fast-nnls

This is a Python implementation of the algorithm described in the paper "A Fast Non-Negativity-Constrained Least Squares Algorithm" by Rasmus Bro and Sumen De Jong.

Give a matrix A and a vector y, this algorithm solves argmin_x ||Ax - y||.

At the time of writing this, there are no readily available Python bindings for this algorithm that I know of. scipy.optimize.nnls implements the slower Lawson-Hanson version.

Installation

From the top level fast-nnls directory, run

python3 setup.py install

Usage

from fastnnls import fnnls
import numpy as np

A = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([1, 2, 3])
AtA = A.T.dot(A)
Aty = A.T.dot(y)
x = fnnls(AtA, Aty)

Testing

Install fastnnls then run python3 tests/test.py.