constraintprogramming 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 i...
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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 assu...
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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/...
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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 comput...
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the paper introduces value precedence on integer and set sequences. A useful application of the notion is in breaking symmetries of indistinguishable values, an important class of symmetries in practice. Although valu...
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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 struct...
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(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.
this paper presents an algorithm that achieves hyper-arc consistency for the soft alldifferent constraint. To this end, we prove and exploit the equivalence with a minimum-cost flow problem. Consistency of the constra...
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In CP, the most efficient model solving the TSP is the Weighted Circuit constraint (WCC) combined withthe k-cutset constraint. the WCC is mainly based on the edges cost of a given graph whereas the k-cutset constrain...
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Although algorithms for Distributed constraint Optimization Problems (DCOPs) have emerged as a key technique for distributed reasoning, their application faces significant hurdles in many multiagent domains due to the...
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We propose a unifying dynamic-programming framework to compute exact literal-weighted model counts of formulas in conjunctive normal form. At the center of our framework are project-join trees, which specify efficient...
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