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**Guarantees of Riemannian optimization for low rank matrix completion.**
*(English)*
Zbl 1439.65059

Summary: We establish the exact recovery guarantees for a class of Riemannian optimization methods based on the embedded manifold of low rank matrices for matrix completion. Assume \( m\) entries of an \( n\times n\) rank \( r\) matrix are sampled independently and uniformly with replacement. We first show that with high probability the Riemannian gradient descent and conjugate gradient descent algorithms initialized by one step hard thresholding are guaranteed to converge linearly to the measured matrix provided \[m\geq C_\kappa n^{1.5}r\log^{1.5}(n),\] where \( C_\kappa\) is a numerical constant depending on the condition number of the measured matrix. Then the sampling complexity is further improved to \[m\geq C_\kappa nr^2\log^2(n)\] via the resampled Riemannian gradient descent initialization. The analysis of the new initialization procedure relies on an asymmetric restricted isometry property of the sampling operator and the curvature of the low rank matrix manifold. Numerical simulation shows that the algorithms are able to recover a low rank matrix from nearly the minimum number of measurements.

### MSC:

65F55 | Numerical methods for low-rank matrix approximation; matrix compression |

15A83 | Matrix completion problems |

65K10 | Numerical optimization and variational techniques |

### Keywords:

matrix completion; Riemannian optimization; low-rank matrix manifold; tangent space; gradient descent methods
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XMLCite

\textit{K. Wei} et al., Inverse Probl. Imaging 14, No. 2, 233--265 (2020; Zbl 1439.65059)

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