Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors

Xu, Lin; Hutter, Frank; Hoos, Holger H.; Leyton‐Brown, Kevin

doi:10.1007/978-3-642-31612-8_18

Cited by 72 publications

(91 citation statements)

References 22 publications

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“…In other terms, an important factor for the success of a portfolio is the marginal contribution [187] of the constituent solvers, where with marginal contribution of a solver S we mean the difference in performance between a portfolio solver including S and a portfolio solver excluding S.…”

Section: Datasetmentioning

confidence: 99%

“…As it can be seen, Mistral is by far the best solver, since it is faster than the others for 1622 instances (36% of D). In Figure 4.1b, following [187], we show instead a measure of the marginal contribution of each solver, i.e., how many times a solver is able to solve instances that no other solver can solve. Even in this case Mistral is by far the best solver, almost one order of magnitude better than the second one.…”

Section: Solversmentioning

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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Section: Datasetmentioning

confidence: 99%

Section: Solversmentioning

confidence: 99%

Portfolio approaches in constraint programming

Amadini

2015

Constraints

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“…In practice they propose [26] to train trees to compare each pair of solvers. At the end the approach selects the solver accordingly to a majority vote.…”

Section: Satzilla 2012mentioning

confidence: 99%

SNNAP: Solver-Based Nearest Neighbor for Algorithm Portfolios

Collautti

Malitsky

Mehta

et al. 2013

Advanced Information Systems Engineering

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A Mamma e Papà e ai miei fratelli AbstractThe success of portfolio algorithms in competitions in the area of combinatorial problem solving, as well as in practice, has motivated interest in the development of new approaches to determine the best solver for the problem at hand. Yet, although there are a number of ways in which this decision can be made, it always relies on a rich set of features to identify and distinguish the structure of the problem instances.In this thesis, however, it is firstly shown how not all the features in the problem have the same relevancy. Then it is presented how one of the most successful portfolio approaches, ISAC, can be augmented by taking into account the past performance of solvers as part of the feature vector. Testing on a variety of SAT datasets, it is here proved how the new formulation continuously outperforms an unmodified/standard version of ISAC.This thesis presents a novel strategy that tackles the problem of algorithm portfolios applying machine learning techniques and improves a state-of-the-art portfolio algorithms approaches (ISAC).Starting from the basic assumptions behind the standard ISAC methodology that the given collection of features can be used to correctly identify the structure of each instance, an important step has been to be able to improve clustering, grouping together instances that prefer to be solved by the same solver. It has been created, and here is presented, a methodology to redefine the feature vector including past performance of the solvers. The final result is named SNNAP: Solver-based Nearest Neighbor for Algorithm Portfolios which is here presented.

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“…Based on the observation that solvers have complementary strengths and thus exhibit incomparable behavior on different problem instances, the ideas of running multiple solvers in parallel or to select one solver based on the features of a given instance were introduced. Portfolio research has led to a wealth of different approaches and an amazing boost in solver performance in the past decade [8,16].…”

Section: Solver Portfoliosmentioning

confidence: 99%

“…These features are inspired by the knearest-neighbor classifier that 3S employs. For other approaches like the voting mechanism in [16] one can also craft features (e.g., number of votes for a solver).…”

Section: Accuracy Predictionmentioning

confidence: 99%

Boosting Sequential Solver Portfolios: Knowledge Sharing and Accuracy Prediction

Malitsky

Sabharwal²,

Samulowitz³

et al. 2013

Lecture Notes in Computer Science

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Abstract. Sequential algorithm portfolios for satisfiability testing (SAT), such as SATzilla and 3S, have enjoyed much success in the last decade. By leveraging the differing strengths of individual SAT solvers, portfolios employing older solvers have often fared as well or better than newly designed ones, in several categories of the annual SAT Competitions and Races. We propose two simple yet powerful techniques to further boost the performance of sequential portfolios, namely, a generic way of knowledge sharing suitable for sequential SAT solver schedules which is commonly employed in parallel SAT solvers, and a meta-level guardian classifier for judging whether to switch the main solver suggested by the portfolio with a recourse action solver. With these additions, we show that the performance of the sequential portfolio solver 3S, which dominated other sequential categories but was ranked 10th in the application category of the 2011 SAT Competition, can be boosted significantly, bringing it just one instance short of matching the performance of the winning application track solver, while still outperforming all other solvers submitted to the crafted and random categories.

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Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors

Cited by 72 publications

References 22 publications

Portfolio approaches in constraint programming

Portfolio approaches in constraint programming

SNNAP: Solver-Based Nearest Neighbor for Algorithm Portfolios

Boosting Sequential Solver Portfolios: Knowledge Sharing and Accuracy Prediction

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