A_mul_Bt

A_mul_Bt(A, B)

For matrices or vectors $A$ and $B$, calculates $Aâ‹…Báµ€$

Examples

julia> A = [1 2 3; 4 5 6];
       B = [7 8; 9 10; 11 12];

julia> A_mul_Bt(A, B)
2×2 Array{Int64,2}:
 58   64
 139  154

Multiply a matrix with a transposed matrix:

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

This example multiplies matrix A with the transpose of matrix B.

Multiply a vector with a transposed matrix:
```
julia> A = [1, 2, 3];
julia> B = [4 5 6];
julia> A_mul_Bt(A, B)
1×1 Array{Int64,2}:
32
```
It calculates the dot product of vector A with the transpose of matrix B.
Multiply a row vector with a column vector:
```
julia> A = [1 2 3];
julia> B = [4, 5, 6];
julia> A_mul_Bt(A, B)
1×1 Array{Int64,2}:
32
```
This example demonstrates the multiplication of a row vector (A) with a column vector (B).

Common mistake example:

julia> A = [1 2 3; 4 5 6];
julia> B = [7 8 9];
julia> A_mul_Bt(A, B)
ERROR: DimensionMismatch("A has dimensions (2,3) but B has dimensions (1,3)")

In this case, the dimensions of matrix A and matrix B are incompatible for multiplication. Make sure the number of columns in A matches the number of columns in the transposed B for a valid multiplication.

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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A_mul_Bt

Examples

See Also

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