# Source code for pm4py.util.lp.variants.cvxopt_solver_custom_align_arm

```'''

PM4Py is free software: you can redistribute it and/or modify
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

PM4Py is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
import sys

from cvxopt import blas
from cvxopt import glpk

[docs]def custom_solve_lp(c, G, h, A, b):
status, x, z, y = glpk.lp(c, G, h, A, b)

if status == 'optimal':
pcost = blas.dot(c, x)
else:
pcost = None

return {'status': status, 'x': x, 'primal objective': pcost}

[docs]def apply(c, Aub, bub, Aeq, beq, parameters=None):
"""
Gets the overall solution of the problem

Parameters
------------
c
c parameter of the algorithm
Aub
A_ub parameter of the algorithm
bub
b_ub parameter of the algorithm
Aeq
A_eq parameter of the algorithm
beq
b_eq parameter of the algorithm
parameters
Possible parameters of the algorithm

Returns
-------------
sol
Solution of the LP problem by the given algorithm
"""
sol = custom_solve_lp(c, Aub, bub, Aeq, beq)

return sol

[docs]def get_prim_obj_from_sol(sol, parameters=None):
"""
Gets the primal objective from the solution of the LP problem

Parameters
-------------
sol
Solution of the ILP problem by the given algorithm
parameters
Possible parameters of the algorithm

Returns
-------------
prim_obj
Primal objective
"""
return sol["primal objective"]

[docs]def get_points_from_sol(sol, parameters=None):
"""
Gets the points from the solution

Parameters
-------------
sol
Solution of the LP problem by the given algorithm
parameters
Possible parameters of the algorithm

Returns
-------------
points
Point of the solution
"""
if parameters is None:
parameters = {}

maximize = parameters["maximize"] if "maximize" in parameters else False
return_when_none = parameters["return_when_none"] if "return_when_none" in parameters else False
var_corr = parameters["var_corr"] if "var_corr" in parameters else {}

if sol and 'x' in sol and sol['x'] is not None:
return list(sol['x'])
else:
if return_when_none:
if maximize:
return [sys.float_info.max] * len(list(var_corr.keys()))
return [sys.float_info.min] * len(list(var_corr.keys()))
```