.png){kind=icon}
返回
Two-time scale Controlled Markov Chains: A decomposition and parallel processing approach
.png){kind=icon}
PRE
AI
DOI：10.1109/TAC.2007.910692.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This correspondence deals with a class of ergodic control problems for systems described by Markov chains with strong and weak inter actions. These systems are composed of a set of m subchains that are weakly coupled. Using results already available in the literature one formulates a limit control problem the solution of which can be obtained via an associated nondifferentiable convex programming (NDCP) problem. The technique used to solve the NDCP problem is the Analytic Center Cutting Plane Method (ACCPM) which implements a dialogue between, on one hand, a master program computing the analytical center of a localization set containing the solution and, on the other hand, an oracle proposing cutting planes that reduce the size of the localization set at each main iteration. The interesting aspect of this implementation comes from two characteristics: (i) the oracle proposes cutting planes by solving reduced sized Markov Decision Problems (MDP) via a linear program (LP) or a policy iteration method; (ii) several cutting planes can be proposed simultaneously through a parallel implementation on m processors. The correspondence concentrates on these two aspects and shows, on a large scale MDP obtained from the numerical approximation a la Kushner-Dupuis of a singularly perturbed hybrid stochastic control problem, the important computational speed-up obtained.
Keyword:
decomposition technique
interior point method
singular perturbations
two-timescale Markov chain
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:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
.png){kind=icon}
Critical Evaluation and Thermodynamic Optimization of the Li2O-Al2O3 and Li2O-MgO-Al2O3 Systems
PREAI
分享收藏
Failed institution building: Understanding the interplay between agency, social skill and context
PREAI
分享收藏
查看更多内容