Image from Google Jackets

Linear Optimization Problems with Inexact Data [recurso electrónico] / by M. Fiedler, J. Nedoma, J. Ramík, J. Rohn, K. Zimmermann.

Por: Colaborador(es): Tipo de material: TextoTextoEditor: Boston, MA : Springer US, 2006Descripción: XV, 214 p. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • recurso en línea
ISBN:
  • 9780387326986
  • 99780387326986
Tema(s): Formatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD:
  • 519.6 23
Recursos en línea:
Contenidos:
Matrices -- Solvability of systems of interval linear equations and inequalities -- Interval linear programming -- Linear programming with set coefficients -- Fuzzy linear optimization -- Interval linear systems and optimization problems over max-algebras.
En: Springer eBooksResumen: Linear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems-for example, providing logistical support for the U.S. Armed Forces or modeling national economies. Early attempts to apply linear programming methods to solve practical problems failed to satisfy expectations. There were various reasons for the failure. One of them, which is the central topic of this book, was the inexactness of the data used to create the models. This phenomenon, inherent in most pratical problems, has been dealt with in several ways. At first, linear programming models used "average" values of inherently vague coefficients, but the optimal solutions of these models were not always optimal for the original problem itself. Later researchers developed the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the literature has not presented a unified theory. Linear Optimization Problems with Inexact Data attempts to present a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework. Audience This book is intended for postgraduate or graduate students in the areas of operations research, optimization theory, linear algebra, interval analysis, reliable computing, and fuzzy sets. The book will also be useful for researchers in these respective areas.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode
Libros electrónicos Libros electrónicos CICY Libro electrónico Libro electrónico 519.6 (Browse shelf(Opens below)) Available

Matrices -- Solvability of systems of interval linear equations and inequalities -- Interval linear programming -- Linear programming with set coefficients -- Fuzzy linear optimization -- Interval linear systems and optimization problems over max-algebras.

Linear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems-for example, providing logistical support for the U.S. Armed Forces or modeling national economies. Early attempts to apply linear programming methods to solve practical problems failed to satisfy expectations. There were various reasons for the failure. One of them, which is the central topic of this book, was the inexactness of the data used to create the models. This phenomenon, inherent in most pratical problems, has been dealt with in several ways. At first, linear programming models used "average" values of inherently vague coefficients, but the optimal solutions of these models were not always optimal for the original problem itself. Later researchers developed the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the literature has not presented a unified theory. Linear Optimization Problems with Inexact Data attempts to present a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework. Audience This book is intended for postgraduate or graduate students in the areas of operations research, optimization theory, linear algebra, interval analysis, reliable computing, and fuzzy sets. The book will also be useful for researchers in these respective areas.

There are no comments on this title.

to post a comment.