一种求解凸-PL极小极大优化问题的随机交替梯度投影算法  

A Stochastic Alternating Gradient Projection Algorithm for Convex-PL Minimax Optimization Problems

作　　者：覃媛媛 王福胜 王静[1] 

机构地区：[1]太原师范学院数学与统计学院,山西 晋中

出　　处：《运筹与模糊学》2023年第1期292-297,共6页Operations Research and Fuzziology

摘　　要：针对机器学习中常见的一类非凸极小极大优化问题,本文提出了一种新的随机交替梯度投影算法,该算法基于交替梯度投影算法,在随机环境中用随机梯度进行更新,和随机交替梯度下降–上升算法相比,运算效率大大提高。在高斯分布数据集上的数值实验结果表明新算法是可行有效的。To solve a class of nonconvex minimax optimization problems in machine learning, we propose a new stochastic alternating gradient projection algorithm (SAGP). This algorithm is based on the alternating gradient projection algorithm (AGP), which is updated with stochastic gradients in a stochastic environment. Compared with the stochastic alternating gradient descent ascent algorithm (SAGDA), the operation efficiency is greatly improved. Numerical experiments on Gaussian distributed data sets show that the new algorithm is feasible and effective.

关 键 词：机器学习 凸-PL GDA算法 随机梯度 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]