Control of Diffusions via Linear Programming

Jiarui Han () and Benjamin Roy ()
Additional contact information
Jiarui Han: Stanford University
Benjamin Roy: Stanford University

Chapter Chapter 16 in Stochastic Programming, 2010, pp 329-353 from Springer

Abstract: Abstract This chapter presents an approach that leverages linear programming to approximate optimal policies for controlled diffusion processes, possibly with high-dimensional state and action spaces. The approach fits a linear combination of basis functions to the dynamic programming value function; the resulting approximation guides control decisions. Linear programming is used here to compute basis function weights. This builds on the linear programming approach to approximate dynamic programming, previously developed in the context of discrete-time stochastic control.

Keywords: Portfolio Optimization; Convex Quadratic Program; Linear Programming Approach; Portfolio Optimization Problem; Portfolio Strategy (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4419-1642-6_16

Ordering information: This item can be ordered from
http://www.springer.com/9781441916426

DOI: 10.1007/978-1-4419-1642-6_16

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().