mirror of
https://github.com/ANL-CEEESA/MIPLearn.jl.git
synced 2025-12-06 08:28:52 -06:00
Allow module to be precompiled
This commit is contained in:
@@ -2,12 +2,21 @@
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# Copyright (C) 2020-2021, 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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__precompile__(false)
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module MIPLearn
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using PyCall
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global miplearn = pyimport("miplearn")
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global traceback = pyimport("traceback")
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global DynamicLazyConstraintsComponent = PyNULL()
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global JuMPSolver = PyNULL()
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global MinPrecisionThreshold = PyNULL()
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global miplearn = PyNULL()
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global ObjectiveValueComponent = PyNULL()
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global PrimalSolutionComponent = PyNULL()
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global PyFileInstance = PyNULL()
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global PyJuMPInstance = PyNULL()
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global StaticLazyConstraintsComponent = PyNULL()
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global traceback = PyNULL()
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global UserCutsComponent = PyNULL()
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include("utils/log.jl")
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include("utils/exceptions.jl")
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@@ -19,12 +28,19 @@ include("solvers/learning.jl")
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include("solvers/macros.jl")
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include("utils/benchmark.jl")
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DynamicLazyConstraintsComponent = miplearn.DynamicLazyConstraintsComponent
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UserCutsComponent = miplearn.UserCutsComponent
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ObjectiveValueComponent = miplearn.ObjectiveValueComponent
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PrimalSolutionComponent = miplearn.PrimalSolutionComponent
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StaticLazyConstraintsComponent = miplearn.StaticLazyConstraintsComponent
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MinPrecisionThreshold = miplearn.MinPrecisionThreshold
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function __init__()
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copy!(miplearn, pyimport("miplearn"))
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copy!(traceback, pyimport("traceback"))
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copy!(DynamicLazyConstraintsComponent, miplearn.DynamicLazyConstraintsComponent)
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copy!(UserCutsComponent, miplearn.UserCutsComponent)
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copy!(ObjectiveValueComponent, miplearn.ObjectiveValueComponent)
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copy!(PrimalSolutionComponent, miplearn.PrimalSolutionComponent)
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copy!(StaticLazyConstraintsComponent, miplearn.StaticLazyConstraintsComponent)
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copy!(MinPrecisionThreshold, miplearn.MinPrecisionThreshold)
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__init_PyFileInstance__()
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__init_PyJuMPInstance__()
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__init_JuMPSolver__()
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end
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export DynamicLazyConstraintsComponent,
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UserCutsComponent,
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@@ -3,56 +3,58 @@
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# Released under the modified BSD license. See COPYING.md for more details.
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@pydef mutable struct PyFileInstance <: miplearn.Instance
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function __init__(self, filename)
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self.filename = filename
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self.loaded = nothing
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self.samples = nothing
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end
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function __init_PyFileInstance__()
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@pydef mutable struct Class <: miplearn.Instance
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function __init__(self, filename)
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self.filename = filename
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self.loaded = nothing
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self.samples = nothing
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end
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function to_model(self)
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return self.loaded.py.to_model()
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end
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function to_model(self)
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return self.loaded.py.to_model()
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end
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function get_instance_features(self)
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return self.loaded.py.get_instance_features()
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end
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function get_instance_features(self)
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return self.loaded.py.get_instance_features()
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end
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function get_variable_features(self, var_name)
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return self.loaded.py.get_variable_features(var_name)
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end
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function get_variable_features(self, var_name)
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return self.loaded.py.get_variable_features(var_name)
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end
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function get_variable_category(self, var_name)
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return self.loaded.py.get_variable_category(var_name)
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end
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function get_variable_category(self, var_name)
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return self.loaded.py.get_variable_category(var_name)
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end
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function get_constraint_features(self, cname)
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return self.loaded.py.get_constraint_features(cname)
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end
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function get_constraint_features(self, cname)
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return self.loaded.py.get_constraint_features(cname)
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end
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function get_constraint_category(self, cname)
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return self.loaded.py.get_constraint_category(cname)
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end
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function get_constraint_category(self, cname)
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return self.loaded.py.get_constraint_category(cname)
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end
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function load(self)
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if self.loaded === nothing
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self.loaded = load_instance(self.filename)
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self.samples = self.loaded.py.samples
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function load(self)
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if self.loaded === nothing
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self.loaded = load_instance(self.filename)
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self.samples = self.loaded.py.samples
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end
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end
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function free(self)
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self.loaded = nothing
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self.samples = nothing
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end
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function flush(self)
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self.loaded.py.samples = self.samples
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save(self.filename, self.loaded)
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end
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end
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function free(self)
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self.loaded = nothing
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self.samples = nothing
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end
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function flush(self)
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self.loaded.py.samples = self.samples
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save(self.filename, self.loaded)
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end
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copy!(PyFileInstance, Class)
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end
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struct FileInstance <: Instance
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py::PyCall.PyObject
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filename::AbstractString
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@@ -5,45 +5,47 @@
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using JuMP
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using JLD2
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function __init_PyJuMPInstance__()
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@pydef mutable struct Class <: miplearn.Instance
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function __init__(self, model)
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init_miplearn_ext(model)
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self.model = model
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self.samples = []
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end
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@pydef mutable struct PyJuMPInstance <: miplearn.Instance
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function __init__(self, model)
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init_miplearn_ext(model)
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self.model = model
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self.samples = []
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end
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function to_model(self)
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return self.model
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end
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function to_model(self)
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return self.model
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end
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function get_instance_features(self)
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return self.model.ext[:miplearn][:instance_features]
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end
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function get_instance_features(self)
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return self.model.ext[:miplearn][:instance_features]
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end
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function get_variable_features(self, var_name)
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model = self.model
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v = variable_by_name(model, var_name)
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return get(model.ext[:miplearn][:variable_features], v, nothing)
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end
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function get_variable_features(self, var_name)
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model = self.model
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v = variable_by_name(model, var_name)
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return get(model.ext[:miplearn][:variable_features], v, nothing)
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end
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function get_variable_category(self, var_name)
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model = self.model
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v = variable_by_name(model, var_name)
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return get(model.ext[:miplearn][:variable_categories], v, nothing)
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end
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function get_variable_category(self, var_name)
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model = self.model
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v = variable_by_name(model, var_name)
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return get(model.ext[:miplearn][:variable_categories], v, nothing)
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end
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function get_constraint_features(self, cname)
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model = self.model
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c = constraint_by_name(model, cname)
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return get(model.ext[:miplearn][:constraint_features], c, nothing)
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end
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function get_constraint_features(self, cname)
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model = self.model
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c = constraint_by_name(model, cname)
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return get(model.ext[:miplearn][:constraint_features], c, nothing)
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end
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function get_constraint_category(self, cname)
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model = self.model
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c = constraint_by_name(model, cname)
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return get(model.ext[:miplearn][:constraint_categories], c, nothing)
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function get_constraint_category(self, cname)
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model = self.model
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c = constraint_by_name(model, cname)
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return get(model.ext[:miplearn][:constraint_categories], c, nothing)
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end
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end
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copy!(PyJuMPInstance, Class)
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end
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@@ -467,144 +467,147 @@ function get_constraints(
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end
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@pydef mutable struct JuMPSolver <: miplearn.solvers.internal.InternalSolver
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function __init__(self, optimizer_factory)
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self.data = JuMPSolverData(
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optimizer_factory,
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Dict(), # varname_to_var
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Dict(), # cname_to_constr
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nothing, # instance
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nothing, # model
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[], # bin_vars
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Dict(), # solution
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[], # reduced_costs
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Dict(), # dual_values
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)
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end
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function add_constraints(self, cf)
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lhs = cf.lhs
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if lhs isa Matrix
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# Undo incorrect automatic conversion performed by PyCall
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lhs = [col[:] for col in eachcol(lhs)]
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function __init_JuMPSolver__()
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@pydef mutable struct Class <: miplearn.solvers.internal.InternalSolver
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function __init__(self, optimizer_factory)
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self.data = JuMPSolverData(
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optimizer_factory,
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Dict(), # varname_to_var
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Dict(), # cname_to_constr
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nothing, # instance
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nothing, # model
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[], # bin_vars
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Dict(), # solution
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[], # reduced_costs
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Dict(), # dual_values
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)
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end
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add_constraints(
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self.data,
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lhs=lhs,
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rhs=cf.rhs,
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senses=cf.senses,
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names=cf.names,
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)
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end
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function are_constraints_satisfied(self, cf; tol=1e-5)
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lhs = cf.lhs
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if lhs isa Matrix
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# Undo incorrect automatic conversion performed by PyCall
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lhs = [col[:] for col in eachcol(lhs)]
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function add_constraints(self, cf)
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lhs = cf.lhs
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if lhs isa Matrix
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# Undo incorrect automatic conversion performed by PyCall
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lhs = [col[:] for col in eachcol(lhs)]
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end
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add_constraints(
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self.data,
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lhs=lhs,
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rhs=cf.rhs,
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senses=cf.senses,
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names=cf.names,
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)
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end
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return are_constraints_satisfied(
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function are_constraints_satisfied(self, cf; tol=1e-5)
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lhs = cf.lhs
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if lhs isa Matrix
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# Undo incorrect automatic conversion performed by PyCall
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lhs = [col[:] for col in eachcol(lhs)]
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end
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return are_constraints_satisfied(
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self.data,
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lhs=lhs,
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rhs=cf.rhs,
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senses=cf.senses,
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tol=tol,
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)
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end
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build_test_instance_infeasible(self) =
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build_test_instance_infeasible()
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build_test_instance_knapsack(self) =
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build_test_instance_knapsack()
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clone(self) = JuMPSolver(self.data.optimizer_factory)
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fix(self, solution) =
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fix!(self.data, solution)
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get_solution(self) =
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isempty(self.data.solution) ? nothing : self.data.solution
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get_constraints(
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self;
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with_static=true,
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with_sa=true,
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with_lhs=true,
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) = get_constraints(
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self.data,
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lhs=lhs,
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rhs=cf.rhs,
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senses=cf.senses,
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tol=tol,
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with_static=with_static,
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)
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get_constraint_attrs(self) = [
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# "basis_status",
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"categories",
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"dual_values",
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"lazy",
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"lhs",
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"names",
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"rhs",
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# "sa_rhs_down",
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# "sa_rhs_up",
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"senses",
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# "slacks",
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"user_features",
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]
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get_variables(
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self;
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with_static=true,
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with_sa=true,
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) = get_variables(self.data; with_static=with_static)
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get_variable_attrs(self) = [
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"names",
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# "basis_status",
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"categories",
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"lower_bounds",
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"obj_coeffs",
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"reduced_costs",
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# "sa_lb_down",
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# "sa_lb_up",
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# "sa_obj_down",
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# "sa_obj_up",
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# "sa_ub_down",
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# "sa_ub_up",
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"types",
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"upper_bounds",
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"user_features",
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"values",
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]
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is_infeasible(self) =
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is_infeasible(self.data)
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remove_constraints(self, names) =
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remove_constraints(
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self.data,
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[n for n in names],
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)
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set_instance(self, instance, model=nothing) =
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set_instance!(self.data, instance, model=model)
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set_warm_start(self, solution) =
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set_warm_start!(self.data, solution)
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solve(
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self;
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tee=false,
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iteration_cb=nothing,
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lazy_cb=nothing,
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user_cut_cb=nothing,
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) = solve(
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self.data,
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tee=tee,
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iteration_cb=iteration_cb,
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)
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solve_lp(self; tee=false) =
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solve_lp(self.data, tee=tee)
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end
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build_test_instance_infeasible(self) =
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build_test_instance_infeasible()
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build_test_instance_knapsack(self) =
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build_test_instance_knapsack()
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clone(self) = JuMPSolver(self.data.optimizer_factory)
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fix(self, solution) =
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fix!(self.data, solution)
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get_solution(self) =
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isempty(self.data.solution) ? nothing : self.data.solution
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get_constraints(
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self;
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with_static=true,
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with_sa=true,
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with_lhs=true,
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) = get_constraints(
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self.data,
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with_static=with_static,
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)
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get_constraint_attrs(self) = [
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# "basis_status",
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"categories",
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"dual_values",
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"lazy",
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"lhs",
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"names",
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"rhs",
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# "sa_rhs_down",
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# "sa_rhs_up",
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"senses",
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# "slacks",
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"user_features",
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]
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get_variables(
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self;
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with_static=true,
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with_sa=true,
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) = get_variables(self.data; with_static=with_static)
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get_variable_attrs(self) = [
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"names",
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# "basis_status",
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"categories",
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"lower_bounds",
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"obj_coeffs",
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"reduced_costs",
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# "sa_lb_down",
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# "sa_lb_up",
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# "sa_obj_down",
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# "sa_obj_up",
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# "sa_ub_down",
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# "sa_ub_up",
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"types",
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"upper_bounds",
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"user_features",
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"values",
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]
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is_infeasible(self) =
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is_infeasible(self.data)
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remove_constraints(self, names) =
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remove_constraints(
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self.data,
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[n for n in names],
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)
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set_instance(self, instance, model=nothing) =
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set_instance!(self.data, instance, model=model)
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set_warm_start(self, solution) =
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set_warm_start!(self.data, solution)
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solve(
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self;
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tee=false,
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iteration_cb=nothing,
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lazy_cb=nothing,
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user_cut_cb=nothing,
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) = solve(
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self.data,
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tee=tee,
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iteration_cb=iteration_cb,
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)
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solve_lp(self; tee=false) =
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solve_lp(self.data, tee=tee)
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copy!(JuMPSolver, Class)
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end
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|
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|
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|
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