Design And Analysis Of Algorithms For Stochastic Integer Programming
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Design And Analysis Of Algorithms For Stochastic Integer Programming
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Author : L. Stougie
language : en
Publisher:
Release Date : 1987
Design And Analysis Of Algorithms For Stochastic Integer Programming written by L. Stougie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Algorithms categories.
Convexification And Global Optimization In Continuous And Mixed Integer Nonlinear Programming
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Author : Mohit Tawarmalani
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Convexification And Global Optimization In Continuous And Mixed Integer Nonlinear Programming written by Mohit Tawarmalani and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-17 with Mathematics categories.
Interest in constrained optimization originated with the simple linear pro gramming model since it was practical and perhaps the only computationally tractable model at the time. Constrained linear optimization models were soon adopted in numerous application areas and are perhaps the most widely used mathematical models in operations research and management science at the time of this writing. Modelers have, however, found the assumption of linearity to be overly restrictive in expressing the real-world phenomena and problems in economics, finance, business, communication, engineering design, computational biology, and other areas that frequently demand the use of nonlinear expressions and discrete variables in optimization models. Both of these extensions of the linear programming model are NP-hard, thus representing very challenging problems. On the brighter side, recent advances in algorithmic and computing technology make it possible to re visit these problems with the hope of solving practically relevant problems in reasonable amounts of computational time. Initial attempts at solving nonlinear programs concentrated on the de velopment of local optimization methods guaranteeing globality under the assumption of convexity. On the other hand, the integer programming liter ature has concentrated on the development of methods that ensure global optima. The aim of this book is to marry the advancements in solving nonlinear and integer programming models and to develop new results in the more general framework of mixed-integer nonlinear programs (MINLPs) with the goal of devising practically efficient global optimization algorithms for MINLPs.
Stochastic Decomposition
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Author : Julia L. Higle
language : en
Publisher: Springer Science & Business Media
Release Date : 1996-02-29
Stochastic Decomposition written by Julia L. Higle and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-02-29 with Business & Economics categories.
This book summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms. Unlike traditional deterministic algorithms, SD combines sampling approaches from the statistical literature with traditional mathematical programming constructs (e.g. decomposition, cutting planes etc.). This marriage of two highly computationally oriented disciplines leads to a line of work that is most definitely driven by computational considerations. Furthermore, the use of sampled data in SD makes it extremely flexible in its ability to accommodate various representations of uncertainty, including situations in which outcomes/scenarios can only be generated by an algorithm/simulation. The authors report computational results with some of the largest stochastic programs arising in applications. These results (mathematical as well as computational) are the `tip of the iceberg'. Further research will uncover extensions of SD to a wider class of problems. Audience: Researchers in mathematical optimization, including those working in telecommunications, electric power generation, transportation planning, airlines and production systems. Also suitable as a text for an advanced course in stochastic optimization.
Applications Of Stochastic Programming
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Author : Stein W. Wallace
language : en
Publisher: SIAM
Release Date : 2005-06-01
Applications Of Stochastic Programming written by Stein W. Wallace and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-01 with Mathematics categories.
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.
Design And Analysis Of Algorithms For Stochastic Integer Programming
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Author : L. Stougie
language : en
Publisher:
Release Date : 1987
Design And Analysis Of Algorithms For Stochastic Integer Programming written by L. Stougie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Algorithms categories.
Stochastic Linear Programming Algorithms
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Author : Janos Mayer
language : en
Publisher: Taylor & Francis
Release Date : 2022-04-19
Stochastic Linear Programming Algorithms written by Janos Mayer and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-19 with Computers categories.
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.
Network Interdiction And Stochastic Integer Programming
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Author : David L. Woodruff
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-11
Network Interdiction And Stochastic Integer Programming written by David L. Woodruff and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-11 with Mathematics categories.
On March 15, 2002 we held a workshop on network interdiction and the more general problem of stochastic mixed integer programming at the University of California, Davis. Jesús De Loera and I co-chaired the event, which included presentations of on-going research and discussion. At the workshop, we decided to produce a volume of timely work on the topics. This volume is the result. Each chapter represents state-of-the-art research and all of them were refereed by leading investigators in the respective fields. Problems - sociated with protecting and attacking computer, transportation, and social networks gain importance as the world becomes more dep- dent on interconnected systems. Optimization models that address the stochastic nature of these problems are an important part of the research agenda. This work relies on recent efforts to provide methods for - dressing stochastic mixed integer programs. The book is organized with interdiction papers first and the stochastic programming papers in the second part. A nice overview of the papers is provided in the Foreward written by Roger Wets.
Encyclopedia Of Optimization
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Author : Christodoulos A. Floudas
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-04
Encyclopedia Of Optimization written by Christodoulos A. Floudas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-04 with Mathematics categories.
The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that show the spectrum of research, the richness of ideas, and the breadth of applications that has come from this field. The second edition builds on the success of the former edition with more than 150 completely new entries, designed to ensure that the reference addresses recent areas where optimization theories and techniques have advanced. Particularly heavy attention resulted in health science and transportation, with entries such as "Algorithms for Genomics", "Optimization and Radiotherapy Treatment Design", and "Crew Scheduling".
Stochastic Linear Programming Algorithms
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Author : Janos Mayer
language : en
Publisher: CRC Press
Release Date : 1998-02-25
Stochastic Linear Programming Algorithms written by Janos Mayer and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-02-25 with Computers categories.
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.
Introduction To Stochastic Programming
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Author : John R. Birge
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-15
Introduction To Stochastic Programming written by John R. Birge and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-15 with Business & Economics categories.
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)
