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Mathematical Programming

, Volume 155, Issue 1–2, pp 333–373 | Cite as

A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions

  • Xudong Li
  • Defeng SunEmail author
  • Kim-Chuan Toh
Full Length Paper Series A

Abstract

This paper is devoted to the design of an efficient and convergent semi-proximal alternating direction method of multipliers (ADMM) for finding a solution of low to medium accuracy to convex quadratic conic programming and related problems. For this class of problems, the convergent two block semi-proximal ADMM can be employed to solve their primal form in a straightforward way. However, it is known that it is more efficient to apply the directly extended multi-block semi-proximal ADMM, though its convergence is not guaranteed, to the dual form of these problems. Naturally, one may ask the following question: can one construct a convergent multi-block semi-proximal ADMM that is more efficient than the directly extended semi-proximal ADMM? Indeed, for linear conic programming with 4-block constraints this has been shown to be achievable in a recent paper by Sun et al. (2014, arXiv:1404.5378). Inspired by the aforementioned work and with the convex quadratic conic programming in mind, we propose a Schur complement based convergent semi-proximal ADMM for solving convex programming problems, with a coupling linear equality constraint, whose objective function is the sum of two proper closed convex functions plus an arbitrary number of convex quadratic or linear functions. Our convergent semi-proximal ADMM is particularly suitable for solving convex quadratic semidefinite programming (QSDP) with constraints consisting of linear equalities, a positive semidefinite cone and a simple convex polyhedral set. The efficiency of our proposed algorithm is demonstrated by numerical experiments on various examples including QSDP.

Keywords

Convex quadratic conic programming Multiple-block ADMM Semi-proximal ADMM Convergence QSDP 

Mathematics Subject Classification

90C06 90C20 90C22 90C25 65F10 

Notes

Acknowledgments

The authors would like to thank Mr Liuqin Yang at National University of Singapore for suggestions on the numerical implementations of the algorithms described in the paper and the two referees for their careful reading of our paper.

Supplementary material

10107_2014_850_MOESM1_ESM.pdf (161 kb)
Supplementary material 1 (pdf 161 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2014

Authors and Affiliations

  1. 1.Department of MathematicsNational University of SingaporeSingaporeSingapore
  2. 2.Department of Mathematics and Risk Management InstituteNational University of SingaporeSingaporeSingapore

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