At_ldiv_B

At_ldiv_B(A, B)

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

Examples

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.

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}:
18
21
```
It calculates the transpose-product of vector v and matrix M.
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}:
10
```
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.

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_B

Examples

See Also

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