A_rdiv_Bc
A_rdiv_Bc(A, B)
For matrices or vectors $A$ and $B$, calculates $A / Bá´´$
Examples
In the Julia programming language, the function A_rdiv_Bc(A, B) calculates the division of matrix/vector A by the conjugate transpose of matrix/vector B, denoted as A / Bᴴ.
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> B = [1+2im 3+4im; 5+6im 7+8im]
2×2 Array{Complex{Int64},2}:
1+2im 3+4im
5+6im 7+8im
julia> A_rdiv_Bc(A, B)
2×2 Array{Complex{Float64},2}:
-1.4-0.8im -3.2-1.6im
-3.6-1.2im -8.0-2.4imHere are some common examples of its use:
-
Dividing a matrix by another matrix:
julia> A = [1.0 2.0; 3.0 4.0] 2×2 Array{Float64,2}: 1.0 2.0 3.0 4.0 julia> B = [1.0 2.0; 3.0 4.0] 2×2 Array{Float64,2}: 1.0 2.0 3.0 4.0 julia> A_rdiv_Bc(A, B) 2×2 Array{Complex{Float64},2}: -1.0+0.0im -1.0+0.0im -1.0+0.0im -1.0+0.0im -
Dividing a vector by a matrix:
julia> A = [1.0, 2.0, 3.0] 3-element Array{Float64,1}: 1.0 2.0 3.0 julia> B = [1.0 2.0; 3.0 4.0] 2×2 Array{Float64,2}: 1.0 2.0 3.0 4.0 julia> A_rdiv_Bc(A, B) 2-element Array{Complex{Float64},1}: -1.0+0.0im -1.0+0.0im -
Dividing a matrix by a vector:
julia> A = [1.0 2.0; 3.0 4.0] 2×2 Array{Float64,2}: 1.0 2.0 3.0 4.0 julia> B = [1.0, 2.0] 2-element Array{Float64,1}: 1.0 2.0 julia> A_rdiv_Bc(A, B) 2×2 Array{Complex{Float64},2}: -1.0+0.0im -1.0+0.0im -2.0+0.0im -2.0+0.0im
Please note that the result of A_rdiv_Bc is a matrix or vector of complex numbers.
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,User Contributed Notes
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