parent
72aa1b0cdf
commit
1fefca584e
@ -0,0 +1,137 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
using JuMP
|
||||
using MathOptInterface
|
||||
const MOI = MathOptInterface
|
||||
|
||||
function varname_split(varname::String)
|
||||
m = match(r"([^[]*)\[(.*)\]", varname)
|
||||
return m.captures[1], m.captures[2]
|
||||
end
|
||||
|
||||
@pydef mutable struct JuMPSolver <: InternalSolver
|
||||
function __init__(self; optimizer=CPLEX.Optimizer)
|
||||
self.optimizer = optimizer
|
||||
end
|
||||
|
||||
function add_constraint(self, constraint)
|
||||
@error "JuMPSolver: add_constraint not implemented"
|
||||
end
|
||||
|
||||
function set_warm_start(self, solution)
|
||||
for (basename, subsolution) in solution
|
||||
for (idx, value) in subsolution
|
||||
value != nothing || continue
|
||||
var = self.basename_idx_to_var[basename, idx]
|
||||
JuMP.set_start_value(var, value)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function clear_warm_start(self)
|
||||
@error "JuMPSolver: clear_warm_start not implemented"
|
||||
end
|
||||
|
||||
function fix(self, solution)
|
||||
for (basename, subsolution) in solution
|
||||
for (idx, value) in subsolution
|
||||
value != nothing || continue
|
||||
var = self.basename_idx_to_var[basename, idx]
|
||||
JuMP.fix(var, value, force=true)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function set_instance(self, instance, model)
|
||||
self.instance = instance
|
||||
self.model = model
|
||||
self.var_to_basename_idx = Dict(var => varname_split(JuMP.name(var))
|
||||
for var in JuMP.all_variables(self.model))
|
||||
self.basename_idx_to_var = Dict(varname_split(JuMP.name(var)) => var
|
||||
for var in JuMP.all_variables(self.model))
|
||||
self.bin_vars = [var
|
||||
for var in JuMP.all_variables(self.model)
|
||||
if JuMP.is_binary(var)]
|
||||
JuMP.set_optimizer(self.model, self.optimizer)
|
||||
end
|
||||
|
||||
function solve(self; tee=false)
|
||||
JuMP.optimize!(self.model)
|
||||
self._update_solution()
|
||||
|
||||
primal_bound = JuMP.objective_value(self.model)
|
||||
dual_bound = JuMP.objective_bound(self.model)
|
||||
|
||||
if JuMP.objective_sense(self.model) == MOI.MIN_SENSE
|
||||
sense = "min"
|
||||
lower_bound = dual_bound
|
||||
upper_bound = primal_bound
|
||||
else
|
||||
sense = "max"
|
||||
lower_bound = primal_bound
|
||||
upper_bound = dual_bound
|
||||
end
|
||||
|
||||
@show primal_bound, dual_bound
|
||||
|
||||
return Dict("Lower bound" => lower_bound,
|
||||
"Upper bound" => upper_bound,
|
||||
"Sense" => sense,
|
||||
"Wallclock time" => JuMP.solve_time(self.model),
|
||||
"Nodes" => 1,
|
||||
"Log" => nothing,
|
||||
"Warm start value" => nothing)
|
||||
end
|
||||
|
||||
function solve_lp(self; tee=false)
|
||||
for var in self.bin_vars
|
||||
JuMP.unset_binary(var)
|
||||
JuMP.set_upper_bound(var, 1.0)
|
||||
JuMP.set_lower_bound(var, 0.0)
|
||||
end
|
||||
|
||||
JuMP.optimize!(self.model)
|
||||
obj_value = JuMP.objective_value(self.model)
|
||||
self._update_solution()
|
||||
|
||||
for var in self.bin_vars
|
||||
JuMP.set_binary(var)
|
||||
end
|
||||
|
||||
return Dict("Optimal value" => obj_value)
|
||||
end
|
||||
|
||||
function get_solution(self)
|
||||
return self.solution
|
||||
end
|
||||
|
||||
function _update_solution(self)
|
||||
solution = Dict()
|
||||
for var in JuMP.all_variables(self.model)
|
||||
basename, idx = self.var_to_basename_idx[var]
|
||||
if !haskey(solution, basename)
|
||||
solution[basename] = Dict()
|
||||
end
|
||||
solution[basename][idx] = JuMP.value(var)
|
||||
end
|
||||
self.solution = solution
|
||||
end
|
||||
|
||||
function set_gap_tolerance(self, gap_tolerance)
|
||||
@error "JuMPSolver: set_gap_tolerance not implemented"
|
||||
end
|
||||
|
||||
function set_node_limit(self)
|
||||
@error "JuMPSolver: set_node_limit not implemented"
|
||||
end
|
||||
|
||||
function set_threads(self, threads)
|
||||
@error "JuMPSolver: set_threads not implemented"
|
||||
end
|
||||
|
||||
function set_time_limit(self, time_limit)
|
||||
JuMP.set_time_limit_sec(self.model, time_limit)
|
||||
end
|
||||
end
|
||||
@ -0,0 +1,32 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
using PyCall
|
||||
|
||||
@pydef mutable struct KnapsackInstance <: Instance
|
||||
function __init__(self, weights, prices, capacity)
|
||||
self.weights = weights
|
||||
self.prices = prices
|
||||
self.capacity = capacity
|
||||
end
|
||||
|
||||
function to_model(self)
|
||||
model = Model()
|
||||
n = length(self.weights)
|
||||
@variable(model, x[1:n], Bin)
|
||||
@objective(model, Max, sum(x[i] * self.prices[i] for i in 1:n))
|
||||
@constraint(model, sum(x[i] * self.weights[i] for i in 1:n) <= self.capacity)
|
||||
return model
|
||||
end
|
||||
|
||||
function get_instance_features(self)
|
||||
return [0.]
|
||||
end
|
||||
|
||||
function get_variable_features(self, var, index)
|
||||
return [0.]
|
||||
end
|
||||
end
|
||||
|
||||
export KnapsackInstance
|
||||
Loading…
Reference in new issue