Stochastic Linear Programming Algorithms PDF Download
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| Author | : Janos Mayer |
| Publisher | : Taylor & Francis |
| Total Pages | : 164 |
| Release | : 2022-04-19 |
| Genre | : Computers |
| ISBN | : 1351413694 |
Download Stochastic Linear Programming Algorithms Book in PDF, ePub and Kindle
A computationally oriented comparison of solution algorithms for two stage and jointly chance constrained stochastic linear programming problems, this is the first book to present comparative computational results with several major stochastic programming solution approaches. The following methods are considered: regularized decomposition, stochastic decomposition and successive discrete approximation methods for two stage problems; cutting plane methods, and a reduced gradient method for jointly chance constrained problems. The first part of the book introduces the algorithms, including a unified approach to decomposition methods and their regularized counterparts. The second part addresses computer implementation of the methods, describes a testing environment based on a model management system, and presents comparative computational results with the various algorithms. Emphasis is on the computational behavior of the algorithms.
| Author | : Janos Mayer |
| Publisher | : CRC Press |
| Total Pages | : 174 |
| Release | : 1998-02-25 |
| Genre | : Computers |
| ISBN | : 9789056991449 |
Download Stochastic Linear Programming Algorithms Book in PDF, ePub and Kindle
A computationally oriented comparison of solution algorithms for two stage and jointly chance constrained stochastic linear programming problems, this is the first book to present comparative computational results with several major stochastic programming solution approaches. The following methods are considered: regularized decomposition, stochastic decomposition and successive discrete approximation methods for two stage problems; cutting plane methods, and a reduced gradient method for jointly chance constrained problems. The first part of the book introduces the algorithms, including a unified approach to decomposition methods and their regularized counterparts. The second part addresses computer implementation of the methods, describes a testing environment based on a model management system, and presents comparative computational results with the various algorithms. Emphasis is on the computational behavior of the algorithms.
| Author | : Julia L. Higle |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 237 |
| Release | : 2013-11-27 |
| Genre | : Mathematics |
| ISBN | : 1461541158 |
Download Stochastic Decomposition Book in PDF, ePub and Kindle
Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications.
| Author | : Peter Kall |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 439 |
| Release | : 2010-11-02 |
| Genre | : Mathematics |
| ISBN | : 1441977295 |
Download Stochastic Linear Programming Book in PDF, ePub and Kindle
This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC’s and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup. To facilitate use as a text, exercises are included throughout the book, and web access is provided to a student version of the authors’ SLP-IOR software. Additionally, the authors have updated the Guide to Available Software, and they have included newer algorithms and modeling systems for SLP. The book is thus suitable as a text for advanced courses in stochastic optimization, and as a reference to the field. From Reviews of the First Edition: "The book presents a comprehensive study of stochastic linear optimization problems and their applications. ... The presentation includes geometric interpretation, linear programming duality, and the simplex method in its primal and dual forms. ... The authors have made an effort to collect ... the most useful recent ideas and algorithms in this area. ... A guide to the existing software is included as well." (Darinka Dentcheva, Mathematical Reviews, Issue 2006 c) "This is a graduate text in optimisation whose main emphasis is in stochastic programming. The book is clearly written. ... This is a good book for providing mathematicians, economists and engineers with an almost complete start up information for working in the field. I heartily welcome its publication. ... It is evident that this book will constitute an obligatory reference source for the specialists of the field." (Carlos Narciso Bouza Herrera, Zentralblatt MATH, Vol. 1104 (6), 2007)
| Author | : P. Kall |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 103 |
| Release | : 2012-12-06 |
| Genre | : Business & Economics |
| ISBN | : 3642662528 |
Download Stochastic Linear Programming Book in PDF, ePub and Kindle
Todaymanyeconomists, engineers and mathematicians are familiar with linear programming and are able to apply it. This is owing to the following facts: during the last 25 years efficient methods have been developed; at the same time sufficient computer capacity became available; finally, in many different fields, linear programs have turned out to be appropriate models for solving practical problems. However, to apply the theory and the methods of linear programming, it is required that the data determining a linear program be fixed known numbers. This condition is not fulfilled in many practical situations, e. g. when the data are demands, technological coefficients, available capacities, cost rates and so on. It may happen that such data are random variables. In this case, it seems to be common practice to replace these random variables by their mean values and solve the resulting linear program. By 1960 various authors had already recog nized that this approach is unsound: between 1955 and 1960 there were such papers as "Linear Programming under Uncertainty", "Stochastic Linear Pro gramming with Applications to Agricultural Economics", "Chance Constrained Programming", "Inequalities for Stochastic Linear Programming Problems" and "An Approach to Linear Programming under Uncertainty".
| Author | : Lewis Ntaimo |
| Publisher | : Springer Nature |
| Total Pages | : 518 |
| Release | : |
| Genre | : |
| ISBN | : 3031524640 |
Download Computational Stochastic Programming Book in PDF, ePub and Kindle
| Author | : Julia L. Higle |
| Publisher | : |
| Total Pages | : 332 |
| Release | : 1996 |
| Genre | : Mathematical optimization |
| ISBN | : |
Download Stochastic Programming, Algorithms and Models Book in PDF, ePub and Kindle
| Author | : Stein W. Wallace |
| Publisher | : SIAM |
| Total Pages | : 701 |
| Release | : 2005-06-01 |
| Genre | : Mathematics |
| ISBN | : 0898715555 |
Download Applications of Stochastic Programming Book in PDF, ePub and Kindle
Consisting of two parts, this book presents papers describing publicly available stochastic programming systems that are operational. It presents a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity.
| Author | : John R. Birge |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 500 |
| Release | : 2011-06-15 |
| Genre | : Business & Economics |
| ISBN | : 1461402379 |
Download Introduction to Stochastic Programming Book in PDF, ePub and Kindle
The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods. The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition: "The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)
| Author | : Stanislav Uryasev |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 456 |
| Release | : 2001-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 9780792369516 |
Download Stochastic Optimization Book in PDF, ePub and Kindle
Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.
