Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization

• URL: http://arxiv.org/abs/2108.01772v1
• Date: Tue, 3 Aug 2021 22:14:01 GMT
• Title: Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization
• Authors: Yuetian Luo and Xudong Li and Anru R. Zhang
• Abstract summary: We consider the geometric landscape connection of the widely studied manifold and factorization formulations in low-rank positive semidefinite (PSD) and general matrix optimization. We establish an equivalence on the set of first-order stationary points (FOSPs) and second-order stationary points (SOSPs) between the manifold and the factorization formulations. Similarities and differences on the landscape connection under the PSD case and the general case are discussed.
• Score: 6.462179538647346
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this paper, we consider the geometric landscape connection of the widely studied manifold and factorization formulations in low-rank positive semidefinite (PSD) and general matrix optimization. We establish an equivalence on the set of first-order stationary points (FOSPs) and second-order stationary points (SOSPs) between the manifold and the factorization formulations. We further give a sandwich inequality on the spectrum of Riemannian and Euclidean Hessians at FOSPs, which can be used to transfer more geometric properties from one formulation to another. Similarities and differences on the landscape connection under the PSD case and the general case are discussed. To the best of our knowledge, this is the first geometric landscape connection between the manifold and the factorization formulations for handling rank constraints. In the general low-rank matrix optimization, the landscape connection of two factorization formulations (unregularized and regularized ones) is also provided. By applying these geometric landscape connections, we are able to solve unanswered questions in literature and establish stronger results in the applications on geometric analysis of phase retrieval, well-conditioned low-rank matrix optimization, and the role of regularization in factorization arising from machine learning and signal processing.

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