A feasible method for optimization with orthogonality constraints
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 transforma Crank-Nicolson-like update schemeto 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 5min on a typical laptop.
orthogonality constraint spherical constraint polynomial optimization curvilinear search nearest correlation matrix quadratic assignment problem stiefel manifold cayley transformation maxcut sdp eigenvalue and eigenvector invariant subspace 49q99 65k05 90c22 90c26 90c27 90c30