Mean field methods for stochastic control and optimization problems
Abstract/Contents
- Abstract
- In probability theory and physics, mean field methods study the dynamics of high dimensional stochastic models by applying the law of large numbers in a sophisticated form, in order to obtain simpler models that approximate the original system. In the setting of a multi agent system, the basic idea is to replace the individual interaction effects induced by other agents upon a single agent with an averaged effect. We can reduce a complex, multi agent system to a single agent problem. In this thesis, we study the use of mean field methods in some stochastic control and stochastic optimization problems. In particular, we study mean field analysis of distributed optimization algorithms, and mean field analysis of a class of stochastic control problems with many entities.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Hui, Yue |
|---|---|
| Degree supervisor | Blanchet, Jose H |
| Degree supervisor | Glynn, Peter W |
| Thesis advisor | Blanchet, Jose H |
| Thesis advisor | Glynn, Peter W |
| Thesis advisor | Papanicolaou, George |
| Degree committee member | Papanicolaou, George |
| Associated with | Stanford University, Department of Mathematics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yue Hui. |
|---|---|
| Note | Submitted to the Department of Mathematics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/md244pv2062 |
Access conditions
- Copyright
- © 2021 by Yue Hui
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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