Optimization methods
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Optimization methods by O. V. VasilК№ev

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Published by World Federation Publishers in Atlanta .
Written in English

Subjects:

  • Mathematical optimization.

Book details:

Edition Notes

Includes bibliographical references (p. [267]-275).

StatementO.V. Vasiliev.
SeriesAdvanced series in mathematical science and engineering ;, 5
Classifications
LC ClassificationsQA402.5 .V3688 1996
The Physical Object
Pagination275 p. :
Number of Pages275
ID Numbers
Open LibraryOL816090M
ISBN 101885978243
LC Control Number95054153
OCLC/WorldCa34076571

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This book covers state-of-the-art optimization methods and their applications in wide range especially for researchers and practitioners who wish to improve their knowledge in this field. It consists of 13 chapters divided into two parts: (I) Engineering applications, which presents some new applications of different methods, and (II) Applications in various areas, where recent contributions Cited by: 1. This book has become the standard for a complete, state-of-the-art description of the methods for unconstrained optimization and systems of nonlinear equations. Originally published in , it provides information needed to understand both the theory and the practice of these methods and provides pseudocode for the problems. "The book contains a comprehensive presentation of methods for unconstrained and constrained optimization problems. The main strength of the book is the precise convergence analysis of most nonlinear programming algorithms presented, and it is especially comprehensive for line search, Newton, quasi-Newton, trust region and SQP methods. With innovative coverage and a straightforward approach, An Introduction to Optimization, Third Edition is an excellent book for courses in optimization theory and methods at the upper-undergraduate and graduate levels. It also serves as a useful, self-contained reference for researchers and professionals in a wide array of fields.

optimization software. Optimization methods are somewhat generic in nature in that many methods work for wide variety of problems. After the connection has been made such that the optimization software can “talk” to the engineering model, we specify the set of design variables and objectives File Size: 2MB. Optimization Vocabulary Your basic optimization problem consists of •The objective function, f(x), which is the output you’re trying to maximize or minimize. •Variables, x 1 x 2 x 3 and so on, which are the inputs – things you can control. They are abbreviated x n . Introduction: In optimization of a design, the design objective could be simply to minimize the cost of production or to maximize the efficiency of production. An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till File Size: KB. This book covers the fundamentals of optimization methods for solving engineering problems. Written by an engineer, it introduces fundamentals of mathematical optimization methods in a manner that engineers can easily understand. The treatment of the topics presented here is /5(26).

"The book is an excellent introduction to the world of continuous optimization. The authors are successful in balancing the theoretical background and the usable algorithms and optimization methods The authors deserve an appreciation of the connection between theory and usage of mathematical tools as Matlab and Maple. This book provides a clear and comprehensive introduction to the subject of optimization methods. Optimal mix design for both ordinary and special concretes (such as fibre- reinforced concretes, polymer cement concretes and water permeable concretes). Different problems of optimization are considere. stage for the development of optimization methods in the subsequent chapters. Scope of Optimization Problems From a practical standpoint, we define the optimization task as follows: given a system or process, find the best solution to this process within Size: KB. Applications of discrete optimization: Branch and bound and cutting planes: Lagrangean methods: Heuristics and approximation algorithms: Dynamic programming: Applications of nonlinear optimization: Optimality conditions and gradient methods: Line searches and Newton's method: Conjugate gradient methods: