constraint logic programming (CLP) is a multidisciplinary research area which can be located between Artificial Intelligence, Operation Research, and programming Languages, and has to do with modeling, solving, and pr...
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This paper presents a system for solving mixed infinite and finite domains linear problem using a constraint Logic programming (CLP) Environment. The contributions of our system are introduced in two directions. First...
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We seek a timetable for courses offered in S sections to maximize contact among K student cohorts over T terms. For this combinatorial optimization problem we propose both integer programming and constraint programmin...
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We seek a timetable for courses offered in S sections to maximize contact among K student cohorts over T terms. For this combinatorial optimization problem we propose both integer programming and constraint programming models. The latter straightforwardly reduces the size of solution equivalence classes, thereby facilitating the search for an optimum. (C) 2002 Elsevier Science B.V. All rights reserved.
The single most serious issue in the development of a parallel implementation of non-deterministic programming languages and systems (e.g., logic programming, constraint programming, search-based artificial intelligen...
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The single most serious issue in the development of a parallel implementation of non-deterministic programming languages and systems (e.g., logic programming, constraint programming, search-based artificial intelligence systems) is the dynamic management of the binding environments-i.e., the ability to associate with each parallel computation the correct set of bindings/values representing the solution generated by that particular branch of the non-deterministic computation. The problem has been abstracted and formally studied previously (ACM Trans. Program. Lang. Syst. 15(4) (1993) 659;New Generation Comput. 17(3) (1999) 285), but to date only relatively inefficient data structures (ACM Trans. Program. Lang. Syst. (2002);New Generation Comput. 17(3) (1999) 285;J. Funct. Logic Program. Special issue #1 (1999)) have been developed to solve it. We provide a very efficient solution to the problem (O(Ig n) per operation). This is a significant improvement over previously best known Omega((3)rootn) solution. Our solution is provably optimal for the pointer machine model. We also show how the solution can be extended to handle the abstraction of search problems in object-oriented systems, with the same time complexity. (C) 2002 Elsevier Science Ltd. All rights reserved.
This paper presents operators searching large neighborhoods in order to solve the vehicle routing problem. They make use of the pruning and propagation techniques of constraint programming which allow an efficient sea...
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This paper presents operators searching large neighborhoods in order to solve the vehicle routing problem. They make use of the pruning and propagation techniques of constraint programming which allow an efficient search of such neighborhoods. The advantages of using a large neighborhood are not only the increased probability of finding a better solution at each iteration but also the reduction of the need to invoke specially-designed methods to avoid local minima. These operators are combined in a variable neighborhood descent in order to take advantage of the different neighborhood structures they generate.
A common telecommunications network design strategy, in order to protect it against failures, is to achieve a decomposition into a set of rings covering the network. We propose a hybrid algorithm for the corresponding...
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A common telecommunications network design strategy, in order to protect it against failures, is to achieve a decomposition into a set of rings covering the network. We propose a hybrid algorithm for the corresponding Constrained Cycle Cover Problem which draws from constraint programming and graph algorithms. Optimal solutions are obtained efficiently on networks of realistic size.
The configuration of complex industrial products is often costly and inaccurate because of the numerous variants of products and their components. Improving the configuration of technical products using computer-suppo...
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The configuration of complex industrial products is often costly and inaccurate because of the numerous variants of products and their components. Improving the configuration of technical products using computer-supported methods is a prerequisite for modern, efficient and flexible customer-oriented production. We describe an approach to the configuration and reconfiguration of technical devices that is based on constraint programming over finite domains. The system ConBaCon, which uses this method, combines automatic processing with interactive user interactions. Benchmark tests are used to investigate the relations between the number of components, hierarchy depth and efficiency. Furthermore, clustering and special treatment of large networks of arithmetic constraints are presented as methods of dealing with high complexity.
We present cost based filtering methods for Knapsack Problems (KPs). Cost based filtering aims at fixing variables with respect to the objective function. It is an important technique when solving complex problems suc...
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We present cost based filtering methods for Knapsack Problems (KPs). Cost based filtering aims at fixing variables with respect to the objective function. It is an important technique when solving complex problems such as Quadratic Knapsack Problems, or KPs with additional constraints (Constrained Knapsack Problems (CKPs)). They evolve, e.g., when constraint Based Column Generation is applied to appropriate optimization problems. We develop new reduction algorithms for KP. They are used as propagation routines for the CKP with Theta(n log n) preprocessing time and Theta(n) time per call. This sums up to an amortized time Theta(n) for Omega(log n) incremental calls where the subsequent problems may differ with respect to arbitrary sets of necessarily included and excluded items.
Automatic differentiation (AD) automatically transforms programs which calculate elementary functions into programs which calculate the gradients of these functions. Unlike other differentiation techniques, AD allows ...
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ISBN:
(纸本)354043593X
Automatic differentiation (AD) automatically transforms programs which calculate elementary functions into programs which calculate the gradients of these functions. Unlike other differentiation techniques, AD allows one to calculate the gradient of any function at the cost of at most 5 values of the function (in terms of time). Interval constraint programming (ICP) is a part of constraint programming focused on representation and processing of nonlinear constraints. We adapt AD to the context of ICP and obtain an algorithm which transforms elementary functions into constraints specifying their gradient. We describe some experiments with implementation of our algorithm in the logic programming language (ECLPSe)-P-i.
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