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Extract value of LP relaxation

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Alinson S. Xavier
2020-02-22 22:40:54 -06:00
parent 10ad8e0a9e
commit b60afab5ef
2 changed files with 15 additions and 4 deletions
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14
miplearn/solvers.py
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@@ -27,9 +27,12 @@ class InternalSolver():
var.setub(ub) var.setub(ub)
var.domain = Reals var.domain = Reals
self.solver.set_instance(model) self.solver.set_instance(model)
self.solver.solve(tee=True) results = self.solver.solve(tee=True)
for var in model.component_data_objects(Var): for var in model.component_data_objects(Var):
var.domain = original_domain[str(var)] var.domain = original_domain[str(var)]
return {
"Optimal value": results["Problem"][0]["Lower bound"],
}
def clear_values(self, model): def clear_values(self, model):
for var in model.component_objects(Var): for var in model.component_objects(Var):
@@ -117,6 +120,9 @@ class CPLEXSolver(InternalSolver):
lp.set_problem_type(cplex.Cplex.problem_type.LP) lp.set_problem_type(cplex.Cplex.problem_type.LP)
results = self.solver.solve(tee=tee) results = self.solver.solve(tee=tee)
lp.variables.set_types(zip(range(n_vars), var_types)) lp.variables.set_types(zip(range(n_vars), var_types))
return {
"Optimal value": results["Problem"][0]["Lower bound"],
}
class LearningSolver: class LearningSolver:
@@ -177,8 +183,10 @@ class LearningSolver:
self.internal_solver = self._create_internal_solver() self.internal_solver = self._create_internal_solver()
# Solve LP relaxation # Solve LP relaxation
self.internal_solver.solve_lp(model, tee=tee) results = self.internal_solver.solve_lp(model, tee=tee)
instance.lp_solution = self.internal_solver.get_solution(model) instance.lp_solution = self.internal_solver.get_solution(model)
print(results)
instance.lp_value = results["Optimal value"]
# Invoke before_solve callbacks # Invoke before_solve callbacks
for component in self.components.values(): for component in self.components.values():
@@ -226,6 +234,7 @@ class LearningSolver:
"Results": results, "Results": results,
"Solution": instance.solution, "Solution": instance.solution,
"LP solution": instance.lp_solution, "LP solution": instance.lp_solution,
"LP value": instance.lp_value,
"Upper bound": instance.upper_bound, "Upper bound": instance.upper_bound,
"Lower bound": instance.lower_bound, "Lower bound": instance.lower_bound,
} }
@@ -237,6 +246,7 @@ class LearningSolver:
for (idx, r) in enumerate(p_map_results): for (idx, r) in enumerate(p_map_results):
instances[idx].solution = r["Solution"] instances[idx].solution = r["Solution"]
instances[idx].lp_solution = r["LP solution"] instances[idx].lp_solution = r["LP solution"]
instances[idx].lp_value = r["LP value"]
instances[idx].lower_bound = r["Lower bound"] instances[idx].lower_bound = r["Lower bound"]
instances[idx].upper_bound = r["Upper bound"] instances[idx].upper_bound = r["Upper bound"]

5
miplearn/tests/test_solver.py
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@@ -27,14 +27,15 @@ def test_solver():
assert instance.solution["x"][1] == 0.0 assert instance.solution["x"][1] == 0.0
assert instance.solution["x"][2] == 1.0 assert instance.solution["x"][2] == 1.0
assert instance.solution["x"][3] == 1.0 assert instance.solution["x"][3] == 1.0
assert instance.lower_bound == 1183.0
assert instance.upper_bound == 1183.0
assert round(instance.lp_solution["x"][0], 3) == 1.000 assert round(instance.lp_solution["x"][0], 3) == 1.000
assert round(instance.lp_solution["x"][1], 3) == 0.923 assert round(instance.lp_solution["x"][1], 3) == 0.923
assert round(instance.lp_solution["x"][2], 3) == 1.000 assert round(instance.lp_solution["x"][2], 3) == 1.000
assert round(instance.lp_solution["x"][3], 3) == 0.000 assert round(instance.lp_solution["x"][3], 3) == 0.000
assert round(instance.lp_value, 3) == 1287.923
assert instance.lower_bound == 1183.0
assert instance.upper_bound == 1183.0
solver.fit() solver.fit()
solver.solve(instance) solver.solve(instance)