Computational methods in optimal control : theory and practice /

Using material from many different sources in a systematic and unified way, this self-contained book provides both rigorous mathematical theory and practical numerical insights while developing a framework for determining the convergence rate of discretizations to optimal control problems. Elements...

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Bibliographic Details
Main Author: Hager, William W., 1948- (Author)
Format: Book
Language:English
Published: Philadelphia : Society for Industrial and Applied Mathematics, [2025].
Series:CBMS-NSF regional conference series in applied mathematics ; 100.
Subjects:

MARC

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100 1 |a Hager, William W.,  |d 1948-  |e author. 
245 1 0 |a Computational methods in optimal control :  |b theory and practice /  |c William W. Hager, University of Florida, Gainesville, Florida. 
264 1 |a Philadelphia :  |b Society for Industrial and Applied Mathematics,  |c [2025]. 
264 4 |c ©2025. 
300 |a xiii, 160 pages :  |b illustrations (some color) ;  |c 26 cm. 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a CBMS-NSF regional conference series in applied mathematics ;  |v 100 
500 |a "This book is based on a series of ten lectures given at an NSF-CBMS conference entitled Computational Methods in Optimal Control. The conference took place between July 23 and July 27, 2018, at Jackson State University"--page xiii. 
504 |a Includes bibliographical references (pages 155-158) and index. 
505 0 |a 1. Introduction -- 2. Convergence theory -- 3. Truncation errors in Runge-Kutta methods -- 4. Convergence theory for Runge-Kutta methods -- 5. Orthogonal collocation schemes -- 6. Endpoint constraints and discontinuous controls -- Appendix A. Minimum principle. 
520 |a Using material from many different sources in a systematic and unified way, this self-contained book provides both rigorous mathematical theory and practical numerical insights while developing a framework for determining the convergence rate of discretizations to optimal control problems. Elements of the framework include the reference point, the truncation error, and a stability theory for the linearized first-order optimality conditions. Within this framework, the discretized control problem has a stationary point whose distance to the reference point is bounded in terms of the truncation error. The theory applies to a broad range of discretizations and provides completely new insights into the convergence theory for discrete approximations in optimal control, including the relationship between orthogonal collocation and Runge-Kutta methods. Throughout the book, derivatives associated with the discretized control problem are expressed in terms of a back-propagated costate. In particular, the objective derivative of a bang-bang or singular control problem with respect to a switch point of the control are obtained, which leads to the efficient solution of a class of nonsmooth control problems using a gradient-based optimizer. 
650 0 |a Control theory. 
650 0 |a Mathematical optimization. 
830 0 |a CBMS-NSF regional conference series in applied mathematics ;  |v 100. 
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