A Nonconvex Free Lunch for Low-Rank plus Sparse Matrix Recovery
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We study the problem of low-rank plus sparse matrix recovery. We propose a generic and efficient nonconvex optimization algorithm based on projected gradient descent and double thresholding operator, with much lower computational complexity. Compared with existing convex-relaxation based methods, the proposed algorithm recovers the low-rank plus sparse matrices for free, without incurring any additional statistical cost. It not only enables exact recovery of the unknown low-rank and sparse matrices in the noiseless setting, and achieves minimax optimal statistical error rate in the noisy case, but also matches the best-known robustness guarantee (i.e., tolerance for sparse corruption). At the core of our theory is a novel structural Lipschitz gradient condition for low-rank plus sparse matrices, which is essential for proving the linear convergence rate of our algorithm, and we believe is of independent interest to prove fast rates for general superposition-structured models. We demonstrate the superiority of our generic algorithm, both theoretically and experimentally, through three concrete applications: robust matrix sensing, robust PCA and one-bit matrix decomposition.
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