Deterministic and Stochastic Optimal Control (Stochastic Modelling and Applied Probability, 1)

Fleming, Wendell H.; Rishel, Raymond W.

ISBN 10: 0387901558 / ISBN 13: 9780387901558

Published by Springer, 1975

About this Item

Good hardcover, without dust jacket, seemingly as issued, from a personal collection (NOT ex-library). Binding is tight, sturdy, and square. Corners of boards and ends of spine bumped. Sort of bumpy areas to boards, not sure how that came about. see our photo (uploaded within a week or so of listing). Age-toning to spine and extremities. Previous owner name blacked-out inside front cover, seemingly only one other marking, a brief pencil notation to page 213 (first page of bibliography). Foxing to page block and endpapers. Ships same or next day from Dinkytown, Minneapolis, Minnesota.

Seller Inventory # 337681

Bibliographic Details

• Title: Deterministic and Stochastic Optimal Control (Stochastic Modelling and Applied Probability, 1)
• Publisher: Springer
• Publication Date: 1975
• Language: English
• ISBN 10: 0387901558
• ISBN 13: 9780387901558
• Binding: hardcover
• Condition: Good
• Weight: 40 ounces
• Seller catalogs:
  • Mathematics

About this title

Synopsis

This book may be regarded as consisting of two parts. In Chapters I-IV we pre­ sent what we regard as essential topics in an introduction to deterministic optimal control theory. This material has been used by the authors for one semester graduate-level courses at Brown University and the University of Kentucky. The simplest problem in calculus of variations is taken as the point of departure, in Chapter I. Chapters II, III, and IV deal with necessary conditions for an opti­ mum, existence and regularity theorems for optimal controls, and the method of dynamic programming. The beginning reader may find it useful first to learn the main results, corollaries, and examples. These tend to be found in the earlier parts of each chapter. We have deliberately postponed some difficult technical proofs to later parts of these chapters. In the second part of the book we give an introduction to stochastic optimal control for Markov diffusion processes. Our treatment follows the dynamic pro­ gramming method, and depends on the intimate relationship between second­ order partial differential equations of parabolic type and stochastic differential equations. This relationship is reviewed in Chapter V, which may be read inde­ pendently of Chapters I-IV. Chapter VI is based to a considerable extent on the authors' work in stochastic control since 1961. It also includes two other topics important for applications, namely, the solution to the stochastic linear regulator and the separation principle.