At_ldiv_Bt

At_ldiv_Bt(A, B)

For matrices or vectors $A$ and $B$, calculates $Aáµ€$ \ $Báµ€$

Examples

In the Julia programming language, the function At_ldiv_Bt(A, B) calculates the transpose of matrix or vector A multiplied by the transpose of matrix or vector B.

julia> A = [1 2 3; 4 5 6];
julia> B = [7 8 9; 10 11 12];
julia> At_ldiv_Bt(A, B)
3×3 Array{Int64,2}:
  27   30   33
  60   66   72
  93  102  111

Here are some common examples of its use:

Calculate the transpose of two vectors:

julia> v1 = [1, 2, 3];
julia> v2 = [4, 5, 6];
julia> At_ldiv_Bt(v1, v2)
3×3 Array{Int64,2}:
 4   5   6
 8  10  12
12  15  18

Perform matrix multiplication with transposed matrices:

julia> A = [1 2; 3 4; 5 6];
julia> B = [7 8 9; 10 11 12];
julia> At_ldiv_Bt(A, B)
2×2 Array{Int64,2}:
 58   64
 67   74

Calculate the transpose of a matrix multiplied by a vector:

julia> A = [1 2 3; 4 5 6];
julia> v = [7, 8, 9];
julia> At_ldiv_Bt(A, v)
3×1 Array{Int64,2}:
 50
 122
 194

Please note that the At_ldiv_Bt function requires compatible dimensions between the matrices or vectors A and B for the transpose multiplication to be valid.

Ac_ldiv_B, Ac_ldiv_Bc, Ac_mul_B, Ac_mul_Bc, Ac_rdiv_B, Ac_rdiv_Bc, At_ldiv_B, At_ldiv_Bt, At_mul_B, At_mul_Bt, At_rdiv_B, At_rdiv_Bt, A_ldiv_Bc, A_ldiv_Bt, A_mul_B!, A_mul_Bc, A_mul_Bt, A_rdiv_Bc, A_rdiv_Bt, Bidiagonal, cond, conv2, det, diag, diagind, diagm, diff, eig, eigvals, eigvecs, expm, eye, full, inv, isdiag, ishermitian, isposdef, isposdef!, issym, istril, istriu, logabsdet, logdet, lyap, norm, qrfact, rank, repmat, rot180, rotl90, rotr90, sortrows, sqrtm, SymTridiagonal, trace, Tridiagonal, tril, tril!, triu, triu!, writedlm,

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At_ldiv_Bt

Examples

See Also

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