# `tensorly`.solve

solve(a, b)

Solve a linear matrix equation, or system of linear scalar equations.

Computes the “exact” solution, x, of the well-determined, i.e., full rank, linear matrix equation ax = b.

Parameters:
a(…, M, M) array_like

Coefficient matrix.

b{(M,), (…, M, K)}, array_like

Ordinate or “dependent variable” values.

Returns:
x{(…, M,), (…, M, K)} ndarray

Solution to the system a x = b. Returned shape is (…, M) if b is shape (M,) and (…, M, K) if b is (…, M, K), where the “…” part is broadcasted between a and b.

Raises:
LinAlgError

If a is singular or not square.

`scipy.linalg.solve`

Similar function in SciPy.

Notes

Broadcasting rules apply, see the numpy.linalg documentation for details.

The solutions are computed using LAPACK routine `_gesv`.

a must be square and of full-rank, i.e., all rows (or, equivalently, columns) must be linearly independent; if either is not true, use lstsq for the least-squares best “solution” of the system/equation.

Changed in version 2.0: The b array is only treated as a shape (M,) column vector if it is exactly 1-dimensional. In all other instances it is treated as a stack of (M, K) matrices. Previously b would be treated as a stack of (M,) vectors if b.ndim was equal to a.ndim - 1.

References

[1]

G. Strang, Linear Algebra and Its Applications, 2nd Ed., Orlando, FL, Academic Press, Inc., 1980, pg. 22.

Examples

Solve the system of equations: `x0 + 2 * x1 = 1` and `3 * x0 + 5 * x1 = 2`:

```>>> a = np.array([[1, 2], [3, 5]])
>>> b = np.array([1, 2])
>>> x = np.linalg.solve(a, b)
>>> x
array([-1.,  1.])
```

Check that the solution is correct:

```>>> np.allclose(np.dot(a, x), b)
True
```