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Mathematical Programming

, Volume 142, Issue 1–2, pp 397–434 | Cite as

A feasible method for optimization with orthogonality constraints

  • Zaiwen WenEmail author
  • Wotao Yin
Full Length Paper Series A

Abstract

Minimization with orthogonality constraints (e.g., \(X^\top X = I\)) and/or spherical constraints (e.g., \(\Vert x\Vert _2 = 1\)) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, p-harmonic flows, 1-bit compressive sensing, matrix rank minimization, etc. These problems are difficult because the constraints are not only non-convex but numerically expensive to preserve during iterations. To deal with these difficulties, we apply the Cayley transform—a Crank-Nicolson-like update scheme—to preserve the constraints and based on it, develop curvilinear search algorithms with lower flops compared to those based on projections and geodesics. The efficiency of the proposed algorithms is demonstrated on a variety of test problems. In particular, for the maxcut problem, it exactly solves a decomposition formulation for the SDP relaxation. For polynomial optimization, nearest correlation matrix estimation and extreme eigenvalue problems, the proposed algorithms run very fast and return solutions no worse than those from their state-of-the-art algorithms. For the quadratic assignment problem, a gap 0.842 % to the best known solution on the largest problem “tai256c” in QAPLIB can be reached in 5 min on a typical laptop.

Keywords

Orthogonality constraint Spherical constraint Stiefel manifold Cayley transformation Curvilinear search  Polynomial optimization Maxcut SDP Nearest correlation matrix Eigenvalue and eigenvector Invariant subspace Quadratic assignment problem 

Mathematics Subject Classification (2010)

49Q99 65K05 90C22 90C26 90C27 90C30 

Notes

Acknowledgments

We would like to thank Yin Zhang, Xin Liu, and Shiqian Ma for the discussions on optimization conditions, Jiawang Nie for the discussions on polynomial optimization, Chao Yang for the discussions on the Kohn–Sham equation, as well as Franz Rendl and Etienne de Klerk for their comments on QAPs. We would also like to thank Defeng Sun and Yan Gao for sharing their code PenCorr and their improvement for Major, as well as sharing the test data for the nearest correlation matrix problem. The authors are grateful to Adrian Lewis, the Associate Editor and three anonymous referees for their detailed and valuable comments and suggestions.

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Copyright information

© Springer and Mathematical Optimization Society 2012

Authors and Affiliations

  1. 1.Department of Mathematics and Institute of Natural SciencesShanghai Jiaotong UniversityShanghaiChina
  2. 2.Department of Computational and Applied MathematicsRice UniversityHoustonUSA

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