jax.numpy.vdot

jax.numpy.vdot(a, b, *, precision=None, preferred_element_type=None)

Perform a conjugate multiplication of two 1D vectors.

JAX implementation of numpy.vdot().

Parameters:

• a (Array | ndarray | bool | number | bool | int | float | complex) – first input array, if not 1D it will be flattened.

• b (Array | ndarray | bool | number | bool | int | float | complex) – second input array, if not 1D it will be flattened. Must have a.size == b.size.

• precision (None | str | Precision | tuple[str, str] | tuple[Precision, Precision] | DotAlgorithm | DotAlgorithmPreset) – either None (default), which means the default precision for the backend, a Precision enum value (Precision.DEFAULT, Precision.HIGH or Precision.HIGHEST) or a tuple of two such values indicating precision of a and b.

• preferred_element_type (str | type[Any] | dtype | SupportsDType | None) – either None (default), which means the default accumulation type for the input types, or a datatype, indicating to accumulate results to and return a result with that datatype.

Returns:

Scalar array (shape ()) containing the conjugate vector product of the inputs.

Return type:

Array

See also

• jax.numpy.vecdot(): batched vector product.

• jax.numpy.matmul(): general matrix multiplication.

• jax.lax.dot_general(): general N-dimensional batched dot product.

Examples

>>> x = jnp.array([1j, 2j, 3j])
>>> y = jnp.array([1., 2., 3.])
>>> jnp.vdot(x, y)
Array(0.-14.j, dtype=complex64)

Note the difference between this and dot(), which does not conjugate the first input when complex:

>>> jnp.dot(x, y)
Array(0.+14.j, dtype=complex64)