29 research outputs found
SAT Competition 2020
The SAT Competitions constitute a well-established series of yearly open international algorithm implementation competitions, focusing on the Boolean satisfiability (or propositional satisfiability, SAT) problem. In this article, we provide a detailed account on the 2020 instantiation of the SAT Competition, including the new competition tracks and benchmark selection procedures, overview of solving strategies implemented in top-performing solvers, and a detailed analysis of the empirical data obtained from running the competition. (C) 2021 The Authors. Published by Elsevier B.V.Peer reviewe
SAT Competition 2020
The SAT Competitions constitute a well-established series of yearly open international algorithm implementation competitions, focusing on the Boolean satisfiability (or propositional satisfiability, SAT) problem. In this article, we provide a detailed account on the 2020 instantiation of the SAT Competition, including the new competition tracks and benchmark selection procedures, overview of solving strategies implemented in top-performing solvers, and a detailed analysis of the empirical data obtained from running the competition
Limits of CDCL Learning via Merge Resolution
In their seminal work, Atserias et al. and independently Pipatsrisawat and Darwiche in 2009 showed that CDCL solvers can simulate resolution proofs with polynomial overhead. However, previous work does not address the tightness of the simulation, i.e., the question of how large this overhead needs to be. In this paper, we address this question by focusing on an important property of proofs generated by CDCL solvers that employ standard learning schemes, namely that the derivation of a learned clause has at least one inference where a literal appears in both premises (aka, a merge literal). Specifically, we show that proofs of this kind can simulate resolution proofs with at most a linear overhead, but there also exist formulas where such overhead is necessary or, more precisely, that there exist formulas with resolution proofs of linear length that require quadratic CDCL proofs
Limits of CDCL Learning via Merge Resolution
In their seminal work, Atserias et al. and independently Pipatsrisawat and
Darwiche in 2009 showed that CDCL solvers can simulate resolution proofs with
polynomial overhead. However, previous work does not address the tightness of
the simulation, i.e., the question of how large this overhead needs to be. In
this paper, we address this question by focusing on an important property of
proofs generated by CDCL solvers that employ standard learning schemes, namely
that the derivation of a learned clause has at least one inference where a
literal appears in both premises (aka, a merge literal). Specifically, we show
that proofs of this kind can simulate resolution proofs with at most a linear
overhead, but there also exist formulas where such overhead is necessary or,
more precisely, that there exist formulas with resolution proofs of linear
length that require quadratic CDCL proofs
A Time Leap Challenge for SAT Solving
We compare the impact of hardware advancement and algorithm advancement for
SAT solving over the last two decades. In particular, we compare 20-year-old
SAT-solvers on new computer hardware with modern SAT-solvers on 20-year-old
hardware. Our findings show that the progress on the algorithmic side has at
least as much impact as the progress on the hardware side.Comment: Authors' version of a paper which is to appear in the proceedings of
CP'202
Reducing SAT to Max2SAT
In the literature we find reductions from 3SAT to
Max2SAT. These reductions are based on the usage
of a gadget, i.e., a combinatorial structure that allows translating constraints of one problem to constraints of another. Unfortunately, the generation of
these gadgets lacks an intuitive or efficient method.
In this paper, we provide an efficient and constructive method for Reducing SAT to Max2SAT and
show empirical results of how MaxSAT solvers are
more efficient than SAT solvers solving the translation of hard formulas for Resolution.Supported by projects PROOFS (PID2019-109137GB-C21) and EU-H2020-RIP LOGISTAR (No. 769142)
Refined Core Relaxation for Core-Guided MaxSAT Solving
Maximum satisfiability (MaxSAT) is a viable approach to solving NP-hard optimization problems. In the realm of core-guided MaxSAT solving - one of the most effective MaxSAT solving paradigms today - algorithmic variants employing so-called soft cardinality constraints have proven very effective. In this work, we propose to combine weight-aware core extraction (WCE) - a recently proposed approach that enables relaxing multiple cores instead of a single one during iterations of core-guided search - with a novel form of structure sharing in the cardinality-based core relaxation steps performed in core-guided MaxSAT solvers. In particular, the proposed form of structure sharing is enabled by WCE, which has so-far not been widely integrated to MaxSAT solvers, and allows for introducing fewer variables and clauses during the MaxSAT solving process. Our results show that the proposed techniques allow for avoiding potential overheads in the context of soft cardinality constraint based core-guided MaxSAT solving both in theory and in practice. In particular, the combination of WCE and structure sharing improves the runtime performance of a state-of-the-art core-guided MaxSAT solver implementing the central OLL algorithm