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  Scalable Semidefinite Programming using Convex Perturbations

Kulis, B., Sra, S., Jegelka, S., & Dhillon, I.(2007). Scalable Semidefinite Programming using Convex Perturbations (TR-07-47). Austin, TX, USA: University of Texas.

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TR-07-47.pdf (Publisher version), 161KB
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Kulis, B, Author
Sra, S1, 2, Author              
Jegelka, S1, 2, Author              
Dhillon, IS, Author
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Several important machine learning problems can be modeled and solved via semidefinite programs. Often, researchers invoke off-the-shelf software for the associated optimization, which can be inappropriate for many applications due to computational and storage requirements. In this paper, we introduce the use of convex perturbations for semidefinite programs (SDPs). Using a particular perturbation function, we arrive at an algorithm for SDPs that has several advantages over existing techniques: a) it is simple, requiring only a few lines of MATLAB, b) it is a first-order method which makes it scalable, c) it can easily exploit the structure of a particular SDP to gain efficiency (e.g., when the constraint matrices are low-rank). We demonstrate on several machine learning applications that the proposed algorithm is effective in finding fast approximations to large-scale SDPs.

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 Dates: 2007-09
 Publication Status: Published in print
 Pages: 14
 Publishing info: Austin, TX, USA : University of Texas
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: TR-07-47
BibTex Citekey: 5119
 Degree: -

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