Modern SAT Solving: CDCL and Inprocessing

Abstract

Boolean satisfiability (SAT) has become an attractive approach to solving hard decision and optimization problems arising from artificial intelligence, knowledge representation, and various industrially relevant domains. The success of the SAT-based approach relies heavily on the development of increasingly robust and efficient SAT solvers. This talk gives a two-part overview of the current state-of-the-art SAT solver technology based on the conflict-driven clause learning (CDCL) paradigm. In the first part, I will provide a basic overview of the most important components of CDCL SAT solvers today. The second part of the talk concentrates on the important aspect of practical preprocessing for SAT and the inprocessing SAT solving paradigm in which more extensive reasoning is interleaved with the core satisfiability search (not only before search). I will review some of the most successful SAT preprocessing techniques, and give an overview of our recent work (joint work with Armin Biere and Marijn Heule) on developing new reasoning techniques for pre- and inprocessing.

Biographic sketch

Dr. Matti Järvisalo received his doctorate in computer science from Helsinki University of Technology TKK in 2008, focusing on Boolean constraint reasoning. After the doctorate, Järvisalo has been a postdoctoral researcher in the Computational Logic Group at TKK and a visiting researcher in the Institute for Formal Models and Verification at Johannes Kepler University Linz, Austria. Currently he holds the position of Postdoctoral Researcher in the Department of Computer Science at University of Helsinki, and is a postdoctoral fellow of Academy of Finland. Järvisalo's main research interests include theoretical and practical aspects of computationally hard constraint satisfaction and optimization problems, with an emphasis on efficient Boolean-based decision and optimization procedures with real-world applications, and on the interplay between practical (in)tractability and structural aspects of constraint satisfaction problems.