At_ldiv_B(A, B)

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


  1. Calculate the transpose-product of matrices:

    julia> A = [1 2 3; 4 5 6];
    julia> B = [7 8; 9 10; 11 12];
    julia> At_ldiv_B(A, B)
    3×2 Array{Int64,2}:
    58   64
    139  154
    220  244

    This example calculates the transpose-product of matrices A and B.

  2. Compute the transpose-product of a vector and a matrix:

    julia> v = [1, 2, 3];
    julia> M = [4 5 6; 7 8 9];
    julia> At_ldiv_B(v, M)
    2-element Array{Int64,1}:

    It calculates the transpose-product of vector v and matrix M.

  3. Handle the case of a single-element matrix:
    julia> A = [5];
    julia> B = [2];
    julia> At_ldiv_B(A, B)
    1-element Array{Int64,1}:

    It correctly handles the case where both A and B are single-element matrices.

Common mistake example:

julia> A = [1 2 3; 4 5 6];
julia> B = [7 8];
julia> At_ldiv_B(A, B)
ERROR: DimensionMismatch("matrix A has dimensions (2,3), vector B has length 2")

In this example, the dimensions of A and B are not compatible for matrix multiplication. It's important to ensure that the number of columns in A matches the number of elements in B for the transpose-product calculation.

See Also

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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