A resampling-free stochastic projection contraction algorithm for solving stochastic variational inequalities

报告人：蔡邢菊 教授

报告题目：A resampling-free stochastic projection contraction algorithm for solving stochastic variational inequalities

报告时间：2026年11月7日（周六）下午3:50

报告地点：云龙校区智华楼204教室

主办单位：数学与统计学院、数学研究院、科学技术研究院

报告人简介：

蔡邢菊，南京师范大学教授，博导。主要从事最优化理论与算法、变分不等式、数值优化方向研究工作。主持多项国家基金，获江苏省科技进步奖一等奖一项，发表SCI论文70余篇。担任中国运筹学会副秘书长、算法软件与应用分会常务理事兼秘书长、数学规划分会常务理事，江苏省运筹学会理事长。

报告摘要：

Sampling is a major computational bottleneck in stochastic algorithms. This paper proposes a stochastic projection contraction algorithm for stochastic variational inequality problems, significantly reducing runtime by eliminating resampling in the correction step. We introduce an adjustable offset weight to optimize search direction, along with different adaptive step size strategies in prediction and correction steps. We further present discrete differential equation interpretations for specific offset weight values. To address bias due to the absence of resampling in the correction step, we develop an error control scheme and provide convergence guarantees. Numerical experiments demonstrate the algorithm's efficiency.