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Solving semidefinitequadraticlinear programs using SDPT3
 MATHEMATICAL PROGRAMMING
, 2003
"... This paper discusses computational experiments with linear optimization problems involving semidefinite, quadratic, and linear cone constraints (SQLPs). Many test problems of this type are solved using a new release of SDPT3, a Matlab implementation of infeasible primaldual pathfollowing algorithm ..."
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Cited by 243 (19 self)
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This paper discusses computational experiments with linear optimization problems involving semidefinite, quadratic, and linear cone constraints (SQLPs). Many test problems of this type are solved using a new release of SDPT3, a Matlab implementation of infeasible primaldual path
Extending Mehrotra And Gondzio Higher Order Methods To Mixed SemidefiniteQuadraticLinear Programming
 OPTIMIZATION METHODS AND SOFTWARE
, 1999
"... We discuss extensions of Mehrotra's higher order corrections scheme and Gondzio's multiple centrality corrections scheme to mixed semidefinitequadraticlinear programming (SQLP). These extensions have been included in a solver for SQLP written in C and based on LAPACK. The code implements ..."
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We discuss extensions of Mehrotra's higher order corrections scheme and Gondzio's multiple centrality corrections scheme to mixed semidefinitequadraticlinear programming (SQLP). These extensions have been included in a solver for SQLP written in C and based on LAPACK. The code
Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming
"... Kernel learning is a powerful framework for nonlinear data modeling. Using the kernel trick, a number of problems have been formulated as semidefinite programs (SDPs). These include Maximum Variance Unfolding (MVU) (Weinberger et al., 2004) in nonlinear dimensionality reduction, and Pairwise Constra ..."
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Cited by 8 (1 self)
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that a large class of kernel learning problems can be reformulated as semidefinitequadraticlinear programs (SQLPs), which only contain a simple positive semidefinite constraint, a secondorder cone constraint and a number of linear constraints. These constraints are much easier to process numerically
On the implementation and usage of SDPT3  a Matlab software package for semidefinitequadraticlinear programming, version 4.0
, 2006
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SDPT3  a MATLAB software package for semidefinitequadraticlinear programming, version 3.0
, 2001
"... This document describes a new release, version 3.0, of the software SDPT3. This code is designed to solve conic programming problems whose constraint cone is a product of semidefinite cones, secondorder cones, and/or nonnegative orthants. It employs a predictorcorrector primaldual pathfollowing ..."
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Cited by 22 (5 self)
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This document describes a new release, version 3.0, of the software SDPT3. This code is designed to solve conic programming problems whose constraint cone is a product of semidefinite cones, secondorder cones, and/or nonnegative orthants. It employs a predictorcorrector primaldual path
M.J.: On the implementation of SDPT3 (version 3.1) – a Matlab software package for semidefinitequadraticlinear programming
 IEEE Conference on ComputerAided Control System Design, 2004
, 2004
"... This code is designed to solve conic programming problems whose constraint cone is a product of semidefinite cones, secondorder cones, nonnegative orthants and Euclidean spaces. It employs a primaldual predictorcorrector pathfollowing method, with either the HKM or the NT search direction. The ba ..."
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Cited by 10 (2 self)
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This code is designed to solve conic programming problems whose constraint cone is a product of semidefinite cones, secondorder cones, nonnegative orthants and Euclidean spaces. It employs a primaldual predictorcorrector pathfollowing method, with either the HKM or the NT search direction
Interiorpoint Methods
, 2000
"... The modern era of interiorpoint methods dates to 1984, when Karmarkar proposed his algorithm for linear programming. In the years since then, algorithms and software for linear programming have become quite sophisticated, while extensions to more general classes of problems, such as convex quadrati ..."
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Cited by 612 (15 self)
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quadratic programming, semidefinite programming, and nonconvex and nonlinear problems, have reached varying levels of maturity. We review some of the key developments in the area, including comments on both the complexity theory and practical algorithms for linear programming, semidefinite programming
Learning the Kernel Matrix with SemiDefinite Programming
, 2002
"... Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
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Cited by 775 (21 self)
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problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied
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 547 (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
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
Results 1  10
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