The Smallest Singular Value of a Shifted Random Matrix

被引:0
作者
Xiaoyu Dong
机构
[1] University of Michigan,Department of Mathematics
来源
Journal of Theoretical Probability | 2023年 / 36卷
关键词
Random matrices; Smallest singular value; Smoothed analysis; The Littlewood–Offord problem; Primary 60B20;
D O I
暂无
中图分类号
学科分类号
摘要
Let Rn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_n$$\end{document} be an n×n\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n \times n$$\end{document} random matrix with i.i.d. subgaussian entries. Let M be an n×n\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n \times n$$\end{document} deterministic matrix with norm ‖M‖≤nγ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Vert M \Vert \le n^\gamma $$\end{document} where 1/2<γ<1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1/2<\gamma <1$$\end{document}. The goal of this paper is to give a general estimate of the smallest singular value of the sum Rn+M\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_n + M$$\end{document}, which improves an earlier result of Tao and Vu.
引用
收藏
页码:2448 / 2475
页数:27
相关论文
共 24 条
  • [1] Edelman Alan(1988)Eigenvalues and condition numbers of random matrices SIAM J. Matrix Anal. Appl. 9 543-560
  • [2] Farrell Brendan(2016)Smoothed analysis of symmetric random matrices with continuous distributions Proc. Am. Math. Soc. 144 2257-2261
  • [3] Vershynin Roman(2022)On the smoothed analysis of the smallest singular value with discrete noise Bull. Lond. Math. Soc. 54 369-388
  • [4] Jain Vishesh(2021)The smallest singular value of inhomogeneous square random matrices Ann. Probab. 49 1286-1309
  • [5] Sah Ashwin(2008)Invertibility of random matrices: norm of the inverse Ann. Math. 168 575-600
  • [6] Sawhney Mehtaab(2014)Recent developments in non-asymptotic theory of random matrices Mod. Asp. Random Matrix Theory 72 83-633
  • [7] Livshyts Galyna V(2008)The Littlewood–Offord problem and invertibility of random matrices Adv. Math. 218 600-1776
  • [8] Tikhomirov Konstantin(2016)No-gaps delocalization for general random matrices Geom. Funct. Anal. 26 1716-476
  • [9] Vershynin Roman(2006)Smoothed analysis of the condition numbers and growth factors of matrices SIAM J. Matrix Anal. Appl. 28 446-463
  • [10] Rudelson M(2004)Smoothed analysis of algorithms J. ACM 51 385-2352