# jax.numpy.dot#

jax.numpy.dot(a, b, *, precision=None, preferred_element_type=None)[source]#

Dot product of two arrays. Specifically,

LAX-backend implementation of `numpy.dot()`.

In addition to the original NumPy arguments listed below, also supports `precision` for extra control over matrix-multiplication precision on supported devices. `precision` may be set to `None`, which means default precision for the backend, a `Precision` enum value (`Precision.DEFAULT`, `Precision.HIGH` or `Precision.HIGHEST`) or a tuple of two `Precision` enums indicating separate precision for each argument.

Original docstring below.

• If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).

• If both a and b are 2-D arrays, it is matrix multiplication, but using `matmul()` or `a @ b` is preferred.

• If either a or b is 0-D (scalar), it is equivalent to `multiply()` and using `numpy.multiply(a, b)` or `a * b` is preferred.

• If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.

• If a is an N-D array and b is an M-D array (where `M>=2`), it is a sum product over the last axis of a and the second-to-last axis of b:

```dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
```

It uses an optimized BLAS library when possible (see numpy.linalg).

Parameters:
• a (array_like) – First argument.

• b (array_like) – Second argument.

• preferred_element_type (dtype, optional) – If specified, accumulate results and return a result of the given data type. If not specified, the accumulation dtype is determined from the type promotion rules of the input array dtypes.

• precision (PrecisionLike) –

Returns:

output – Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. If out is given, then it is returned.

Return type:

ndarray