Feasible and Accurate Algorithms for Covering Semidefinite Programs

  • Garud Iyengar
  • David J. Phillips
  • Cliff Stein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6139)


In this paper we describe an algorithm to approximately solve a class of semidefinite programs called covering semidefinite programs. This class includes many semidefinite programs that arise in the context of developing algorithms for important optimization problems such as Undirected Sparsest Cut, wireless multicasting, and pattern classification. We give algorithms for covering SDPs whose dependence on ε is ε − 1. These algorithms, therefore, have a better dependence on ε than other combinatorial approaches, with a tradeoff of a somewhat worse dependence on the other parameters. For many reasons, including numerical stability and a variety of implementation concerns, the dependence on ε is critical, and the algorithms in this paper may be preferable to those of the previous work. Our algorithms exploit the structural similarity between packing and covering semidefinite programs and packing and covering linear programs.


Lagrangian Relaxation Semidefinite Program Saddle Point Problem Interior Point Algorithm Accurate 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 2010

Authors and Affiliations

  • Garud Iyengar
    • 1
  • David J. Phillips
    • 2
  • Cliff Stein
    • 1
  1. 1.The Department of Industrial Engineering & Operations ResearchColumbia University 
  2. 2.Mathematics DepartmentThe College of William & Mary 

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