Near-Optimal Nonconvex Matrix Completion

Jian-Feng Cai; Xiliang Lu; Juntao You

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arXiv · 2609.17048

Near-Optimal Nonconvex Matrix Completion

Jian-Feng Cai·Xiliang Lu·Juntao You

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Abstract

We study nonconvex methods for matrix completion, the problem of recovering a low-rank matrix from a subset of its entries. Convex methods achieve sample complexity linear in the matrix dimension and the rank, up to logarithmic factors, whereas global guarantees for commonly used nonconvex methods require a higher polynomial dependence on the rank. We close this gap by analyzing Riemannian gradient descent (RGD) and Riemannian Gauss--Newton (RGN) methods. For an $n\times n$ matrix of rank $r$ with incoherence parameter $μ$ and condition number $κ$, the two methods achieve exact recovery with high probability from $O(μnr\log n\log(nκ))$ and $O(μnr\log n\log(2μrκ))$ observations, respectively. The methods use a multiscale residual initialization, while the analysis simultaneously controls the spectral error and incoherence. The resulting RGD iterates converge linearly, whereas RGN eventually converges Q-quadratically.

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Jian-Feng Cai, Xiliang Lu, Juntao You. 2026-09-15. Near-Optimal Nonconvex Matrix Completion. https://arxiv.org/abs/2609.17048

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