Large Scale Optimization
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Large Scale Optimization

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Published by Pergamon Pr .
Written in English


Book details:

The Physical Object
FormatHardcover
Number of Pages100
ID Numbers
Open LibraryOL9976709M
ISBN 10008030270X
ISBN 109780080302706
OCLC/WorldCa35419801

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Decomposition methods aim to reduce large-scale problems to simpler problems. This monograph presents selected aspects of the dimension-reduction problem. Exact and approximate aggregations of multidimensional systems are developed and from a known model of input-output balance, aggregationBrand: Springer US. Large scale optimization has seen a dramatic increase in activities in the past decade. This has been a natural consequence of new algorithmic developments and of the increased power of computers. For example, decomposition ideas proposed by G. Dantzig and P. Wolfe in the 's, are now implement able in distributed process­ ing systems, and. The book will prove useful to researchers, students, and engineers in different domains who encounter large scale optimization problems and will encourage them to undertake research in this timely and practical field. The book splits into two parts. The first part covers a general perspective and challenges in a smart society and in industry. Large scale optimization has seen a dramatic increase in activities in the past decade. This has been a natural consequence of new algorithmic developments and of the increased power of computers. For example, decomposition ideas proposed by G. Dantzig and P. Wolfe in the 's, are now implement able in distributed process­ ing systems, and Author: William W. Hager.

Therefore it is very easy to solve a large scale linear optimization problem, but it can be very difficult to solve a complex optimization problem (to find its global minimum), even with a small.   Presents a new and systematic viewpoint for power system optimization inspired by microeconomics and game theory A timely and important advanced reference with the fast growth of smart grids Professor Chen is a pioneer of applying experimental economics to the electricity market trading mechanism, and this work brings together the latest research.   Large-Scale Optimization with Applications: Part II: Optimal With contributions by specialists in optimization and practitioners in the fields of aerospace engineering, chemical engineering, and fluid and solid mechanics, the major themes include an assessment of the state of the art in optimization algorithms as well as challenging Author: Vladimir Tsurkov. In this book, theory of large scale optimization is introduced with case studies of real-world problems and applications of structured mathematical modeling. The large scale optimization methods are represented by various theories such as Benders’ decomposition, logic-based Benders’ decomposition, Lagrangian relaxation, Dantzig –Wolfe.

Get this from a library! Large-scale optimization: problems and methods. [V I T︠S︡urkov] -- "Decomposition methods aim to reduce large-scale problems to simpler problems. This monograph presents selected aspects of the dimension-reduction problem. Exact and approximate aggregations of. This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale. The book will prove useful to researchers, students, and engineers in different domains who encounter large scale optimization problems and will encourage them to . This book provides an up-to-date, comprehensive, and rigorous account of nonlinear programming at the first year graduate student level. It covers descent algorithms for unconstrained and constrained optimization, Lagrange multiplier theory, interior point and augmented Lagrangian methods for linear and nonlinear programs, duality theory, and major aspects of large-scale optimization.