Modern Numerical Nonlinear Optimization
Neculai Andrei ()
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Neculai Andrei: Center for Advanced Modeling and Optimization
in Springer Optimization and Its Applications from Springer, currently edited by Pardalos, Panos, Thai, My T. and Du, Ding-Zhu
Date: 2022
ISBN: 978-3-031-08720-2
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Chapters in this book:
- Ch 1 Introduction
- Neculai Andrei
- Ch 2 Fundamentals on Unconstrained Optimization. Stepsize Computation
- Neculai Andrei
- Ch 3 Steepest Descent Methods
- Neculai Andrei
- Ch 4 The Newton Method
- Neculai Andrei
- Ch 5 Conjugate Gradient Methods
- Neculai Andrei
- Ch 6 Quasi-Newton Methods
- Neculai Andrei
- Ch 7 Inexact Newton Methods
- Neculai Andrei
- Ch 8 The Trust-Region Method
- Neculai Andrei
- Ch 9 Direct Methods for Unconstrained Optimization
- Neculai Andrei
- Ch 10 Constrained Nonlinear Optimization Methods: An Overview
- Neculai Andrei
- Ch 11 Optimality Conditions for Nonlinear Optimization
- Neculai Andrei
- Ch 12 Simple Bound Constrained Optimization
- Neculai Andrei
- Ch 13 Quadratic Programming
- Neculai Andrei
- Ch 14 Penalty and Augmented Lagrangian Methods
- Neculai Andrei
- Ch 15 Sequential Quadratic Programming
- Neculai Andrei
- Ch 16 Primal Methods: The Generalized Reduced Gradient with Sequential Linearization
- Neculai Andrei
- Ch 17 Interior-Point Methods
- Neculai Andrei
- Ch 18 Filter Methods
- Neculai Andrei
- Ch 19 Interior-Point Filter Line-Search
- Neculai Andrei
- Ch 20 Direct Methods for Constrained Optimization
- Neculai Andrei
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spopap:978-3-031-08720-2
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DOI: 10.1007/978-3-031-08720-2
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