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Sparse Approximate Solutions to Semidefinite Programs

  • Elad Hazan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4957)

Abstract

We propose an algorithm for approximately maximizing a concave function over the bounded semi-definite cone, which produces sparse solutions. Sparsity for SDP corresponds to low rank matrices, and is a important property for both computational as well as learning theoretic reasons. As an application, building on Aaronson’s recent work, we derive a linear time algorithm for Quantum State Tomography.

Keywords

Curvature Constant Interior Point Method Linear Time Algorithm Sparse Solution Lanczos Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Elad Hazan
    • 1
  1. 1.IBM Almaden Research CenterSan JoseUSA

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