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检索条件"任意字段=9th International Conference on Principles and Practice of Constraint Programming"

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Computing Relaxations for the three-Dimensional Stable Matching Problem with Cyclic Preferences 28

Computing Relaxations for the Three-Dimensional Stable Match...

引用

28th international conference on principles and practice of constraint programming, CP 2022

作者： Cseh, Ágnes Escamocher, Guillaume Quesada, Luis Institute of Economics Centre for Economic and Regional Studies Budapest Hungary Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Ireland

ISBN: (纸本)9783959772402

constraint programming has proven to be a successful framework for determining whether a given instance of the three-dimensional stable matching problem with cyclic preferences (3dsm-cyc) admits a solution. If such an instance is satisfiable, constraint models can even compute its optimal solution for several different objective functions. On the other hand, the only existing output for unsatisfiable 3dsm-cyc instances is a simple declaration of impossibility. In this paper, we explore four ways to adapt constraint models designed for 3dsm-cyc to the maximum relaxation version of the problem, that is, the computation of the smallest part of an instance whose modification leads to satisfiability. We also extend our models to support the presence of costs on elements in the instance, and to return the relaxation with lowest total cost for each of the four types of relaxation. Empirical results reveal that our relaxation models are efficient, as in most cases, they show little overhead compared to the satisfaction version. © Ágnes Cseh, Guillaume Escamocher, and Luis Quesada.

关键词： constraint theory

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Learning a Generic Value-Selection Heuristic Inside a constraint programming Solver 29

Learning a Generic Value-Selection Heuristic Inside a Constr...

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29th international conference on principles and practice of constraint programming, CP 2023

作者： Marty, Tom François, Tristan Tessier, Pierre Gautier, Louis Rousseau, Louis-Martin Cappart, Quentin Polytechnique Montréal Montreal Canada Ecole Polytechnique Palaiseau France

ISBN: (纸本)9783959773003

constraint programming is known for being an efficient approach to solving combinatorial problems. Important design choices in a solver are the branching heuristics, designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise. this observation has motivated many efforts to use machine learning to automatically learn efficient heuristics without expert intervention. Although several generic variable-selection heuristics are available in the literature, the options for value-selection heuristics are more scarce. We propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver. this has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network. Experiments on graph coloring, maximum independent set, and maximum cut problems show that this framework competes with the well-known impact-based and activity-based search heuristics and can find solutions close to optimality without requiring a large number of backtracks. © Tom Marty, Tristan François, Pierre Tessier, Louis Gautier, Louis-Martin Rousseau, and Quentin Cappart.

关键词： Reinforcement learning

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An interval constraint approach to handle parametric ordinary differential equations for decision support 5th

引用

5th international conference on principles and practice of constraint programming, CP 1999

作者： Cruz, Jorge Barahona, Pedro Dep. de Informática Universidade Nova de Lisboa Caparica Portugal

ISBN: (纸本)3540666265

Model-based decision support systems rely on an explicit representation of some system whose dynamics is often qualitatively described by specifying the rates at which the system variables change. Such models are naturally represented as a set of ordinary differential equations (ODEs) which are parametric (they include parameters whose value is not known exactly). If safe decisions are to be made based on the values of these parameters, it is important to know them with sufficient precision.

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Large Neighborhood Search for Robust Solutions for constraint Satisfaction Problems with Ordered Domains 28

Large Neighborhood Search for Robust Solutions for Constrain...

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28th international conference on principles and practice of constraint programming, CP 2022

作者： López, Jheisson Arbelaez, Alejandro Climent, Laura University College Cork School of Computer Science Ireland SFI Centre for Research Training in Artificial Intelligence Cork Ireland Department of Computer Engineering Autonomous University of Madrid Spain

ISBN: (纸本)9783959772402

Often, real-world constraint Satisfaction Problems (CSPs) are subject to uncertainty/dynamism not known in advance. Some techniques in the literature offer robust solutions for CSPs. Here, we analyze a previous exact/complete approach from the state-of-the-art that focuses on CSPs with ordered domains and dynamic bounds. However, this approach has low performance in large-scale CSPs. For this reason, in this paper, we present an inexact/incomplete approach that is faster at finding robust solutions for large-scale CSPs. It is useful when the computation time available for finding a solution is limited and/or in situations where a new one must be re-computed online because the dynamism invalidated the original one. Specifically, we present a Large Neighbourhood Search (LNS) algorithm combined with constraint programming (CP) and Branch-and-bound (B&B) that searches for robust solutions. We also present a robust-value selection heuristic that guides the search toward more promising branches. We evaluate our approach with large-scale CSPs instances, including the case study of scheduling problems. the evaluation shows a considerable improvement in the robustness of the solutions achieved by our algorithm for large-scale CSPs. © Jheisson López, Alejandro Arbelaez, and Laura Climent.

关键词： constraint satisfaction problems

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Restart policies with dependence among runs: A dynamic programming approach 8th

引用

8th international conference on principles and practice of constraint programming, CP 2002

作者： Ruan, Yongshao Horvitz, Eric Kautz, Henry University of Washington SeattleWA98195 United States Microsoft Research RedmondWA98052 United States

ISBN: (纸本)3540441204

the time required for a backtracking search procedure to solve a problem can be minimized by employing randomized restart procedures. To date, researchers designing restart policies have relied on the simplifying assumption that runs are probabilistically independent from one another. We relax the assumption of independence among runs and address the challenge of identifying an optimal restart policy for the dependent case. We show how offline dynamic programming can be used to generate an ideal restart policy, and how the policy can be used in conjunction with real-time observations to control the timing of restarts. We present results of experiments on applying the methods to create ideal restart policies for several challenging search problems using two different solvers. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Dynamic programming

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A constraint program for solving the job-shop problem 2nd

A constraint program for solving the job-shop problem

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2nd international conference on principles and practice of constraint programming, CP 1996

作者： Zhou, Jianyang Laboratoire d’Informatique de Marseille 163 avenue de Luminy Marseille Cedex 913288 France

ISBN: (纸本)3540615512

In this paper, a method within the framework of propagation of interval constraints and based on the branch-and-bound optimization scheme for solving the job-shop scheduling problem will be presented. the goal is to provide a constraint program which is clean, flexible and robust. the design of the constraint program is based on an idea of sorting the release and due dates of tasks, which is a successful application of a previous but not yet published work on a distinct integers constraint. Based on the sorting constraint, by assembling redundant constraints and applying an efficient search strategy, the current program for the job-shop problem can solve the ten 10 x 10 instances in the paper of Applegate and Cook (1991) in satisfactory computational time. Moreover, good results have been achieved on some harder instances. © Springer-Verlag Berlin Heidelberg 1996.

关键词： Job shop scheduling

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Improved bound computation in presence of several clique constraints

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10th international conference on principles and practice of constraint programming, CP 2004

作者： Puget, Jean-Francois ILOG 9 avenue de Verdun Gentilly94253 France

ISBN: (纸本)3540232419

Bound consistency can easily and efficiently be enforced on linear constraint. However, bound consistency techniques deal with every constraint separately. We show that in some cases much stronger bounds can be computed by looking globally at the set of linear constraints. A simple algorithm for computing such bounds is derived from a new decomposition scheme. We prove that these bounds can be as tight as possible. In some cases this can be better than the standard reduced cost fixings. this algorithm has been implemented within a global constraint. Experimental results on balanced incomplete block design (BIBD) show its effectiveness: we were able to significantly improve over the best published results for this class of problems. © Springer-Verlag Berlin Heidelberg 2004.

关键词： constraint theory

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Machine learning for portfolio selection using structure at the instance level

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10th international conference on the principles and practice of constraint programming

作者： Gebruers, C Guerri, A Natl Univ Ireland Univ Coll Cork Cork Constraint Computat Ctr Cork Ireland Univ Bologna DEIS I-40136 Bologna Italy

ISBN: (纸本)3540232419

Many combinatorial optimization problems do not have a clear structure, may present many side constraints, and may include subproblems. In addition, different instances within the same domain can have different structure and characteristics. As a consequence it is commonplace that a single algorithm is not the best performer on every problem instance. We consider an algorithm portfolio approach to try to help us select the best algorithm for a given problem instance. Our purpose is twofold: firstly, to show that structure at the instance level is tightly connected to algorithm performance, and secondly to demonstrate that different machine learning and modelling methodologies, specifically Decision Trees (DT), Case Based Reasoning (CBR) and Multinomial Logistic Regression (MLR), can be used to perform effective algorithm portfolio selection. We test our claims by applying the above mentioned techniques to a large set of instances of the Bid Evaluation Problem (BEP) in Combinatorial Auctions. A BEP consists of a Winner Determination Problem (a well-known NP-hard problem best solved by a IP-based approach), and additional temporal information and precedence constraints (which favour a CP-based approach). We solved the BEP instances using a set of different algorithms. We observed that two algorithms; one IP-based and the other a hybrid combining both CP and IP elements, outperformed all the others on all instances. Hence we divided the instances into 2 classes based on which of these 2 algorithms solves them best. In order to perform our analysis we extract a set of structure-based features, that are cheap to determine, from each instance . We apply the Machine Learning methodologies using the extracted features as input data and the best algorithms as prediction classes.

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An interval constraint programming approach for quasi capture tube validation 27

An interval constraint programming approach for quasi captur...

引用

27th international conference on principles and practice of constraint programming, CP 2021

作者： Bedouhene, Abderahmane Neveu, Bertrand Trombettoni, Gilles Jaulin, Luc Le Menec, Stéphane LIGM Ecole des Ponts ParisTech Université Gustave Eiffel CNRS Marne-la-Vallée France LIRMM Université de Montpellier CNRS France Lab-STICC ENSTA-Bretagne Brest France MBDA Le Plessis Robinson France

ISBN: (纸本)9783959772112

Proving that the state of a controlled nonlinear system always stays inside a time moving bubble (or capture tube) amounts to proving the inconsistency of a set of nonlinear inequalities in the time-state space. In practice however, even with a good intuition, it is difficult for a human to find such a capture tube except for simple examples. In 2014, Jaulin et al. established properties that support a new interval approach for validating a quasi capture tube, i.e. a candidate tube (with a simple form) from which the mobile system can escape, but into which it enters again before a given time. A quasi capture tube is easy to find in practice for a controlled system. Merging the trajectories originated from the candidate tube yields the smallest capture tube enclosing it. this paper proposes an interval constraint programming solver dedicated to the quasi capture tube validation. the problem is viewed as a differential CSP where the functional variables correspond to the state variables of the system and the constraints define system trajectories that escape from the candidate tube "for ever". the solver performs a branch and contract procedure for computing the trajectories that escape from the candidate tube. If no solution is found, the quasi capture tube is validated and, as a side effect, a corrected smallest capture tube enclosing the quasi one is computed. the approach is experimentally validated on several examples having 2 to 5 degrees of freedom. © Abderahmane Bedouhene, Bertrand Neveu, Gilles Trombettoni, Luc Jaulin, and Stéphane Le Menec.

关键词： Dynamical systems

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A linear time algorithm for the k-cutset constraint 27

A linear time algorithm for the k-cutset constraint

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27th international conference on principles and practice of constraint programming, CP 2021

作者： Isoart, Nicolas Régin, Jean-Charles Université Côte d'Azur Nice France

ISBN: (纸本)9783959772112

In CP, the most efficient model solving the TSP is the Weighted Circuit constraint (WCC) combined with the k-cutset constraint. the WCC is mainly based on the edges cost of a given graph whereas the k-cutset constraint is a structural constraint. Specifically, for each cutset in a graph, the k-cutset constraint imposes that the size of the cutset is greater than or equal to two. In addition, any solution contains an even number of elements from this cutset. Isoart and Régin introduced an algorithm for this constraint. Unfortunately, their approach leads to a time complexity growing with the size of the considered cutsets, i.e. with k. thus, they introduced an algorithm with a quadratic complexity dealing with k lower or equal to three. In this paper, we introduce a linear time algorithm for any k based on a DFS checking the consistency of this constraint and performing its filtering. Experimental results show that the size of most of the k-cutsets is lower or equal than 3. In addition, since the time complexity is improved, our algorithm also improves the solving times. © Nicolas Isoart and Jean-Charles Régin.

关键词： Clustering algorithms

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