In engineering and economics a certain vector of inputs or decisions must often be chosen, subject to some constraints, such that the expected costs arising from the deviation between the output of a stochastic linear system and a desired stochastic target vector are minimal. In many cases the loss function u is convex and the occuring random variables have, at least approximately, a joint discrete distribution. Concrete problems of this type are stochastic linear programs with recourse, portfolio optimization problems, error minimization and optimal design problems. In solving stochastic optimization problems of this type by standard optimization software, the main difficulty is that the objective function F and its derivatives are defined by multiple integrals. Hence, one wants to omit, as much as possible, the time-consuming computation of derivatives of F. Using the special structure of the problem, the mathematical foundations and several concrete methods for the computation of feasible descent directions, in a certain part of the feasible domain, are presented first, without any derivatives of the objective function F. It can also be used to support other methods for solving discretely distributed stochastic programs, especially large scale linear programming and stochastic approximation methods.

Descent Directions and Efficient Solutions in Discretely Distributed Stochastic Programs (Lecture Notes in Economics & Mathematical Systems)

Science & Math

Marti, Kurt

Mathematics

Springer, Verlag

This book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease expense in corrective measures. Basic types of deterministic substitute problems occurring mostly in practice involve i) minimization of the expected primary costs subject to expected recourse cost constraints (reliability constraints) and remaining deterministic constraints, e.g. box constraints, as well as ii) minimization of the expected total costs (costs of construction, design, recourse costs, etc.) subject to the remaining deterministic constraints. After an introduction into the theory of dynamic control systems with random parameters, the major control laws are described, as open-loop control, closed-loop, feedback control and open-loop feedback control, used for iterative construction of feedback controls. For approximate solution of optimization and control problems with random parameters and involving expected cost/loss-type objective, constraint functions, Taylor expansion procedures, and Homotopy methods are considered, Examples and applications to stochastic optimization of regulators are given. Moreover, for reliability-based analysis and optimal design problems, corresponding optimization-based limit state functions are constructed. Because of the complexity of concrete optimization/control problems and their lack of the mathematical regularity as required of Mathematical Programming (MP) techniques, other optimization techniques, like random search methods (RSM) became increasingly important. Basic results on the convergence and convergence rates of random search methods are presented. Moreover, for the improvement of the - sometimes very low - convergence rate of RSM, search methods based on optimal stochastic decision processes are presented. In order to improve the convergence behavior of RSM, the random search procedure is embedded into a stochastic decision process for an optimal control of the probability distributions of the search variates (mutation random variables).

Optimization Under Stochastic Uncertainty: Methods, Control and Random Search Methods (International Series in Operations Research & Management Science, 296)

Computers & Technology

Computer Science

Springer

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 stochastic 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 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. The fourth edition of this classic text has been carefully and thoroughly revised. It includes new chapters on the solution of stochastic 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 stochastic optimization methods to machine learning problems with different loss functions. For the computation of optimal feedback controls under stochastic&nbsp;uncertainty, besides the open-loop feedback procedures, a new method based on Taylor expansions with respect to the gain parameters is presented.&nbsp; The book is intended for researchers and graduate students who are interested in stochastics, stochastic optimization, and control. It will also benefit professionals and practitioners whose work involves technical, economicand/or operations research problems under stochastic uncertainty.

Stochastic Optimization Methods: Applications in Engineering and Operations Research

Woher eine Ethik nehmen? Streitgespräch über Vernunft und Glauben.

Mächler, Robert

Nagel & Kimche

Zärtlichkeit und Schmerz: Notizen (German Edition)

Luchterhand

Descent Directions and Efficient Solutions in Discretely Distributed Stochastic Programs (Lecture Notes in Economics and Mathematical Systems, 299)

Reference

Das Buch gibt eine Einführung in das neue Gebiet der Analyse und Optimierung von Tragwerken unter stochastischer Unsicherheit. Es werden die Grundlagen ausführlich dargestellt und zum Teil von unterschiedlichen Standpunkten aus beleuchtet.  In Teil I wird die lineare Theorie der Stabtragwerke als Grundlage für die FEM entwickelt. Vorausgesetzt werden dabei nur wenige Kenntnisse aus  der Technischen Mechanik und der Ingenieurmathematik, insbesondere eine gewisse Vertrautheit mit der Matrizenrechnung.  In Teil II wird dargestellt, wie sich die Wahrscheinlichkeitsverteilungen der Verschiebungen und Spannungen in den Knoten aus denen der stochastischen Stabparameter und äußeren Lasten – zumindest approximativ – berechnen lassen.  In Teil III schließlich wird die Optimierung von Tragwerken mit stochastischen Parametern behandelt. Dazu wird ein geeignetes deterministisches Ersatzproblem des Ausgangsproblems mit stochastischen Modellparametern formuliert.  Eine kurze Beschreibung einiger Optimierungsverfahren findet man im letzten Abschnitt. Besondere Mühe wurde auf die zahlreichen und eingehend behandelten Beispiele verwandt. Das Buch ist geschrieben für Studierende, praktisch tätige Ingenieure und Mathematiker.

Stochastische Strukturoptimierung von Stab- und Balkentragwerken (German Edition)

Gröger, Detlef

Foreign Language Study & Reference

Optimization problems arising in practice involve random model parameters. This book features many illustrations, several examples, and applications to concrete problems from engineering and operations research.

Stochastic Optimization Methods

Business & Money

Economics

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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