def _scip_solve(self, solver):
"""solves a mip/lp using scip"""
if solver is None:
solver = Model()
solver.hideOutput()
if self.config.disable_maxcuts:
for param in [
'separating/maxcuts', 'separating/maxcutsroot', 'propagating/maxrounds',
'propagating/maxroundsroot', 'presolving/maxroundsroot'
]:
solver.setIntParam(param, 0)
solver.setBoolParam('conflict/enable', False)
solver.setPresolve(SCIP_PARAMSETTING.OFF)
solver.setBoolParam('randomization/permutevars', True)
# seed is set to 0 permanently.
solver.setIntParam('randomization/permutationseed', 0)
solver.setIntParam('randomization/randomseedshift', 0)
with U.Timer() as timer:
solver.optimize()
assert solver.getStatus() == 'optimal', solver.getStatus()
obj = float(solver.getObjVal())
ass = {var.name: solver.getVal(var) for var in solver.getVars()}
mip_stats = ConfigDict(mip_work=solver.getNNodes(),
n_cuts=solver.getNCuts(),
n_cuts_applied=solver.getNCutsApplied(),
n_lps=solver.getNLPs(),
solving_time=solver.getSolvingTime(),
pre_solving_time=solver.getPresolvingTime(),
time_elapsed=timer.to_seconds())
return ass, obj, mip_stats
def convert():
milp = MILP()
milp.problem_type = args.problem_type
model = Model()
model.hideOutput()
heur = LogBestSol()
model.includeHeur(heur,
"PyHeur",
"custom heuristic implemented in python",
"Y",
timingmask=SCIP_HEURTIMING.BEFORENODE)
model.setRealParam('limits/gap', args.gap)
model.readProblem(args.inp_file)
milp.mip = scip_to_milps(model)
model.optimize()
heur.done()
milp.optimal_objective = model.getObjVal()
milp.optimal_solution = {
var.name: model.getVal(var)
for var in model.getVars()
}
milp.is_optimal = (model.getStatus() == 'optimal')
milp.optimal_sol_metadata.n_nodes = model.getNNodes()
milp.optimal_sol_metadata.gap = model.getGap()
milp.optimal_sol_metadata.primal_integral = heur.primal_integral
milp.optimal_sol_metadata.primal_gaps = heur.l
feasible_sol = model.getSols()[-1]
milp.feasible_objective = model.getSolObjVal(feasible_sol)
milp.feasible_solution = {
var.name: model.getSolVal(feasible_sol, var)
for var in model.getVars()
}
model = Model()
model.hideOutput()
relax_integral_constraints(milp.mip).add_to_scip_solver(model)
model.optimize()
assert model.getStatus() == 'optimal'
milp.optimal_lp_sol = {
var.name: model.getVal(var)
for var in model.getVars()
}
return milp
