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Set Branching in Constraint Optimization
"... Branch and bound is an effective technique for solving constraint optimization problems (COP’s). However, its search space expands very rapidly as the domain sizes of the problem variables grow. In this paper, we present an algorithm that clusters the values of a variable’s domain into sets. Branch ..."
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Cited by 2 (0 self)
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Branch and bound is an effective technique for solving constraint optimization problems (COP’s). However, its search space expands very rapidly as the domain sizes of the problem variables grow. In this paper, we present an algorithm that clusters the values of a variable’s domain into sets. Branch
Lecture Notes: Constraint Optimization
, 2015
"... In constraint optimization we want to maximize a function f(x) under the constraints that gi(x) = 0. For simplicity, we will only consider equality constraints. We can formalize this problem as argmaxxf(x) s.t.:gi(xi) = 0,∀i. (1) We look for a point x ∗ that lies on the constraint surface and max ..."
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In constraint optimization we want to maximize a function f(x) under the constraints that gi(x) = 0. For simplicity, we will only consider equality constraints. We can formalize this problem as argmaxxf(x) s.t.:gi(xi) = 0,∀i. (1) We look for a point x ∗ that lies on the constraint surface
Distributed Constraint Optimization (DCOP)...
"... Distributed Constraint Optimization (DCOP) is useful for solving agentcoordination problems. Anyspace DCOP search algorithms require only a small amount of memory but can be sped up by caching information. However, their current caching schemes do not exploit the cached information when deciding w ..."
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Distributed Constraint Optimization (DCOP) is useful for solving agentcoordination problems. Anyspace DCOP search algorithms require only a small amount of memory but can be sped up by caching information. However, their current caching schemes do not exploit the cached information when deciding
Dynamic programming algorithm optimization for spoken word recognition
 IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
, 1978
"... This paper reports on an optimum dynamic programming (DP) based timenormalization algorithm for spoken word recognition. First, a general principle of timenormalization is given using timewarping function. Then, two timenormalized distance definitions, ded symmetric and asymmetric forms, are der ..."
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Cited by 766 (3 self)
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words in different The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimentat comparison with various DPalgorithms, previously applied
Solving distributed constraint optimization problems using cooperative mediation
 In Proceedings of AAMAS
, 2004
"... Distributed Constraint Optimization Problems (DCOP) have, for a long time, been considered an important research area for multiagent systems because a vast number of realworld situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve th ..."
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Cited by 193 (6 self)
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Distributed Constraint Optimization Problems (DCOP) have, for a long time, been considered an important research area for multiagent systems because a vast number of realworld situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve
Diagnosis as Semiringbased Constraint Optimization
, 2004
"... Constraint optimization is at the core of many problems in Artificial Intelligence. In this paper, we frame modelbased diagnosis as a constraint optimization problem over lattices. We then show how it can be captured in a framework for "soft" constraints known as semiringCSPs. The welld ..."
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Cited by 16 (7 self)
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Constraint optimization is at the core of many problems in Artificial Intelligence. In this paper, we frame modelbased diagnosis as a constraint optimization problem over lattices. We then show how it can be captured in a framework for "soft" constraints known as semiringCSPs. The well
Constrained model predictive control: Stability and optimality
 AUTOMATICA
, 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
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Cited by 706 (15 self)
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Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence
An Asynchronous Complete Method for Distributed Constraint Optimization
 In AAMAS
, 2003
"... We present a new polynomialspace algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multiagent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to pr ..."
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Cited by 130 (30 self)
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We present a new polynomialspace algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multiagent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able
Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization
 SIAM Journal on Optimization
, 1993
"... We study the semidefinite programming problem (SDP), i.e the problem of optimization of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. First we review the classical cone duality as specialized to S ..."
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Cited by 544 (12 self)
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We study the semidefinite programming problem (SDP), i.e the problem of optimization of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. First we review the classical cone duality as specialized
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 1317 (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 11  20
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920,211