scipy.optimize.nnls¶
-
scipy.optimize.
nnls
(A, b, maxiter=None)[source]¶ Solve
argmin_x || Ax - b ||_2
forx>=0
. This is a wrapper for a FORTRAN non-negative least squares solver.- Parameters
- Andarray
Matrix
A
as shown above.- bndarray
Right-hand side vector.
- maxiter: int, optional
Maximum number of iterations, optional. Default is
3 * A.shape[1]
.
- Returns
- xndarray
Solution vector.
- rnormfloat
The residual,
|| Ax-b ||_2
.
Notes
The FORTRAN code was published in the book below. The algorithm is an active set method. It solves the KKT (Karush-Kuhn-Tucker) conditions for the non-negative least squares problem.
References
Lawson C., Hanson R.J., (1987) Solving Least Squares Problems, SIAM