無約束最優化與非線性方程的數值方法（簡體書）

This book is a standard for a complete description of the methods for unconstrained optimization and the solution ofnonlinear equations....this republication is most welcome and this volume should be in every library. Of course, there exist more recent books on the topics and somebody interested in the subject cannot be satiated by looking only at this book. However, it contains much quite-well-presented material and I recommend reading it before going ,to other.publications.

PREFACE TO THE CLASSICS EDITION
PREFACE
1 INTRODUCTION
1.1 Problems to be considered
1.2 Characteristics ofreal-world problems
1.3 Finite-precision arithmetic and measurement of error
　1.4 Exercises
2 NONLINEAR PROBLEMS IN ONE VARIABLE
　2.1 What is not possible
　2.2 Newtons method for solving one equation in one unknown
　2.3 Convergence of sequences of real numbers
　2.4 Convergence of Newtons method
　2.5 Globally convergent methods for solving one equation in one unknown
　2.6 Methods when derivatives are unavailable
　2.7 Minimization of a function of one variable
　2.8 Exercises
3 NUMERICAL LINEAR ALGEBRA BACKGROUND
　3.1 Vector and matrix norms and orthogonality
　3.2 Solving systems of linear equations--matrix factorizations
　3.3 Errors in solving linear systems
　3.4 Updating matrix factorizations
　3.5 Eigenvalues and positive definiteness
　3.6 Linear least squares
　3.7 Exercises
4 MULTIVARIABLE CALCULUS BACKGROUND
　4.1 Derivatives and multivariable models
　4.2 Multivariable finite-difference derivatives
　4.3 Necessary and sufficient conditions for unconstrained minimization
　4.4 Exercises 83
5 NEWTONS METHOD FOR NONLINEAR EQUATIONS AND UNCONSTRAINED MINIMIZATION
　5.1 Newtons method for systems of nonlinear equations
　5.2 Local convergence of Newtons method
　5.3 The Kantorovich and contractive mapping theorems
　5.4 Finite-difference derivative methods for systems of nonlinear equations
　5.5 Newtons method for unconstrained minimization
　5.6 Finite-difference derivative methods for unconstrained minimization
　5.7 Exercises
6 GLOBALLY CONVERGENT MODIFICATIONS OF NEWTONS METHOD
　6.1 The quasi-Newton framework
　6.2 Descent directions
　6.3 Line searches
6.3.1 Convergence results for properly chosen steps
6.3.2 Step selection by backtracking
　6.4 The model-trust region approach
6.4.1 The locally constrained optimal (hook) step
6.4.2 The double dogleg step
6.4.3 Updating the trust region
　6.5 Global methods for systems of nonlinear equations
　6.6 Exercises
7 STOPPING, SCALING, AND TESTING
　7.1 Scaling
　7.2 Stopping criteria
　7.3 Testing
　7.4 Exercises
8 SECANT METHODS FOR SYSTEMS OF NONLINEAR EQUATIONS
　8.1 Broydens method
　8.2 Local convergence analysis of Broydens method
　8.3 Implementation of quasi-Newton algorithms using Broydens update
　8.4 Other secant updates for nonlinear equations
　8.5 Exercises
9 SECANT METHODS FOR UNCONSTRAINED MINIMIZATION
　9.1 The symmetric secant update of Powell
　9.2 Symmetric positive definite secant updates
　9.3 Local convergence of positive definite secant methods
　9.4 Implementation of quasi-Newton algorithms using the positive definite secant update
　9.5 Another convergence result for the positive definite secant method
　9.6 Other secant updates for unconstrained minimization
　9.7 Exercises
10 NONLINEAR LEAST SQUARES
　10.1 The nonlinear least-squares problem
　10.2 Gauss-Newton-type methods
　10.3 Full Newton-type methods
　10.4 Other considerations in solving nonlinear least-squares problems
　10.5 Exercises
11 METHODS FOR PROBLEMS WITH SPECIAL STRUCTURE
　11.1 The sparse finite-difference Newton method
　11.2 Sparse secant methods
　11.3 Deriving least-change secant updates
　11.4 Analyzing least-change secant methods
　11.5 Exercises
A APPENDIX: A MODULAR SYSTEM OF ALGORITHMS FOR UNCONSTRAINED MINIMIZATION AND NONLINEAR EQUATIONS (by Robert Schnabel)
B APPENDIX: TEST PROBLEMS (by Robert SchnabeI)
REFERENCES
AUTHOR INDEX
SUBJECT INDEX