# Julia is slower than matlab when it comes to matrix diagonalization?

**URL:** <https://discourse.julialang.org/t/julia-is-slower-than-matlab-when-it-comes-to-matrix-diagonalization/108658>\
**Category:** General Usage\
**Tags:** question, eigs, eigenvalue-problem\
**Created:** [January 11, 2024, 3:42am UTC](https://discourse.julialang.org/t/julia-is-slower-than-matlab-when-it-comes-to-matrix-diagonalization/108658 "2024-01-11T03:42:27Z")\
**Posts on this page:** 1\
**Showing post:** 17

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**Author:** ![Ralph\_Smith](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ralph_smith/32/10344_2.png) [@Ralph\_Smith](https://discourse.julialang.org/u/Ralph_Smith)\
**Post date:** [January 12, 2024, 2:44am UTC](https://discourse.julialang.org/t/julia-is-slower-than-matlab-when-it-comes-to-matrix-diagonalization/108658/17 "2024-01-12T02:44:40Z")

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Here is my source:

> [@Some eigenpairs from a large, sparse, nonsymmetric matrix: Julia vs Matlab](https://discourse.julialang.org/t/some-eigenpairs-from-a-large-sparse-nonsymmetric-matrix-julia-vs-matlab/93742/18):
>
> Actually, Matlab does not use Arpack anymore. Instead, they have implemented their own version of the Krylov-Schur method accessible from the Matlab prompt with edit eigs.m. I am not sure whether the following tweak will resolve your issue, however, I remember from my own work on a modesolver that SuiteSparse conducts an iterative refinement of the solutions by default. Disabling this refinement using SuiteSparse.UMFPACK.umf\_ctrl[8] = 0 improved the performance of eigs significantly for my us…

Further down that thread is an updated way to get the faster sparse solution, which may help @jiang_ming_zhang as well.

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