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As described in the publication by Van Benthem and Keenan (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.
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 $\mathbf{K}\geq 0$.
import{fcnnlsVector}from'ml-fcnnls';constX=[[1,1,2],[10,11,-9],[-1,0,0],[-5,6,-7],];consty=[-1,11,0,1];constk=fcnnlsVector(X,y).K.to1DArray();/* k = [0.4610, 0.5611, 0] */
Multiple RHS, using Matrix instances as inputs.
import{fcnnls}from'ml-fcnnls';import{Matrix}from'ml-matrix';//npm i ml-matrix// Example with multiple RHSconstX=newMatrix([[1,1,2],[10,11,-9],[-1,0,0],[-5,6,-7],]);// Y can either be a Matrix or an array of arraysconstY=newMatrix([[-1,0,0,9],[11,-20,103,5],[0,0,0,0],[1,2,3,4],]);constK=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],])*/
Using the options
constK=fcnnls(X,Y,{info: true,// returns the error/iteration.maxIterations: 5,gradientTolerance: 0,});/* same result than 2*/
