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Approximating semidefinite packing problems

G. Iyengar (garud***at***ieor.columbia.edu)
D. J. Phillips (phillips***at***math.wm.edu)
C. Stein (cliff***at***ieor.columbia.edu)

Abstract: In this paper we define semidefinite packing programs and describe an algorithm to approximately solve these problems. Semidefinite packing programs arise in many applications such as semidefinite programming relaxations for combinatorial optimization problems, sparse principal component analysis, and sparse variance unfolding technique for dimension reduction. Our algorithm exploits the structural similarity between semidefinite packing programs and linear packing programs.

Keywords: Semidefinite programming, combinatorial optimization, approximation algorithms, nonsmooth optimization

Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming )

Category 2: Combinatorial Optimization (Approximation Algorithms )

Category 3: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Citation: Accepted to SIAM J. Optimization.

Download: [PDF]

Entry Submitted: 06/22/2009
Entry Accepted: 06/22/2009
Entry Last Modified: 10/25/2010

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