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140 lines
3.8 KiB
140 lines
3.8 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import logging
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from abc import ABC, abstractmethod
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logger = logging.getLogger(__name__)
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class InternalSolver(ABC):
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"""
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Abstract class representing the MIP solver used internally by LearningSolver.
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"""
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@abstractmethod
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def solve_lp(self, tee=False):
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"""
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Solves the LP relaxation of the currently loaded instance. After this
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method finishes, the solution can be retrieved by calling `get_solution`.
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Parameters
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----------
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tee: bool
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If true, prints the solver log to the screen.
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Returns
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-------
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dict
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A dictionary of solver statistics containing the following keys:
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"Optimal value".
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"""
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pass
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@abstractmethod
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def get_solution(self):
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"""
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Returns current solution found by the solver.
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If called after `solve`, returns the best primal solution found during
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the search. If called after `solve_lp`, returns the optimal solution
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to the LP relaxation.
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The solution is a dictionary `sol`, where the optimal value of `var[idx]`
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is given by `sol[var][idx]`.
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"""
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pass
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@abstractmethod
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def set_warm_start(self, solution):
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"""
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Sets the warm start to be used by the solver.
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The solution should be a dictionary following the same format as the
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one produced by `get_solution`. Only one warm start is supported.
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Calling this function when a warm start already exists will
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remove the previous warm start.
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"""
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pass
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@abstractmethod
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def clear_warm_start(self):
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"""
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Removes any existing warm start from the solver.
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"""
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pass
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@abstractmethod
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def set_instance(self, instance, model=None):
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"""
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Loads the given instance into the solver.
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Parameters
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----------
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instance: miplearn.Instance
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The instance to be loaded.
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model:
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The concrete optimization model corresponding to this instance
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(e.g. JuMP.Model or pyomo.core.ConcreteModel). If not provided,
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it will be generated by calling `instance.to_model()`.
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"""
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pass
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@abstractmethod
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def fix(self, solution):
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"""
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Fixes the values of a subset of decision variables.
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The values should be provided in the dictionary format generated by
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`get_solution`. Missing values in the solution indicate variables
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that should be left free.
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"""
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pass
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@abstractmethod
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def add_constraint(self, constraint):
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"""
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Adds a single constraint to the model.
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"""
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pass
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@abstractmethod
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def solve(self, tee=False):
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"""
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Solves the currently loaded instance. After this method finishes,
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the best solution found can be retrieved by calling `get_solution`.
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Parameters
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----------
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tee: bool
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If true, prints the solver log to the screen.
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Returns
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-------
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dict
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A dictionary of solver statistics containing the following keys:
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"Lower bound", "Upper bound", "Wallclock time", "Nodes", "Sense",
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"Log" and "Warm start value".
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"""
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pass
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@abstractmethod
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def set_threads(self, threads):
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pass
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@abstractmethod
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def set_time_limit(self, time_limit):
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pass
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@abstractmethod
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def set_node_limit(self, node_limit):
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pass
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@abstractmethod
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def set_gap_tolerance(self, gap_tolerance):
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pass
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