Making the First Solution Good!

Fages, Jean-Guillaume; Prud’Homme, Charles

doi:10.1109/ictai.2017.00164

Cited by 8 publications

(19 citation statements)

References 20 publications

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“…It shows that for ABS and WDeg adding an objective-based feature seems to dominate their performance. Finally it shows that the objective as a feature can play an important role in finding a good solution faster, as already claimed [3].…”

Section: Miscellaneous Discussionsupporting

confidence: 65%

“…Different variants have been tried: first to select the value minimizing ∆ O (t), with possibly different values for a and b. Second to select the value using the new value heuristic from [3]. However, even if on some instances such as ghoulomb or openstack these selector showed a real improvement, they seem to globally be dominated in the Minizinc problems set by minVal.…”

Section: Miscellaneous Discussionmentioning

confidence: 99%

“…A constraint C i contains a set of all its allowed tuples defined over a subset S Ci ⊆ X of variables. 3 A solution is a tuple of values (a 1 , a 2 , ..., a n ) such that the assignments x 1 = a 1 , x 2 = a 2 ..., x n = a n respect all the constraints. The solving process for a CSP generally involves a depth first search algorithm with backtracks in a decision tree.…”

Section: Constraint Satisfaction Problem (Csp)mentioning

confidence: 99%

“…A decision often corresponds to a pair (variable, value) which can be seen as a backtrackable constraint variable = value. Search strategies are crucially important to find good solutions, to reduce the search space, and even to quickly find an initial feasible or good solution [3].…”

Section: Search Strategiesmentioning

confidence: 99%

“…Our idea, inspired by integer programming [4], is to extract good features from the objective variable to make good decisions. A recent CP work started exploring this area by using the objective to design a value selector in order to find a first good solution [3]. An advantage of using the objective as a feature is its ability to both optimize the objective value and to reduce the search tree at the same time.…”

Section: Introductionmentioning

confidence: 99%

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Objective as a Feature for Robust Search Strategies

Palmieri

Perez

2018

Lecture Notes in Computer Science

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In constraint programming the search strategy entirely guides the solving process, and drastically affects the running time for solving particular problem instances. Many features have been defined so far for the design of efficient and robust search strategies, such as variables' domains, constraint graph, or even the constraints triggering fails. In this paper, we propose to use the objective functions of constraint optimization problems as a feature to guide search strategies. We define an objective-based function, to monitor the objective bounds modifications and to extract information. This function is the main feature to design a new variable selection heuristic, whose results validate human intuitions about the objective modifications. Finally, we introduce a simple but efficient combination of features, to incorporate the objective in the state-of-the-art search strategies. We illustrate this new method by testing it on several classic optimization problems, showing that the new feature often yields to a better running time and finds better solutions in the given time.

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Section: Miscellaneous Discussionsupporting

confidence: 65%

Section: Miscellaneous Discussionmentioning

confidence: 99%

Section: Constraint Satisfaction Problem (Csp)mentioning

confidence: 99%

Section: Search Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Objective as a Feature for Robust Search Strategies

Palmieri

Perez

2018

Lecture Notes in Computer Science

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Automatic Streamlining for Constrained Optimisation

Spracklen

Dang

Akgün

et al. 2019

Lecture Notes in Computer Science

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Augmenting a base constraint model with additional constraints can strengthen the inferences made by a solver and therefore reduce search effort. We focus on the automatic addition of streamliner constraints, which trade completeness for potentially very significant reduction in search. Recently an automated approach has been proposed, which produces streamliners via a set of streamliner generation rules. This existing automated approach to streamliner generation has two key limitations. First, it outputs a single streamlined model. Second, the approach is limited to satisfaction problems. We remove both limitations by providing a method to produce automatically a portfolio of streamliners, each representing a different balance between three criteria: how aggressively the search space is reduced, the proportion of training instances for which the streamliner admitted at least one solution, and the average reduction in quality of the objective value versus the unstreamlined model. In support of our new method, we present an automated approach to training and test instance generation, and provide several approaches to the selection and application of the streamliners from the portfolio. Empirical results demonstrate drastic improvements both to the time required to find good solutions early and to prove optimality on three problem classes.

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Learning Objective Boundaries for Constraint Optimization Problems

Spieker¹,

Gotlieb²

2020

Machine Learning, Optimization, and Data Science

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Making the First Solution Good!

Cited by 8 publications

References 20 publications

Objective as a Feature for Robust Search Strategies

Objective as a Feature for Robust Search Strategies

Automatic Streamlining for Constrained Optimisation

Learning Objective Boundaries for Constraint Optimization Problems

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