| nnls {limSolve} | R Documentation |
Solves the following inverse problem:
\min(||Ax-b||^2)
subject to
x>=0
Uses subroutine nnls (FORTRAN) from Linpack
nnls(A, B, tol = sqrt(.Machine$double.eps), verbose = TRUE)
A |
numeric matrix containing the coefficients of the equality
constraints Ax~=B; if the columns of |
B |
numeric vector containing the right-hand side of the equality constraints. |
tol |
tolerance (for singular value decomposition and for the "equality" constraints). |
verbose |
logical to print |
a list containing:
X |
vector containing the solution of the nonnegative least squares problem. |
residualNorm |
scalar, the sum of absolute values of residuals of violated inequalities (i.e. sumof x[<0]); should be zero or very small if the problem is feasible. |
solutionNorm |
scalar, the value of the quadratic function at the solution, i.e. the value of \min(||Ax-b||^2). |
IsError |
logical, |
type |
the string "nnls", such that how the solution was obtained can be traced. |
Karline Soetaert <karline.soetaert@nioz.nl>
Lawson C.L.and Hanson R.J. 1974. Solving Least Squares Problems, Prentice-Hall
Lawson C.L.and Hanson R.J. 1995. Solving Least Squares Problems. SIAM classics in applied mathematics, Philadelphia. (reprint of book)
ldei, which includes equalities
A <- matrix(nrow = 2, ncol = 3, data = c(3, 2, 2, 4, 2, 1)) B <- c(-4, 3) nnls(A, B)