3 papers
math.NA2026
Precision-induced Adaptive Randomized Low-Rank Approximation for SVD and Matrix Inversion
Weiwei Xu, Weijie Shen, Zhengjian Bai +1
Singular value decomposition (SVD) and matrix inversion are ubiquitous in scientific computing. Both tasks are computationally demanding for large scale matrices. Existing algorith…
stat.CO2025
Accelerating Randomized Algorithms for Low-Rank Matrix Approximation
Dandan Jiang, Bo Fu, Weiwei Xu
Randomized algorithms are overwhelming methods for low-rank approximation that can alleviate the computational expenditure with great reliability compared to deterministic algorith…
math.OC2025
A Learning-Based Inexact ADMM for Solving Quadratic Programs
Xi Gao, Jinxin Xiong, Linxin Yang +3
Convex quadratic programs (QPs) constitute a fundamental computational primitive across diverse domains including financial optimization, control systems, and machine learning. The…