**MWE:**

```
using LinearAlgebra, SparseArrays, Arpack, PyCall, BenchmarkTools
@pyimport scipy.sparse as pysparse
@pyimport scipy.sparse.linalg as pylinalg
N = 100_000
X = sparse(Tridiagonal(1.0:N-1, 1.0:N, 1.0:N-1))
X = X + X'
Xpy = pysparse.csc_matrix((X.nzval, X.rowval .- 1, X.colptr .- 1), shape=size(X))
@btime Arpack.eigs($X, nev=6, which=:LM); # julia
@btime pylinalg.eigs($Xpy, k=6, which=:LM); # python
# Output:
# 3.475 s (4467 allocations: 23.83 MiB)
# 3.116 s (180 allocations: 9.16 MiB)
```

I’m setting up a sparse (about 1e-5 nz entries) tridiagonal real matrix. I want to know the, say, 6 largest eigenvalues.

**Why is Julia slower here?**

AFAIU, both `scipy.sparse.linalg.eigs`

and Arpack.jl’s `eigs`

are using ARPACK. Correct?

Any explanation is highly appreciated!