An enhanced features extractor for a portfolio of constraint solvers

Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo

doi:10.1145/2554850.2555114

Cited by 22 publications

(35 citation statements)

References 21 publications

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“…For every instance of the benchmark we have extracted a set of 155 different features by exploiting the tool mzn2feat (Amadini et al 2014a). Among the extracted features, 144 were static, i.e., obtained by parsing the source problem instance while 11 were dynamic, i.e., obtained by running the Gecode solver for a short run of 2 seconds.…”

Section: Solvers Dataset and Featuresmentioning

confidence: 99%

SUNNY: a Lazy Portfolio Approach for Constraint Solving

Amadini

Gabbrielli

Mauro

2014

Theory and Practice of Logic Programming

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Abstract*** To appear in Theory and Practice of Logic Programming (TPLP) *** Within the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to computewithout learning an explicit model -a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of sunny-csp conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming.

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Section: Solvers Dataset and Featuresmentioning

confidence: 99%

SUNNY: a Lazy Portfolio Approach for Constraint Solving

Amadini

Gabbrielli

Mauro

2014

Theory and Practice of Logic Programming

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“…Most of the work in Chapter 4 has been published in [7,8,10]. A journal version of [7] is currently under revision.…”

Section: Outlinementioning

confidence: 99%

“…Chapter 6 presents an extended version of [11]. Papers [7,8,10,9,11] are joint works with Dr. Jacopo Mauro and…”

Section: Outlinementioning

confidence: 99%

“…8 This solver uses a k-NN algorithm in order to compute a schedule of the constituent solvers that maximizes the number of instances solved in the neighbourhood. A weak point of CPHydra is that it is not very scalable w.r.t.…”

Section: Other Approachesmentioning

confidence: 99%

“…This however was found to be ineffective. 8 At the time when the experiments were conducted, CPHydra was actually the only available CSP portfolio solver.…”

Section: Other Approachesmentioning

confidence: 99%

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Portfolio approaches in constraint programming

Amadini

2015

Constraints

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Recent research has shown that the performance of a single, arbitrarily efficient algorithm can be significantly outperformed by using a portfolio of -possibly onaverage slower-algorithms. Within the Constraint Programming (CP) context, a portfolio solver can be seen as a particular constraint solver that exploits the synergy between the constituent solvers of its portfolio for predicting which is (or which are) the best solver(s) to run for solving a new, unseen instance.In this thesis we examine the benefits of portfolio solvers in CP. Despite portfolio approaches have been extensively studied for Boolean Satisfiability (SAT) problems, in the more general CP field these techniques have been only marginally studied and used. We conducted this work through the investigation, the analysis and the construction of several portfolio approaches for solving both satisfaction and optimization problems. We focused in particular on sequential approaches, i.e., single-threaded portfolio solvers always running on the same core.We started from a first empirical evaluation on portfolio approaches for solving Constraint Satisfaction Problems (CSPs), and then we improved on it by introducing new data, solvers, features, algorithms, and tools. Afterwards, we addressed the more general Constraint Optimization Problems (COPs) by implementing and testing a number of models for dealing with COP portfolio solvers. Finally, we have come full circle by developing sunny-cp: a sequential CP portfolio solver that turned out to be competitive also in the MiniZinc Challenge, the reference competition for CP solvers.iii iv

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Discriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIP

Akgün

Dang

Miguel

et al. 2020

Integration of Constraint Programming, Artificial Intelligence, and Operations Research

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We extend automatic instance generation methods to allow cross-paradigm comparisons. We demonstrate that it is possible to completely automate the search for benchmark instances that help to discriminate between solvers. Our system starts from a high level humanprovided problem specification, which is translated into a specification for valid instances. We use the automated algorithm configuration tool irace to search for instances, which are translated into inputs for both MIP and CP solvers by means of the Conjure, Savile Row, and MiniZinc tools. These instances are then solved by CPLEX and Chuffed, respectively. We constrain our search for instances by requiring them to exhibit a significant advantage for MIP over CP, or vice versa. Experimental results on four optimisation problem classes demonstrate the effectiveness of our method in identifying instances that highlight differences in performance of the two solvers.

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An enhanced features extractor for a portfolio of constraint solvers

Cited by 22 publications

References 21 publications

SUNNY: a Lazy Portfolio Approach for Constraint Solving

SUNNY: a Lazy Portfolio Approach for Constraint Solving

Portfolio approaches in constraint programming

Discriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIP

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