Automatic Algorithm Selection for Pseudo-Boolean Optimization with Given Computational Time Limits

• URL: http://arxiv.org/abs/2309.03924v1
• Date: Thu, 7 Sep 2023 03:04:50 GMT
• Title: Automatic Algorithm Selection for Pseudo-Boolean Optimization with Given Computational Time Limits
• Authors: Catalina Pezo and Dorit Hochbaum and Julio Godoy and Roberto Asin-Acha
• Abstract summary: Machine learning (ML) techniques have been proposed to automatically select the best solver from a portfolio of solvers. These methods, known as meta-solvers, take an instance of a problem and a portfolio of solvers as input. "Anytime" meta-solvers predict the best-performing solver within the specified time limit.
• Score: 0.9831489366502301
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning (ML) techniques have been proposed to automatically select the best solver from a portfolio of solvers, based on predicted performance. These techniques have been applied to various problems, such as Boolean Satisfiability, Traveling Salesperson, Graph Coloring, and others. These methods, known as meta-solvers, take an instance of a problem and a portfolio of solvers as input. They then predict the best-performing solver and execute it to deliver a solution. Typically, the quality of the solution improves with a longer computational time. This has led to the development of anytime selectors, which consider both the instance and a user-prescribed computational time limit. Anytime meta-solvers predict the best-performing solver within the specified time limit. Constructing an anytime meta-solver is considerably more challenging than building a meta-solver without the "anytime" feature. In this study, we focus on the task of designing anytime meta-solvers for the NP-hard optimization problem of Pseudo-Boolean Optimization (PBO), which generalizes Satisfiability and Maximum Satisfiability problems. The effectiveness of our approach is demonstrated via extensive empirical study in which our anytime meta-solver improves dramatically on the performance of Mixed Integer Programming solver Gurobi, which is the best-performing single solver in the portfolio. For example, out of all instances and time limits for which Gurobi failed to find feasible solutions, our meta-solver identified feasible solutions for 47% of these.

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