Optimization Using Linear Programming
Optimization Using Linear Programming

Author: A. J. Metei

Publisher: Mercury Learning and Information

Published: 2019-03-21

Total Pages: 435

ISBN-13: 1683923464

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Designed for engineers, mathematicians, computer scientists, financial analysts, and anyone interested in using numerical linear algebra, matrix theory, and game theory concepts to maximize efficiency in solving applied problems. The book emphasizes the solution of various types of linear programming problems by using different types of software, but includes the necessary definitions and theorems to master theoretical aspects of the topics presented. Features: Emphasizes the solution of various types of linear programming problems by using different kinds of software, e.g., MS-Excel, solutions of LPPs by Mathematica, MATLAB, WinQSB, and LINDO Provides definitions, theorems, and procedures for solving problems and all cases related to various linear programming topics Includes numerous application examples and exercises, e.g., transportation, assignment, and maximization Presents numerous topics that can be used to solve problems involving systems of linear equations, matrices, vectors, game theory, simplex method, and more.

Modeling and Solving Linear Programming with R
Modeling and Solving Linear Programming with R

Author: Jose M. Sallan

Publisher: OmniaScience

Published: 2015-09-09

Total Pages: 108

ISBN-13: 8494422936

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Linear programming is one of the most extensively used techniques in the toolbox of quantitative methods of optimization. One of the reasons of the popularity of linear programming is that it allows to model a large variety of situations with a simple framework. Furthermore, a linear program is relatively easy to solve. The simplex method allows to solve most linear programs efficiently, and the Karmarkar interior-point method allows a more efficient solving of some kinds of linear programming. The power of linear programming is greatly enhanced when came the opportunity of solving integer and mixed integer linear programming. In these models all or some of the decision variables are integers, respectively. In this book we provide a brief introduction to linear programming, together with a set of exercises that introduce some applications of linear programming. We will also provide an introduction to solve linear programming in R. For each problem a possible solution through linear programming is introduced, together with the code to solve it in R and its numerical solution.

Linear Optimization Problems with Inexact Data
Linear Optimization Problems with Inexact Data

Author: Miroslav Fiedler

Publisher: Springer Science & Business Media

Published: 2006-07-18

Total Pages: 222

ISBN-13: 0387326987

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Linear programming has attracted the interest of mathematicians since World War II when the first computers were constructed. Early attempts to apply linear programming methods practical problems failed, in part because of the inexactness of the data used to create the models. This book presents a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework.

Linear Programming
Linear Programming

Author: Robert J Vanderbei

Publisher: Springer Science & Business Media

Published: 2013-07-16

Total Pages: 420

ISBN-13: 1461476305

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This Fourth Edition introduces the latest theory and applications in optimization. It emphasizes constrained optimization, beginning with a substantial treatment of linear programming and then proceeding to convex analysis, network flows, integer programming, quadratic programming, and convex optimization. Readers will discover a host of practical business applications as well as non-business applications. Topics are clearly developed with many numerical examples worked out in detail. Specific examples and concrete algorithms precede more abstract topics. With its focus on solving practical problems, the book features free C programs to implement the major algorithms covered, including the two-phase simplex method, primal-dual simplex method, path-following interior-point method, and homogeneous self-dual methods. In addition, the author provides online JAVA applets that illustrate various pivot rules and variants of the simplex method, both for linear programming and for network flows. These C programs and JAVA tools can be found on the book's website. The website also includes new online instructional tools and exercises.

Understanding and Using Linear Programming
Understanding and Using Linear Programming

Author: Jiri Matousek

Publisher: Springer Science & Business Media

Published: 2007-07-04

Total Pages: 230

ISBN-13: 3540307176

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The book is an introductory textbook mainly for students of computer science and mathematics. Our guiding phrase is "what every theoretical computer scientist should know about linear programming". A major focus is on applications of linear programming, both in practice and in theory. The book is concise, but at the same time, the main results are covered with complete proofs and in sufficient detail, ready for presentation in class. The book does not require more prerequisites than basic linear algebra, which is summarized in an appendix. One of its main goals is to help the reader to see linear programming "behind the scenes".

Linear Programming with MATLAB
Linear Programming with MATLAB

Author: Michael C. Ferris

Publisher: SIAM

Published: 2007-01-01

Total Pages: 270

ISBN-13: 0898716438

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A self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Exercises are included in each chapter, and additional information is provided in two appendices and an accompanying Web site. Only a basic knowledge of linear algebra and calculus is required.

Optimization Models
Optimization Models

Author: Giuseppe C. Calafiore

Publisher: Cambridge University Press

Published: 2014-10-31

Total Pages: 651

ISBN-13: 1107050871

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This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects.

Linear and Integer Optimization
Linear and Integer Optimization

Author: Gerard Sierksma

Publisher: CRC Press

Published: 2015-05-01

Total Pages: 676

ISBN-13: 1498743129

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Presenting a strong and clear relationship between theory and practice, Linear and Integer Optimization: Theory and Practice is divided into two main parts. The first covers the theory of linear and integer optimization, including both basic and advanced topics. Dantzig's simplex algorithm, duality, sensitivity analysis, integer optimization models

Linear Optimization and Extensions
Linear Optimization and Extensions

Author: Manfred Padberg

Publisher: Springer Science & Business Media

Published: 2013-04-17

Total Pages: 521

ISBN-13: 3662122731

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From the reviews: "Do you know M.Padberg's Linear Optimization and Extensions? [...] Now here is the continuation of it, discussing the solutions of all its exercises and with detailed analysis of the applications mentioned. Tell your students about it. [...] For those who strive for good exercises and case studies for LP this is an excellent volume." Acta Scientiarum Mathematicarum

Linear Programming: Foundations and Extensions
Linear Programming: Foundations and Extensions

Author: Robert J. Vanderbei

Publisher: Springer

Published: 1998-03-31

Total Pages: 440

ISBN-13: 0792381416

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This book focuses largely on constrained optimization. It begins with a substantial treatment of linear programming and proceeds to convex analysis, network flows, integer programming, quadratic programming, and convex optimization. Along the way, dynamic programming and the linear complementarity problem are touched on as well. This book aims to be the first introduction to the topic. Specific examples and concrete algorithms precede more abstract topics. Nevertheless, topics covered are developed in some depth, a large number of numerical examples worked out in detail, and many recent results are included, most notably interior-point methods. The exercises at the end of each chapter both illustrate the theory, and, in some cases, extend it. Optimization is not merely an intellectual exercise: its purpose is to solve practical problems on a computer. Accordingly, the book comes with software that implements the major algorithms studied. At this point, software for the following four algorithms is available: The two-phase simplex method The primal-dual simplex method The path-following interior-point method The homogeneous self-dual methods.£/LIST£.