Perform a conjugate multiplication of two batched vectors.
JAX implementation of numpy.vecdot().
a – left-hand side array.
b – right-hand side array. Size of b[axis] must match size of a[axis], and remaining dimensions must be broadcast-compatible.
axis (int) – axis along which to compute the dot product (default: -1)
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.
x1 (Array | ndarray | bool | number | bool | int | float | complex)
x2 (Array | ndarray | bool | number | bool | int | float | complex)
array containing the conjugate dot product of a and b along axis. The non-contracted dimensions are broadcast together.
Examples
Vector conjugate-dot product of two 1D arrays:
>>> a = jnp.array([1j, 2j, 3j]) >>> b = jnp.array([4., 5., 6.]) >>> jnp.linalg.vecdot(a, b) Array(0.-32.j, dtype=complex64)
Batched vector dot product of two 2D arrays:
>>> a = jnp.array([[1, 2, 3], ... [4, 5, 6]]) >>> b = jnp.array([[2, 3, 4]]) >>> jnp.linalg.vecdot(a, b, axis=-1) Array([20, 47], dtype=int32)
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