No-regret algorithms in on-line learning, games and convex optimization

被引：1
|
作者：
Sorin, Sylvain [1 ]
机构：
[1] Sorbonne Univ, Inst Math Jussieu, CNRS UMR 7586, PRG, Campus Pierre Marie Curie, Paris, France
来源：
MATHEMATICAL PROGRAMMING | 2024年 / 203卷 / 1-2期
关键词：
68T05; 68W27; 68W50; 90C25; 91A26; PROJECTED SUBGRADIENT METHODS; DYNAMICAL-SYSTEMS; 1ST-ORDER METHODS; VARIATIONAL-INEQUALITIES; MONOTONE-OPERATORS; GRADIENT; CONVERGENCE; DESCENT; APPROXIMATIONS; MINIMIZATION;
D O I：
10.1007/s10107-023-01927-7
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
The purpose of this article is to underline the links between some no-regret algorithms used in on-line learning, games and convex optimization and to compare the continuous and discrete time versions.
引用
收藏
页码：645 / 686
页数：42
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