From Wakapon
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| + | http://www.seas.ucla.edu/~vandenbe/133A/ | ||
| + | https://en.wikipedia.org/wiki/Gaussian_quadrature | ||
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This page is a rough summary of the various methods for optimization, curve fitting/linear regression, etc. | This page is a rough summary of the various methods for optimization, curve fitting/linear regression, etc. | ||
First, some definitions: | First, some definitions: | ||
* In statistics, '''linear regression''' is basically a way to make a curve fit a set of data points | * In statistics, '''linear regression''' is basically a way to make a curve fit a set of data points | ||
| − | [[File:Linear_Regression.png | + | [[File:Linear_Regression.png]] |
* | * | ||
== Optimization == | == 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 | ||
Latest revision as of 11:01, 5 August 2017
http://www.seas.ucla.edu/~vandenbe/133A/ https://en.wikipedia.org/wiki/Gaussian_quadrature
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