
An Algorithmic Approach to Nonlinear Analysis and Optimization
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Content
- Front Cover
- An Algorithmic Approach to Nonlinear Analysis and Optimization
- Copyright Page
- CONTENTS
- Preface
- Acknowledgments
- Chapter 1. Iterative Methods on Normed Linear Spaces
- 1.1 Introduction
- 1.2 Contraction Mappings
- 1.3 Differentiation on Normed Spaces
- 1.4 Gradient Techniques
- 1.5 Least Squares Approximation
- 1.6 Two-Point Boundary Value Problems
- 1.7 Further Remarks on Stability
- Chapter 2. Constrained Optimization on En
- 2.1 Introduction
- 2.2 Semicontinuity and Convexity
- 2.3 A Penalty Argument
- 2.4 The Kuhn-Tucker and Lagrange Multiplier Rules
- 2.5 The Regularity Assumption
- 2.6 Bibliographic Notes and Other Remarks
- Chapter 3. Computational Techniques for Constrained Optimization on En
- 3.1 Introduction
- 3.2 Implementation of the Penalty Argument
- 3.3 A Conjugate Direction Method
- 3.4 Gradient Projection
- 3.5 Numerical Examples and One-Dimensional Minimization
- 3.6 Bibliographic Notes and Remarks
- Chapter 4. Constrained Optimization in Function Space
- 4.1 Introduction
- 4.2 Problems of Optimal Control
- 4.3 The Neyman-Pearson Multiplier Rule
- Chapter 5. Weak Convergence in Hilbert Space
- 5.1 Introduction
- 5.2 Weak Convergence
- 5.3 Fixed Points of Nonexpansive Maps
- 5.4 The Penalty Argument in Hilbert Space
- 5.5 An Existence Question in Optimization
- Appendix: Computer Program for the Solution of Two-Point Boundary Value Problems
- Bibliography
- AUTHOR INDEX
- SUBJECT INDEX
- Mathematics in Science and Engineering
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