Left Matrix Division and Numpy Solve
Solution 1
From MathWorks documentation for left matrix division:
If A is an m-by-n matrix with m ~= n and B is a column vector with m components, or a matrix with several such columns, then X = A\B is the solution in the least squares sense to the under- or overdetermined system of equations AX = B. In other words, X minimizes norm(A*X - B), the length of the vector AX - B.
The equivalent in numpy is np.linalg.lstsq:
In [15]: B = np.array([[2],[4]])
In [16]: b = np.array([[4],[4]])
In [18]: x,resid,rank,s = np.linalg.lstsq(B,b)
In [19]: x
Out[19]: array([[ 1.2]])
Solution 2
Matlab will actually do a number of different operations when the \ operator is used, depending on the shape of the matrices involved (see here for more details). In you example, Matlab is returning a least squares solution, rather than solving the linear equation directly, as would happen with a square matrix. To get the same behaviour in numpy, do this:
import numpy as np
import numpy.linalg as lin
B = np.array([[2],[4]])
b = np.array([[4],[4]])
print np.linalg.lstsq(B,b)[0]
which should give you the same solution as Matlab.
Solution 3
You can form the left inverse:
import numpy as np
import numpy.linalg as lin
B = np.array([[2],[4]])
b = np.array([[4],[4]])
B_linv = lin.solve(B.T.dot(B), B.T)
c = B_linv.dot(b)
print('c\n', c)
Result:
c
[[ 1.2]]
Actually, we can simply run the solver once, without forming an inverse, like this:
c = lin.solve(B.T.dot(B), B.T.dot(b))
print('c\n', c)
Result:
c
[[ 1.2]]
.... as before
Why? Because:
We have:
Multiply through by B.T
, gives us:
Now, B.T.dot(B)
is square, full rank, does have an inverse. And therefore we can multiply through by the inverse of B.T.dot(B)
, or use a solver, as above, to get c
.
BBSysDyn
Updated on July 09, 2022Comments
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BBSysDyn almost 2 years
I am trying to convert code that contains the \ operator from Matlab (Octave) to Python. Sample code
B = [2;4] b = [4;4] B \ b
This works and produces 1.2 as an answer. Using this web page
http://mathesaurus.sourceforge.net/matlab-numpy.html
I translated that as:
import numpy as np import numpy.linalg as lin B = np.array([[2],[4]]) b = np.array([[4],[4]]) print lin.solve(B,b)
This gave me an error:
numpy.linalg.linalg.LinAlgError: Array must be square
How come Matlab \ works with non square matrix for B?
Any solutions for this?