The nuclear norm minimization (NNM) problem is to recover a matrix that minimizes the sum of its singular values and satisfies some linear constraints simultaneously. The alternating direction method (ADM) has been used to solve this problem recently. However, the subproblems in ADM are usually not easily solvable when the linear mappings in the constraints are not identities. In this paper, we propose a proximity algorithm with adaptive penalty (PA-AP). First, we formulate the nuclear norm minimization problems into a unified model. To solve this model, we improve the ADM by adding a proximal term to the subproblems that are difficult to solve. An adaptive tactic on the proximity parameters is also put forward for acceleration. By employing subdifferentials and proximity operators, an equivalent fixed-point equation system is constructed, and we use this system to further prove the convergence of the proposed algorithm under certain conditions, e.g., the precondition matrix is symmetric positive definite. Finally, experimental results and comparisons with state-of-the-art methods, e.g., ADM, IADM-CG and IADM-BB, show that the proposed algorithm is effective.
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