Statistical Decision Analysis of Stochastic Linear Programming Problems

Statistical Decision Analysis of Stochastic Linear Programming Problems
Author: Jerome Bracken
Publisher:
Total Pages: 30
Release: 1966
Genre:
ISBN:

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This paper presents a statistical decision analysis of a one-stage linear programming problem with deterministic constraints and stochastic criterion function. Procedures for obtaining numerical results are given that are applicable to any problem having this general form. After stating the statistical decision problems to be considered, the expected value of perfect information and the expected value of sample information are discussed. In obtaining these quantities, use is made of the distribution of the optimal value of the linear programming problem with stochastic criterion function; therefore Monte Carlo and numerical integration procedures for estimating the mean of this distribution are discussed. The case in which the random criterion vector has a multivariate Normal distribution is presented separately, and more detailed methods are offered. (Author).

Stochastic Linear Programming

Stochastic Linear Programming
Author: Peter Kall
Publisher: Springer
Total Pages: 397
Release: 2005
Genre: Electronic books
ISBN: 9786610613410

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Peter Kall and Janos Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization. "Stochastic Linear Programming: Models, Theory, and Computation" is a definitive presentation and discussion of the theoretical properties of the models, the conceptual algorithmic approaches, and the computational issues relating to the implementation of these methods to solve problems that are stochastic in nature. The application area of stochastic programming includes portfolio analysis, financial optimization, energy problems, random yields in manufacturing, risk analysis, etc. In this book, models in financial optimization and risk analysis are discussed as examples, including solution methods and their implementation.Stochastic programming is a fast developing area of optimization and mathematical programming.; Numerous papers and conference volumes, and several monographs have been published in the area; however, the Kall and Mayer book will be particularly useful in presenting solution methods including their solid theoretical basis and their computational issues, based in many cases on implementations by the authors. The book is also suitable for advanced courses in stochastic optimization.

Stochastic Linear Regulator Problem in Optimal Control Theory

Stochastic Linear Regulator Problem in Optimal Control Theory
Author: Md. Azizul Baten
Publisher: LAP Lambert Academic Publishing
Total Pages: 160
Release: 2012-03
Genre:
ISBN: 9783848429332

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Stochastic optimization problems are the study of dynamical systems subject to random perturbations which can be controlled in order to optimize some performance criterion. The research on control theory has developed considerably over last few years, inspired in particular by stochastic optimization problems emerging from mathematical nance. Problems involving linear dynamics and quadratic performance criteria are generally called linear regulator problems. The usual framework of control is the one given in probably the most studied control problem, the linear quadratic optimal control problem or the linear regulator problem, which deals with minimizing a performance index of a system governed by a set of dierential equations. The object of linear regulator control problems is to control the position of a certain process and at the same time, the force with which this process is being regulated, by punishing quadratic deviations from some targets of the process and the rate of regulation, respectively.

Two-Stage Stochastic Linear Programming with Recourse

Two-Stage Stochastic Linear Programming with Recourse
Author: David Mills
Publisher:
Total Pages: 93
Release: 1999-03-01
Genre:
ISBN: 9781423546153

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The LP recourse problem applies to two-stage optimization problems where uncertainty in resource availability of the second stage hinders informed decision making. The recourse function affords a way to compensate "later" for an error in prediction "now." The literature provides a rich body of work on the optimization of such problems, but little research has been accomplished regarding the characterization of the surface in the local region of optimality, in particular sensitivity analysis. A decision maker faced with considerations other than the modeled objective function must be presented with a way to estimate the impact of operating at non-optimal decision variable values. This work develops and demonstrates a technique for characterizing the surface using response surface methodology. Specifically, the flexibility and utility of RSM techniques applied to this class of problems is demonstrated, and a methodology for characterizing the surface in the local region using a low-order polynomial is developed.

Index to IEEE Publications

Index to IEEE Publications
Author: Institute of Electrical and Electronics Engineers
Publisher:
Total Pages: 848
Release: 1990
Genre: Electric engineering
ISBN:

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Issues for 1973- cover the entire IEEE technical literature.

Applied mechanics reviews

Applied mechanics reviews
Author:
Publisher:
Total Pages: 400
Release: 1948
Genre: Mechanics, Applied
ISBN:

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Government Reports Index

Government Reports Index
Author:
Publisher:
Total Pages: 1088
Release: 1972
Genre: Government publications
ISBN:

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