#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""This module contains the definition of the solver class.
"""
import logging
import pyoptinterface as poi
from pyoptinterface import mosek
from pyoptinterface import gurobi
from pyoptinterface import highs
from pyoptinterface import copt
from prepshot.logs import timer
from prepshot._model.head_iteration import run_model_iteration
[docs]def get_solver(params : dict) -> object:
"""Retrieve the solver object based on parameters.
Parameters
----------
params : dict
Configuration dictionary with solver details.
Returns
-------
object
Solver object based on the configuration.
"""
solver_map = {
'mosek': mosek,
'gurobi': gurobi,
'highs': highs,
'copt': copt
}
solver = params['solver']['solver']
if solver in solver_map:
poi_solver = solver_map[solver]
else:
raise ValueError(f"Unsupported solver: {solver}")
if not poi_solver.autoload_library():
logging.warning(
"%s library failed to load automatically."
+ "Attempting to load manually.", solver
)
if 'solver_path' not in params:
raise ValueError(
f"Solver path for {solver} not found in the configuration."
)
if not poi_solver.load_library(params['solver']['solver_path']):
raise ValueError(f"Failed to load {solver} library.")
logging.info("Loaded %s library manually.", solver)
else:
logging.info("Loaded %s library automatically.", solver)
return poi_solver
[docs]def set_solver_parameters(model : object) -> None:
"""Set the solver-specific parameters for the model.
Parameters
----------
model : object
Model to configurable.
"""
for key, value in model.params['solver'].items():
if key not in ('solver', 'solver_path'):
model.set_raw_parameter(key, value)
[docs]@timer
def solve_model(model : object, params : dict) -> bool:
"""Solve the model using the provided parameters.
Parameters
----------
model : object
Model to solve.
params : dict
Configuration parameters for solving the model.
Returns
-------
bool
True if the model is solved optimally, False otherwise.
"""
if params['isinflow']:
error_threshold = params['error_threshold']
iteration_number = params['iteration_number']
return run_model_iteration(
model, params, error_threshold, iteration_number
)
model.optimize()
status = model.get_model_attribute(poi.ModelAttribute.TerminationStatus)
if status != poi.TerminationStatusCode.OPTIMAL:
return False
return True