`BatchedBLAS.batched_dot!`

— Method`batched_dot!(o, x, y)`

In-place batched vector-vector multiplication, equivalent to `o[k] = transpose(x[:,k]) * y[:,k]`

for all `k`

. All inputs can have eltypes of either AbstractFloats or Integers.

`BatchedBLAS.batched_gemv!`

— Method`batched_gemv!(tA, alpha, A, x, beta, y)`

In-place batched matrix-vector multiplication and addition, equivalent to `y[:,k] = alpha[k] * A[:,:,k] * x[:,k] + beta[k] * y[:,k]`

for all `k`

. `A`

can optionally be transposed with `tA`

as `N`

, `T`

, or `C`

. All other inputs can have eltypes of either AbstractFloats or Integers. `alpha`

and `beta`

can also be scalars.

`BatchedBLAS.batched_ger!`

— Method`batched_ger!(alpha, x, y, A)`

In-place rank-1 update of matrix `A`

with vectors `x`

and `y`

as `alpha[k] * x[:,k] * transpose(y[:,k]) + A[:,:,k]`

for all `k`

. All nputs can have eltypes of either AbstractFloats or Integers. `alpha`

can also be a scalar.

`BatchedBLAS.batched_spmv!`

— Method`batched_spmv!(ul, alpha, A, x, beta, y)`

In-place batched matrix-vector multiplication and addition, equivalent to `y[:,k] = alpha[k] * A[:,:,k] * x[:,k] + beta[k] * y[:,k]`

for all `k`

. `A`

must be packed symmetric, and `uplo`

specifies whether the upper ('U') or lower ('L') triangle was packed. All other inputs can have eltypes of either AbstractFloats or Integers. `alpha`

and `beta`

can also be scalars.

`BatchedBLAS.batched_spr!`

— Method`batched_spr!(uplo, alpha, x, A)`

In-place rank-1 update of packed symmetric matrix `A`

with vector `x`

as `alpha[k] * x[:,k] * transpose(x[:,k]) + A[:,:,k]`

for all `k`

. `A`

must be symmetric, and `uplo`

specifies whether the upper ('U') or lower ('L') triangle was packed. All other inputs can have eltypes of either AbstractFloats or Integers. `alpha`

can also be a scalar.

`BatchedBLAS.batched_symv!`

— Method`batched_symv!(uplo, alpha, A, x, beta, y)`

In-place batched matrix-vector multiplication and addition, equivalent to `y[:,k] = alpha[k] * A[:,:,k] * x[:,k] + beta[k] * y[:,k]`

for all `k`

. `A`

is assumed to be symmetric. Only the `uplo`

(either 'U' or 'L') triangle of `A`

is used. All other inputs can have eltypes of either AbstractFloats or Integers. `alpha`

and `beta`

can also be scalars.

`BatchedBLAS.batched_syr!`

— Method`batched_syr!(uplo, alpha, x, A)`

In-place rank-1 update of symmetric matrix `A`

with vector `x`

as `alpha[k] * x[:,k] * transpose(x[:,k]) + A[:,:,k]`

for all `k`

. `A`

is assumed to be symmetric. Only the `uplo`

(either 'U' or 'L') triangle of `A`

is used. All other inputs can have eltypes of either AbstractFloats or Integers. `alpha`

can also be a scalar.