Block Coordinate Descent Methods for Semidefinite Programming

  • Zaiwen Wen
  • Donald Goldfarb
  • Katya Scheinberg
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 166)


We consider in this chapter block coordinate descent (BCD) methods for solving semidefinite programming (SDP) problems. These methods are based on sequentially minimizing the SDP problem’s objective function over blocks of variables corresponding to the elements of a single row (and column) of the positive semidefinite matrix X; hence, we will also refer to these methods as row-by-row (RBR) methods. Using properties of the (generalized) Schur complement with respect to the remaining fixed (n − 1)-dimensional principal submatrix of X, the positive semidefiniteness constraint on X reduces to a simple second-order cone constraint. It is well known that without certain safeguards, BCD methods cannot be guaranteed to converge in the presence of general constraints. Hence, to handle linear equality constraints, the methods that we describe here use an augmented Lagrangian approach. Since BCD methods are first-order methods, they are likely to work well only if each subproblem minimization can be performed very efficiently. Fortunately, this is the case for several important SDP problems, including the maxcut SDP relaxation and the minimum nuclear norm matrix completion problem, since closed-form solutions for the BCD subproblems that arise in these cases are available. We also describe how BCD can be applied to solve the sparse inverse covariance estimation problem by considering a dual formulation of this problem. The BCD approach is further generalized by using a rank-two update so that the coordinates can be changed in more than one row and column at each iteration. Finally, numerical results on the maxcut SDP relaxation and matrix completion problems are presented to demonstrate the robustness and efficiency of the BCD approach, especially if only moderately accurate solutions are desired.


Cholesky Factorization Augmented Lagrangian Method Matrix Completion Augmented Lagrangian Function Coordinate Descent Method 
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.



The research presented in this chapter was supported in part by NSF Grants DMS-0439872, DMS 06-06712, DMS 10-16571, ONR Grant N00014-08-1-1118 and DOE Grant DE-FG02-08ER58562. The authors would like to thank Shiqian Ma for his help in writing and testing the codes, especially for the matrix completion problem, and the editors and two anonymous referees for their valuable comments and suggestions.


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© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Mathematics and Institute of Natural SciencesShanghai Jiaotong UniversityShanghaiChina
  2. 2.Department of Industrial Engineering and Operations ResearchColumbia UniversityNew YorkUSA
  3. 3.Department of Industrial and Systems EngineeringLehigh UniversityBethlehemUSA

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