# Julia versus MATLAB

**URL:** <https://discourse.julialang.org/t/julia-versus-matlab/79360>\
**Category:** Performance\
**Tags:** matlab, linearalgebra\
**Created:** [April 12, 2022, 1:39am UTC](https://discourse.julialang.org/t/julia-versus-matlab/79360 "2022-04-12T01:39:09Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![Elrod](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/elrod/32/22461_2.png) [@Elrod](https://discourse.julialang.org/u/Elrod)\
**Post date:** [April 12, 2022, 2:12am UTC](https://discourse.julialang.org/t/julia-versus-matlab/79360/6 "2022-04-12T02:12:56Z")

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See this comment from an earlier discussion:

> [@Julia is slower than MATLAB at diagonalizing matrices](https://discourse.julialang.org/t/julia-is-slower-than-matlab-at-diagonalizing-matrices/78174/7):
>
> I guess that with one or no output argument Matlab’s [eig](https://www.mathworks.com/help/matlab/ref/eig.html) just computes the eigenvalues and does not particularly compute/store the eigenvectors. In contrast, in Julia the [eigen](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#LinearAlgebra.eigen) computes full eigendecomposition, that is, both eigenvalues and eigenvectors. If only eigenvalues are required, use [eigvals](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#LinearAlgebra.eigvals) instead. The performance of Matlab and Julia are then pretty much identical on my old laptop (running Linux). In fact, Julia even a bit faster. In Matlab 2022a (and yes, I did run the code a few tim…

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