Showing papers on "Constraint satisfaction published in 1981"
TL;DR: The programming language aspects of a graphic simulation laboratory named ThingLab, which is an object-oriented system that employs inheritance and part-whole hierarchies to describe the structure of a simulation, are presented.
Abstract: The programming language aspects of a graphic simulation laboratory named ThingLab are presented. The design and implementation of ThingLab are extensions to Smalltalk. In ThingLab, constraints are used to specify the relations that must hold among the parts of the simulation. The system is object-oriented and employs inheritance and part-whole hierarchies to describe the structure of a simulation. An interactive, graphic user interface is provided that allows the user to view and edit a simulation.
660 citations
TL;DR: This paper presents a simplex-based solution procedure for the multiple objective linear fractional programming problem that solves for all weakly efficient vertices of the augmented feasible region.
Abstract: This paper presents a simplex-based solution procedure for the multiple objective linear fractional programming problem. By 1 departing slightly from the traditional notion of efficiency and 2 augmenting the feasible region as in goal programming, the solution procedure solves for all weakly efficient vertices of the augmented feasible region. The article discusses the difficulties that must be addressed in multiple objective linear fractional programming and motivates the solution algorithm that is developed.
158 citations
Proceedings Article•
24 Aug 1981TL;DR: An algorithm for finding all the solutions of a constraint satisfaction problem with worst case time bound 0(m*kf+1) and space bound 0 (n*kF+1), where n is the number of variables in the problem and k the cardinality of the domain of the variables.
Abstract: This paper deals with the combinatorial search problem of finding values for a set of variables subject to a set of constraints. This problem is referred to as a constraint satisfaction problem.
We present an algorithm for finding all the solutions of a constraint satisfaction problem with worst case time bound 0(m*kf+1) and space bound 0(n*kf+1), where n is the number of variables in the problem, m the number of constraints, k the cardinality of the domain of the variables, and f
80 citations
TL;DR: The role of Farkas-Minkowski systems in semi-infinite programming is studied in this paper by means of duality, and, in the nonlinear case, in connection with optimality conditions.
Abstract: The Farkas-Minkowski systems are characterized through a convex cone associated to the system, and some sufficient conditions are given that guarantee the mentioned property. The role of such systems in semi-infinite programming is studied in the linear case by means of the duality, and, in the nonlinear case, in connection with optimality conditions. In the last case the property appears as a constraint qualification.
44 citations