.png){kind=icon}
返回
Value function gradient learning for large-scale multistage stochastic programming problems
.png){kind=icon}
OA
AI
DOI：10.1016/j.ejor.2022.10.011.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A stagewise decomposition algorithm called value function gradient learning (VFGL) is proposed for large-scale multistage stochastic convex programs. VFGL finds the parameter values that best fit the gra-dient of the value function within a given parametric family. Widely used decomposition algorithms for multistage stochastic programming, such as stochastic dual dynamic programming (SDDP), approximate the value function by adding linear subgradient cuts at each iteration. Although this approach has been successful for linear problems, nonlinear problems may suffer from the increasing size of each subprob-lem as the iteration proceeds. On the other hand, VFGL has a fixed number of parameters; thus, the size of the subproblems remains constant throughout the iteration. Furthermore, VFGL can learn the param-eters by means of stochastic gradient descent, which means that it can be easil0y parallelized and does not require a scenario tree approximation of the underlying uncertainties. VFGL was compared with a deterministic equivalent formulation of the multistage stochastic programming problem and SDDP ap-proaches for three illustrative examples: production planning, hydrothermal generation, and the lifetime financial planning problem. Numerical examples show that VFGL generates high-quality solutions and is computationally efficient.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Decision processes
Large-scale optimization
Multistage stochastic programming
Stagewise decomposition
Value function approximation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
暂无机构信息
引用论文
Tensile Properties of Al-12Si Fabricated via Selective Laser Melting (SLM) at Different Temperatures.png)
TechnologiesOAAI
TechnologiesIF0
OAAI分享收藏
查看更多内容