# Julia is slower than MATLAB at diagonalizing matrices

**URL:** <https://discourse.julialang.org/t/julia-is-slower-than-matlab-at-diagonalizing-matrices/78174>\
**Category:** New to Julia\
**Tags:** question, matlab, linearalgebra, matrices, eigenvalues\
**Created:** [March 20, 2022, 7:45pm UTC](https://discourse.julialang.org/t/julia-is-slower-than-matlab-at-diagonalizing-matrices/78174 "2022-03-20T19:45:57Z")\
**Posts on this page:** 1\
**Showing post:** 7

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**Author:** ![zdenek\_hurak](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/zdenek_hurak/32/53118_2.png) [@zdenek\_hurak](https://discourse.julialang.org/u/zdenek_hurak)\
**Post date:** [March 21, 2022, 9:27am UTC](https://discourse.julialang.org/t/julia-is-slower-than-matlab-at-diagonalizing-matrices/78174/7 "2022-03-21T09:27:19Z")

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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 times):

```matlab
>> A = rand(1000, 1000) + 1i * rand(1000, 1000); A = A + A';
>> tic, eig(A); toc
Elapsed time is 0.401051 seconds.
>> maxNumCompThreads

ans =

     2

```

and in Julia 1.7.2

```julia
julia> using LinearAlgebra

julia> using MKL

julia> using BenchmarkTools

julia> A = rand(1000, 1000) + im * rand(1000, 1000);

julia> A = A + A';

julia> A = Hermitian(A);

julia> LinearAlgebra.BLAS.set_num_threads(2)

julia> @btime eigen(A);
  628.196 ms (16 allocations: 46.57 MiB)

julia> @btime eigvals(A);
  297.065 ms (13 allocations: 16.05 MiB)

```

Indeed, I am afraid that even the title of the post mislead us a bit because in your Matlab code you are really just computing the eigenvalues, which are not quite sufficient to perform diagonalization of a matrix.

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