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U SUR                  S   4 3-   5      eU(       d  SU-  n[        XU5      u  pgnUS:X  a  [        S5      eXg4$ )a  
Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``.

This problem, often called as NonNegative Least Squares, is a convex
optimization problem with convex constraints. It typically arises when
the ``x`` models quantities for which only nonnegative values are
attainable; weight of ingredients, component costs and so on.

Parameters
----------
A : (m, n) ndarray
    Coefficient array
b : (m,) ndarray, float
    Right-hand side vector.
maxiter: int, optional
    Maximum number of iterations, optional. Default value is ``3 * n``.
atol : float, optional
    .. deprecated:: 1.18.0
        This parameter is deprecated and will be removed in SciPy 1.18.0.
        It is not used in the implementation.

Returns
-------
x : ndarray
    Solution vector.
rnorm : float
    The 2-norm of the residual, ``|| Ax-b ||_2``.

See Also
--------
lsq_linear : Linear least squares with bounds on the variables

Notes
-----
The code is based on the classical algorithm of [1]_. It utilizes an active
set method and solves the KKK (Karush-Kuhn-Tucker) conditions for the
non-negative least squares problem.

References
----------
.. [1] : Lawson C., Hanson R.J., "Solving Least Squares Problems", SIAM,
   1995, :doi:`10.1137/1.9781611971217`

 Examples
--------
>>> import numpy as np
>>> from scipy.optimize import nnls
...
>>> A = np.array([[1, 0], [1, 0], [0, 1]])
>>> b = np.array([2, 1, 1])
>>> nnls(A, b)
(array([1.5, 1. ]), 0.7071067811865475)

>>> b = np.array([-1, -1, -1])
>>> nnls(A, b)
(array([0., 0.]), 1.7320508075688772)

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npasarray_chkfinitefloat64lenshape
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