0

I can solve a system equation (using NumPY) like this:

>>> a = np.array([[3,1], [1,2]])
>>> b = np.array([9,8])
>>> y = np.linalg.solve(a, b)
>>> y
array([ 2.,  3.])

But, if I got something like this:

>>> x = np.linspace(1,10)
>>> a = np.array([[3*x,1-x], [1/x,2]])
>>> b = np.array([x**2,8*x])
>>> y = np.linalg.solve(a, b)

It doesnt work, where the matrix's coefficients are arrays and I want calculate the array solution "y" for each element of the array "x". Also, I cant calculate

>>> det(a)

The question is: How can do that?

nandhos
  • 681
  • 2
  • 16
  • 31

1 Answers1

1

Check out the docs page. If you want to solve multiple systems of linear equations you can send in multiple arrays but they have to have shape (N,M,M). That will be considered a stack of N MxM arrays. A quote from the docs page below,

Several of the linear algebra routines listed above are able to compute results for several matrices at once, if they are stacked into the same array. This is indicated in the documentation via input parameter specifications such as a : (..., M, M) array_like. This means that if for instance given an input array a.shape == (N, M, M), it is interpreted as a “stack” of N matrices, each of size M-by-M. Similar specification applies to return values, for instance the determinant has det : (...) and will in this case return an array of shape det(a).shape == (N,). This generalizes to linear algebra operations on higher-dimensional arrays: the last 1 or 2 dimensions of a multidimensional array are interpreted as vectors or matrices, as appropriate for each operation.

When I run your code I get, 

>>> a.shape
(2, 2)

>>> b.shape
(2, 50)

Not sure exactly what problem you're trying to solve, but you need to rethink your inputs. You want a to have shape (N,M,M) and b to have shape (N,M). You will then get back an array of shape (N,M) (i.e. N solution vectors).

Gabriel
  • 10,524
  • 1
  • 23
  • 28