A Parametric Propagator for Discretely Convex Pairs of Sum Constraints

Monette, Jean-Noël; Beldiceanu, Nicolas; Flener, Pierre; Pearson, Justin

doi:10.1007/978-3-642-40627-0_40

Cited by 5 publications

(16 citation statements)

References 11 publications

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“…The instances solved in Schaus (2009) and Monette et al (2013) do not include the original restriction on the number of courses per semester -we take it into account here, which makes the instances significantly harder to solve to optimality. Figure 7 reports the percentage of instances that could be solved to optimality within a given number of backtracks.…”

Section: Balanced Academic Curriculum Problem (Bacp)mentioning

confidence: 99%

“…For the looser instances the dashed curves show the clear superiority of DC maxSD / dom;least loaded over DC dom;least loaded: all 100 instances are solved to optimality in less than a second each, almost all of them within a few hundred backtracks. In comparison Monette et al (2013), implementing BC dom;least loaded, report that 33 out of 100 instances required more that a second to be solved to optimality and even that two instances timed out after 12 hours.…”

Section: Balanced Academic Curriculum Problem (Bacp)mentioning

confidence: 99%

“…A simpler algorithm with the same characteristics was initially proposed by Schaus et al (2007b) for the case of a fixed integral mean. More recently Monette et al (2013) describe a propagator for certain pairs of sum constraints, of which spread and deviation are special cases. It achieves bounds(Z) consistency on X for the latter in O(n) time and for the former (as well as for higher norms) in O(n • | ∪ n i=1 D i |) time.…”

Section: Introductionmentioning

confidence: 99%

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Achieving Domain Consistency and Counting Solutions for Dispersion Constraints

Pesant

2015

INFORMS Journal on Computing

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Many combinatorial problems require of their solutions that they achieve a certain balance of given features. For this important aspect of modeling, the spread and deviation constraints have been proposed in Constraint Programming to express balance among a set of variables by constraining their mean and their overall deviation from the mean. Currently the only practical filtering algorithms known for these constraints achieve bounds consistency. In this paper we improve that filtering by presenting an efficient domain consistency algorithm that applies to both constraints. We also extend it to count solutions so that it can be used in counting-based search, a generic and effective family of branching heuristics that free the user from having to write problem-specific search heuristics. We provide a time complexity analysis of our contributions and also evaluate them empirically on benchmark problems.

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Section: Balanced Academic Curriculum Problem (Bacp)mentioning

confidence: 99%

Section: Balanced Academic Curriculum Problem (Bacp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Achieving Domain Consistency and Counting Solutions for Dispersion Constraints

Pesant

2015

INFORMS Journal on Computing

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“…Positive results have already been achieved on some families of propagators (e.g., [26,21]). More general results are to be expected but will require thorough inspection of existing propagators to discover and factor out recurring abstractions.…”

Section: Verification and Synthesis Of Propagatorsmentioning

confidence: 99%

Toward sustainable development in constraint programming

Beldiceanu¹,

Flener

Monette

et al. 2013

Constraints

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“…where d is the size of the largest domain, 1 . our propagator, once instantiated, matches the theoretical time complexity and practical efficiency of several previously published specialised propagators, as shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%