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@ -31,9 +31,6 @@ def _parallel_solve(instance_idx):
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"Results": results,
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"Solution": instance.solution,
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"LP solution": instance.lp_solution,
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"LP value": instance.lp_value,
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"Upper bound": instance.upper_bound,
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"Lower bound": instance.lower_bound,
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"Violations": instance.found_violations,
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}
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@ -97,6 +94,45 @@ class LearningSolver:
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model=None,
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tee=False,
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relaxation_only=False):
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"""
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Solves the given instance. If trained machine-learning models are
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available, they will be used to accelerate the solution process.
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This method modifies the instance object. Specifically, the following
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properties are set:
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- instance.lp_solution
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- instance.lp_value
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- instance.lower_bound
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- instance.upper_bound
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- instance.solution
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- instance.found_violations
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- instance.solver_log
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Additional solver components may set additional properties. Please
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see their documentation for more details.
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Parameters
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----------
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instance: miplearn.Instance
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The instance to be solved
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model: pyomo.core.ConcreteModel
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The corresponding Pyomo model. If not provided, it will be created.
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tee: bool
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If true, prints solver log to screen.
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relaxation_only: bool
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If true, solve only the root LP relaxation.
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Returns
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-------
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dict
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A dictionary of solver statistics containing at least the following
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keys: "Lower bound", "Upper bound", "Wallclock time", "Nodes",
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"Sense", "Log", "Warm start value" and "LP value".
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Additional components may generate additional keys. For example,
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ObjectiveValueComponent adds the keys "Predicted LB" and
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"Predicted UB". See the documentation of each component for more
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details.
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"""
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if model is None:
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model = instance.to_model()
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@ -118,10 +154,12 @@ class LearningSolver:
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return results
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results = self.internal_solver.solve(tee=tee)
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results["LP value"] = instance.lp_value
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# Read MIP solution and bounds
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instance.lower_bound = results["Lower bound"]
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instance.upper_bound = results["Upper bound"]
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instance.solver_log = results["Log"]
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instance.solution = self.internal_solver.get_solution()
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logger.debug("Calling after_solve callbacks...")
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@ -147,10 +185,11 @@ class LearningSolver:
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for (idx, r) in enumerate(p_map_results):
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instances[idx].solution = r["Solution"]
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instances[idx].lp_solution = r["LP solution"]
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instances[idx].lp_value = r["LP value"]
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instances[idx].lower_bound = r["Lower bound"]
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instances[idx].upper_bound = r["Upper bound"]
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instances[idx].lp_value = r["Results"]["LP value"]
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instances[idx].lower_bound = r["Results"]["Lower bound"]
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instances[idx].upper_bound = r["Results"]["Upper bound"]
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instances[idx].found_violations = r["Violations"]
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instances[idx].solver_log = r["Results"]["Log"]
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return results
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