus, using the same proof, we can separate C from any class that contains Π. As mentioned above, in the original proof of Williams, instead of separating the class C from E N P , the class C is separated from N E X P . Indeed, for many specific circuit classes C , if N E X P ⊆ C , then for any x ∈ L there exists a concise representation of a proof π x for x by a C -circuit (see [Wil13, Wil11, SW13] for further details). However, this fact is highly non-trivial, and follows from the work of Impagliazzo, Kabanets, and Wigderson [IKW02]. Finally, one can immediately strengthen Theorem 1 in two ways. First, we don’t have to unconditionally assume that the algorithm D exists; in fact, it suffices to only assume that D exists under the hypothesis that E N P ⊆ C . Secondly, since we are using the algorithm D as a sub-routine of a non-deterministic algorithm, we can allow D to be non-deterministic itself. However, the non-determinism of D should be such that there exists a proof that leads D to accept circuits with acceptance probability one, whereas circuits with low acceptance probability are rejected by D (regardless of the proof); it’s not a-priori clear how non-determinism might be useful for this task.

References [BGH+ 05]

Eli Ben-Sasson, Oded Goldreich, Prahladh Harsha, Madhu Sudan, and Salil Vadhan. Short pcps verifiable in polylogarithmic time. In Proc. 20th Annual IEEE Conference on Computational Complexity (CCC), pages 120–134, 2005.

[BS05]

Eli Ben-Sasson and Madhu Sudan. Simple PCPs with poly-log rate and query complexity. In Proc. 37th Annual ACM Symposium on Theory of Computing (STOC), pages 266–275. 2005.

[BV14]

Eli Ben-Sasson and Emanuele Viola. Short PCPs with projection queries. In Proc. 41st International Colloquium on Automata, Languages and Programming (ICALP), pages 163–173. 2014.

[IKW02]

Russell Impagliazzo, Valentine Kabanets, and Avi Wigderson. In search of an easy witness: exponential time vs. probabilistic polynomial time. Journal of Computer and System Sciences, 65(4):672–694, 2002.

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[SW13]

Rahul Santhanam and Ryan Williams. On medium-uniformity and circuit lower bounds. In Proc. 28th Annual IEEE Conference on Computational Complexity (CCC), pages 15–23. 2013.

[Wil11]

Ryan Williams. Non-uniform ACC circuit lower bounds. In Proc. 26th Annual IEEE Conference on Computational Complexity (CCC), pages 115–125. 2011.

[Wil13]

Ryan Williams. Improving exhaustive search implies superpolynomial lower bounds. SIAM Journal of Computing, 42(3):1218–1244, 2013.

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Non-trivial derandomization implies weak lower bounds

Aug 15, 2017 - fixed size and depth, one needs a non-trivial derandomization algorithm .... Journal of Computer and System Sciences, 65(4):672–694, 2002. 3 ...

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