Global and Local Analyses of Nonlinear Low-Rank Matrix Recovery Problems
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Abstract (arXiv)
The restricted isometry property (RIP) is a well-known condition that guarantees the absence of spurious local minima in low-rank matrix recovery problems with linear measurements. In this paper, we introduce a novel property named bound difference property (BDP) to study low-rank matrix recovery problems with nonlinear measurements. Using RIP and BDP jointly, we first focus on the rank-1 matrix recovery problem, for which we propose a new criterion to certify the nonexistence of spurious local minima over the entire space. We then analyze the general case with an arbitrary rank and derive a condition to rule out the possibility of having a spurious solution in a ball around the true solution. The developed conditions lead to much stronger theoretical guarantees than the existing bounds on RIP.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2022Improved Global Guarantees for the Nonconvex Burer--Monteiro Factorization via Rank Overparameterizationcited by 13
- 2021Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methodscited by 6
- 2022Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensingcited by 5
- 2025An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU functioncited by 4
- 2025An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function31 Mar 2025
- 2022Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing15 Aug 2022
- 2022Improved Global Guarantees for the Nonconvex Burer--Monteiro Factorization via Rank Overparameterization5 Jul 2022
- 2021Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods19 Oct 2021