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Stochastic optimization methods[electronic resource] :applications in engineering and operations research /

Record Type:	Language materials, printed : Monograph/item
[NT 15000414]:	519.23
Title/Author:	Stochastic optimization methods : applications in engineering and operations research // by Kurt Marti.
Author:	Marti, Kurt.
Published:	Berlin, Heidelberg : : Springer Berlin Heidelberg :, 2015.
Description:	xxiv, 368 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
Subject:	Stochastic processes.
Subject:	Mathematical optimization.
Subject:	Economics/Management Science.
Subject:	Operation Research/Decision Theory.
Subject:	Optimization.
Subject:	Computational Intelligence.
ISBN:	9783662462140 (electronic bk.)
ISBN:	9783662462133 (paper)
[NT 15000228]:	Stochastic Optimization Methods -- Optimal Control Under Stochastic Uncertainty -- Stochastic Optimal Open-Loop Feedback Control -- Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) -- Optimal Design of Regulators -- Expected Total Cost Minimum Design of Plane Frames -- Stochastic Structural Optimization with Quadratic Loss Functions -- Maximum Entropy Techniques.
[NT 15000229]:	This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect 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 stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic 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, stochastic approximation and gradient procedures, and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research.
Online resource:	http://dx.doi.org/10.1007/978-3-662-46214-0

Stochastic optimization methods[electronic resource] :applications in engineering and operations research /
Marti, Kurt.

Stochastic optimization methodsapplications in engineering and operations research /[electronic resource] :by Kurt Marti. - 3rd ed. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - xxiv, 368 p. :ill., digital ;24 cm.

Stochastic Optimization Methods -- Optimal Control Under Stochastic Uncertainty -- Stochastic Optimal Open-Loop Feedback Control -- Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) -- Optimal Design of Regulators -- Expected Total Cost Minimum Design of Plane Frames -- Stochastic Structural Optimization with Quadratic Loss Functions -- Maximum Entropy Techniques.

This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect 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 stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic 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, stochastic approximation and gradient procedures, and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research.

ISBN: 9783662462140 (electronic bk.)

Standard No.: 10.1007/978-3-662-46214-0doiSubjects--Topical Terms:

177592
Stochastic processes.

LC Class. No.: QA274

Dewey Class. No.: 519.23

Stochastic optimization methods[electronic resource] :applications in engineering and operations research /
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520 $a This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect 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 stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic 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, stochastic approximation and gradient procedures, and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research.
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