AxisAlgorithms.A_ldiv_B_md!
— MethodA_ldiv_B_md!(dest, F, src, dim)
solves a tridiagonal system along dimension dim
of src
, storing the result in dest
. Currently, F
must be an LU-factorized tridiagonal matrix. If desired, you may safely use the same array for both src
and dest
, so that this becomes an in-place algorithm.
AxisAlgorithms.A_ldiv_B_md
— MethodA_ldiv_B_md(F, src, dim)
solves F
for slices b
of src
along dimension dim
, storing the result along the same dimension of the output. Currently, F
must be an LU-factorized tridiagonal matrix or a Woodbury matrix.
AxisAlgorithms.A_mul_B_md!
— MethodA_mul_B_md!(dest, M, src, dim)
computes M*x
for slices x
of src
along dimension dim
, storing the result in dest
. M
must be an AbstractMatrix
. This uses an in-place naive algorithm.
AxisAlgorithms.A_mul_B_md
— MethodA_mul_B_md(M, src, dim)
computes M*x
for slices x
of src
along dimension dim
, storing the resulting vector along the same dimension of the output. M
must be an AbstractMatrix
. This uses an in-place naive algorithm.
AxisAlgorithms.A_mul_B_perm!
— MethodA_mul_B_perm!(dest, M, src, dim)
computes M*x
for slices x
of src
along dimension dim
, storing the result in dest
. M
must be an AbstractMatrix
. This uses permutedims
to make dimension dim
into the first dimension, performs a standard matrix multiplication, and restores the original dimension ordering. In many cases, this algorithm exhibits the best cache behavior.
AxisAlgorithms.A_mul_B_perm
— MethodA_mul_B_perm(M, src, dim)
computes M*x
for slices x
of src
along dimension dim
, storing the resulting vector along the same dimension of the output. M
must be an AbstractMatrix
. This uses permutedims
to make dimension dim
into the first dimension, performs a standard matrix multiplication, and restores the original dimension ordering. In many cases, this algorithm exhibits the best cache behavior.