Download or read book Numerical Techniques for Stochastic Optimization written by I︠U︡riĭ Mikhaĭlovich Ermolʹev and published by Springer. This book was released on 1988 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Numerical Techniques for Stochastic Optimization written by Yuri Ermoliev and published by Springer. This book was released on 1988 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid changes in today's environment emphasize the need for models and meth ods capable of dealing with the uncertainty inherent in virtually all systems re lated to economics, meteorology, demography, ecology, etc. Systems involving interactions between man, nature and technology are subject to disturbances which may be unlike anything which has been experienced in the past. In the technological revolution increases uncertainty-as each new stage particular, perturbs existing knowledge of structures, limitations and constraints. At the same time, many systems are often too complex to allow for precise measure ment of the parameters or the state of the system. Uncertainty, nonstationarity, disequilibrium are pervasivE' characteristics of most modern systems. In order to manage such situations (or to survive in such an environment) we must develop systems which can facilitate oar response to uncertainty and changing conditions. In our individual behavior we often follow guidelines that are conditioned by the need to be prepared for all (likely) eventualities: insur ance, wearing seat·belts, savings versus investments, annual medical check.ups, even keeping an umbrella at the office, etc. One can identify two major types of mechanisms: the short term adaptive adjustments (defensive driving, mar keting, inventory control, etc.) that are made after making some observations of the system's parameters, and the long term anticipative actions (engineer ing design, policy setting, allocation of resources, investment strategies, etc.).
Download or read book Numerical Techniques for Stochastic Optimization Problems written by Yuri Ermoliev and published by . This book was released on 1984 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Numerical Optimization written by Jorge Nocedal and published by Springer Science & Business Media. This book was released on 2006-12-11 with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.
Download or read book Numerical Methods for Stochastic Control Problems in Continuous Time written by Harold Kushner and published by Springer Science & Business Media. This book was released on 2013-11-27 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic control is a very active area of research. This monograph, written by two leading authorities in the field, has been updated to reflect the latest developments. It covers effective numerical methods for stochastic control problems in continuous time on two levels, that of practice and that of mathematical development. It is broadly accessible for graduate students and researchers.
Download or read book Stochastic Optimization written by Kurt Marti and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume includes a selection of refereed papers presented at the GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications", held at the Federal Armed Forces University Munich, May 29 - 31, 1990. The objective of this meeting was to bring together scientists from Stochastic Programming and from those Engineering areas, where Mathematical Programming models are common tools, as e. g. Optimal Structural Design, Power Dispatch, Acid Rain Management etc. The first, theoretical part includes the papers by S. D. Flam. H. Niederreiter, E. Poechinger and R. Schultz. The second part on methods and applications contains the articles by N. Baba, N. Grwe and W. Roemisch, J. Mayer, E. A. Mc Bean and A. Vasarhelyi.
Download or read book Stochastic Optimization written by Johannes Schneider and published by Springer Science & Business Media. This book was released on 2007-08-06 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses stochastic optimization procedures in a broad manner. The first part offers an overview of relevant optimization philosophies; the second deals with benchmark problems in depth, by applying a selection of optimization procedures. Written primarily with scientists and students from the physical and engineering sciences in mind, this book addresses a larger community of all who wish to learn about stochastic optimization techniques and how to use them.
Download or read book Stochastic Programming written by Kurt Marti and published by Springer. This book was released on 1995-04-06 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the 2nd GAMM/IFIP-Workshop on "Stochastic Optimization:Numerical Methods and Technical Applications" held at the Federal Armed Forces University, Munich, Neubiberg/München, Germany, June 15-17, 1993
Download or read book Stochastic Programming written by Kurt Marti and published by Springer Science & Business Media. This book was released on 2013-12-14 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Lectures on Stochastic Programming written by Alexander Shapiro and published by SIAM. This book was released on 2009-01-01 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. Readers will find coverage of the basic concepts of modeling these problems, including recourse actions and the nonanticipativity principle. The book also includes the theory of two-stage and multistage stochastic programming problems; the current state of the theory on chance (probabilistic) constraints, including the structure of the problems, optimality theory, and duality; and statistical inference in and risk-averse approaches to stochastic programming.
Download or read book Numerical Methods for Convex Multistage Stochastic Optimization written by Guanghui Lan and published by . This book was released on 2024-05-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization problems involving sequential decisions in a stochastic environment were studied in Stochastic Programming (SP), Stochastic Optimal Control (SOC) and Markov Decision Processes (MDP). This monograph concentrates on SP and SOC modeling approaches. In these frameworks, there are natural situations when the considered problems are convex. The classical approach to sequential optimization is based on dynamic programming. It has the problem of the so-called "curse of dimensionality", in that its computational complexity increases exponentially with respect to the dimension of state variables. Recent progress in solving convex multistage stochastic problems is based on cutting plane approximations of the cost-to-go (value) functions of dynamic programming equations. Cutting plane type algorithms in dynamical settings is one of the main topics of this monograph. Also discussed in this work are stochastic approximation type methods applied to multistage stochastic optimization problems. From the computational complexity point of view, these two types of methods seem to be complimentary to each other. Cutting plane type methods can handle multistage problems with a large number of stages but a relatively smaller number of state (decision) variables. On the other hand, stochastic approximation type methods can only deal with a small number of stages but a large number of decision variables.
Download or read book Stochastic Optimization Techniques written by Kurt Marti and published by Springer. This book was released on 2002 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization problems arising in practice mostly contain several random parameters. Hence, in order to get robust optimal solutions with respect to random parameter variations, the available statistical information about the random data should be considered already at the planning phase. Thus, the original problem with random coefficients must be replaced by an appropriate deterministic substitute problem. This proceedings volume of the 4th GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications" held June 27-29, 2000 at 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 mean value and probability functions as objective and/or constraint functions. Moreover, many concrete applications from engineering and operations research can be found in this book.
Download or read book Stochastic Optimization Techniques written by Kurt Marti and published by Springer. This book was released on 2001-12-14 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization problems arising in practice mostly contain several random parameters. Hence, in order to get robust optimal solutions with respect to random parameter variations, the available statistical information about the random data should be considered already at the planning phase. Thus, the original problem with random coefficients must be replaced by an appropriate deterministic substitute problem. This proceedings volume of the 4th GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications" held June 27-29, 2000 at 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 mean value and probability functions as objective and/or constraint functions. Moreover, many concrete applications from engineering and operations research can be found in this book.
Download or read book Stochastic Programming Methods and Technical Applications written by Kurt Marti and published by Springer. This book was released on 2012-05-29 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Numerical Solution of Stochastic Differential Equations written by Peter E. Kloeden and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: The numerical analysis of stochastic differential equations (SDEs) differs significantly from that of ordinary differential equations. This book provides an easily accessible introduction to SDEs, their applications and the numerical methods to solve such equations. From the reviews: "The authors draw upon their own research and experiences in obviously many disciplines... considerable time has obviously been spent writing this in the simplest language possible." --ZAMP
Download or read book Stochastic Optimization written by Kurt Marti and published by Springer My Copy UK. This book was released on 1992 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume includes a selection of refereed papers presented at the GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications," held at the Federal Armed Forces University Munich, May 29 - 31, 1990. The objective of this meeting was to bring together scientists from Stochastic Programming and from those Engineering areas, where Mathematical Programming models are common tools, as e. g. Optimal Structural Design, Power Dispatch, Acid Rain Management etc. The first, theoretical part includes the papers by S. D. Flam. H. Niederreiter, E. Pl-chinger and R. Schultz. The second part on methods and applications contains the articles by N. Baba, N. Gr-we and W. R-misch, J. Mayer, E. A. Mc Bean and A. Vasarhelyi.
Download or read book First order and Stochastic Optimization Methods for Machine Learning written by Guanghui Lan and published by Springer Nature. This book was released on 2020-05-15 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
