Numerical Optimization

Review of the book
This book is written for people who are interested in solving optimization problems. Due to the wide use and growth of optimization in basic sciences, engineering, economics and industry, students and researchers should have a good understanding of optimization algorithms. Knowing the capabilities and limitations of these algorithms leads to a better understanding of their effects on a variety of problems and opens the way for future research in improving and expanding optimization algorithms and software. Our goal in this book is to provide a comprehensive description of powerful and state-of-the-art techniques for solving continuous optimization problems. By providing useful ideas for each algorithm, we try to increase the reader’s intuition and follow the technical details.

Table of Contents
The contents of this book are as follows:

1. Introduction
2- Principles of unrestricted optimization
3- Linear search methods
4- confidence interval methods
5- Conjugate gradient methods
6- Newton’s methods
7- Calculate the derivative
8- Newton’s method
9- Newton’s quasi-method for large-scale problems
10- Nonlinear least square problems
11- Nonlinear equations
12- Theory of constrained optimization
13- Simplex method for linear programming
14- Midpoint method for linear programming
15- The principles of algorithms of limited nonlinear models
16- Second degree planning
17- Lagrange method and penalty function and barrier
18- consecutive second degree planning
19- Required background