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Numerical Optimization

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Cover of 'Numerical Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Fundamentals of Unconstrained Optimization
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    Chapter 3 Line Search Methods
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    Chapter 4 Trust-Region Methods
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    Chapter 5 Conjugate Gradient Methods
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    Chapter 6 Quasi-Newton Methods
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    Chapter 7 Large-Scale Unconstrained Optimization
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    Chapter 8 Calculating Derivatives
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    Chapter 9 Derivative-Free Optimization
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    Chapter 10 Least-Squares Problems
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    Chapter 11 Nonlinear Equations
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    Chapter 12 Theory of Constrained Optimization
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    Chapter 13 Linear Programming: The Simplex Method
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    Chapter 14 Linear Programming: Interior-Point Methods
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    Chapter 15 Fundamentals of Algorithms for Nonlinear Constrained Optimization
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    Chapter 16 Quadratic Programming
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    Chapter 17 Penalty and Augmented Lagrangian Methods
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    Chapter 18 Sequential Quadratic Programming
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    Chapter 19 Interior-Point Methods for Nonlinear Programming
Attention for Chapter 17: Penalty and Augmented Lagrangian Methods
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