I tried to have the result of `` but it gave error. So, I tried adj_factor.L*adj_factor.U*adj_factor.F*V but it gave different result with adj*V.
Could you please tell me what is the correct order of L,U,P to have the same result between?

Edit: as stevengj mentions, normally you decompose to solve a system, not to multiply the matrix with something (in that case you could directly use your matrix)

My main goal is to find a way to accelerate the multiplication of two matrices (one or both are sparse/s). So, I though that factorizing one and multiplying it by the other can be faster.

I am profiling my code (takes 5 sec) against another program (coded in Fortran and takes 2.13 sec). It is a time loop simulation 1:25e-6:0.6.
I found my code spends 1.5 sec at the line of multiplication (as in my first post). So, I started to think if there is a faster way to do the multiplication. I tried to use â€śMKLSparse.jlâ€ť but no gain. I am not sure if it is activated correctly in my julia.

How long is the Fortran spending in each part? The Fortran is likely using the same libraries as Julia for the multiplication, so I would think the time difference would be in a different part.

I would suggest to describe the problem in detail. It is possible to find examples of poor performance when multiplying matrices, especially when mixing dense and sparse matrices.