Portfolios with Deadlines for Backtracking Search

Backtracking search is often the method of choice for solving constraint satisfaction and propositional satisfiability problems. Previous studies have shown that portfolios of backtracking algorithms—a selection of one or more algorithms plus a schedule for executing the algorithms—can dramatically improve performance on some instances. In this paper, we consider a setting that often arises in practice where the instances to be solved arise over time, the instances all belong to some class of problem instances, and a limit or deadline is placed on the computational resources that can be consumed in solving any instance. For such a scenario, we present a simple scheme for learning a good portfolio of backtracking algorithms from a small sample of instances. We demonstrate the effectiveness of our approach through an extensive empirical evaluation using two testbeds: real-world instruction scheduling problems and the widely used quasigroup completion problems.