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A PrimalDual TrustRegion Algorithm for Minimizing a NonConvex Function Subject to General Inequality and Linear Equality Constraints
, 1999
"... A new primaldual algorithm is proposed for the minimization of nonconvex objective functions subject to general inequality and linear equality constraints. The method uses a primaldual trustregion model to ensure descent on a suitable merit function. Convergence is proved to secondorder critical ..."
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Cited by 7 (0 self)
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A new primaldual algorithm is proposed for the minimization of nonconvex objective functions subject to general inequality and linear equality constraints. The method uses a primaldual trustregion model to ensure descent on a suitable merit function. Convergence is proved to second
Quadratic Optimization over a SecondOrder Cone with Linear Equality Constraints Xiaoling Guo · Zhibin Deng · ShuCherng Fang ·
, 2013
"... Abstract This paper studies the nonhomogeneous quadratic programming problem over a secondorder cone with linear equality constraints. When the feasible region is bounded, we show that an optimal solution of the problem can be found in polynomial time. When the feasible region is unbounded, a semi ..."
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Abstract This paper studies the nonhomogeneous quadratic programming problem over a secondorder cone with linear equality constraints. When the feasible region is bounded, we show that an optimal solution of the problem can be found in polynomial time. When the feasible region is unbounded, a
We consider the maximum entropy problem with linear equality constraints ME: max Ap=c
"... pi log pi qi (1.1) where A = (aij) ∈ R m×n, q = (qj) ∈ R n and c = (ci) ∈ R m, with m < n, qi> 0 and ci> 0. Suppose p ∗ is the solution of (1.1), then it is easy to verify that p ∗ ∏ n πj s=1 tasj s for some t = (t1,..., tn), where ts> 0 and where πj = elog qj−1. We will assume that A ..."
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applications, including the multidimensional contingency table computation [4], self assembly[5] and natural language processing [1]. When the constraints that n∑ n∑ cs = 1, asj = 1, and asj ≥ 0, for all s, j. (1.2) s=1 s=1 a standard method solving (1.1) is Iterative Proportional Scaling method [3
Solving nuclear norm regularized and semidefinite matrix least squares problems with linear equality constraints
, 2012
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Constraint Networks
, 1992
"... Constraintbased reasoning is a paradigm for formulating knowledge as a set of constraints without specifying the method by which these constraints are to be satisfied. A variety of techniques have been developed for finding partial or complete solutions for different kinds of constraint expression ..."
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Cited by 1149 (43 self)
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Constraintbased reasoning is a paradigm for formulating knowledge as a set of constraints without specifying the method by which these constraints are to be satisfied. A variety of techniques have been developed for finding partial or complete solutions for different kinds of constraint
Guaranteed minimumrank solutions of linear matrix equations via nuclear norm minimization
, 2007
"... The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and collaborative ..."
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Cited by 568 (23 self)
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The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
, 2008
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Making LargeScale Support Vector Machine Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 620 (1 self)
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Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large
Making LargeScale SVM Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 1846 (17 self)
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Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large
The Semantics Of Constraint Logic Programs
 JOURNAL OF LOGIC PROGRAMMING
, 1996
"... This paper presents for the first time the semantic foundations of CLP in a selfcontained and complete package. The main contributions are threefold. First, we extend the original conference paper by presenting definitions and basic semantic constructs from first principles, giving new and comp ..."
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Cited by 872 (14 self)
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and complete proofs for the main lemmas. Importantly, we clarify which theorems depend on conditions such as solution compactness, satisfaction completeness and independence of constraints. Second, we generalize the original results to allow for incompleteness of the constraint solver. This is important
Results 11  20
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