This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT),
an archetypal NP-complete problem, with the aid of machine learning (ML) techniques. Over …

The Boolean satisfiability problem (SAT) is a famous NP-complete problem in computer
science. An effective way for solving a satisfiable SAT problem is the stochastic local search …

Modern SAT solvers are based on a paradigm named conflict driven clause learning
(CDCL), while local search is an important alternative. Although there have been attempts …

Given a formula ϕ, the problem of uniform sampling seeks to sample solutions of ϕ uniformly
at random. Uniform sampling is a fundamental problem with a wide variety of applications …

Abstract Satisfiability Modulo Linear Integer Arithmetic, SMT (LIA) for short, has significant
applications in many domains. In this paper, we develop the first local search algorithm for …

Partial maximum satisfiability (PMS) is a significant generalization of Boolean satisfiability
(SAT) and maximum satisfiability (MaxSAT), by introducing hard clauses and soft clauses …

Satisfiability Modulo Theories (SMT) refers to the problem of deciding the satisfiability of a
formula with respect to certain background first-order theories. In this article, we focus on …

Abstract Maximum Satisfiability (MaxSAT) is the optimization version of the Satisfiability
(SAT) problem. Partial Maximum Satisfiability (PMS) is a generalization of MaxSAT which …

Satisfiability Modulo Theories (SMT) has significant application in various domains. In this
paper, we focus on quantifier-free Satisfiablity Modulo Real Arithmetic, referred to as SMT …

Abstract Maximum Satisfiability (MaxSAT) is an important NP-hard combinatorial
optimization problem with many applications and MaxSAT solving has attracted much …