One of the most important paradigm shifts in the use of SAT solvers for solving industrial
problems has been the introduction of clause learning. Clause learning entails adding a …

This introductory chapter explains the motivation for developing decision diagrams as a new
discrete optimization technology. It shows how decision diagrams implement the five main …

Recent work proposed a toolkit PySAT aiming at fast and easy prototyping with propositional
satisfiability (SAT) oracles in Python, which enabled one to exploit the power of the original …

Maximum satisfiability (MaxSAT) is an optimization version of SAT that is solved by finding
an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function …

Core-guided algorithms proved to be effective on industrial instances of MaxSAT, the
optimization variant of the satisfiability problem for propositional formulas. These algorithms …

A central challenge in software analysis concerns balancing different competing tradeoffs.
To address this challenge, we propose an approach based on the Maximum Satisfiability …

EvalMaxSAT1 is a MaxSAT solver written in modern C++ language mainly using the
Standard Template Library (STL). The solver is built on top of the SAT solver Glucose [1], but …

Boolean Satisfiability (SAT) is arguably the archetypical NP-complete decision problem.
Progress in SAT solving algorithms has motivated an ever increasing number of practical …

Abstract Maximum Satisfiability (MaxSAT) is a well-known optimization version of
Propositional Satisfiability (SAT) that finds a wide range of practical applications. This work …

The computation of minimal conflict sets is a central task when the goal is to find relaxations
or explanations for overconstrained problem formulations and in particular in the context of …