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- from __future__ import division, print_function, absolute_import
- from . import _nnls
- from numpy import asarray_chkfinite, zeros, double
- __all__ = ['nnls']
- def nnls(A, b, maxiter=None):
- """
- Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. This is a wrapper
- for a FORTRAN non-negative least squares solver.
- Parameters
- ----------
- A : ndarray
- Matrix ``A`` as shown above.
- b : ndarray
- Right-hand side vector.
- maxiter: int, optional
- Maximum number of iterations, optional.
- Default is ``3 * A.shape[1]``.
- Returns
- -------
- x : ndarray
- Solution vector.
- rnorm : float
- 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
- """
- A, b = map(asarray_chkfinite, (A, b))
- if len(A.shape) != 2:
- raise ValueError("expected matrix")
- if len(b.shape) != 1:
- raise ValueError("expected vector")
- m, n = A.shape
- if m != b.shape[0]:
- raise ValueError("incompatible dimensions")
- maxiter = -1 if maxiter is None else int(maxiter)
- w = zeros((n,), dtype=double)
- zz = zeros((m,), dtype=double)
- index = zeros((n,), dtype=int)
- x, rnorm, mode = _nnls.nnls(A, m, n, b, w, zz, index, maxiter)
- if mode != 1:
- raise RuntimeError("too many iterations")
- return x, rnorm
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