https://github.com/benfred/fmin

Unconstrained function minimization in Javascript
https://github.com/benfred/fmin

numerical-optimization optimization-algorithms visualization

Last synced: 13 days ago
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Unconstrained function minimization in Javascript

• Host: GitHub
• URL: https://github.com/benfred/fmin
• Owner: benfred
• License: bsd-3-clause
• Created: 2016-11-24T23:39:25.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-06-03T23:55:38.000Z (over 6 years ago)
• Last Synced: 2024-10-14T18:40:21.635Z (about 1 month ago)
• Topics: numerical-optimization, optimization-algorithms, visualization
• Language: JavaScript
• Size: 1.32 MB
• Stars: 354
• Watchers: 20
• Forks: 61
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# fmin [![Build Status](https://travis-ci.org/benfred/fmin.svg?branch=master)](https://travis-ci.org/benfred/fmin)

Unconstrained function minimization in javascript.

This package implements some basic numerical optimization algorithms: Nelder-Mead, Gradient

Descent, Wolf Line Search and Non-Linear Conjugate Gradient methods are all provided.

Interactive visualizations with D3 explaining how these algorithms work are also included in this package.

Descriptions of the algorithms as well as most of the visualizations are available on my blog post

[An Interactive Tutorial on Numerical

Optimization](http://www.benfrederickson.com/numerical-optimization/).

## Installing

If you use NPM, `npm install fmin`. Otherwise, download the [latest release](https://github.com/benfred/fmin/releases/latest).

## API Reference

# nelderMead(f, initial)

Uses the [Nelder-Mead method](https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method) to

minimize a function f starting at location initial.

Example usage  minimizing the function f(x, y) = x2 + y2 + x sin y + y

sin x is:

![nelder mead demo](./images/nelder_mead.gif)

```js

function loss(X) {

    var x = X[0], y = X[1];

    return Math.sin(y) * x  + Math.sin(x) * y  +  x * x +  y *y;

}

var solution = fmin.nelderMead(loss, [-3.5, 3.5]);

console.log("solution is at " + solution.x);

```

# conjugateGradient(f, initial)

Minimizes a function using the [Polak–Ribière non-linear conjugate gradient method

](https://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method). The function f should

compute both the loss and the gradient.

An example minimizing [Rosenbrock's Banana

function](https://en.wikipedia.org/wiki/Rosenbrock_function) is:

![conjugate gradient demo](./images/conjugate_gradient.gif)

```js

function banana(X, fxprime) {

    fxprime = fxprime || [0, 0];

    var x = X[0], y = X[1];

    fxprime[0] = 400 * x * x * x - 400 * y * x + 2 * x - 2;

    fxprime[1] = 200 * y - 200 * x * x;

    return (1 - x) * (1 - x) + 100 * (y - x * x) * (y - x * x);

}

var solution = fmin.conjugateGradient(banana, [-1, 1]);

console.log("solution is at " + solution.x);

```