About: Constraint programming is a(n) research topic. Over the lifetime, 8175 publication(s) have been published within this topic receiving 190683 citation(s).
01 Jan 1967-
Abstract: Overview of the operations research modelling approach introduction to linear programming solving linear programming problems - the Simplex Method the theory of the Simplex Method duality theory and sensitivity analysis other algorithms for linear programming the transportation and assignment problems network analysis, including Pert-CPM dynamic programming game theory integer programming non-linear programming Markov chains queueing theory the application of queueing theory inventory theory forecasting Markovian decision processes and applications decision analysis simulation.
Hans-Jürgen Zimmermann1•Institutions (1)
01 Jan 1978-Fuzzy Sets and Systems
TL;DR: It is shown that solutions obtained by fuzzy linear programming are always efficient solutions and the consequences of using different ways of combining individual objective functions in order to determine an “optimal” compromise solution are shown.
Abstract: In the recent past numerous models and methods have been suggested to solve the vectormaximum problem. Most of these approaches center their attention on linear programming problems with several objective functions. Apart from these approaches the theory of fuzzy sets has been employed to formulate and solve fuzzy linear programming problems. This paper presents the application of fuzzy linear programming approaches to the linear vectormaximum problem. It shows that solutions obtained by fuzzy linear programming are always efficient solutions. It also shows the consequences of using different ways of combining individual objective functions in order to determine an “optimal” compromise solution.
01 Jan 1981-
TL;DR: This second edition of ''Programming in Prolog'' is a textbook as well as a reference work for everyone who wants to study and use Prolog as a practical programming language.
Abstract: Since the first publication of ''Programming in Prolog'' in 1981, Prolog has continued to attract an unexpectedly great deal of interest in the computer science community and is now seen as a potential basis for an important new generation of programming languages and systems. In this second edition, the authors have improved the presentation and corrected various minor errors to provide a textbook as well as a reference work for everyone who wants to study and use Prolog as a practical programming language. Various examples show how useful programs can be written with the Prolog system that exists today. The authors concentrate on teaching the ''core'' Prolog; all examples conform to this standard and will run on most existing Prolog implementations. Some of the existing Prolog implementations are listed in the appendices with indications as to how diverge from the standard.
01 Jan 2003-
TL;DR: Rina Dechter synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics to provide the first comprehensive examination of the theory that underlies constraint processing algorithms.
Abstract: Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning. In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms. ·Examines the basic practical aspects of each topic and then tackles more advanced issues, including current research challenges ·Builds the reader's understanding with definitions, examples, theory, algorithms and complexity analysis ·Synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics Table of Contents Preface; Introduction; Constraint Networks; Consistency-Enforcing Algorithms: Constraint Propagation; Directional Consistency; General Search Strategies; General Search Strategies: Look-Back; Local Search Algorithms; Advanced Consistency Methods; Tree-Decomposition Methods; Hybrid of Search and Inference: Time-Space Trade-offs; Tractable Constraint Languages; Temporal Constraint Networks; Constraint Optimization; Probabilistic Networks; Constraint Logic Programming; Bibliography
01 Oct 1987-
Abstract: We address the problem of designing programming systems to reason with and about constraints. Taking a logic programming approach, we define a class of programming languages, the CLP languages, all of which share the same essential semantic properties. From a conceptual point of view, CLP programs are highly declarative and are soundly based within a unified framework of formal semantics. This framework not only subsumes that of logic programming, but satisfies the core properties of logic programs more naturally. From a user's point of view, CLP programs have great expressive power due to the constraints which they naturally manipulate. Intuition in the reasoning about programs is enhanced as a result of working directly in the intended domain of discourse. This contrasts with working in the Herbrand Universe wherein every semantic object has to be explicitly coded into a Herbrand term; this enforces reasoning at a primitive level. Finally, from an implementor's point of view, CLP systems can be efficient because of the exploitation of constraint solving techniques over specific domains.