Eldon Hansen, Consultant, Los Altos, California. G. William Walster, Sun Microsystems Laboratories , Mountain View, California, U.S.A.
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
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.
Employing a closed set-theoretic foundation for interval computations, this work simplifies algorithm construction and increases generality of interval arithmetic. It also contains discussions of interval methods for solving systems of nonlinear equations and global optimization problems.
Über den Autor
Eldon Hansen, G. William Walster
Klappentext
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.
