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Adaptive Constraint Satisfaction
 WORKSHOP OF THE UK PLANNING AND SCHEDULING
, 1996
"... Many different approaches have been applied to constraint satisfaction. These range from complete backtracking algorithms to sophisticated distributed configurations. However, most research effort in the field of constraint satisfaction algorithms has concentrated on the use of a single algorithm fo ..."
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Cited by 952 (43 self)
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for solving all problems. At the same time, a consensus appears to have developed to the effect that it is unlikely that any single algorithm is always the best choice for all classes of problem. In this paper we argue that an adaptive approach should play an important part in constraint satisfaction
Partial Constraint Satisfaction
, 1992
"... . A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying ..."
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Cited by 471 (21 self)
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. A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying
Algorithms for ConstraintSatisfaction Problems: A Survey
, 1992
"... A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraintsatisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, an ..."
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Cited by 449 (0 self)
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A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraintsatisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
 ARTIF. INTELL
, 1992
"... This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a valueorderin ..."
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Cited by 457 (6 self)
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This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a value
Nonnegative matrix factorization with sparseness constraints,”
 Journal of Machine Learning Research,
, 2004
"... Abstract Nonnegative matrix factorization (NMF) is a recently developed technique for finding partsbased, linear representations of nonnegative data. Although it has successfully been applied in several applications, it does not always result in partsbased representations. In this paper, we sho ..."
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Cited by 498 (0 self)
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show how explicitly incorporating the notion of 'sparseness' improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data
Distributed Constraint Satisfaction for Formalizing Distributed Problem Solving
, 1992
"... Viewing cooperative distributed problem solving (CDPS) as distributed constraint satisfaction provides a useful formalism for characterizing CDPS techniques. In this paper, we describe this formalism and compare algorithms for solving distributed constraint satisfaction problems (DCSPs). In particul ..."
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Cited by 295 (23 self)
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Viewing cooperative distributed problem solving (CDPS) as distributed constraint satisfaction provides a useful formalism for characterizing CDPS techniques. In this paper, we describe this formalism and compare algorithms for solving distributed constraint satisfaction problems (DCSPs
Analogical Mapping by Constraint Satisfaction
 COGNITIVE SCIENCE 13, 295 (1989)
, 1989
"... A theory of analogical mopping between source and target analogs based upon interacting structural, semantic, and pragmatic constraints is proposed here. The structural constraint of fsomorphfsm encourages mappings that maximize the consistency of relational corresondences between the elements of th ..."
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Cited by 397 (29 self)
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the anology Is being used. The theory is implemented in a computer progrom called ACME (Analogical Constraint Mapping Engine), which represents constraints by means of a network of supporting and competing hypotheses regarding what elements to map. A coop erative algorithm for parallel constraint satisfaction
Hybrid Algorithms for the Constraint Satisfaction Problem
 Computational Intelligence
, 1993
"... problem (csp), namely, naive backtracking (BT), backjumping (BJ), conflictdirected backjumping ..."
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Cited by 380 (8 self)
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problem (csp), namely, naive backtracking (BT), backjumping (BJ), conflictdirected backjumping
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 597 (24 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones
, 1998
"... SeDuMi is an addon for MATLAB, that lets you solve optimization problems with linear, quadratic and semidefiniteness constraints. It is possible to have complex valued data and variables in SeDuMi. Moreover, large scale optimization problems are solved efficiently, by exploiting sparsity. This pape ..."
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Cited by 1368 (5 self)
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SeDuMi is an addon for MATLAB, that lets you solve optimization problems with linear, quadratic and semidefiniteness constraints. It is possible to have complex valued data and variables in SeDuMi. Moreover, large scale optimization problems are solved efficiently, by exploiting sparsity
Results 1  10
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20,490