Package Exports

ml-direct
ml-direct/lib/index.js
ml-direct/src/index.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (ml-direct) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

ml-direct

Direct - DIviding RECTangles algorithm.

The algorithm is intended to minimize real valued multivariate scalar fields over a hyper-rectangular region of N, theoretically the only prerequisite to achieve convergence is that the function must be continuous in the domain or at least continuous over a neighborhood of the global minimum.

Advanced example

import direct from 'ml-direct';

const options = {
  iterations: 50,
};

const lowerBoundaries = [-1, -1.5];
const upperBoundaries = [2, 6];

const predicted = direct(griewank, lowerBoundaries, upperBoundaries, options);

function griewank(x) {
  let d = x.length;
  let s = 0;
  let p = 1;
  for (let i = 0; i < d; i++) {
    s += Math.pow(x[i], 2) / Math.sqrt(4000);
    p *= Math.cos(x[i] / Math.sqrt(i + 1));
  }
  let result = s - p + 1;
  return result;
}

// predicted.minFunctionValue = 0;
// predicted.optima[0] = [0, 0]; This are the points where the function has minimum value

A tool for global optimization of real valued functions .

Installation

$ npm i ml-direct

Usage

import direct from 'ml-direct';

const options = {
  iterations: 25,
};

// for x we explore values between -5 and 4
// for y we explore values between -2 and 3

const lowerBoundaries = [-5, -2];
const upperBoundaries = [4, 3];

const quadratic = function (parameters) {
  let [x, y] = parameters;
  return Math.pow(x, 2) + Math.pow(y, 2);
};

const predicted = direct(quadratic, lowerBoundaries, upperBoundaries, options);

// predicted.minFunctionValue = 0;
// predicted.optima[0] = [0, 0];

API Documentation

References

Jones, D. R., Perttunen, C. D., & Stuckman, B. E. (1993). Lipschitzian optimization without the Lipschitz constant. Journal of optimization Theory and Applications, 79(1), 157-181.
Björkman, M., & Holmström, K. (1999). Global optimization using the DIRECT algorithm in Matlab.
Preparata, F. P., & Shamos, M. I. (2012). Computational geometry: an introduction. Springer Science & Business Media.

License

MIT