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Articoli di riviste sul tema "Constraint programming"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

APT, KRZYSZTOF R., and ERIC MONFROY. "Constraint programming viewed as rule-based programming." Theory and Practice of Logic Programming 1, no. 6 (2001): 713–50. http://dx.doi.org/10.1017/s1471068401000072.

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We study here a natural situation when constraint programming can be entirely reduced to rule-based programming. To this end we explain first how one can compute on constraint satisfaction problems using rules represented by simple first-order formulas. Then we consider constraint satisfaction problems that are based on predefined, explicitly given constraints. To solve them we first derive rules from these explicitly given constraints and limit the computation process to a repeated application of these rules, combined with labeling. We consider two types of rule here. The first type, that we

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Van Hentenryck, Pascal, Laurent Michel, and Frédéric Benhamou. "Constraint programming over nonlinear constraints." Science of Computer Programming 30, no. 1-2 (1998): 83–118. http://dx.doi.org/10.1016/s0167-6423(97)00008-7.

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O'Sullivan, Barry. "Automated Modelling and Solving in Constraint Programming." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1493–97. http://dx.doi.org/10.1609/aaai.v24i1.7530.

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Constraint programming can be divided very crudely into modeling and solving. Modeling defines the problem, in terms of variables that can take on different values, subject to restrictions (constraints) on which combinations of variables are allowed. Solving finds values for all the variables that simultaneously satisfy all the constraints. However, the impact of constraint programming has been constrained by a lack of "user-friendliness''. Constraint programming has a major "declarative" aspect, in that a problem model can be handed off for solution to a variety of standard solving methods. T

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Dao, Thi-Bich-Hanh, Khanh-Chuong Duong, and Christel Vrain. "Constrained clustering by constraint programming." Artificial Intelligence 244 (March 2017): 70–94. http://dx.doi.org/10.1016/j.artint.2015.05.006.

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5

SCHRIJVERS, TOM, PETER STUCKEY, and PHILIP WADLER. "Monadic constraint programming." Journal of Functional Programming 19, no. 6 (2009): 663–97. http://dx.doi.org/10.1017/s0956796809990086.

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AbstractA constraint programming system combines two essential components: a constraint solver and a search engine. The constraint solver reasons about satisfiability of conjunctions of constraints, and the search engine controls the search for solutions by iteratively exploring a disjunctive search tree defined by the constraint program. In this paper we give a monadic definition of constraint programming in which the solver is defined as a monad threaded through the monadic search tree. We are then able to define search and search strategies as first-class objects that can themselves be buil

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Mattenet, Alex, Ian Davidson, Siegfried Nijssen, and Pierre Schaus. "Generic Constraint-based Block Modeling using Constraint Programming." Journal of Artificial Intelligence Research 70 (February 9, 2021): 597–630. http://dx.doi.org/10.1613/jair.1.12280.

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Block modeling has been used extensively in many domains including social science, spatial temporal data analysis and even medical imaging. Original formulations of the problem modeled it as a mixed integer programming problem, but were not scalable. Subsequent work relaxed the discrete optimization requirement, and showed that adding constraints is not straightforward in existing approaches. In this work, we present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of c

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7

Dincbas, M. "Constraint programming." ACM Computing Surveys 28, no. 4es (1996): 62. http://dx.doi.org/10.1145/242224.242303.

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8

Van Hentenryck, Pascal. "Constraint Programming." Revue Ouverte d'Intelligence Artificielle 5, no. 2-3 (2024): 139–59. http://dx.doi.org/10.5802/roia.76.

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Van Hentenryck, Pascal. "Constraint programming." ACM SIGSOFT Software Engineering Notes 25, no. 1 (2000): 89–90. http://dx.doi.org/10.1145/340855.341036.

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Booth, Kyle E. C., Bryan O'Gorman, Jeffrey Marshall, Stuart Hadfield, and Eleanor Rieffel. "Quantum-accelerated constraint programming." Quantum 5 (September 28, 2021): 550. http://dx.doi.org/10.22331/q-2021-09-28-550.

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Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction and combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions and solved with backtracking tree search augmented with logical inference. In this paper, we show how quantum algorithms can accelerate CP, at both the levels of inference and search. Leveraging existing quantum algorithms, we introduce a quantum-accelerated filtering algorithm for the alldifferent global constraint and discuss its applicability to a broader family of global constraints

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11

ARIAS, JOAQUIN, MANUEL CARRO, ELMER SALAZAR, KYLE MARPLE, and GOPAL GUPTA. "Constraint Answer Set Programming without Grounding." Theory and Practice of Logic Programming 18, no. 3-4 (2018): 337–54. http://dx.doi.org/10.1017/s1471068418000285.

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AbstractExtending ASP with constraints (CASP) enhances its expressiveness and performance. This extension is not straightforward as the grounding phase, present in most ASP systems, removes variables and the links among them, and also causes a combinatorial explosion in the size of the program. Several methods to overcome this issue have been devised: restricting the constraint domains (e.g., discrete instead of dense), or the type (or number) of models that can be returned. In this paper we propose to incorporate constraints into s(ASP), a goal-directed, top-down execution model which impleme

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PUGET, JEAN-FRANÇOIS, and IRVIN LUSTIG. "Constraint programming and maths programming." Knowledge Engineering Review 16, no. 1 (2001): 5–23. http://dx.doi.org/10.1017/s0269888901000042.

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Maths programming (MP) and constraint programming (CP) are two techniques that are able to solve difficult industrial optimisation problems. The purpose of this paper is to compare them from an algorithmic and a modelling point of view. Algorithmic principles of each approach are described and contrasted. Some ways of combining both techniques are also introduced.

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Sitek, Pawel, and Jaroslaw Wikarek. "A Hybrid Method for the Modelling and Optimisation of Constrained Search Problems." Foundations of Management 5, no. 3 (2014): 7–22. http://dx.doi.org/10.2478/fman-2014-0016.

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AbstractThe paper presents a concept and the outline of the implementation of a hybrid approach to modelling and solving constrained problems. Two environments of mathematical programming (in particular, integer programming) and declarative programming (in particular, constraint logic programming) were integrated. The strengths of integer programming and constraint logic programming, in which constraints are treated in a different way and different methods are implemented, were combined to use the strengths of both. The hybrid method is not worse than either of its components used independentl

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14

Milano, Michela, Greger Ottosson, Philippe Refalo, and Erlendur S. Thorsteinsson. "The Role of Integer Programming Techniques in Constraint Programming's Global Constraints." INFORMS Journal on Computing 14, no. 4 (2002): 387–402. http://dx.doi.org/10.1287/ijoc.14.4.387.2830.

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15

Baykasoğlu, Adil, Şeyda Topaloğlu, and Filiz Şenyüzlüler. "Manufacturing cell formation with flexible processing capabilities and worker assignment: Comparison of constraint programming and integer programming approaches." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, no. 11 (2017): 2054–68. http://dx.doi.org/10.1177/0954405416682281.

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Cell formation deals with grouping of machines and parts in manufacturing systems according to their compatibility. Manufacturing processes are surrounded with an abundance of complex constraints which should be considered carefully and represented clearly for obtaining high efficiency and productivity. Constraint programming is a new approach to combinatorial optimization and provides a rich language to represent complex constraints easily. However, the cell formation problems are well suited to be solved by constraint programming approach since the problem has many constraints such as part-m

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Oleynik, Yu A., and A. A. Zuenko. "Global constraints in modeling and solving problems within the Constraint Programming paradigm." Transaction Kola Science Centre 11, no. 8-2020 (2020): 67–83. http://dx.doi.org/10.37614/2307-5252.2020.8.11.006.

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At the moment, constraint programming technology is a powerful tool for solving combinatorial search and combinatorial optimization problems. To use this technology, any task must be formulated as a task of satisfying constraints. The role of the concept of global constraints in modeling and solving applied problems within the framework of the constraint programming paradigm can hardly be overestimated. The procedures that implement the algorithms of filtering global constraints are the elementary “building blocks” from which the model of a specific applied problem is built. Algorithms for fil

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17

Kjenstad, Dag. "Concurrent Constraint Programming." AI Communications 8, no. 2 (1995): 102–3. http://dx.doi.org/10.3233/aic-1995-8206.

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18

Jaffar, J., and R. Yap. "Constraint programming 2000." ACM Computing Surveys 28, no. 4es (1996): 65. http://dx.doi.org/10.1145/242224.242307.

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19

McAloon, Ken. "Constraint-based programming." ACM Computing Surveys 28, no. 4es (1996): 69. http://dx.doi.org/10.1145/242224.242313.

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20

Van Hentenryck, Pascal. "Constraint logic programming." Knowledge Engineering Review 6, no. 3 (1991): 151–94. http://dx.doi.org/10.1017/s0269888900005798.

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AbstractConstraint logic programming (CLP) is a generalization of logic programming (LP) where unification, the basic operation of LP languages, is replaced by constraint handling in a constraint system. The resulting languages combine the advantages of LP (declarative semantics, nondeterminism, relational form) with the efficiency of constraint-solving algorithms. For some classes of combinatorial search problems, they shorten the development time significantly while preserving most of the efficiency of imperative languages. This paper surveys this new class of programming languages from thei

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21

Faltings, Boi, and Santiago Macho-Gonzalez. "Open constraint programming." Artificial Intelligence 161, no. 1-2 (2005): 181–208. http://dx.doi.org/10.1016/j.artint.2004.10.005.

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DUNDUA, BESIK, MÁRIO FLORIDO, TEMUR KUTSIA, and MIRCEA MARIN. "CLP(H):Constraint logic programming for hedges." Theory and Practice of Logic Programming 16, no. 2 (2015): 141–62. http://dx.doi.org/10.1017/s1471068415000071.

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AbstractCLP(H) is an instantiation of the general constraint logic programming scheme with the constraint domain of hedges. Hedges are finite sequences of unranked terms, built over variadic function symbols and three kinds of variables: for terms, for hedges, and for function symbols. Constraints involve equations between unranked terms and atoms for regular hedge language membership. We study algebraic semantics of CLP(H) programs, define a sound, terminating, and incomplete constraint solver, investigate two fragments of constraints for which the solver returns a complete set of solutions,

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23

Van Hentenryck, Pascal, Helmut Simonis, and Mehmet Dincbas. "Constraint satisfaction using constraint logic programming." Artificial Intelligence 58, no. 1-3 (1992): 113–59. http://dx.doi.org/10.1016/0004-3702(92)90006-j.

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MAHER, MICHAEL J. "Contractibility for open global constraints." Theory and Practice of Logic Programming 17, no. 4 (2017): 365–407. http://dx.doi.org/10.1017/s1471068417000126.

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AbstractOpen forms of global constraints allow the addition of new variables to an argument during the execution of a constraint program. Such forms are needed for difficult constraint programming problems, where problem construction and problem solving are interleaved, and fit naturally within constraint logic programming. However, in general, filtering that is sound for a global constraint can be unsound when the constraint is open. This paper provides a simple characterization, called contractibility, of the constraints, where filtering remains sound when the constraint is open. With this c

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25

Cohen, Jacques. "Logic programming and constraint logic programming." ACM Computing Surveys 28, no. 1 (1996): 257–59. http://dx.doi.org/10.1145/234313.234416.

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26

Yu, Chun-Mei, Dang-Jun Zhao, and Ye Yang. "Efficient Convex Optimization of Reentry Trajectory via the Chebyshev Pseudospectral Method." International Journal of Aerospace Engineering 2019 (May 2, 2019): 1–9. http://dx.doi.org/10.1155/2019/1414279.

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A novel sequential convex (SCvx) optimization scheme via the Chebyshev pseudospectral method is proposed for efficiently solving the hypersonic reentry trajectory optimization problem which is highly constrained by heat flux, dynamic pressure, normal load, and multiple no-fly zones. The Chebyshev-Gauss Legend (CGL) node points are used to transcribe the original dynamic constraint into algebraic equality constraint; therefore, a nonlinear programming (NLP) problem is concave and time-consuming to be solved. The iterative linearization and convexification techniques are proposed to convert the

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27

FIERBINTEANU, CRISTINA. "CONSTRAINT LOGIC PROGRAMMING APPROACH OF NETWORK FLOW PROBLEMS WITHIN A DECISION SUPPORT SYSTEMS GENERATOR FOR TRANSPORTATION PLANNIG." International Journal on Artificial Intelligence Tools 07, no. 04 (1998): 453–62. http://dx.doi.org/10.1142/s0218213098000214.

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In this paper we propose a model of a decision support systems (DSS) generator for unstructured problems. The model is developed within the constraint logic programming (CLP) paradigm. At the center of the generator there is an ontology defining the concepts and relationships necessary and sufficient to describe the domain to be reasoned about, in a manner suitable for a particular class of tasks. The constraint solver of the constraint logic programming host language has to be extended with constraints which are relevant to the domain studied, but can not be found among the general constraint

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Bessa, Swann, Darius Dabert, Max Bourgeat, Louis-Martin Rousseau, and Quentin Cappart. "Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11113–21. https://doi.org/10.1609/aaai.v39i11.33208.

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Lagrangian decomposition (LD) is a relaxation method that provides a dual bound for constrained optimization problems by decomposing them into more manageable sub-problems. This bound can be used in branch-and-bound algorithms to prune the search space effectively.In brief, a vector of Lagrangian multipliers is associated with each sub-problem, and an iterative procedure (e.g., a sub-gradient optimization) adjusts these multipliers to find the tightest bound. Initially applied to integer programming, Lagrangian decomposition also had success in constraint programming due to its versatility and

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29

Peng, Yunfang, Dandan Lu, and Yarong Chen. "A Constraint Programming Method for Advanced Planning and Scheduling System with Multilevel Structured Products." Discrete Dynamics in Nature and Society 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/917685.

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This paper deals with the advanced planning and scheduling (APS) problem with multilevel structured products. A constraint programming model is constructed for the problem with the consideration of precedence constraints, capacity constraints, release time and due date. A new constraint programming (CP) method is proposed to minimize the total cost. This method is based on iterative solving via branch and bound. And, at each node, the constraint propagation technique is adapted for domain filtering and consistency check. Three branching strategies are compared to improve the search speed. The

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30

ZHANG, YUANLIN, and ROLAND H. C. YAP. "Solving functional constraints by variable substitution." Theory and Practice of Logic Programming 11, no. 2-3 (2011): 297–322. http://dx.doi.org/10.1017/s1471068410000591.

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AbstractFunctional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ Constraint Satisfaction Problem(s)-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other nonfunctional constraints. It also sol

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FAGES, FRANÇOIS, and EMMANUEL COQUERY. "Typing constraint logic programs." Theory and Practice of Logic Programming 1, no. 6 (2001): 751–77. http://dx.doi.org/10.1017/s1471068401001120.

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We present a prescriptive type system with parametric polymorphism and subtyping for constraint logic programs. The aim of this type system is to detect programming errors statically. It introduces a type discipline for constraint logic programs and modules, while maintaining the capabilities of performing the usual coercions between constraint domains, and of typing meta-programming predicates, thanks to the exibility of subtyping. The property of subject reduction expresses the consistency of a prescriptive type system w.r.t. the execution model: if a program is ‘well-typed’, then all deriva

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32

MANCARELLA, PAOLO, GIACOMO TERRENI, FARIBA SADRI, FRANCESCA TONI, and ULLE ENDRISS. "The CIFF proof procedure for abductive logic programming with constraints: Theory, implementation and experiments." Theory and Practice of Logic Programming 9, no. 6 (2009): 691–750. http://dx.doi.org/10.1017/s1471068409990093.

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AbstractWe present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over variable quantification (allowedness conditions) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming. Finally, we describe the CIFF system, comparing it with state-of-the-art abductive systems and answer set solvers and showing how to use it to program some applications.

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33

Yin, Minghao, Tingting Zou, and Wenxiang Gu. "Reverse Bridge Theorem under Constraint Partition." Mathematical Problems in Engineering 2010 (2010): 1–18. http://dx.doi.org/10.1155/2010/617398.

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Reverse bridge theorem (RBTH) has been proved to be both a necessary and sufficient condition for solving Nonlinear programming problems. In this paper, we first propose three algorithms for finding constraint minimum points of continuous, discrete, and mixed-integer nonlinear programming problems based on the reverse bridge theorem. Moreover, we prove that RBTH under constraint partition is also a necessary and sufficient condition for solving nonlinear programming problems. This property can help us to develop an algorithm using RBTH under constraints. Specifically, the algorithm first parti

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34

Huang, Y. Q., S. L. Nie, and H. Ji. "Identification of Contamination Control Strategy for Fluid Power System Using an Inexact Chance-Constrained Integer Program." Journal of Applied Mathematics 2014 (2014): 1–19. http://dx.doi.org/10.1155/2014/146413.

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An inexact chance-constrained integer programming (ICIP) method is developed for planning contamination control of fluid power system (FPS). The ICIP is derived by incorporating chance-constrained programming (CCP) within an interval mixed integer linear programming (IMILP) framework, such that uncertainties presented in terms of probability distributions and discrete intervals can be handled. It can also help examine the reliability of satisfying (or risk of violating) system constraints under uncertainty. The developed method is applied to a case of contamination control planning for one typ

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ZHOU, NENG-FA. "Programming finite-domain constraint propagators in Action Rules." Theory and Practice of Logic Programming 6, no. 5 (2006): 483–507. http://dx.doi.org/10.1017/s1471068405002590.

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In this paper, we propose a new language, called AR (Action Rules), and describe how various propagators for finite-domain constraints can be implemented in it. An action rule specifies a pattern for agents, an action that the agents can carry out, and an event pattern for events that can activate the agents. AR combines the goal-oriented execution model of logic programming with the event-driven execution model. This hybrid execution model facilitates programming constraint propagators. A propagator for a constraint is an agent that maintains the consistency of the constraint and is activated

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36

Ram, Balasubramanian, and A. J. G. Babu. "Reduction of dimensionality in dynamic programming-based solution methods for nonlinear integer programming." International Journal of Mathematics and Mathematical Sciences 11, no. 4 (1988): 811–14. http://dx.doi.org/10.1155/s0161171288000985.

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This paper suggests a method of formulating any nonlinear integer programming problem, with any number of constraints, as an equivalent single constraint problem, thus reducing the dimensionality of the associated dynamic programming problem.

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Finkel, Raphael, Victor W. Marek, and Miros?aw Truszczy?ski. "Constraint Lingo: towards high-level constraint programming." Software: Practice and Experience 34, no. 15 (2004): 1481–504. http://dx.doi.org/10.1002/spe.623.

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Aoga, John O. R., Tias Guns, and Pierre Schaus. "Mining Time-constrained Sequential Patterns with Constraint Programming." Constraints 22, no. 4 (2017): 548–70. http://dx.doi.org/10.1007/s10601-017-9272-3.

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Upadhyay, Balendu Bhooshan, Shubham Kumar Singh, I. M. Stancu-Minasian, and Andreea Mădălina Rusu-Stancu. "Robust Optimality and Duality for Nonsmooth Multiobjective Programming Problems with Vanishing Constraints Under Data Uncertainty." Algorithms 17, no. 11 (2024): 482. http://dx.doi.org/10.3390/a17110482.

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This article investigates robust optimality conditions and duality results for a class of nonsmooth multiobjective programming problems with vanishing constraints under data uncertainty (UNMPVC). Mathematical programming problems with vanishing constraints constitute a distinctive class of constrained optimization problems because of the presence of complementarity constraints. Moreover, uncertainties are inherent in various real-life problems. The aim of this article is to identify an optimal solution to an uncertain optimization problem with vanishing constraints that remains feasible in eve

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Barnier, Nicolas, and Cyril Allignol. "Trajectory deconfliction with constraint programming." Knowledge Engineering Review 27, no. 3 (2012): 291–307. http://dx.doi.org/10.1017/s0269888912000227.

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AbstractAs acknowledged by the SESAR (Single European Sky ATM (Air Traffic Management) Research) program, current Air Traffic Control (ATC) systems must be drastically improved to accommodate the predicted traffic growth in Europe. In this context, the Episode 3 project aims at assessing the performance of new ATM concepts, like 4D-trajectory planning and strategic deconfliction.One of the bottlenecks impeding ATC performances is the hourly capacity constraints defined on each en-route ATC sector to limit the rate of aircraft. Previous works were mainly focused on optimizing the current ground

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Elffers, Jan, Stephan Gocht, Ciaran McCreesh, and Jakob Nordström. "Justifying All Differences Using Pseudo-Boolean Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1486–94. http://dx.doi.org/10.1609/aaai.v34i02.5507.

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Constraint programming solvers support rich global constraints and propagators, which make them both powerful and hard to debug. In the Boolean satisfiability community, proof-logging is the standard solution for generating trustworthy outputs, and this has become key to the social acceptability of computer-generated proofs. However, reusing this technology for constraint programming requires either much weaker propagation, or an impractical blowup in proof length. This paper demonstrates that simple, clean, and efficient proof logging is still possible for the all-different constraint, throug

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Kirchner, Claude, and Christophe Ringeissen. "Rule-Based Constraint Programming." Fundamenta Informaticae 34, no. 3 (1998): 225–62. http://dx.doi.org/10.3233/fi-1998-34302.

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Réty, Jean-Hugues. "Distributed Concurrent Constraint Programming." Fundamenta Informaticae 34, no. 3 (1998): 323–46. http://dx.doi.org/10.3233/fi-1998-34305.

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Montanari, Ugo, and Francesca Rossi. "Constraint solving and programming." ACM Computing Surveys 28, no. 4es (1996): 70. http://dx.doi.org/10.1145/242224.242314.

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Puget, Jean-Francois. "Future of constraint programming." ACM Computing Surveys 28, no. 4es (1996): 72. http://dx.doi.org/10.1145/242224.242317.

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Cohen, Jacques. "Constraint logic programming languages." Communications of the ACM 33, no. 7 (1990): 52–68. http://dx.doi.org/10.1145/79204.79209.

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Gupta, V., R. Jagadeesan, and V. A. Saraswat. "Truly concurrent constraint programming." Theoretical Computer Science 278, no. 1-2 (2002): 223–55. http://dx.doi.org/10.1016/s0304-3975(00)00337-6.

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Olarte, Carlos, Elaine Pimentel, and Vivek Nigam. "Subexponential concurrent constraint programming." Theoretical Computer Science 606 (November 2015): 98–120. http://dx.doi.org/10.1016/j.tcs.2015.06.031.

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Flener, Pierre, Mats Carlsson, and Christian Schulte. "Constraint Programming in Sweden." IEEE Intelligent Systems 24, no. 2 (2009): 87–89. http://dx.doi.org/10.1109/mis.2009.25.

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Pesant, Gilles. "A constraint programming primer." EURO Journal on Computational Optimization 2, no. 3 (2014): 89–97. http://dx.doi.org/10.1007/s13675-014-0026-3.

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