Using CBR to Select Solution Strategies in Constraint Programming

Gebruers, Cormac; Hnich, Brahim; Bridge, Derek; Freuder, Eugene C.

doi:10.1007/11536406_19

Cited by 28 publications

(21 citation statements)

References 11 publications

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“…Such algorithm portfolios include methods that select a single algorithm on a per-instance basis [21,7,10,31,24,26], methods that make online decisions between algorithms [16,3,23], and methods that run multiple algorithms independently on one instance, either in parallel or sequentially [13,9,20,6,27,14,22,11].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors

Hutter

Hoos

et al. 2012

Lecture Notes in Computer Science

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Abstract. Portfolio-based methods exploit the complementary strengths of a set of algorithms and-as evidenced in recent competitions-represent the state of the art for solving many NP-hard problems, including SAT. In this work, we argue that a state-of-the-art method for constructing portfolio-based algorithm selectors, SATzilla, also gives rise to an automated method for quantifying the importance of each of a set of available solvers. We entered a substantially improved version of SATzilla to the inaugural "analysis track" of the 2011 SAT competition, and draw two main conclusions from the results that we obtained. First, automatically-constructed portfolios of sequential, non-portfolio competition entries perform substantially better than the winners of all three sequential categories. Second, and more importantly, a detailed analysis of these portfolios yields valuable insights into the nature of successful solver designs in the different categories. For example, we show that the solvers contributing most to SATzilla were often not the overall best-performing solvers, but instead solvers that exploit novel solution strategies to solve instances that would remain unsolved without them.

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Section: Introductionmentioning

confidence: 99%

Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors

Hutter

Hoos

et al. 2012

Lecture Notes in Computer Science

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“…CPHydra, using a CBR system for configuring a set of solvers to maximize the chances of solving an instance in 1800 seconds, was overall winner of the 2008 International CSP Solver Competition. Gebruers et al [53] also use casebased reasoning to select solution strategies for constraint satisfaction. In contrast, SATzilla [131] relies on runtime prediction models to select the solver from its portfolio that (hopefully) has the fastest running time on a given problem instance.…”

Section: Related Workmentioning

confidence: 99%

Constraints Meet Concurrency

Mauro

2014

Atlantis Studies in Computing

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We investigate the benefits that emerge when the fields of constraint programming and concurrency meet. On one hand, constraints can be use in concurrency theory to increase the conciseness and the expressive power of concurrent languages from a pragmatic point of view. On the other hand, problems modeled by using constraints can be solved faster and more efficiently using a concurrent system. We explore both directions providing two separate lines of contribution. Firstly we study the expressive power of a concurrent language, namely Constraint Handling Rules, that supports constraints as a primitive construct. We show what features of this language make it Turing powerful. Then we propose a framework to solve constraint problems that is intended to be deployed on a concurrent system. For the development of this framework we used the concurrent language Jolie following the Service Oriented paradigm. Based on this experience, we also propose an extension to Service Oriented Languages to overcome some of their limitations and to improve the development of concurrent applications. Needless to say, I am also in debt with my coauthors:

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“…Given a set of solvers and a set of constraint satisfaction problems (CSPs), no one solver may consistently outperform all the others on every problem (e.g., [1][2][3][4][5]). Informally, an algorithm portfolio is a set of algorithms that run according to some schedule on a set of problems.…”

Section: Introductionmentioning

confidence: 99%

Learning Algorithm Portfolios for Parallel Execution

Epstein

2012

Lecture Notes in Computer Science

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Abstract. Portfolio-based solvers are both effective and robust, but their promise for parallel execution with constraint satisfaction solvers has received relatively little attention. This paper proposes an approach that constructs algorithm portfolios intended for parallel execution based on a combination of case-based reasoning, a greedy algorithm, and three heuristics. Empirical results show that this method is efficient, and can significantly improve performance with only a few additional processors. On problems from solver competitions, the resultant algorithm portfolios perform nearly as well as an oracle.

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Using CBR to Select Solution Strategies in Constraint Programming

Cited by 28 publications

References 11 publications

Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors

Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors

Constraints Meet Concurrency

Learning Algorithm Portfolios for Parallel Execution

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