Marti K Stochastic Optimization Methods Applications in Engineering 4ed 2024
General:
Name: Marti K Stochastic Optimization Methods Applications in Engineering 4ed 2024
Format: pdf
Size: 11.45 MB
Book:
Title: Stochastic Optimization Methods
Author: Kurt Marti
Language: polski
Year: 2015
Subjects: Science & Technology, Business, Engineering, Engineering – General & Miscellaneous, Mathematics, Business Technology, Research & Development – Operations Research, Mathematical Programming & Operations Research, Operations research
Publisher: Springer-Verlag New York, LLC
ISBN: 9783662462140
Total pages: 389
Description:
This book examines optimization problems that in practice involve random model parameters. It outlines the computation of robust optimal solutions, i.e., optimal solutions that are insensitive to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under shastic uncertainty are converted into corresponding deterministic problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and shastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, shastic approximation and gradient procedures, and differentiation formulas for probabilities and expectations.
The fourth edition of this classic text has been carefully and thoroughly revised. It includes new chapters on the solution of shastic linear programs by discretization of the underlying probability distribution, and on solving deterministic optimization problems by means of controlled random search methods and multiple random search procedures. It also presents a new application of shastic optimization methods to machine learning problems with different loss functions. For the computation of optimal feedback controls under shastic uncertainty, besides the open-loop feedback procedures, a new method based on Taylor expansions with respect to the gain parameters is presented.
The book is intended for researchers and graduate students who are interested in shastics, shastic optimization, and control. It will also benefit professionals and practitioners whose work involves technical, economicand/or operations research problems under shastic uncertainty.
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