# ml-fcnnls

> Fast Combinatorial Non-negative Least Squares

Latest version **3.1.0** (published 2026-06-23) · MIT license · 0 weekly downloads

## Install

```sh
npm install ml-fcnnls
pnpm add ml-fcnnls
yarn add ml-fcnnls
bun add ml-fcnnls
```

## Health

**Score 55/100 (C)** — status: active.

Positive: esm support; no vulnerabilities; recently updated.

Warnings: low downloads; no types.

## Facts

| | |
|---|---|
| Version | 3.1.0 |
| Published | 2026-06-23 |
| First published | 2019-08-16 |
| Weekly downloads | 0 |
| License | MIT |
| TypeScript types | none |
| Module format | ESM |
| Dependencies | 1 |
| Unpacked size | 76.3 KB |
| Known vulnerabilities | 0 |
| Install scripts | no |
| GitHub stars | 7 |
| Author | Jeremy Weymann |
| Maintainers | stropitek, targos, lpatiny, mljs-bot, cheminfo-bot |
| Keywords | non-negative, least-squares, nnls |

## Links

- npm: https://www.npmjs.com/package/ml-fcnnls
- Repository: https://github.com/mljs/fcnnls
- Homepage: https://github.com/mljs/fcnnls#readme
- Issues: https://github.com/mljs/fcnnls/issues
- npm.io page: https://npm.io/package/ml-fcnnls

## Dependencies (1)

- [ml-matrix](https://npm.io/package/ml-matrix.md) ^6.13.0

## Recent versions

- 3.1.0 (latest) — 2026-06-23
- 3.0.0 — 2023-10-12
- 2.1.0 — 2023-08-10
- 2.0.1 — 2023-08-07
- 2.0.0 — 2023-08-04
- 1.1.4 — 2023-07-27
- 1.1.2 — 2022-10-08
- 1.1.1 — 2021-05-17
- 1.1.0 — 2019-08-20
- 1.0.0 — 2019-08-16

## README

# fcnnls

[![NPM version][npm-image]][npm-url]
[![build status][ci-image]][ci-url]
[![Test coverage][codecov-image]][codecov-url]
[![npm download][download-image]][download-url]
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8189402.svg)](https://doi.org/10.5281/zenodo.8189402)

Fast Combinatorial Non-negative Least Squares.

As described in the publication by Van Benthem and Keenan ([10.1002/cem.889](http://doi.org/10.1002/cem.889)), which is in turn based on the active-set method algorithm previously published by Lawson and Hanson. The basic active-set method is implemented in the [nnls repository.](https://github.com/mljs/nnls)

Given the matrices $\mathbf{X}$ and $\mathbf{Y}$, the code finds the matrix $\mathbf{K}$ that minimises the squared Frobenius norm $$\mathrm{argmin}_K ||\mathbf{XK} -\mathbf{Y}||^2_F$$ subject to $\mathbf{K}\geq 0$.

https://en.wikipedia.org/wiki/Non-negative_least_squares

## Installation

```bash
npm i ml-fcnnls
```

## Usage Example

1. Single $y$, using arrays as inputs.

```js
import { fcnnlsVector } from 'ml-fcnnls';

const X = [
  [1, 1, 2],
  [10, 11, -9],
  [-1, 0, 0],
  [-5, 6, -7],
];
const y = [-1, 11, 0, 1];

const k = fcnnlsVector(X, y).K.to1DArray();
/* k = [0.4610, 0.5611, 0] */
```

2. Multiple RHS, using `Matrix` instances as inputs.

```js
import { fcnnls } from 'ml-fcnnls';
import { Matrix } from 'ml-matrix'; //npm i ml-matrix

// Example with multiple RHS

const X = new Matrix([
  [1, 1, 2],
  [10, 11, -9],
  [-1, 0, 0],
  [-5, 6, -7],
]);

// Y can either be a Matrix or an array of arrays
const Y = new Matrix([
  [-1, 0, 0, 9],
  [11, -20, 103, 5],
  [0, 0, 0, 0],
  [1, 2, 3, 4],
]);

const K = fcnnls(X, Y).K;
// `K.to2DArray()` converts the matrix to array.
/*
K = Matrix([
  [0.4610, 0, 4.9714, 0],
  [0.5611, 0, 4.7362, 2.2404],
  [0, 1.2388, 0, 1.9136],
])
*/
```

3. Using the options

```js
const K = fcnnls(X, Y, {
  info: true, // returns the error/iteration.
  maxIterations: 5,
  gradientTolerance: 0,
});
/* same result than 2*/
```

## [API Documentation](https://mljs.github.io/fcnnls/)

## License

[MIT](./LICENSE)

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

---
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