Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence
This work addresses optimization problems in machine learning and statistics where rank constraints are crucial, offering a novel deterministic sketching approach that improves upon existing methods.
The paper tackles rank-constrained least squares optimization by proposing the RISRO algorithm, which achieves local quadratic-linear and quadratic convergence rates and demonstrates superior numerical performance in applications like low-rank matrix trace regression and phase retrieval.
In this paper, we propose {\it \underline{R}ecursive} {\it \underline{I}mportance} {\it \underline{S}ketching} algorithm for {\it \underline{R}ank} constrained least squares {\it \underline{O}ptimization} (RISRO). The key step of RISRO is recursive importance sketching, a new sketching framework based on deterministically designed recursive projections, which significantly differs from the randomized sketching in the literature \citep{mahoney2011randomized,woodruff2014sketching}. Several existing algorithms in the literature can be reinterpreted under this new sketching framework and RISRO offers clear advantages over them. RISRO is easy to implement and computationally efficient, where the core procedure in each iteration is to solve a dimension-reduced least squares problem. We establish the local quadratic-linear and quadratic rate of convergence for RISRO under some mild conditions. We also discover a deep connection of RISRO to the Riemannian Gauss-Newton algorithm on fixed rank matrices. The effectiveness of RISRO is demonstrated in two applications in machine learning and statistics: low-rank matrix trace regression and phase retrieval. Simulation studies demonstrate the superior numerical performance of RISRO.