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FaCiLe: a Functional Constraint Library
, 2001
"... FaCiLe is an open source constraint programming library over integer nite domain written in OCaml, a functional language of the ML family. It oers all usual constraint system facilities to create and handle nite domain variables, arithmetic constraints (possibly nonlinear) , builtin global cons ..."
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Cited by 19 (12 self)
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FaCiLe is an open source constraint programming library over integer nite domain written in OCaml, a functional language of the ML family. It oers all usual constraint system facilities to create and handle nite domain variables, arithmetic constraints (possibly nonlinear) , builtin global constraints and search goals. FaCiLe allows as well to build easily userdened constraints and goals from scratch or by combining simple primitives, making pervasive use of higherorder functionals to provide a simple and exible user interface. As FaCiLe is an OCaml library and not yet another language, the user benets from polymorphic type inference and strong typing discipline, high level of abstraction, generic modules and object system, as well as native code compilation eciency, garbage collection and replay debugger. All these features allow to prototype and experiment quickly: modelling, data processing and interface are implemented in the same powerful language with a high level of safety.
HighLevel Nondeterministic Abstractions in C++
"... Abstract. This paper presents highlevel abstractions for nondeterministic search in C++ which provide the counterpart to advanced features found in recent constraint languages. The abstractions have several benefits: they explicitly highlight the nondeterministic nature of the code, provide a natur ..."
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Cited by 4 (1 self)
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Abstract. This paper presents highlevel abstractions for nondeterministic search in C++ which provide the counterpart to advanced features found in recent constraint languages. The abstractions have several benefits: they explicitly highlight the nondeterministic nature of the code, provide a natural iterative style, simplify debugging, and are efficiently implementable using macros and continuations. Their efficiency is demonstrated by comparing their performance with the C++ library Gecode, both for programming search procedures and search engines. 1
Constraint propagation in presence of arrays
 Proc. of 6th Workshop of the ERCIM Working Group on Constraints
, 2001
"... www.cwi.nl/˜sbrand/ Abstract. We describe the use of array expressions as constraints, which represents a consequent generalisation of the element constraint. Constraint propagation for array constraints is studied theoretically, and for a set of domain reduction rules the local consistency they enf ..."
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Cited by 4 (1 self)
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www.cwi.nl/˜sbrand/ Abstract. We describe the use of array expressions as constraints, which represents a consequent generalisation of the element constraint. Constraint propagation for array constraints is studied theoretically, and for a set of domain reduction rules the local consistency they enforce, arcconsistency, is proved. An efficient algorithm is described that encapsulates the rule set and so inherits the capability to enforce arcconsistency from the rules. 1
DOCTORAT DE L'I.N.P.T. Spe'cialite ' : INFORMATIQUE ET TE'LE'COMMUNICATIONS
"... Titre de la the`se: Application de la programmation par contraintes a ` des proble`mes de gestion du trafic ae'rien Soutenue le: 6 de'cembre 2002 Salle des the`ses de l'ENSEEIHT ..."
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Titre de la the`se: Application de la programmation par contraintes a ` des proble`mes de gestion du trafic ae'rien Soutenue le: 6 de'cembre 2002 Salle des the`ses de l'ENSEEIHT
Towards the new modelling language Zinc
"... Combinatorial optimization problems are usually tackled in two steps: modelling and solving. Three main approaches are used for solving: Mathematical Methods (MM), Constraint Programming (CP) and Local Search (LS). For modelling the main tools are constraint programming languages, constraint program ..."
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Combinatorial optimization problems are usually tackled in two steps: modelling and solving. Three main approaches are used for solving: Mathematical Methods (MM), Constraint Programming (CP) and Local Search (LS). For modelling the main tools are constraint programming languages, constraint programming libraries and (mathematical) modelling languages. Modelling languages provide the best approach to modelling for nonprogrammers since they do not require sophisticated programming skills. However, current modelling languages are tied to their underlying solvers and cannot support all three solving techniques. This is unfortunate since it is often not clear which technique is most suitable. This paper presents the preliminary design of Zinc, a new solver independent modelling language which is intended to support all three solving techniques. 1
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLECLICK HERE TO EDIT) < 1 DEPICT: A HighLevel Formal Language For Modeling Constraint Satisfaction Problems*
"... Abstract: The past decade witnessed rapid development of constraint satisfaction technologies, where algorithms are now able to cope with larger and harder problems. However, owing to the fact that constraints are inherently declarative, attention is quickly turning toward developing highlevel prog ..."
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Abstract: The past decade witnessed rapid development of constraint satisfaction technologies, where algorithms are now able to cope with larger and harder problems. However, owing to the fact that constraints are inherently declarative, attention is quickly turning toward developing highlevel programming languages within which such problems can be modeled and also solved. Along these lines, this paper presents DEPICT, the language. Its use is illustrated through modeling a number of benchmark examples. The paper continues with a description of a prototype system within which such models may be interpreted. The paper concludes with a description of a sample run of this interpreter showing how a problem modeled as such is typically solved.
Constraints in Procedural and Concurrent Languages
, 2006
"... This chapter addresses the integration of constraints and search into programming languages from three different points of views. It first focuses on the use of constraints to model combinatorial optimization problem and to easily implement search procedures, then it considers the use of constraint ..."
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This chapter addresses the integration of constraints and search into programming languages from three different points of views. It first focuses on the use of constraints to model combinatorial optimization problem and to easily implement search procedures, then it considers the use of constraints for supporting concurrent computations and finally turns to the use of constraints to enable open implementations of constraints solvers. The idea of approaching hard combinatorial optimization problems through a combination of search and constraint solving appeared first in logic programming. The genesis and growth of constraint programming within logic programming is not surprising as it catered to two fundamental needs: a declarative style and nondeterminism. Despite the continued support of logic programming for constraint programmers, research efforts were initiated to import constraint technologies into other paradigms (in particular procedural and objectoriented paradigms) to cater to a broader audience and leverage constraintbased techniques in novel areas. The first motivation behind a transition is a desire to ease the adoption of a successful technology. Moving constraints to a platform and paradigm widely accepted would facilitate their adoption within existing software systems
Using UML and OCL for Representing Multiobjective Combinatorial Optimization Problems
"... This paper describes the results of a preliminary feasibility study of an approach to representing Multiobjective Combinatorial Optimization Problems in UML (structural constraints) and OCL (procedural constraints) and then automatically translating the representations to a Constraint Satisfaction s ..."
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This paper describes the results of a preliminary feasibility study of an approach to representing Multiobjective Combinatorial Optimization Problems in UML (structural constraints) and OCL (procedural constraints) and then automatically translating the representations to a Constraint Satisfaction solving language (Oz) for execution. The paper presents two examples of the application of the approach a job scheduling problem and a (fixture) design problem. The main goal of this paper is to investigate directions towards a standard, graphical language for representing combinatorial optimization problems. The paper shows that for the two selected problems it is easy to represent structural constraints in UML and that procedural constraints are representable in OCL. The results also show that a developed translator automatically converts the UML/OCL representations to Oz and that the resulting Oz program performs very reasonably, in some cases outperforming the handwritten benchmark programs. 1