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| Author | : X. Wang |
| Publisher | : |
| Total Pages | : |
| Release | : 1987 |
| Genre | : |
| ISBN | : |
| Author | : David A. Wismer |
| Publisher | : |
| Total Pages | : 335 |
| Release | : 1971 |
| Genre | : |
| ISBN | : |
| Author | : David A. Wismer |
| Publisher | : McGraw-Hill Companies |
| Total Pages | : 358 |
| Release | : 1971 |
| Genre | : Mathematics |
| ISBN | : |
| Author | : Vladimir Tsurkov |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 322 |
| Release | : 2013-03-09 |
| Genre | : Computers |
| ISBN | : 1475732430 |
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, aggregation methods are categorized. The issues of loss of accuracy, recovery of original variables (disaggregation), and compatibility conditions are analyzed in detail. The method of iterative aggregation in large-scale problems is studied. For fixed weights, successively simpler aggregated problems are solved and the convergence of their solution to that of the original problem is analyzed. An introduction to block integer programming is considered. Duality theory, which is widely used in continuous block programming, does not work for the integer problem. A survey of alternative methods is presented and special attention is given to combined methods of decomposition. Block problems in which the coupling variables do not enter the binding constraints are studied. These models are worthwhile because they permit a decomposition with respect to primal and dual variables by two-level algorithms instead of three-level algorithms. Audience: This book is addressed to specialists in operations research, optimization, and optimal control.
| Author | : Kaisa Miettinen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 304 |
| Release | : 2012-12-06 |
| Genre | : Business & Economics |
| ISBN | : 1461555639 |
Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization. Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980. Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well. This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers.
| Author | : José M. Maestre |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 601 |
| Release | : 2013-11-10 |
| Genre | : Technology & Engineering |
| ISBN | : 9400770065 |
The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available.
| Author | : H. P. Geering |
| Publisher | : Pergamon |
| Total Pages | : 500 |
| Release | : 1987 |
| Genre | : Science |
| ISBN | : |
This volume deals with production and management planning, decision making and control using large scale systems. Computer modelling systems and their applications - such as transport, communication and information and energy systems - are discussed, and future trends examined to give an up-to-date account of systems theory today.
| Author | : William W. Hager |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 416 |
| Release | : 2006-06-18 |
| Genre | : Mathematics |
| ISBN | : 038729550X |
As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towards the solution of original problem, using a solution from a previous level as a starting guess at the next level. This volume contains 22 chapters highlighting some recent research. The topics of the chapters selected for this volume are focused on the development of new solution methodologies, including general multilevel solution techniques, for tackling difficult, large-scale optimization problems that arise in science and industry. Applications presented in the book include but are not limited to the circuit placement problem in VLSI design, a wireless sensor location problem, optimal dosages in the treatment of cancer by radiation therapy, and facility location.
| Author | : Duan Li |
| Publisher | : |
| Total Pages | : 364 |
| Release | : 1987 |
| Genre | : |
| ISBN | : |
| Author | : Vladimir Tsurkov |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 320 |
| Release | : 2013-11-21 |
| Genre | : Science |
| ISBN | : 1461546672 |
This book should be considered as an introduction to a special dass of hierarchical systems of optimal control, where subsystems are described by partial differential equations of various types. Optimization is carried out by means of a two-level scheme, where the center optimizes coordination for the upper level and subsystems find the optimal solutions for independent local problems. The main algorithm is a method of iterative aggregation. The coordinator solves the problern with macrovariables, whose number is less than the number of initial variables. This problern is often very simple. On the lower level, we have the usual optimal control problems of math ematical physics, which are far simpler than the initial statements. Thus, the decomposition (or reduction to problems ofless dimensions) is obtained. The algorithm constructs a sequence of so-called disaggregated solutions that are feasible for the main problern and converge to its optimal solutionunder certain assumptions ( e.g., under strict convexity of the input functions). Thus, we bridge the gap between two disciplines: optimization theory of large-scale systems and mathematical physics. The first motivation was a special model of branch planning, where the final product obeys a preset assortment relation. The ratio coefficient is maximized. Constraints are given in the form of linear inequalities with block diagonal structure of the part of a matrix that corresponds to subsystems. The central coordinator assem bles the final production from the components produced by the subsystems.
