Year: 2026
Type: article
Abstract: In this paper, we present a rapid gradient descent method for solving low-rank matrix recovery problems. Our method extends the conventional gradient descent framework by exploiting the problem’s unique features to develop an innovative fast gradient computation technique that lowers the computational cost of gradient evaluation. The introduced adaptive step size selection strategy not only eliminates the need for the heavy calculations usually involved in finding the descent direction but also guarantees a consistent decrease in the objective function at every iteration. Additionally, we offer a proof confirming the algorithm’s convergence. Numerical experiments are provided to show the efficiency of the proposed algorithm.
Source: Mathematics
DOI: 10.3390/math14020343
Authors: Yujing Zhang, Peng Wang, Detong Zhu
Institutions: Hainan Normal University, Shanghai Normal University
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G3004089585, G4219501560, G8324964907
OpenAlex ID: W7125023672