A Simpler Approach to Matrix Completion

This paper provides the best bounds to date on the number of randomly sampled entries required to reconstruct an unknown low rank matrix. These results improve on prior work by Candes and Recht, Candes and Tao, and Keshavan, Montanari, and Oh. The reconstruction is accomplished by minimizing the nuclear norm, or sum of the singular values, of the hidden matrix subject to agreement with the provided entries. If the underlying matrix satisfies a certain incoherence condition, then the number of entries required is equal to a quadratic logarithmic factor times the number of parameters in the singular value decomposition. The proof of this assertion is short, self contained, and uses very elementary analysis. The novel techniques herein are based on recent work in quantum information theory.

💡 Research Summary

The paper addresses the fundamental problem of low‑rank matrix completion: given a matrix (M) of unknown entries but known to have rank (r), recover the entire matrix from a subset of its entries that are observed at random. The authors focus on the convex relaxation based on nuclear‑norm minimization, i.e., solving
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