Ruiwen Chen

Correlation Bounds and #SAT Algorithms for Small Linear-Size Circuits

We revisit the gate elimination method, generalize it to prove correlation bounds of boolean circuits with Parity, and also derive deterministic #SAT algorithms for small linear-size circuits. In particular, we prove that, for boolean circuits of size \(3n - n^{0.51}\), the correlation with Parity is at most \(2^{-n^{\varOmega (1)}}\), and there is a #SAT algorithm running in time \(2^{n-n^{\varOmega (1)}}\); for circuit size 2.99n, the correlation with Parity is at most \(2^{-{\varOmega (n)}}\), and there is a #SAT algorithm running in time \(2^{n-{\varOmega (n)}}\). Similar correlation bounds and algorithms are also proved for circuits of size almost 2.5n over the full binary basis \(B_2\).

Mining Circuit Lower Bound Proofs for Meta-Algorithms

AbstractWe show that circuit lower bound proofs based on the method of random restrictions yield non-trivial compression algorithms for “easy” Boolean functions from the corresponding circuit classes. The compression problem is defined as follows: given the truth table of an n-variate Boolean function f computable by some unknown small circuit from a known class of circuits, find in deterministic time poly(2n) a circuit C (no restriction on the type of C) computing f so that the size of C is less than the trivial circuit size

Improved Algorithms for Sparse MAX-SAT and MAX-k-CSP

{2^n/n}

Lower Bounds against Weakly Uniform Circuits

2n/n. We get non-trivial compression for functions computable by AC0 circuits, (de Morgan) formulas, and (read-once) branching programs of the size for which the lower bounds for the corresponding circuit class are known.These compression algorithms rely on the structural characterizations of “easy” functions, which are useful both for proving circuit lower bounds and for designing “meta-algorithms” (such as Circuit-SAT). For (de Morgan) formulas, such structural characterization is provided by the “shrinkage under random restrictions” results by Subbotovskaya (Doklady Akademii Nauk SSSR 136(3):553–555, 1961) and Håstad (SIAM J Comput 27:48–64, 1998), strengthened to the “high-probability” version by Santhanam (Proceedings of the Fifty-First Annual IEEE Symposium on Foundations of Computer Science, pp 183–192, 2010), Impagliazzo, Meka & Zuckerman (Proceedings of the Fifty-Third Annual IEEE Symposium on Foundations of Computer Science, pp 111–119, 2012b), and Komargodski & Raz (Proceedings of the Forty-Fifth Annual ACM Symposium on Theory of Computing, pp 171–180, 2013). We give a new, simple proof of the “high-probability” version of the shrinkage result for (de Morgan) formulas, with improved parameters. We use this shrinkage result to get both compression and #SAT algorithms for (de Morgan) formulas of size about n2. We also use this shrinkage result to get an alternative proof of the result by Komargodski & Raz (Proceedings of the Forty-Fifth Annual ACM Symposium on Theory of Computing, pp 171–180, 2013) of the average-case lower bound against small (de Morgan) formulas.Finally, we show that the existence of any non-trivial compression algorithm for a circuit class

GRN model of probabilistic databases: construction, transition and querying

{\mathcal{C} \subseteq \mathsf{P}/ \mathsf{poly}}

Generator-Recognizer Networks: A unified approach to probabilistic databases

C⊆P/poly would imply the circuit lower bound