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46 lines
1.1 KiB
46 lines
1.1 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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from typing import TypedDict, Optional, Dict, Callable, Any
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TrainingSample = TypedDict(
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"TrainingSample",
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{
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"LP log": Optional[str],
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"LP solution": Optional[Dict],
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"LP value": Optional[float],
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"Lower bound": Optional[float],
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"MIP log": Optional[str],
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"Solution": Optional[Dict],
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"Upper bound": Optional[float],
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},
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total=False,
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)
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LPSolveStats = TypedDict(
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"LPSolveStats",
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{
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"Optimal value": float,
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"Log": str,
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},
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)
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MIPSolveStats = TypedDict(
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"MIPSolveStats",
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{
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"Lower bound": Optional[float],
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"Upper bound": Optional[float],
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"Wallclock time": float,
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"Nodes": Optional[int],
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"Sense": str,
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"Log": str,
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"Warm start value": Optional[float],
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"LP value": Optional[float],
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},
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)
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IterationCallback = Callable[[], bool]
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LazyCallback = Callable[[Any, Any], None]
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