mirror of
https://github.com/ANL-CEEESA/MIPLearn.jl.git
synced 2025-12-06 00:18:51 -06:00
Make JuMPSolver pass all tests
This commit is contained in:
@@ -83,6 +83,33 @@ function _update_solution!(data::JuMPSolverData)
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end
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function add_constraints(
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data::JuMPSolverData;
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lhs::Vector{Vector{Tuple{String, Float64}}},
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rhs::Vector{Float64},
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senses::Vector{String},
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names::Vector{String},
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)::Nothing
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for (i, sense) in enumerate(senses)
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lhs_expr = AffExpr(0.0)
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for (varname, coeff) in lhs[i]
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var = data.varname_to_var[varname]
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add_to_expression!(lhs_expr, var, coeff)
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end
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if sense == "<"
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constr = @constraint(data.model, lhs_expr <= rhs[i])
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elseif sense == ">"
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constr = @constraint(data.model, lhs_expr >= rhs[i])
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else
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constr = @constraint(data.model, lhs_expr == rhs[i])
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end
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set_name(constr, names[i])
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data.cname_to_constr[names[i]] = constr
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end
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return
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end
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function are_constraints_satisfied(
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data::JuMPSolverData;
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lhs::Vector{Vector{Tuple{String, Float64}}},
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@@ -109,30 +136,28 @@ function are_constraints_satisfied(
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end
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function add_constraints(
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data::JuMPSolverData;
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lhs::Vector{Vector{Tuple{String, Float64}}},
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rhs::Vector{Float64},
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senses::Vector{String},
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names::Vector{String},
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)::Nothing
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for (i, sense) in enumerate(senses)
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lhs_expr = AffExpr(0.0)
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for (varname, coeff) in lhs[i]
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var = data.varname_to_var[varname]
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add_to_expression!(lhs_expr, var, coeff)
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function build_test_instance_knapsack()
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weights = [23.0, 26.0, 20.0, 18.0]
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prices = [505.0, 352.0, 458.0, 220.0]
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capacity = 67.0
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model = Model()
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n = length(weights)
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@variable(model, x[0:n-1], Bin)
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@variable(model, z, lower_bound=0.0, upper_bound=capacity)
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@objective(model, Max, sum(x[i-1] * prices[i] for i in 1:n))
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@constraint(model, eq_capacity, sum(x[i-1] * weights[i] for i in 1:n) - z == 0)
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return JuMPInstance(model)
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end
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if sense == "<"
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constr = @constraint(data.model, lhs_expr <= rhs[i])
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elseif sense == ">"
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constr = @constraint(data.model, lhs_expr >= rhs[i])
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else
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constr = @constraint(data.model, lhs_expr == rhs[i])
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end
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set_name(constr, names[i])
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data.cname_to_constr[names[i]] = constr
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end
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return
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function build_test_instance_infeasible()
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model = Model()
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@variable(model, x, Bin)
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@objective(model, Max, x)
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@constraint(model, x >= 2)
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return JuMPInstance(model)
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end
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@@ -166,9 +191,15 @@ function solve(
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break
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end
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end
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if is_infeasible(data)
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data.solution = Dict()
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primal_bound = nothing
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dual_bound = nothing
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else
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_update_solution!(data)
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primal_bound = JuMP.objective_value(model)
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dual_bound = JuMP.objective_bound(model)
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end
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if JuMP.objective_sense(model) == MOI.MIN_SENSE
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sense = "min"
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lower_bound = dual_bound
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@@ -200,8 +231,13 @@ function solve_lp(data::JuMPSolverData; tee::Bool=false)
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wallclock_time = @elapsed begin
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log = _optimize_and_capture_output!(model, tee=tee)
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end
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if is_infeasible(data)
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data.solution = Dict()
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obj_value = nothing
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else
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_update_solution!(data)
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obj_value = JuMP.objective_value(model)
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end
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for var in bin_vars
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JuMP.set_binary(var)
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end
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@@ -213,17 +249,24 @@ function solve_lp(data::JuMPSolverData; tee::Bool=false)
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end
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function set_instance!(data::JuMPSolverData, instance, model::JuMP.Model)
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function set_instance!(
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data::JuMPSolverData,
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instance;
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model::Union{Nothing,JuMP.Model},
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)::Nothing
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data.instance = instance
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if model === nothing
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model = instance.to_model()
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end
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data.model = model
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data.bin_vars = [
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var
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for var in JuMP.all_variables(data.model)
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for var in JuMP.all_variables(model)
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if JuMP.is_binary(var)
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]
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data.varname_to_var = Dict(
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JuMP.name(var) => var
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for var in JuMP.all_variables(data.model)
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for var in JuMP.all_variables(model)
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)
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JuMP.set_optimizer(model, data.optimizer_factory)
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data.cname_to_constr = Dict()
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@@ -234,6 +277,7 @@ function set_instance!(data::JuMPSolverData, instance, model::JuMP.Model)
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data.cname_to_constr[name] = constr
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end
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end
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return
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end
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@@ -256,7 +300,10 @@ end
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function is_infeasible(data::JuMPSolverData)
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return JuMP.termination_status(data.model) == MOI.INFEASIBLE
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return JuMP.termination_status(data.model) in [
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MOI.INFEASIBLE,
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MOI.INFEASIBLE_OR_UNBOUNDED,
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]
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end
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@@ -409,22 +456,6 @@ function get_constraints(
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end
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function build_test_instance_knapsack()
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weights = [23.0, 26.0, 20.0, 18.0]
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prices = [505.0, 352.0, 458.0, 220.0]
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capacity = 67.0
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model = Model()
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n = length(weights)
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@variable(model, x[0:n-1], Bin)
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@variable(model, z, lower_bound=0.0, upper_bound=capacity)
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@objective(model, Max, sum(x[i-1] * prices[i] for i in 1:n))
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@constraint(model, eq_capacity, sum(x[i-1] * weights[i] for i in 1:n) - z == 0)
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return JuMPInstance(model)
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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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@@ -459,19 +490,18 @@ end
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)...)
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build_test_instance_infeasible(self) =
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error("not implemented")
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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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# FIXME: Actually clone instead of returning self
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clone(self) = self
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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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self.data.solution
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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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@@ -529,8 +559,8 @@ end
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[n for n in names],
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)
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set_instance(self, instance, model) =
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set_instance!(self.data, instance, model)
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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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