A self-adaptive multi-engine solver for quantified Boolean formulas

Pulina, Luca; Tacchella, Armando

doi:10.1007/s10601-008-9051-2

Cited by 70 publications

(72 citation statements)

References 27 publications

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“…In some cases the over-abundance of solvers hinders the effectiveness of the considered approach. As seen in Section 4.1, and pointed out also by [155], usually the best results are obtained by adopting a relatively small portfolio (e.g., ten or even less solvers).…”

Section: Sunny Algorithmmentioning

confidence: 88%

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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Section: Sunny Algorithmmentioning

confidence: 88%

Portfolio approaches in constraint programming

Amadini

2015

Constraints

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“…Finally, we remark that portfolio approaches can be successfully applied in the most disparate domains. Besides SAT and CSP fields, successful portfolio solvers have been developed also for Answer-Set Programming (ASP) [15], Quantified Boolean Formula (QBF) [38], Planning [44], Constraint Optimization Problems (COPs) [6].…”

Section: Discussionmentioning

confidence: 99%

An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems

Amadini¹,

Gabbrielli²,

Mauro³

2016

IJIMAI

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-In the context of Constraint Programming, a portfolio approach exploits the complementary strengths of a portfolio of different constraint solvers. The goal is to predict and run the best solver(s) of the portfolio for solving a new, unseen problem. In this work we reproduce, simulate, and evaluate the performance of different portfolio approaches on extensive benchmarks of Constraint Satisfaction Problems. Empirical results clearly show the benefits of portfolio solvers in terms of both solved instances and solving time.

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“…Portfolio solvers have been successful in combinatorially cleaner domains such as SAT solving [27,35,42], quantified boolean satisfiability (QSAT) [32,33,36], answer set programming (ASP) [20,29], and various constraint satisfaction problems (CSP) [21,28,30]. In contrast to software verification, in these areas constituent tools are usually assumed to be correct.…”

Section: Related Workmentioning

confidence: 99%

Empirical software metrics for benchmarking of verification tools

Demyanova

Pani

Veith

et al. 2017

Form Methods Syst Des

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We study empirical metrics for software source code, which can predict the performance of verification tools on specific types of software. Our metrics comprise variable usage patterns, loop patterns, as well as indicators of control-flow complexity and are extracted by simple data-flow analyses. We demonstrate that our metrics are powerful enough to devise a machine-learning based portfolio solver for software verification. We show that this portfolio solver would be the (hypothetical) overall winner of the international competition on software verification (SV-COMP) in three consecutive years (2014)(2015)(2016). This gives strong empirical evidence for the predictive power of our metrics and demonstrates the viability of portfolio solvers for software verification. Moreover, we demonstrate the flexibility of our algorithm for portfolio construction in novel settings: originally conceived for SV-COMP'14, the construction works just as well for SV-COMP'15 (considerably more verification tasks) and for SV-COMP'16 (considerably more candidate verification tools).

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A self-adaptive multi-engine solver for quantified Boolean formulas

Cited by 70 publications

References 27 publications

Portfolio approaches in constraint programming

Portfolio approaches in constraint programming

An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems

Empirical software metrics for benchmarking of verification tools

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