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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 Practical Newton Methods
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    Chapter 7 Calculating Derivatives
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    Chapter 8 Quasi-Newton Methods
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    Chapter 9 Large-Scale Quasi-Newton and Partially Separable Optimization
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    Chapter 10 Nonlinear 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, Barrier, and Augmented Lagrangian Methods
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    Chapter 18 Sequential Quadratic Programming
Attention for Chapter 9: Large-Scale Quasi-Newton and Partially Separable Optimization
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