Parallelizing Constraint Solvers for Hard RCPSP Instances

Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo

doi:10.1007/978-3-319-50349-3_16

Cited by 3 publications

(7 citation statements)

References 28 publications

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“…In particular, it appears quite strange that sunny-cp − performed far worse than sunny-cp −− although having more, and ideally better, solvers. We then thoroughly investigated this anomaly since, as also shown in Amadini et al (2015a;2016a), the dynamic scheduling of the available solvers is normally more fruitful than statically running an arbitrarily good subset of them over the available cores.…”

Section: Minizinc Challenge 2016mentioning

confidence: 94%

SUNNY-CP and the MiniZinc challenge

Amadini¹,

Gabbrielli²,

Mauro³

2017

Theory and Practice of Logic Programming

Self Cite

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In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver sunny-cp, and we discuss its performance in the MiniZinc Challenge-the annual international competition for CP solvers-where it won two gold medals in 2015 and 2016.

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Section: Minizinc Challenge 2016mentioning

confidence: 94%

SUNNY-CP and the MiniZinc challenge

Amadini¹,

Gabbrielli²,

Mauro³

2017

Theory and Practice of Logic Programming

Self Cite

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show abstract

“…Without domain knowledge, more generic features have to be used to capture the characteristics and variance of different constraint models and their instances. One approach is the design of portfolio solvers, where a learning model is used to decide which solver to run for a given problem instance [28][29][30][31][32][33]. Feature extraction exploits the structure of the general constraint model and the specific instance, its constraints and variables, and their domains.…”

Section: Feature Selectionmentioning

confidence: 99%

“…The previously discussed work considered algorithm configuration and selection within one solver to optimize its performance. As mentioned earlier, other approaches are focused towards combining multiple distinct solvers into a portfolio solver [33]. Using machine learning and heuristics, the planning component of the portfolio solver determines the execution schedule of the solver [30,31].…”

Section: Algorithm Selection and Configurationmentioning

confidence: 99%

Predictive Machine Learning of Objective Boundaries for Solving COPs

Spieker

Gotlieb

2021

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Solving Constraint Optimization Problems (COPs) can be dramatically simplified by boundary estimation, that is providing tight boundaries of cost functions. By feeding a supervised Machine Learning (ML) model with data composed of the known boundaries and extracted features of COPs, it is possible to train the model to estimate the boundaries of a new COP instance. In this paper, we first give an overview of the existing body of knowledge on ML for Constraint Programming (CP), which learns from problem instances. Second, we introduce a boundary estimation framework that is applied as a tool to support a CP solver. Within this framework, different ML models are discussed and evaluated regarding their suitability for boundary estimation, and countermeasures to avoid unfeasible estimations that avoid the solver finding an optimal solution are shown. Third, we present an experimental study with distinct CP solvers on seven COPs. Our results show that near-optimal boundaries can be learned for these COPs with only little overhead. These estimated boundaries reduce the objective domain size by 60-88% and can help the solver find near-optimal solutions early during the search.

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“…Without domain knowledge, more generic features have to be used to capture the characteristics and variance of different constraint models and their instances. One approach is the design of portfolio solvers, where a learning model is used to decide which solver to run for a given problem instance [74,57,50,64,7,8]. Feature extraction exploits the structure of the general constraint model and the specific instance, its constraints, variables and their domains.…”

Section: Representationmentioning

confidence: 99%

“…The previously discussed work considered algorithm configuration and selection within one solver to optimize its performance. As mentioned earlier, other approaches are focused towards combining multiple distinct solvers into a portfolio solver [8]. Using machine learning and heuristics, the planning component of the portfolio solver determines the execution schedule of the solver [50,64].…”

Section: Algorithm Selection and Configurationmentioning

confidence: 99%

Predictive Machine Learning of Objective Boundaries for Solving COPs

Spieker,

Gotlieb

2021

Preprint

View full text Add to dashboard Cite

Solving Constraint Optimization Problems (COPs) can be dramatically simplified by boundary estimation, that is, providing tight boundaries of cost functions. By feeding a supervised Machine Learning (ML) model with data composed of known boundaries and extracted features of COPs, it is possible to train the model to estimate boundaries of a new COP instance. In this paper, we first give an overview of the existing body of knowledge on ML for Constraint Programming (CP) which learns from problem instances. Second, we introduce a boundary estimation framework that is applied as a tool to support a CP solver. Within this framework, different ML models are discussed and evaluated regarding their suitability for boundary estimation, and countermeasures to avoid unfeasible estimations that avoid the solver to find an optimal solution are shown. Third, we present an experimental study with distinct CP solvers on seven COPs. Our results show that near-optimal boundaries can be learned for these COPs with only little overhead. These estimated boundaries reduce the objective domain size by 60-88% and can help the solver to find near-optimal solutions early during search.

show abstract

Parallelizing Constraint Solvers for Hard RCPSP Instances

Cited by 3 publications

References 28 publications

SUNNY-CP and the MiniZinc challenge

SUNNY-CP and the MiniZinc challenge

Predictive Machine Learning of Objective Boundaries for Solving COPs

Predictive Machine Learning of Objective Boundaries for Solving COPs

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