# numpy.linalg.matmul#

linalg.matmul(x1, x2, /)[source]#

Computes the matrix product.

This function is Array API compatible, contrary to `numpy.matmul`.

Parameters:
x1array_like

The first input array.

x2array_like

The second input array.

Returns:
outndarray

The matrix product of the inputs. This is a scalar only when both `x1`, `x2` are 1-d vectors.

Raises:
ValueError

If the last dimension of `x1` is not the same size as the second-to-last dimension of `x2`.

If a scalar value is passed in.

Examples

For 2-D arrays it is the matrix product:

```>>> a = np.array([[1, 0],
...               [0, 1]])
>>> b = np.array([[4, 1],
...               [2, 2]])
>>> np.linalg.matmul(a, b)
array([[4, 1],
[2, 2]])
```

For 2-D mixed with 1-D, the result is the usual.

```>>> a = np.array([[1, 0],
...               [0, 1]])
>>> b = np.array([1, 2])
>>> np.linalg.matmul(a, b)
array([1, 2])
>>> np.linalg.matmul(b, a)
array([1, 2])
```

Broadcasting is conventional for stacks of arrays

```>>> a = np.arange(2 * 2 * 4).reshape((2, 2, 4))
>>> b = np.arange(2 * 2 * 4).reshape((2, 4, 2))
>>> np.linalg.matmul(a,b).shape
(2, 2, 2)
>>> np.linalg.matmul(a, b)[0, 1, 1]
98
>>> sum(a[0, 1, :] * b[0 , :, 1])
98
```

Vector, vector returns the scalar inner product, but neither argument is complex-conjugated:

```>>> np.linalg.matmul([2j, 3j], [2j, 3j])
(-13+0j)
```

Scalar multiplication raises an error.

```>>> np.linalg.matmul([1,2], 3)
Traceback (most recent call last):
...
ValueError: matmul: Input operand 1 does not have enough dimensions ...
```