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An Interior-Point Method for Semidefinite Programming
TLDR
We propose a new interior-point-based method to minimize a linear function of a matrix variable subject to linear equality and inequality constraints over the set of positive semidefinite matrices. Expand
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Positive definite completions of partial Hermitian matrices
Abstract The question of which partial Hermitian matrices (some entries specified, some free) may be completed to positive definite matrices is addressed. It is shown that if the diagonal entries areExpand
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Handbook of semidefinite programming : theory, algorithms, and applications
Contributing Authors. List of Figures. List of Tables. Preface. 1. Introduction H. Wolkowicz, et al. Part I: Theory. 2. Convex Analysis on Symmetric Matrices F. Jarre. 3. The Geometry of SemidefiniteExpand
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Semidefinite Programming Relaxations for the Quadratic Assignment Problem
TLDR
Semidefinite programming (SDP) relaxations for the quadratic assignment problem (QAP) are derived using the dual of the (homogenized) Lagrangian dual of appropriate equivalent representations of QAP. Expand
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SIAM Journal on Optimization
TLDR
The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. Expand
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A semidefinite framework for trust region subproblems with applications to large scale minimization
TLDR
Primal-dual pairs of semidefinite programs provide a general framework for the theory and algorithms for the trust region subproblem (TRS) as a parametric eigenvalue problem. Expand
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Solving Euclidean Distance Matrix Completion Problems Via Semidefinite Programming
TLDR
We solve the Euclidean distance matrix completion problem by generalizing the completion problem to allow for approximate completions. Expand
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Strong Duality for Semidefinite Programming
TLDR
We study strong duality theorems for the semidefinite linear programming problem (P) p∗. Expand
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Regularizing the Abstract Convex Program
Abstract Characterizations of optimality for the abstract convex program μ = inf {p(x) : g(x) ϵ −S, x ϵ Ω} (P) where S is an arbitrary convex cone in a finite dimensional space, Ω is a convex set,Expand
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The Quadratic Assignment Problem: A Survey and Recent Developments
TLDR
Quadratic Assignment Problems model many applications in diverse areas such as operations research, parallel and distributed computing , and combinatorial data analysis. Expand
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