Normal view MARC view ISBD view

Convex Stochastic Optimization [electronic resource] : Dynamic Programming and Duality in Discrete Time / by Teemu Pennanen, Ari-Pekka Perkkiö.

By:

Pennanen, Teemu [author.]

Contributor(s):

Material type:

TextSeries: Probability Theory and Stochastic Modelling ; 107Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XI, 412 p. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783031764325

Subject(s):

-- Mathematics

Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:

QA402.5-402.6

Online resources:

Resursa online

Contents:

- 1. Convex Stochastic Optimization -- 2. Dynamic Programming -- 3. Duality -- 4. Absence of a Duality Gap -- 5. Existence of Dual Solutions.

In: Springer Nature eBookSummary: This book studies a general class of convex stochastic optimization (CSO) problems that unifies many common problem formulations from operations research, financial mathematics and stochastic optimal control. We extend the theory of dynamic programming and convex duality to allow for a unified and simplified treatment of various special problem classes found in the literature. The extensions allow also for significant generalizations to existing problem formulations. Both dynamic programming and duality have played crucial roles in the development of various optimality conditions and numerical techniques for the solution of convex stochastic optimization problems.

Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

No physical items for this record

- 1. Convex Stochastic Optimization -- 2. Dynamic Programming -- 3. Duality -- 4. Absence of a Duality Gap -- 5. Existence of Dual Solutions.

This book studies a general class of convex stochastic optimization (CSO) problems that unifies many common problem formulations from operations research, financial mathematics and stochastic optimal control. We extend the theory of dynamic programming and convex duality to allow for a unified and simplified treatment of various special problem classes found in the literature. The extensions allow also for significant generalizations to existing problem formulations. Both dynamic programming and duality have played crucial roles in the development of various optimality conditions and numerical techniques for the solution of convex stochastic optimization problems.

There are no comments on this title.

to post a comment.