This is a python wrapper for the bounded variable least squares (BVLS)
solver (the fortran source code has been distributed under the GNU LGPL
license and can be found here).
The algorithm finds the vector m such that G.dot(m) is the best
prediction to the observation vector d in a least squares sense
with the constraint that bounds[0] >= m and bounds[1] <= m.
Compiling the Fortran code and creating the python wrapper is done with the following shell commands
$ git clone http://www.github.com/treverhines/BVLS
$ cd BVLS
$ python setup.py install
This will install a python module named bvls which gives access to
all the subroutines in bvls.f90.
The following python code sets up and solves a bounded value least squares problem
import bvls
import numpy as np
G = np.random.random((10,2))
m = np.array([1.0,2.0])
d = G.dot(m)
lower_bounds = np.array([0.0,0.0])
upper_bounds = np.array([1.5,1.5])
bounds = [lower_bounds,upper_bounds]
soln = bvls.bvls(G,d,bounds)See the documentation in bvls.f90 for additional details