Impact-Based Search Strategies for Constraint Programming

Refalo, Philippe

doi:10.1007/978-3-540-30201-8_41

Cited by 161 publications

(154 citation statements)

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“…However, once weights are chosen to be between 50 and 119, it becomes much more useful to use degree-based heuristics. The plot also shows the effects of using impact-based search [46]. These results are nearly as good as the degree-based heuristics, but do not beat tailored heuristics.…”

Section: Does Weight Allocation Affect Algorithm Design?mentioning

confidence: 92%

On Maximum Weight Clique Algorithms, and How They Are Evaluated

McCreesh

Prosser

Simpson

et al. 2017

Lecture Notes in Computer Science

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Abstract. Maximum weight clique and maximum weight independent set solvers are often benchmarked using maximum clique problem instances, with weights allocated to vertices by taking the vertex number mod 200 plus 1. For constraint programming approaches, this rule has clear implications, favouring weight-based rather than degree-based heuristics. We show that similar implications hold for dedicated algorithms, and that additionally, weight distributions affect whether certain inference rules are cost-effective. We look at other families of benchmark instances for the maximum weight clique problem, coming from winner determination problems, graph colouring, and error-correcting codes, and introduce two new families of instances, based upon kidney exchange and the Research Excellence Framework. In each case the weights carry much more interesting structure, and do not in any way resemble the 200 rule. We make these instances available in the hopes of improving the quality of future experiments.

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Section: Does Weight Allocation Affect Algorithm Design?mentioning

confidence: 92%

On Maximum Weight Clique Algorithms, and How They Are Evaluated

McCreesh

Prosser

Simpson

et al. 2017

Lecture Notes in Computer Science

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“…All the uncertainties should be included in the model. Philippe Refalo [13] has presented a new general purpose strategy for constraint programming which is inspired from integer programming technique. The importance of a variable for the reduction of the search space is measured by the impact.…”

Section: Review Analysismentioning

confidence: 99%

A Review of Constraint Programming

Dabas¹,

Cooner²

2014

IJCATR

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A constraint is defined as a logical relation among several unknown quantities or variables, each taking a value in a given domain. Constraint Programming (CP) is an emergent field in operations research. Constraint programming is based on feasibility which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the constraints and variables domain rather than the objective functions. While defining a set of constraints, this may seem a simple way to model a real-world problem but finding a good model that works well with a chosen solver is not that easy. A model could be very hard to solve if it is poorly chosen.

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“…Although not related to constructive disjunction, similar strategies have been applied to finite-domain CSPs [4,13].…”

Section: A New Cid-based Splitting Strategymentioning

confidence: 99%

Constructive Interval Disjunction

Trombettoni

Chabert

2007

Lecture Notes in Computer Science

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Abstract. This paper presents two new filtering operators for numerical CSPs (systems with constraints over the reals) based on constructive disjunction, as well as a new splitting heuristic. The fist operator (CID) is a generic algorithm enforcing constructive disjunction with intervals. The second one (3BCID) is a hybrid algorithm mixing constructive disjunction and shaving, another technique already used with numerical CSPs through the algorithm 3B. Finally, the splitting strategy learns from the CID filtering step the next variable to be split, with no overhead.Experiments have been conducted with 20 benchmarks. On several benchmarks, CID and 3BCID produce a gain in performance of orders of magnitude over a standard strategy. CID compares advantageously to the 3B operator while being simpler to implement. Experiments suggest to fix the CID-related parameter in 3BCID, offering thus to the user a promising variant of 3B.

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Impact-Based Search Strategies for Constraint Programming

Cited by 161 publications

References 10 publications

On Maximum Weight Clique Algorithms, and How They Are Evaluated

On Maximum Weight Clique Algorithms, and How They Are Evaluated

A Review of Constraint Programming

Constructive Interval Disjunction

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