# xorbits.numpy.linalg.solve#

xorbits.numpy.linalg.solve(a, b, sym_pos=False, sparse=None)[source]#

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 – Solution to the system a x = b. Returned shape is identical to b.

Return type

{(…, M,), (…, M, K)} ndarray

Raises

LinAlgError – If a is singular or not square.

`scipy.linalg.solve`

Similar function in SciPy.

Notes

New in version 1.8.0(numpy.linalg).

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.

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
```
sym_posbool

Assume a is symmetric and positive definite. If `True`, use Cholesky decomposition.

sparse: bool, optional

Return sparse value or not.

This docstring was copied from numpy.linalg.