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Global Optimization Using Interval Analysis: Revised And Expanded de Eldon Hansen

Global Optimization Using Interval Analysis: Revised And Expanded

Editat de Eldon Hansen, G. William Walster
en Limba Engleză Hardback – 19 dec 2003
Employing a closed set-theoretic foundation for interval computations, Global Optimization Using Interval Analysis simplifies algorithm construction and increases generality of interval arithmetic. This Second Edition contains an up-to-date discussion of interval methods for solving systems of nonlinear equations and global optimization problems. It expands and improves various aspects of its forerunner and features significant new discussions, such as those on the use of consistency methods to enhance algorithm performance.
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Specificații

ISBN-13: 9780824740597
ISBN-10: 0824740599
Pagini: 520
Dimensiuni: 152 x 229 x 29 mm
Greutate: 0.82 kg
Ediția:Revizuită
Editura: CRC Press
Colecția CRC Press

Public țintă

Professional

Cuprins

Foreword Preface 1 INTRODUCTION 2 INTERVAL NUMBERS AND ARITHMETIC 3 FUNCTIONS OF INTERVALS 4 CLOSED INTERVAL SYSTEMS 5 LINEAR EQUATIONS 6 INEQUALITIES 7 TAYLOR SERIES AND SLOPE EXPANSIONS 8 QUADRATIC EQUATIONS AND INEQUALITIES 9 NONLINEAR EQUATIONS OF ONE VARIABLE 10 CONSISTENCIES 11 SYSTEMS OF NONLINEAR EQUATIONS 12 UNCONSTRAINED OPTIMIZATION 13 CONSTRAINED OPTIMIZATION 14 INEQUALITY CONSTRAINED OPTIMIZATION 15 EQUALITY CONSTRAINED OPTIMIZATION 16 THE FULL MONTY 17 PERTURBED PROBLEMS AND SENSITIVITY ANALYSIS 18 MISCELLANY

Notă biografică

Eldon Hansen, Consultant, Los Altos, California. G. William Walster, Sun Microsystems Laboratories , Mountain View, California, U.S.A.

Descriere

Employing a closed set-theoretic foundation for interval computations, Global Optimization Using Interval Analysis simplifies algorithm construction and increases generality of interval arithmetic. This Second Edition contains an up-to-date discussion of interval methods for solving systems of nonlinear equations and global optimization problems. It expands and improves various aspects of its forerunner and features significant new discussions, such as those on the use of consistency methods to enhance algorithm performance. Provided algorithms are guaranteed to find and bound all solutions to these problems despite bounded errors in data, in approximations, and from use of rounded arithmetic.