Mathematical Programming Computation, Volume 6, Issue 3, September 2014
A partial proximal point algorithm for nuclear norm regularized matrix least squares problems
Kaifeng Jiang, Defeng Sun, Kim-Chuan Toh
We introduce a partial proximal point algorithm for solving nuclear norm regularized matrix least squares problems with equality and inequality constraints. The inner subproblems, reformulated as a system of semismooth equations, are solved by an inexact smoothing Newton method, which is proved to be quadratically convergent under a constraint non-degeneracy condition, together with the strong semismoothness property of the singular value thresholding operator. Numerical experiments on a variety of problems including those arising from low-rank approximations of transition matrices show that our algorithm is efficient and robust.
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