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# torch.addmv

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×m)(n \times m)(n×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.

  out=β input+α (mat@vec)\text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) out=β input+α (mat@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 matrix multiplied

    - vec (Tensor) – vector to be matrix multiplied

  Keyword Arguments:

    - beta (Number, optional) – multiplier for `input` (β\beta β)

    - alpha (Number, optional) – multiplier for m a t@v e c mat @ vec ma t@v ec (α\alpha α)

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

  Example:

      >>> M = torch.randn(2)
      >>> mat = torch.randn(2, 3)
      >>> vec = torch.randn(3)
      >>> torch.addmv(M, mat, vec)
      tensor([-0.3768, -5.5565])

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