Collaborative Research: Ergodic Control of Stochastic Differential Equations Driven By a Class of Pure-Jump Levy Processes, and Applications to Stochastic Networks

合作研究：一类纯跳跃 Levy 过程驱动的随机微分方程的遍历控制及其在随机网络中的应用

基本信息

批准号：
1715210
负责人：
Ari Arapostathis
金额：
$ 21.1万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-06-01 至 2021-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1715210&HistoricalAwards=false
关键词：
Collaborative Research Ergodic Control Stochastic

项目摘要

Queueing processes in large-scale networks are ubiquitous in society as exemplified by patient flow in hospitals, telephone call centers, service networks, and manufacturing and service operations management. This research will provide fundamental theoretical understanding of these queueing processes in the presence of batch arrivals. The results of this research will be put into direct applications to real-world stochastic networks through collaborations with the health systems and system analysis industry. This research will support under-represented minority groups and train young STEM graduates with new mathematical skills. This research concerns ergodic control problems for systems described by Ito stochastic differential equations (SDEs) driven by pure-jump Levy processes. The research objectives are: (1) to develop a comprehensive theoretical framework for ergodic control for a large class of controlled SDEs driven by a pure-jump Levy process; (2) to study a novel fully nonlinear problem that arises in admission control and falls outside the usual paradigm of stochastic control; and (3) to develop value iteration algorithms and spatial approximation methods in order to study large time asymptotics, and also to facilitate the numerical solution of the associated Hamilton-Jacobi-Bellman (HJB) equations, and the design of learning schemes for adaptive control in the presence of unknown parameters. This research will advance the basic science of applied mathematics and stochastic control, and make fundamental contributions to applied probability and stochastic networks.

大规模网络中的排队过程在社会中无处不在，例如医院，电话中心，服务网络以及制造和服务运营管理中的患者流动。这项研究将在批处理到达的情况下对这些排队过程提供基本的理论理解。这项研究的结果将通过与卫生系统和系统分析行业的合作来直接应用于现实世界随机网络。这项研究将支持代表性不足的少数群体，并以新的数学技能培训年轻的STEM毕业生。这项研究涉及由ITO随机微分方程（SDE）所描述的系统的奇异控制问题，该系统由纯粹的征费过程驱动。研究目标是：（1）为由纯粹的征收过程驱动的大量控制的SDE开发全面的理论框架；（2）研究一个新的完全非线性问题，该问题是在入院控制中出现的，并且落在通常的随机控制范式之外；（3）为了开发价值迭代算法和空间近似方法，以研究大型时间渐近学，并促进相关的汉密尔顿 - 雅各布利 - 贝尔曼（HJB）方程的数值解决方案，以及在不知名参数存在下适应控制的学习方案的设计。这项研究将推进应用数学和随机控制的基础科学，并为应用概率和随机网络做出基本贡献。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Variational Formula for Risk-Sensitive Control of Diffusions in $\mathbb{R}^d$

$mathbb{R}^d$ 中风险敏感扩散控制的变分公式

DOI：
10.1137/18m1218704
发表时间：
2020
期刊：
SIAM Journal on Control and Optimization
影响因子：
2.2
作者：
Arapostathis, Ari;Biswas, Anup
通讯作者：
Biswas, Anup

Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching Costs

DOI：
10.1109/tnet.2018.2869874
发表时间：
2018-08
期刊：
IEEE/ACM Transactions on Networking
影响因子：
0
作者：
Subhashini Krishnasamy;P. Akhil;A. Arapostathis;R. Sundaresan;S. Shakkottai
通讯作者：
Subhashini Krishnasamy;P. Akhil;A. Arapostathis;R. Sundaresan;S. Shakkottai

On uniqueness of solutions to viscous HJB equations with a subquadratic nonlinearity in the gradient

梯度次二次非线性粘性HJB方程解的唯一性