Stochastic Decomposition PDF Download
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| 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 | : Michel Talagrand |
| Publisher | : Springer Nature |
| Total Pages | : 727 |
| Release | : 2022-01-01 |
| Genre | : Mathematics |
| ISBN | : 3030825957 |
Download Upper and Lower Bounds for Stochastic Processes Book in PDF, ePub and Kindle
This book provides an in-depth account of modern methods used to bound the supremum of stochastic processes. Starting from first principles, it takes the reader to the frontier of current research. This second edition has been completely rewritten, offering substantial improvements to the exposition and simplified proofs, as well as new results. The book starts with a thorough account of the generic chaining, a remarkably simple and powerful method to bound a stochastic process that should belong to every probabilist’s toolkit. The effectiveness of the scheme is demonstrated by the characterization of sample boundedness of Gaussian processes. Much of the book is devoted to exploring the wealth of ideas and results generated by thirty years of efforts to extend this result to more general classes of processes, culminating in the recent solution of several key conjectures. A large part of this unique book is devoted to the author’s influential work. While many of the results presented are rather advanced, others bear on the very foundations of probability theory. In addition to providing an invaluable reference for researchers, the book should therefore also be of interest to a wide range of readers.
| Author | : Christian Küchler |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 178 |
| Release | : 2010-05-30 |
| Genre | : Mathematics |
| ISBN | : 3834893994 |
Download Stability, Approximation, and Decomposition in Two- and Multistage Stochastic Programming Book in PDF, ePub and Kindle
Christian Küchler studies various aspects of the stability of stochastic optimization problems as well as approximation and decomposition methods in stochastic programming. In particular, the author presents an extension of the Nested Benders decomposition algorithm related to the concept of recombining scenario trees.
| 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 | : Gerd Infanger |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 373 |
| Release | : 2010-11-10 |
| Genre | : Mathematics |
| ISBN | : 1441916423 |
Download Stochastic Programming Book in PDF, ePub and Kindle
From the Preface... The preparation of this book started in 2004, when 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 programming. The field of stochastic programming (also referred to as optimization under uncertainty or planning under uncertainty) had advanced significantly in the last two decades, both theoretically and in practice. George Dantzig and I felt that it would be valuable to showcase some of these advances and to present what one might call the state-of- the-art of the field to a broader audience. We invited researchers whom we considered to be leading experts in various specialties of the field, including a few representatives of promising developments in the making, to write a chapter for the volume. Unfortunately, to the great loss of all of us, George Dantzig passed away on May 13, 2005. Encouraged by many colleagues, I decided to continue with the book and edit it as a volume dedicated to George Dantzig. Management Science published in 2005 a special volume featuring the “Ten most Influential Papers of the first 50 Years of Management Science.” George Dantzig’s original 1955 stochastic programming paper, “Linear Programming under Uncertainty,” was featured among these ten. Hearing about this, George Dantzig suggested that his 1955 paper be the first chapter of this book. The vision expressed in that paper gives an important scientific and historical perspective to the book. Gerd Infanger
| Author | : Julia L. Higle |
| Publisher | : |
| Total Pages | : 248 |
| Release | : 2014-09-01 |
| Genre | : |
| ISBN | : 9781461541165 |
Download Stochastic Decomposition Book in PDF, ePub and Kindle
| 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 | : Quan-Lin Li |
| Publisher | : Springer Nature |
| Total Pages | : 497 |
| Release | : 2019-10-21 |
| Genre | : Computers |
| ISBN | : 981150864X |
Download Stochastic Models in Reliability, Network Security and System Safety Book in PDF, ePub and Kindle
This book is dedicated to Jinhua Cao on the occasion of his 80th birthday. Jinhua Cao is one of the most famous reliability theorists. His main contributions include: published over 100 influential scientific papers; published an interesting reliability book in Chinese in 1986, which has greatly influenced the reliability of education, academic research and engineering applications in China; initiated and organized Reliability Professional Society of China (the first part of Operations Research Society of China) since 1981. The high admiration that Professor Cao enjoys in the reliability community all over the world was witnessed by the enthusiastic response of each contributor in this book. The contributors are leading researchers with diverse research perspectives. The research areas of the book iclude a broad range of topics related to reliability models, queueing theory, manufacturing systems, supply chain finance, risk management, Markov decision processes, blockchain and so forth. The book consists of a brief Preface describing the main achievements of Professor Cao; followed by congratulations from Professors Way Kuo and Wei Wayne Li, and by Operations Research Society of China, and Reliability Professional Society of China; and further followed by 25 articles roughly grouped together. Most of the articles are written in a style understandable to a wide audience. This book is useful to anyone interested in recent developments in reliability, network security, system safety, and their stochastic modeling and analysis.
| Author | : Kurt Marti |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 448 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 3642457673 |
Download Stochastic Programming Methods and Technical Applications Book in PDF, ePub and Kindle
Optimization problems arising in practice usually contain several random parameters. Hence, in order to obtain optimal solutions being robust with respect to random parameter variations, the mostly available statistical information about the random parameters should be considered already at the planning phase. The original problem with random parameters must be replaced by an appropriate deterministic substitute problem, and efficient numerical solution or approximation techniques have to be developed for those problems. 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 systems.
| Author | : Peter Kall |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 405 |
| Release | : 2005-07-25 |
| Genre | : Mathematics |
| ISBN | : 0387244409 |
Download Stochastic Linear Programming Book in PDF, ePub and Kindle
Peter Kall and János 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 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.
