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Semidefinite programming

Known as: Semi-definite programming, Semidefinite programs 
Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function (an objective… Expand
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Papers overview

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Review
2019
Review
2019
In recent years, optimization theory has been greatly impacted by the advent of sum of squares (SOS) optimization. The reliance… Expand
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Highly Cited
2009
Highly Cited
2009
We describe a major update of our Matlab freeware GloptiPoly for parsing generalized problems of moments and solving them… Expand
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Highly Cited
2006
Highly Cited
2006
An SDP relaxation based method is developed to solve the localization problem in sensor networks using incomplete and inaccurate… Expand
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Highly Cited
2003
Highly Cited
2003
Abstract. A hierarchy of convex relaxations for semialgebraic problems is introduced. For questions reducible to a finite number… Expand
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Highly Cited
2003
Highly Cited
2003
Abstract. This paper discusses computational experiments with linear optimization problems involving semidefinite, quadratic, and… Expand
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Highly Cited
2002
Highly Cited
2002
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among… Expand
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Highly Cited
2000
Highly Cited
2000
In the first part of this thesis, we introduce a specific class of Linear Matrix Inequalities (LMI) whose optimal solution can be… Expand
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Highly Cited
2000
Highly Cited
2000
A central drawback of primal-dual interior point methods for semidefinite programs is their lack of ability to exploit problem… Expand
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Highly Cited
1998
Highly Cited
1998
In this paper we consider semidefinite programs (SDPs) whose data depend on some unknown but bounded perturbation parameters. We… Expand
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Highly Cited
1995
Highly Cited
1995
We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2-satisfiability (MAX 2SAT) problems… Expand
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