Source code for pm4py.util.lp.variants.cvxopt_solver

```'''

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 matrix, solvers

[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
"""
if parameters is None:
parameters = {}

solver = parameters["solver"] if "solver" in parameters else None

c = matrix(c)
Aub = matrix(Aub)
bub = matrix(bub)
if Aeq is not None:
Aeq = matrix(Aeq)
if beq is not None:
beq = matrix(beq)

solvers.options['glpk'] = {}
solvers.options['glpk']['LPX_K_MSGLEV'] = 0
solvers.options['glpk']['msg_lev'] = 'GLP_MSG_OFF'
solvers.options['glpk']['show_progress'] = False
solvers.options['glpk']['presolve'] = "GLP_ON"
solvers.options['glpk']['meth'] = "GLP_PRIMAL"
solvers.options['msg_lev'] = 'GLP_MSG_OFF'
solvers.options['show_progress'] = False

if solver:
sol = solvers.lp(c, Aub, bub, A=Aeq, b=beq, solver=solver)
else:
sol = solvers.lp(c, Aub, bub, A=Aeq, b=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
"""
if parameters is None:
parameters = {}

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()))
```