# Use external solvers#

FElupe uses SuperLU as direct sparse solver by default because it is shipped with SciPy (and SciPy is already a dependency of FElupe). While it is definitely a good choice for small to mid-sized problems, faster alternatives are easy to install and use. This section demonstrates several possibilities, e.g. a fast direct solver from PyPardiso (`pip install pypardiso`

) and the `minres`

iterative solver from `SciPy`

. Custom solvers may be passed to the evaluation of a job.

```
import felupe as fem
job = fem.Job(steps)
job.evaluate(solver=solver) # function `x = solver(A, b)`
```

Solvers from SciPy Sparse:

```
# the default solver
from scipy.sparse.linalg import spsolve as solver
```

```
import scipy.sparse.linalg as spla
from scipy.sparse import tril
def solver(A, b):
return spla.spsolve_triangular(tril(A).tocsr(), b).squeeze()
```

**Note**: `minres`

may be replaced by another iterative method.

```
import scipy.sparse.linalg as spla
def solver(A, b):
"Wrapper function for iterative solvers from scipy.sparse."
return spla.minres(A, b)[0]
```

Solvers from external packages:

Ensure to have PyPardiso installed.

```
pip install pypardiso
```

```
from pypardiso import spsolve as solver
# undocumented, untested workaround if multiple blas libaries are installed
# import os
# os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
```

Ensure to have PyPardiso installed.

```
pip install pypardiso
```

```
from pypardiso import PyPardisoSolver
from scipy.sparse import triu
def solver(A, b):
# mtype = 1: real and structurally symmetric, supernode pivoting
# mtype = 2: real and symmetric positive definite
# mtype =-2: real and symmetric indefinite,
# diagonal or Bunch-Kaufman pivoting
# mtype = 6: complex and symmetric
return PyPardisoSolver(mtype=2).solve(triu(A).tocsr(), b).squeeze()
```