© 2014, Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society. 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 semi-smoothness 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.
|Number of pages||45|
|Journal||Mathematical Programming Computation|
|Publication status||Published - 1 Sept 2014|
ASJC Scopus subject areas
- Theoretical Computer Science