torch.addmv(input, mat, vec, *, beta=1, alpha=1, out=None) → Tensor

Performs a matrix-vector product of the matrix mat and the vector vec. The vector input is added to the final result.

If mat is a $(n \times m)$ tensor, vec is a 1-D tensor of size m, then input must be broadcastable with a 1-D tensor of size n and out will be 1-D tensor of size n.

alpha and beta are scaling factors on matrix-vector product between mat and vec and the added tensor input respectively.

$\text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec})$

If beta is 0, then input will be ignored, and nan and inf in it will not be propagated.

For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers

Parameters
• input (Tensor) – vector to be added

• mat (Tensor) – matrix to be multiplied

• vec (Tensor) – vector to be multiplied

Keyword Arguments
• beta (Number, optional) – multiplier for input ($\beta$ )

• alpha (Number, optional) – multiplier for $mat @ vec$ ($\alpha$ )

• out (Tensor, optional) – the output tensor.

Example:

>>> M = torch.randn(2)
>>> mat = torch.randn(2, 3)
>>> vec = torch.randn(3)