Efficiently multiplies two or more matrices by reordering the multiplications so that the fewest arithmetic operations are performed.

## Arguments

- tensors
(

`Sequence[Tensor]`

): two or more tensors to multiply. The first and last tensors may be 1D or 2D. Every other tensor must be 2D.

## Details

Supports inputs of `float`

, `double`

, `cfloat`

and `cdouble`

dtypes.
This function does not support batched inputs.

Every tensor in `tensors`

must be 2D, except for the first and last which
may be 1D. If the first tensor is a 1D vector of shape `(n,)`

it is treated as a row vector
of shape `(1, n)`

, similarly if the last tensor is a 1D vector of shape `(n,)`

it is treated
as a column vector of shape `(n, 1)`

.

If the first and last tensors are matrices, the output will be a matrix. However, if either is a 1D vector, then the output will be a 1D vector.

## Note

This function is implemented by chaining `torch_mm()`

calls after
computing the optimal matrix multiplication order.

The cost of multiplying two matrices with shapes `(a, b)`

and `(b, c)`

is
`a * b * c`

. Given matrices `A`

, `B`

, `C`

with shapes `(10, 100)`

,
`(100, 5)`

, `(5, 50)`

respectively, we can calculate the cost of different
multiplication orders as follows:

$\begin{align*} \operatorname{cost}((AB)C) &= 10 \times 100 \times 5 + 10 \times 5 \times 50 = 7500 \ \operatorname{cost}(A(BC)) &= 10 \times 100 \times 50 + 100 \times 5 \times 50 = 75000 \end{align*}$

In this case, multiplying `A`

and `B`

first followed by `C`

is 10 times faster.

## See also

Other linalg:
`linalg_cholesky_ex()`

,
`linalg_cholesky()`

,
`linalg_det()`

,
`linalg_eigh()`

,
`linalg_eigvalsh()`

,
`linalg_eigvals()`

,
`linalg_eig()`

,
`linalg_householder_product()`

,
`linalg_inv_ex()`

,
`linalg_inv()`

,
`linalg_lstsq()`

,
`linalg_matrix_norm()`

,
`linalg_matrix_power()`

,
`linalg_matrix_rank()`

,
`linalg_norm()`

,
`linalg_pinv()`

,
`linalg_qr()`

,
`linalg_slogdet()`

,
`linalg_solve()`

,
`linalg_svdvals()`

,
`linalg_svd()`

,
`linalg_tensorinv()`

,
`linalg_tensorsolve()`

,
`linalg_vector_norm()`

## Examples

```
if (torch_is_installed()) {
linalg_multi_dot(list(torch_tensor(c(1, 2)), torch_tensor(c(2, 3))))
}
#> torch_tensor
#> 8
#> [ CPUFloatType{} ]
```