# Some eigenpairs from a large, sparse, nonsymmetric matrix: Julia vs Matlab

**URL:** <https://discourse.julialang.org/t/some-eigenpairs-from-a-large-sparse-nonsymmetric-matrix-julia-vs-matlab/93742>\
**Category:** Numerics\
**Tags:** sparse, eigenvalues, eigenvectors\
**Created:** [January 29, 2023, 7:35pm UTC](https://discourse.julialang.org/t/some-eigenpairs-from-a-large-sparse-nonsymmetric-matrix-julia-vs-matlab/93742 "2023-01-29T19:35:54Z")\
**Posts on this page:** 1\
**Showing post:** 18

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**Author:** ![jonas-kr](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jonas-kr/32/1810_2.png) [@jonas-kr](https://discourse.julialang.org/u/jonas-kr)\
**Post date:** [January 31, 2023, 8:43am UTC](https://discourse.julialang.org/t/some-eigenpairs-from-a-large-sparse-nonsymmetric-matrix-julia-vs-matlab/93742/18 "2023-01-31T08:43:25Z")

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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

```julia
SuiteSparse.UMFPACK.umf_ctrl[8] = 0

```

improved the performance of `eigs` significantly for my use case.

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