From Wakapon

This page is a rough summary of the various methods for optimization, curve fitting/linear regression, etc.

First, some definitions:

- In statistics,
**linear regression**is basically a way to make a curve fit a set of data points

## Contents

## Optimization

- Convex optimization

### One-Dimensional Search Methods

- Golden Section Search 91
- Fibonacci Search 95
- Newton's Method 103
- Secant Method

### Unconstrained Optimization and Neural Networks

- Descent methods
- Line search
- Descent methods with trust region
- Steepest descent
- Quadratic models
- Conjugate gradient methods
- Single-Neuron Training
- Backpropagation Algorithm

### Newton-Type Methods

- Newton’s method
- Damped Newton methods
- Quasi–Newton methods
- DFP formula
- BFGS formulas
- Quasi–Newton implementation

### Direct Search

- Simplex method
- Method of Hooke and Jeeves

### Linear Data Fitting

- “Best” fit
- Linear least squares
- Weighted least squares
- Generalized least squares
- Polynomial fit
- Spline fit
- Choice of knots

### Nonlinear Least Squares Problems

- Gauss–Newton method
- The Levenberg–Marquardt method
- Powell’s Dog Leg Method
- Secant version of the L–M method
- Secant version of the Dog Leg method

### Duality

- The Lagrange dual function
- The Lagrange dual problem