# direct-optimization

**Repository Path**: mirrors_mljs/direct-optimization

## Basic Information

- **Project Name**: direct-optimization
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-22
- **Last Updated**: 2026-02-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ml-direct

[![NPM version][npm-image]][npm-url]
[![build status][ci-image]][ci-url]
[![npm download][download-image]][download-url]

# 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

```js
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
```

<p align="center">
  <img src="image/griewandContourplotDirect.png">
</p>

<p align="center">
  A tool for global optimization of real valued functions .
</p>

## Installation

`$ npm i ml-direct`

## Usage

```js
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) {
  const [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](https://mljs.github.io/direct/)

## 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. https://doi.org/10.1007/BF00941892

- 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](./LICENSE)

[npm-image]: https://img.shields.io/npm/v/ml-direct.svg
[npm-url]: https://www.npmjs.com/package/ml-direct
[ci-image]: https://github.com/mljs/direct/workflows/Node.js%20CI/badge.svg?branch=main
[ci-url]: https://github.com/mljs/direct/actions?query=workflow%3A%22Node.js+CI%22
[download-image]: https://img.shields.io/npm/dm/ml-direct.svg
[download-url]: https://www.npmjs.com/package/ml-direct