Initial version

test/jump_solver_test.jl

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+#  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 Test
+using MIPLearn
+using CPLEX
+using Gurobi
+
+@testset "varname_split" begin
+    @test MIPLearn.varname_split("x[1]") == ("x", "1")
+end
+
+
+@testset "JuMPSolver" begin
+    for optimizer in [CPLEX.Optimizer, Gurobi.Optimizer]
+        instance = KnapsackInstance([23., 26., 20., 18.],
+                                    [505., 352., 458., 220.],
+                                    67.0)
+        model = instance.to_model()
+
+        solver = JuMPSolver(optimizer=optimizer)
+        solver.set_instance(instance, model)
+        solver.set_time_limit(30)
+        solver.set_warm_start(Dict("x" => Dict(
+            "1" => 1.0,
+            "2" => 0.0,
+            "3" => 0.0,
+            "4" => 1.0,
+        )))
+        stats = solver.solve()
+
+        @test stats["Lower bound"] == 1183.0
+        @test stats["Upper bound"] == 1183.0
+        @test stats["Sense"] == "max"
+        @test stats["Wallclock time"] > 0
+        @test length(stats["Log"]) > 100
+
+        solution = solver.get_solution()
+        @test solution["x"]["1"] == 1.0
+        @test solution["x"]["2"] == 0.0
+        @test solution["x"]["3"] == 1.0
+        @test solution["x"]["4"] == 1.0
+
+        stats = solver.solve_lp()
+        @test round(stats["Optimal value"], digits=3) == 1287.923
+        @test length(stats["Log"]) > 100
+
+        solution = solver.get_solution()
+        @test round(solution["x"]["1"], digits=3) == 1.000
+        @test round(solution["x"]["2"], digits=3) == 0.923
+        @test round(solution["x"]["3"], digits=3) == 1.000
+        @test round(solution["x"]["4"], digits=3) == 0.000
+
+        solver.fix(Dict("x" => Dict(
+            "1" => 1.0,
+            "2" => 0.0,
+            "3" => 0.0,
+            "4" => 1.0,
+        )))
+        stats = solver.solve()
+        @test stats["Lower bound"] == 725.0
+        @test stats["Upper bound"] == 725.0
+    end
+end

test/knapsack.jl

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+#  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.
+
+import MIPLearn: get_instance_features,
+                 get_variable_features
+                 find_violated_lazy_constraints
+using JuMP
+
+struct KnapsackData
+    weights
+    prices
+    capacity
+end
+
+function to_model(data::KnapsackData)
+    model = Model()
+    n = length(data.weights)
+    @variable(model, x[1:n], Bin)
+    @objective(model, Max, sum(x[i] * data.prices[i] for i in 1:n))
+    @constraint(model, sum(x[i] * data.weights[i] for i in 1:n) <= data.capacity)
+    return model
+end
+
+function get_instance_features(data::KnapsackData)
+    return [0.]
+end
+
+
+function get_variable_features(data::KnapsackData, var, index)
+    return [0.]
+end
+
+KnapsackInstance = @Instance(KnapsackData)

test/learning_solver_test.jl

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+#  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 Test
+using MIPLearn
+using CPLEX
+using Gurobi
+
+
+@testset "Instance" begin
+    weights = [23., 26., 20., 18.]
+    prices = [505., 352., 458., 220.]
+    capacity = 67.0
+    
+    instance = KnapsackInstance(weights, prices, capacity)
+    dump(instance, "tmp/instance.json.gz")
+    
+    instance = KnapsackInstance([0.0], [0.0], 0.0)
+    load!(instance, "tmp/instance.json.gz")
+    @test instance.data.weights == weights
+    @test instance.data.prices == prices
+    @test instance.data.capacity == capacity
+end
+
+
+@testset "LearningSolver" begin
+    for optimizer in [CPLEX.Optimizer, Gurobi.Optimizer]
+        instance = KnapsackInstance([23., 26., 20., 18.],
+                                    [505., 352., 458., 220.],
+                                    67.0)
+        solver = LearningSolver(optimizer=optimizer,
+                                mode="heuristic",
+                                time_limit=90)
+        stats = solve!(solver, instance)
+        @test instance.solution["x"]["1"] == 1.0
+        @test instance.solution["x"]["2"] == 0.0
+        @test instance.solution["x"]["3"] == 1.0
+        @test instance.solution["x"]["4"] == 1.0
+        @test instance.lower_bound == 1183.0
+        @test instance.upper_bound == 1183.0
+        @test round(instance.lp_solution["x"]["1"], digits=3) == 1.000
+        @test round(instance.lp_solution["x"]["2"], digits=3) == 0.923
+        @test round(instance.lp_solution["x"]["3"], digits=3) == 1.000
+        @test round(instance.lp_solution["x"]["4"], digits=3) == 0.000
+        @test round(instance.lp_value, digits=3) == 1287.923
+        fit!(solver, [instance])
+        solve!(solver, instance)
+    end
+end

test/runtests.jl

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+#  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 Test
+using MIPLearn
+
+MIPLearn.setup_logger()
+
+@testset "MIPLearn" begin
+    include("knapsack.jl")
+    include("jump_solver_test.jl")
+    include("learning_solver_test.jl")
+end