A Penalty Method for Rank Minimization Problems in Symmetric Matrices
Abstract
The problem of minimizing the rank of a symmetric positive semidefinite matrix subject to constraints can be cast equivalently as a semidefinite program with complementarity constraints (SDCMPCC). The formulation requires two positive semidefinite matrices to be complementary. This is a continuous and nonconvex reformulation of the rank minimization problem. We investigate calmness of locally optimal solutions to the SDCMPCC formulation and hence show that any locally optimal solution is a KKT point. We develop a penalty formulation of the problem. We present calmness results for locally optimal solutions to the penalty formulation. We also develop a proximal alternating linearized minimization (PALM) scheme for the penalty formulation, and investigate the incorporation of a momentum term into the algorithm. Computational results are presented.
Cite
@article{arxiv.1701.03218,
title = {A Penalty Method for Rank Minimization Problems in Symmetric Matrices},
author = {Xin Shen and John E. Mitchell},
journal= {arXiv preprint arXiv:1701.03218},
year = {2018}
}