scipy/numpy equivalent of c++ eigen3 example

Question

In scipy there is an ldl decomposition and a solve method, But I am not able to get the same result in python as I get using eigen3 for a very simple piece of code. Here is the C++ code:

#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
   Matrix2f A, b;
   A << 2, -1, -1, 3;
   b << 1, 2, 3, 1;
   cout << "Here is the matrix A:\n" << A << endl;
   cout << "Here is the right hand side b:\n" << b << endl;
   Matrix2f x = A.ldlt().solve(b);
   cout << "The solution is:\n" << x << endl;
}

The output of the program is:

Here is the matrix A:
 2 -1
-1  3
Here is the right hand side b:
1 2
3 1
The solution is:
1.2 1.4
1.4 0.8

How can that be written using numpy/scipy to get the same result?

I tried some of the following things:

import scipy.linalg

A = np.array([[2,-1],[-1,3]])
b = np.array([[1,2],[3,1]])
a = scipy.linalg.ldl(A)
print(scipy.linalg.solve(a[0],b))

I tried transposing and doing various combinations of members of a and b. I get a result but its just not the same as in the C++ program, so what am I doing wrong here?

Thanks

Answer 1

Ahhh I got confused by all wording. Its just solving Ax=b. So scipy.linalg.solve(A,b) does the trick.