A majorized PAM method with subspace correction for low-rank composite factorization model

This paper concerns a class of low-rank composite factorization models arising from matrix completion. For this nonconvex and nonsmooth optimization problem, we propose a proximal alternating minimization algorithm (PAMA) with subspace correction, in which a subspace correction step is imposed on every proximal subproblem so as to guarantee that the corrected proximal subproblem has a closed-form solution. For this subspace correction PAMA, we prove the subsequence convergence of the iterate sequence, and establish the convergence of the whole iterate sequence and the column subspace sequences of factor pairs under the KL property of objective function and a restrictive condition that holds automatically for the column ℓ_2,0-norm function. Numerical comparison with the proximal alternating linearized minimization method on one-bit matrix completion problems indicates that PAMA has an advantage in seeking lower relative error within less time.