Add implementation of textbook branch-and-bound method

test/bb/lp_test.jl

@@ -0,0 +1,115 @@
+#  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 Clp
+using JuMP
+using Test
+using MIPLearn.BB
+
+basepath = @__DIR__
+
+function runtests(optimizer_name, optimizer; large = true)
+    @testset "Solve ($optimizer_name)" begin
+        @testset "interface" begin
+            filename = "$basepath/../fixtures/danoint.mps.gz"
+
+            mip = BB.init(optimizer)
+            BB.read!(mip, filename)
+
+            @test mip.sense == 1.0
+            @test length(mip.binary_variables) == 56
+
+            status, obj = BB.solve_relaxation!(mip)
+            @test status == :Optimal
+            @test round(obj, digits = 6) == 62.637280
+
+            @test BB.name(mip, mip.binary_variables[1]) == "xab"
+            @test BB.name(mip, mip.binary_variables[2]) == "xac"
+            @test BB.name(mip, mip.binary_variables[3]) == "xad"
+
+            vals = BB.values(mip, mip.binary_variables)
+            @test round(vals[1], digits = 6) == 0.046933
+            @test round(vals[2], digits = 6) == 0.000841
+            @test round(vals[3], digits = 6) == 0.248696
+
+            # Probe (up and down are feasible)
+            probe_up, probe_down = BB.probe(mip, mip.binary_variables[1])
+            @test round(probe_down, digits = 6) == 62.690000
+            @test round(probe_up, digits = 6) == 62.714100
+
+            # Fix one variable to zero
+            BB.set_bounds!(mip, mip.binary_variables[1:1], [0.0], [0.0])
+            status, obj = BB.solve_relaxation!(mip)
+            @test status == :Optimal
+            @test round(obj, digits = 6) == 62.690000
+
+            # Fix one variable to one and another variable variable to zero
+            BB.set_bounds!(mip, mip.binary_variables[1:2], [1.0, 0.0], [1.0, 0.0])
+            status, obj = BB.solve_relaxation!(mip)
+            @test status == :Optimal
+            @test round(obj, digits = 6) == 62.714777
+
+            # Probe (up is infeasible, down is feasible)
+            BB.set_bounds!(
+                mip,
+                mip.binary_variables[1:3],
+                [1.0, 1.0, 0.0],
+                [1.0, 1.0, 1.0],
+            )
+            status, obj = BB.solve_relaxation!(mip)
+            @test status == :Optimal
+            probe_up, probe_down = BB.probe(mip, mip.binary_variables[3])
+            @test round(probe_up, digits = 6) == Inf
+            @test round(probe_down, digits = 6) == 63.073992
+
+            # Fix all binary variables to one, making problem infeasible
+            N = length(mip.binary_variables)
+            BB.set_bounds!(mip, mip.binary_variables, ones(N), ones(N))
+            status, obj = BB.solve_relaxation!(mip)
+            @test status == :Infeasible
+            @test obj == Inf
+
+            # Restore original problem
+            N = length(mip.binary_variables)
+            BB.set_bounds!(mip, mip.binary_variables, zeros(N), ones(N))
+            status, obj = BB.solve_relaxation!(mip)
+            @test status == :Optimal
+            @test round(obj, digits = 6) == 62.637280
+        end
+
+        @testset "varbranch" begin
+            branch_rules = [
+                BB.RandomBranching(),
+                BB.FirstInfeasibleBranching(),
+                BB.LeastInfeasibleBranching(),
+                BB.MostInfeasibleBranching(),
+                BB.PseudocostBranching(),
+                BB.StrongBranching(),
+                BB.ReliabilityBranching(),
+                BB.HybridBranching(),
+            ]
+            for branch_rule in branch_rules
+                filename = "$basepath/../fixtures/vpm2.mps.gz"
+                mip = BB.init(optimizer)
+                BB.read!(mip, filename)
+                @info optimizer_name, branch_rule
+                @time BB.solve!(
+                    mip,
+                    initial_primal_bound = 13.75,
+                    print_interval = 10,
+                    node_limit = 100,
+                    branch_rule = branch_rule,
+                )
+            end
+        end
+    end
+end
+
+@testset "BB" begin
+    @time runtests("Clp", Clp.Optimizer)
+    if is_gurobi_available
+        using Gurobi
+        @time runtests("Gurobi", Gurobi.Optimizer)
+    end
+end

test/runtests.jl

@@ -6,6 +6,7 @@ using Test
 using MIPLearn
 
 MIPLearn.setup_logger()
+const is_gurobi_available = ("GUROBI_HOME" in keys(ENV))
 
 @testset "MIPLearn" begin
     include("fixtures/knapsack.jl")
@@ -15,4 +16,5 @@ MIPLearn.setup_logger()
     include("solvers/learning_solver_test.jl")
     # include("utils/benchmark_test.jl")
     include("utils/parse_test.jl")
+    include("bb/lp_test.jl")
 end

test/solvers/jump_solver_test.jl

@@ -8,7 +8,6 @@ using MIPLearn
 using PyCall
 using Test
 
-const is_gurobi_available = ("GUROBI_HOME" in keys(ENV))
 if is_gurobi_available
     using Gurobi
 end