Minimization with orthogonality constraints (e.g., $X^\top X = I$) and/or spherical constraints (e.g., $\|x\|_2 = 1$) 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 propose to use a Crank-Nicholson-like update scheme to preserve the constraints and based on it, develop curvilinear search algorithms with lower per-iteration cost 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 quadratic assignment problem, a gap 0.842\% to the best known solution on the largest problem ``tai256c" in QAPLIB can be reached in 5 minutes on a typical laptop.
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@TECHREPORT{OptM-Wen-Yin-2010, author = {Wen, Zaiwen and Yin, Wotao}, title = {A Feasible method for Optimization with Orthogonality Constraints}, institution = {Rice University}, year = {2010} }
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