Maximum satisfiability in software analysis: Applications and techniques
A central challenge in software analysis concerns balancing different competing tradeoffs.
To address this challenge, we propose an approach based on the Maximum Satisfiability …
To address this challenge, we propose an approach based on the Maximum Satisfiability …
Abstract cores in implicit hitting set MaxSat solving
Abstract Maximum Satisfiability (MaxSat) solving is an active area of research motivated by
numerous successful applications to solving NP-hard combinatorial optimization problems …
numerous successful applications to solving NP-hard combinatorial optimization problems …
Efficient learning of interpretable classification rules
Abstract Machine learning has become omnipresent with applications in various safety-
critical domains such as medical, law, and transportation. In these domains, high-stake …
critical domains such as medical, law, and transportation. In these domains, high-stake …
Grover-QAOA for 3-SAT: Quadratic speedup, fair-sampling, and parameter clustering
The SAT problem is a prototypical NP-complete problem of fundamental importance in
computational complexity theory with many applications in science and engineering; as …
computational complexity theory with many applications in science and engineering; as …
Learning from survey propagation: a neural network for MAX-E-3-SAT
R Marino - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Many natural optimization problems are NP-hard, which implies that they are probably hard
to solve exactly in the worst-case. However, it suffices to get reasonably good solutions for …
to solve exactly in the worst-case. However, it suffices to get reasonably good solutions for …
[PDF][PDF] A Core-Guided Approach to Learning Optimal Causal Graphs.
Discovery of causal relations is an important part of data analysis. Recent exact Boolean
optimization approaches enable tackling very general search spaces of causal graphs with …
optimization approaches enable tackling very general search spaces of causal graphs with …
[PDF][PDF] Uncovering and Classifying Bugs in MaxSAT Solvers through Fuzzing and Delta Debugging.
In this study we continue the success story of fuzz testing automated reasoning tools by
providing the first extensive fuzzing study on MaxSAT solvers. As somewhat expected we …
providing the first extensive fuzzing study on MaxSAT solvers. As somewhat expected we …
A MaxSAT-based framework for group testing
The success of MaxSAT (maximum satisfiability) solving in recent years has motivated
researchers to apply MaxSAT solvers in diverse discrete combinatorial optimization …
researchers to apply MaxSAT solvers in diverse discrete combinatorial optimization …
Sade: Learning models that provably satisfy domain constraints
In many real world applications of machine learning, models have to meet certain domain-
based requirements that can be expressed as constraints (for example, safety-critical …
based requirements that can be expressed as constraints (for example, safety-critical …
UpMax: user partitioning for MaxSAT
It has been shown that Maximum Satisfiability (MaxSAT) problem instances can be
effectively solved by partitioning the set of soft clauses into several disjoint sets. The …
effectively solved by partitioning the set of soft clauses into several disjoint sets. The …