Stochastic Programming Methods and Technical Applications

Cover of: Stochastic Programming Methods and Technical Applications |

Published by Springer .

Written in English

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Subjects:

  • Applied mathematics,
  • Probability & statistics,
  • Stochastic Processes,
  • Mathematical optimization,
  • Technology,
  • Operations Research (Engineering),
  • Mathematics,
  • Science/Mathematics,
  • Economics - General,
  • Linear Programming,
  • Operations Research,
  • Business & Economics / Operations Research,
  • Congresses,
  • Stochastic programming

Edition Notes

Book details

ContributionsKurt Marti (Editor), Peter Kall (Editor)
The Physical Object
FormatPaperback
Number of Pages437
ID Numbers
Open LibraryOL9062457M
ISBN 103540639241
ISBN 109783540639244

Download Stochastic Programming Methods and Technical Applications

This proceedings volume contains a selection of papers on modelling techniques, approximation methods, numerical solution procedures for stochastic optimization problems and applications to the reliability-based optimization of concrete technical or economic : Paperback.

Stochastic Programming Methods and Technical Applications Proceedings of the 3rd GAMM/IFIP-Workshop on “Stochastic Optimization: Numerical Methods and Technical Applications” held at the Federal Armed Forces University Munich, Neubiberg/München, Germany, June 17–20, Stochastic Programming Methods and Technical Applications Proceedings of the 3rd GAMM/IFIP-Workshop on “Stochastic Optimization: Numerical Methods and Technical Applications” held at the Federal Armed Forces University Munich, Neubiberg/München, Germany, June 17–20, Editors: Marti, Kurt, Kall, Peter (Eds.).

Hence, ordinary mathematical programs have to be replaced by appropriate stochastic programs. New theoretical insight into several branches of reliability-oriented optimization of stochastic systems, new computational approaches and technical/economic applications of stochastic programming methods can be found in this volume.

Stochastic optimization: numerical methods and technical applications. [Kurt Marti;] Three Approaches for Solving the Stochastic Multiobjective Programming Problem.- A Stochastic Programming Model for Optimal Power Dispatch: Stability and Numerical Treatment.- numerical methods and technical applications\/span> \u00A0\u00A0\u00A0.

LECTURES ON STOCHASTIC PROGRAMMING MODELING AND THEORY Alexander Shapiro Georgia Institute of Technology Atlanta, Georgia Darinka Dentcheva Stevens Institute of Technology Hoboken, New Jersey Andrzej Ruszczynski.

This proceedings volume of the 4th GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications" held Juneat the Federal Armed Forces University Munich, Neubiberg/Munich contains new methods for the approximation and numerical solution of deterministic substitute problems, especially the handling of Price: $ ISBN: OCLC Number: Notes: "Selection of technical papers presented at the 4th GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications" held at the Federal Armed Forces University Munich, Neubiberg/Munich, June"--Preface.

Stochastic Programming Methods and Technical Applications: Proceedings of the 3rd GAMM/IFIP-Workshop on “Stochastic Optimization: Numerical Methods and Technical Applications” held at the Federal Armed Forces University Munich, Neubiberg/München, Germany, P.

Kall (auth.), Prof. Kurt Marti, Prof. Peter Kall (eds.). This is the first book devoted to the full scale of applications of stochastic programming and also the first to provide access to publicly available algorithmic systems.

The 32 contributed papers in this volume are written by leading stochastic programming specialists and reflect the high level of activity in recent years in research on. Here is a nonempty closed subset of, is a random vector whose probability distribution is supported on a set ⊂, and: × →.In the framework of two-stage stochastic programming, (,) is given by the optimal value of the corresponding second-stage problem.

Assume that () is well defined and finite valued for all ∈.This implies that for every ∈ the value (,) is finite almost surely.

Stochastic Programming Methods and Technical Applications: Proceedings of the 3rd GAMM/IFIP-Workshop on “Stochastic Optimization: Numerical Methods and Technical Applications” held at the Federal Armed Forces University Munich, Neubiberg/München, Germany, June 17–20, Author: Prof.

Kurt Marti, Prof. Peter Kall. Stochastic Programming Methods and Technical Applications: Proceedings of the 3rd Gamm/Ifip-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications" Held at the Federal Armed Forces University Munich, Neubiberg/Munchen by Kurt Marti.

Stochastic Linear and Nonlinear Programming Optimal land usage under stochastic uncertainties Extensive form of the stochastic decision program We consider a farmer who has a total of acres of land available for growing wheat, corn and sugar beets.

We denote by x1;x2;x3 the amount of acres of land devoted to wheat, corn and sugar File Size: KB. Continuous-time Stochastic Control and Optimization with Financial Applications - Ebook written by Huyên Pham.

Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Continuous-time Stochastic Control and Optimization with Financial Applications.

• stochastic programming • Monte Carlo sampling methods • validation sources: Nemirovsky & Shapiro EEA — Stochastic Programming 1. Stochasticprogramming • objective and constraint functions fi(x,ω) depend on optimization – (with some technical conditions) File Size: 85KB.

Applications in Finance, Energy, Planning and Logistics. Author: Horand Gassmann,W. Ziemba. Publisher: World Scientific ISBN: X Category: Business & Economics Page: View: DOWNLOAD NOW» This book shows the breadth and depth of stochastic programming applications.

stochastic programming (SP) applications are introduced. It is assumed that they may help people who want to apply their stochastic programming knowledge to real world pro-blems.

• Many examples of various application areas are presented in research papers. They are also different by their applicability level. multi-stage stochastic programming problems, we were able to derive many of these results without resorting to methods of functional analysis.

The. Stochastic programming has been used extensively for decision making under uncertainty, e.g., power systems [64], finance [37], and many engineering applications [42]. Author: Kurt Marti. Full text of "Stochastic programming methods and technical applications: proceedings of the 3rd GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications", held at the Federal Armed Forces University Munich, Neubiberg/München, Germany, June" See other formats.

4 Introductory Lectures on Stochastic Optimization focusing on non-stochastic optimization problems for which there are many so-phisticated methods. Because of our goal to solve problems of the form (), we develop first-order methods that are in some ways robust to many types of noise from sampling.

Overview of Stochastic Programming. Stochastic programming is a framework for modeling optimization problems that involve uncertainty. Whereas deterministic optimization problems are formulated with known parameters, real world problems almost.

the isotropic Weiner process. This type of equation has applications in a variety of fields including stochastic control theory [6], filtering theory [7], and fluid mechanics [8]. Of particu- lar interest in applications is the Langevin equation, a special case of Eq.(l) in which the deter.

Stochastic programming offers a solution to this issue by eliminating uncertainty and characterizing it using probability distributions. Many different types of stochastic problems exist.

The most famous type of stochastic programming model is for recourse problems. This type of problem will be described in detail in the following sections below.

Stochastic programming. Stochastic programming, as the name implies, is mathematical (i.e. linear, integer, mixed-integer, nonlinear) programming but with a stochastic element present in the data. By this we mean that: in deterministic mathematical programming the data (coefficients) are known numbers.

Books shelved as stochastic-processes: Introduction to Stochastic Processes by Gregory F. Lawler, Adventures in Stochastic Processes by Sidney I.

Resnick. Darinka Dentcheva (Bulgarian Römisch, W., Optimal power generation under uncertainty via stochastic programming, in: Stochastic Programming Methods and Technical Applications (K. Marti and P principles, level sets, well-posedness, and ∈-solutions in vector optimization, Journal of Optimization Theory and Applicati Alma mater: Humboldt University, Berlin, Germany.

Economic Dynamics. This is the homepage for Economic Dynamics: Theory and Computation, a graduate level introduction to deterministic and stochastic dynamics, dynamic programming and computational methods with economic applications.

About the Book. The topics covered in the book are fairly similar to those found in “Recursive Methods in Economic Dynamics” by Nancy. ming. The theory and methods of solving stochastic integer programming problems draw heavily from the theory of general integer programming.

Their comprehensive presentation would entail discussion of many concepts and methods of this vast field, which would have little connection with the rest of the Size: 2MB. A Tutorial on Stochastic Programming AlexanderShapiro (although we could not avoid giving some technical de-tails).

Since it is not intended to be a historical overview of the subject, relevant references are given in the “Notes” section at the end of the paper, rather than in the Size: KB.

Book Description. Adaptive Stochastic Optimization Techniques with Applications provides a single, convenient source for state-of-the-art information on optimization techniques used to solve problems with adaptive, dynamic, and stochastic features.

Presenting modern advances in static and dynamic optimization, decision analysis, intelligent systems, evolutionary programming. Stochastic Programming Methods and Technical Applications, () Stability in multistage stochastic programming.

Annals of Operations ResearchCited by: Stochastic Programming. This example illustrates AIMMS capabilities for stochastic programming support. Starting from an existing deterministic LP or MIP model, AIMMS can create a stochastic model automatically, without the need to reformulate constraint definitions.

STOCHASTIC PROGRAMMING IN TRANSPORTATION AND LOGISTICS 1 1. Introduction Operational models of problems in transportation and logistics offer a ripe set of applica-tions for stochastic programming since they are typically characterized by highly dynamic information processes. In freight transportation, it is the norm to call a carrier the day.

Exercises are provided at the end of each chapter. Throughout, the emphasis is on the appropriate use of the techniques, rather than on the underlying mathematical proofs and theories, making the book ideal for researchers and students in mathematical programming and operations research who wish to develop their skills in stochastic programming.

Fractional Programming: Theory, Methods and Applications - Ebook written by I.M. Stancu-Minasian. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Fractional Programming: Theory, Methods and Applications.

History, achievements and problems to be solved. Forty-five years ago, stochastic programming was set up independently by Beale, Dantzig, Charnes and Cooper and others who observed that for many linear programs to be solved, the values of the presumably known coefficients were not available.

They suggested to replace the deterministic view by a Cited by: Stochastic programming has been steadily making its way into a variety of application areas. This chapter demonstrates the use of a class of stochastic programming methods for fleet management applications, using the freight car distribution problem as a problem context.

This book presents a concise treatment of stochastic calculus and its applications. It gives a simple but rigorous treatment of the subject including a range of advanced topics, it is useful for practitioners who use advanced theoretical results. It covers advanced applications, such as models in mathematical finance, biology and -contained and unified in 5/5(2).

Consider the system of two reservoirs (), whose objective is to retain the flood in the protected flood is produced by two random inflows, ξ 1 and ξ danger occurs once a year, say, and ξ 1, ξ 2 appear simultaneously.

The damage from flood of size y≥0 is modeled as a convex nondecreasing function L(y), where L(0)= objective is to determine the reservoir Cited by: 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.

From the Preface The preparation of this book started inwhen George B. Dantzig and I, following a long-standing invitation by Fred Hillier to contribute a volume to his International Series in Operations Research and Management Science, decided finally to go ahead with editing a volume on stochastic : Springer New York.

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