Stochastic optimization based on principal-agent problem

Author

Ren, Xiaoyu ; Shao, Xinping ; Li, Shenghong

Author_Institution

Dept. of Math., Zhejiang Univ., Hangzhou, China

Volume

2

fYear

2009

fDate

8-9 Aug. 2009

Firstpage

176

Lastpage

179

Abstract

By the theory of stochastic dynamic programming, we provide the methods for deriving the optimal rules. In this paper, we make two models in dynamic state process to maximize the expected utility of the agent and then obtain the famous Hamilton-Jacobi-Bellman equation. Furthermore, we derive explicit form solution and closed-form solution of the optimal equations for given utility functions.

Keywords

dynamic programming; optimal control; stochastic programming; utility theory; Hamilton-Jacobi-Bellman equation; closed-form solution; dynamic state process; expected utility maximization; explicit form solution; optimal rule; principal-agent problem; stochastic dynamic programming; stochastic optimization; Communication system control; Differential equations; Dynamic programming; Mathematics; Nonlinear equations; Optimal control; Partial differential equations; Portfolios; Stochastic processes; Utility theory; HJB equation; principal-agent problem; stochastic differential equation; stochastic dynamic programming; stochastic optimal control;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on

Conference_Location

Sanya

Print_ISBN

978-1-4244-4247-8

Type

conf

DOI

10.1109/CCCM.2009.5267952

Filename

5267952