Unsuccessful assignment without any error

[ArmanZarrin97]

I first wrongly created an issue for julia itself, but it is apparently an issue related to SparseArrays.jl. you can find the previous issue here

Hi,

I tried to run this line in one of my functions:

 J[:, isl]=[ki... zeros(length(setdiff(1:nBus,ipq)))...]

J is a sparse matrix with size 120120. J[:, isl] is a column with only four values, which are [11.00 -11.00 44.00 -44.00]. [ki... zeros(length(setdiff(1:nBus,ipq)))...] is a matrix 1201, of which 23 elements are 50.00 and the others equal to zero. The result of running this code is again a column with only four values, which are [0.00 0.00 0.00 0.00]. However, I expect it to be a column with 23, 24,..., or 27 values, which includes 23 values equal to 50.00.

I didn't encounter any error. I believe it should be a bug. By chance I understood that this assignment doesn't work correctly, But it could be in fact dangerous for the others.

I fixed it in this way:

J[:, isl]=[ki... zeros(length(setdiff(1:nBus,ipq)))...]**[:,1]**

Best,
Arman

Here is the reproducible example:

a=spzeros(120,120)

isl=5

a[10,isl]=1
a[14,isl]=1
a[37,isl]=1
a
R=zeros(40,1)
S=50*ones(40,1)
T=zeros(40,1)
b=[R... S... T...]
a
b
a[:,isl]=b ## This is the concerned assignment
sum(b) # =1200.0
sum(a[:,isl]) # =0.0

[adienes]

perhaps the issue is here

_to_same_csc(::AbstractSparseMatrixCSC{Tv, Ti}, V::AbstractMatrix, I...) where {Tv,Ti} = convert(SparseMatrixCSC{Tv,Ti}, V)

that V could be map(length, I) like it is for the AbstractVector case.

I'm not sure though; I don't know this code very well.