The decision problem for Boolean satisfiability, generally referred to as SAT, is the
archetypal NP-complete problem, and encodings of many problems of practical interest exist …

The set of novelties introduced with the SAT solver Glucose is now considered as a standard
for practical SAT solving. In this paper, we review the different strategies and technologies …

In this paper, we propose a framework for viewing solver branching heuristics as
optimization algorithms where the objective is to maximize the learning rate, defined as the …

The Boolean satisfiability problem (SAT) is a fundamental NP-complete decision problem in
automated reasoning and mathematical logic. As evidenced by the results of SAT …

We present an approach to learn SAT solver heuristics from scratch through deep
reinforcement learning with a curriculum. In particular, we incorporate a graph neural …

Many computational problems in modern society account to probabilistic reasoning,
statistics, and combinatorics. A variety of these real-world questions can be solved by …

Learnt clauses in CDCL SAT solvers often contain redundant literals. This may have a
negative impact on performance because redundant literals may deteriorate both the …

Modern conflict-driven clause-learning SAT solvers routinely solve large real-world
instances with millions of clauses and variables in them. Their success crucially depends on …

The VSIDS (variable state independent decaying sum) decision heuristic invented in the
context of the CDCL (conflict-driven clause learning) SAT solver Chaff, is considered crucial …

Modern CDCL SAT solvers routinely solve very large industrial SAT instances in relatively
short periods of time. It is clear that these solvers somehow exploit the structure of real-world …