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Fast Algorithms for Approximate Semidefinite Programming using the Multiplicative Weights Update Method
 

Summary: Fast Algorithms for Approximate Semidefinite Programming using the
Multiplicative Weights Update Method
Sanjeev Arora
Elad Hazan Satyen Kale
Computer Science Department, Princeton University
35 Olden Street, Princeton, NJ 08544
{arora, ehazan, satyen}@cs.princeton.edu
Abstract
Semidefinite programming (SDP) relaxations appear in
many recent approximation algorithms but the only gen-
eral technique for solving such SDP relaxations is via inte-
rior point methods. We use a Lagrangian-relaxation based
technique (modified from the papers of Plotkin, Shmoys,
and Tardos (PST), and Klein and Lu) to derive faster al-
gorithms for approximately solving several families of SDP
relaxations. The algorithms are based upon some improve-
ments to the PST ideas -- which lead to new results even for
their framework-- as well as improvements in approximate
eigenvalue computations by using random sampling.
1. Introduction

  

Source: Arora, Sanjeev - Department of Computer Science, Princeton University

 

Collections: Computer Technologies and Information Sciences