多阶段随机变分不等式的动态随机投影法

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED Computational Optimization and Applications Pub Date : 2024-07-26 DOI:10.1007/s10589-024-00594-4
Bin Zhou, Jie Jiang, Hailin Sun
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引用次数: 0

摘要

对于求解单阶段随机变分不等式(SVI),随机逼近(SA)类型的方法已经得到了深入研究。本文提出了一种用于求解多阶段 SVI 的动态随机投影法 (DSPM)。我们特别研究了一个不精确的单阶段 SVI,并提出了一种用于求解该 SVI 的不精确随机投影法 (ISPM)。然后,我们通过对每个阶段应用 ISPM,将 DSPM 应用于三阶段 SVI。我们证明,对于三阶段 SVI 所需的场景总数,DSPM 可以达到 \(\mathcal {O}(\frac{1}{\epsilon ^2})\)收敛率。当阶段数大于三个时,我们还将 DSPM 扩展到多阶段 SVI。数值实验说明了 DSPM 的有效性和效率。
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Dynamic stochastic projection method for multistage stochastic variational inequalities

Stochastic approximation (SA) type methods have been well studied for solving single-stage stochastic variational inequalities (SVIs). This paper proposes a dynamic stochastic projection method (DSPM) for solving multistage SVIs. In particular, we investigate an inexact single-stage SVI and present an inexact stochastic projection method (ISPM) for solving it. Then we give the DSPM to a three-stage SVI by applying the ISPM to each stage. We show that the DSPM can achieve an \(\mathcal {O}(\frac{1}{\epsilon ^2})\) convergence rate regarding to the total number of required scenarios for the three-stage SVI. We also extend the DSPM to the multistage SVI when the number of stages is larger than three. The numerical experiments illustrate the effectiveness and efficiency of the DSPM.

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来源期刊
CiteScore
3.70
自引率
9.10%
发文量
91
审稿时长
10 months
期刊介绍: Computational Optimization and Applications is a peer reviewed journal that is committed to timely publication of research and tutorial papers on the analysis and development of computational algorithms and modeling technology for optimization. Algorithms either for general classes of optimization problems or for more specific applied problems are of interest. Stochastic algorithms as well as deterministic algorithms will be considered. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. Topics of interest include, but are not limited to the following: Large Scale Optimization, Unconstrained Optimization, Linear Programming, Quadratic Programming Complementarity Problems, and Variational Inequalities, Constrained Optimization, Nondifferentiable Optimization, Integer Programming, Combinatorial Optimization, Stochastic Optimization, Multiobjective Optimization, Network Optimization, Complexity Theory, Approximations and Error Analysis, Parametric Programming and Sensitivity Analysis, Parallel Computing, Distributed Computing, and Vector Processing, Software, Benchmarks, Numerical Experimentation and Comparisons, Modelling Languages and Systems for Optimization, Automatic Differentiation, Applications in Engineering, Finance, Optimal Control, Optimal Design, Operations Research, Transportation, Economics, Communications, Manufacturing, and Management Science.
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