How to solve the inverse square of a matrix without using numpy's solver

Asked Sep 01 '16 at 02:45

Active Sep 01 '16 at 03:09

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I have two matrices, A (a 4 x 4 matrix) and b (a 4 x 1) matrix, as shown:

A = ([[2, 3, 5, 2],
   [2, 4, 1, 1],
   [1, 7, 4, 2],
   [3, 8, 2, 3]])

b = ([[2], 
      [2],
      [1],
      [5]])

I am trying to solve for x in Ax = b. This means that x = A^-1 * b. I tried the built-in matrix solver from numpy but I want to know if there is another way to do so without using this. Perhaps a something with a more lines of code than numpy's solver?

 x = np.linalg.solve(A,b)

edited Sep 01 '16 at 03:09

benten

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asked Sep 01 '16 at 02:45

Caroline.py

1

Fewer than 1 line code is 0, do you want numpy knows what you want and solve it for you, by any chance? :-) – Mazdak Sep 01 '16 at 02:51
Sorry I meant few lines of code, meaning I am fine with more lines than the one line of code using numpy's solver – Caroline.py Sep 01 '16 at 02:55
In that case you have to implement the formula, manually. – Mazdak Sep 01 '16 at 03:04
Meaning I use inv(A) * b ? – Caroline.py Sep 01 '16 at 03:05
Yes, check out the duplicate. – Mazdak Sep 01 '16 at 03:11
Thanks, I did see that but they are only inversing one matrix and not solving for x. – Caroline.py Sep 01 '16 at 03:32

How to solve the inverse square of a matrix without using numpy's solver

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