🤖 AI Summary
This work addresses the efficiency bottleneck in MaxSAT solving caused by frequent calls to ILP solvers. It presents the first systematic investigation into the impact of ILP preprocessing techniques on MaxSAT solving. We propose an ILP constraint reduction and equivalence substitution method specifically tailored for MaxSAT, which structurally simplifies soft and hard constraints in the ILP encoding prior to solving. This significantly reduces the dependency of WMaxCDCL-style solvers on underlying ILP solvers—without compromising solution accuracy or quality—thereby enhancing the robustness and generalizability of solver portfolios. Experimental evaluation on standard benchmarks shows that WMaxCDCL-OpenWbo1200 solves 15 additional instances; ILP solver invocations decrease substantially; and overall throughput and stability improve markedly.
📝 Abstract
The Maximum Satisfiability problem (MaxSAT) is a major optimization challenge with numerous practical applications. In recent MaxSAT evaluations, most MaxSAT solvers have adopted an ILP solver as part of their portfolios. This paper investigates the impact of Integer Linear Programming (ILP) preprocessing techniques on MaxSAT solving. Experimental results show that ILP preprocessing techniques help WMaxCDCL-OpenWbo1200, the winner of the MaxSAT evaluation 2024 in the unweighted track, solve 15 additional instances. Moreover, current state-of-the-art MaxSAT solvers heavily use an ILP solver in their portfolios, while our proposed approach reduces the need to call an ILP solver in a portfolio including WMaxCDCL or MaxCDCL.