Project.toml

@@ -1,7 +1,7 @@
 name = "MIPLearn"
 uuid = "2b1277c3-b477-4c49-a15e-7ba350325c68"
 authors = ["Alinson S Xavier <git@axavier.org>"]
-version = "0.4.2"
+version = "0.4.0"
 
 [deps]
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
@@ -41,5 +41,3 @@ Requires = "1"
 Statistics = "1"
 TimerOutputs = "0.5"
 julia = "1"
-PrecompileTools = "1"
-SCIP = "0.12"

deps/build.jl

@@ -5,7 +5,7 @@ function install_miplearn()
     Conda.update()
     pip = joinpath(dirname(pyimport("sys").executable), "pip")
     isfile(pip) || error("$pip: invalid path")
-    run(`$pip install miplearn==0.4.2`)
+    run(`$pip install miplearn==0.4.0`)
 end
 
 install_miplearn()

src/BB/lp.jl

@@ -134,11 +134,7 @@ function _get_int_variables(
                 var_ub = constr.upper
                 MOI.delete(optimizer, _upper_bound_index(var))
             end
-            MOI.add_constraint(
+            MOI.add_constraint(optimizer, var, MOI.Interval(var_lb, var_ub))
-                optimizer,
-                MOI.VariableIndex(var.index),
-                MOI.Interval(var_lb, var_ub),
-            )
         end
         push!(vars, var)
         push!(lb, var_lb)

src/MIPLearn.jl

@@ -37,48 +37,48 @@ include("Cuts/Cuts.jl")
 # Precompilation
 # =============================================================================
 
-# function __precompile_cuts__()
+function __precompile_cuts__()
-#     function build_model(mps_filename)
+    function build_model(mps_filename)
-#         model = read_from_file(mps_filename)
+        model = read_from_file(mps_filename)
-#         set_optimizer(model, SCIP.Optimizer)
+        set_optimizer(model, SCIP.Optimizer)
-#         return JumpModel(model)
+        return JumpModel(model)
-#     end
+    end
-#     BASEDIR = dirname(@__FILE__)
+    BASEDIR = dirname(@__FILE__)
-#     mps_filename = "$BASEDIR/../test/fixtures/bell5.mps.gz"
+    mps_filename = "$BASEDIR/../test/fixtures/bell5.mps.gz"
-#     h5_filename = "$BASEDIR/../test/fixtures/bell5.h5"
+    h5_filename = "$BASEDIR/../test/fixtures/bell5.h5"
-#     collect_gmi_dual(
+    collect_gmi_dual(
-#         mps_filename;
+        mps_filename;
-#         optimizer=HiGHS.Optimizer,
+        optimizer=HiGHS.Optimizer,
-#         max_rounds = 10,
+        max_rounds = 10,
-#         max_cuts_per_round = 500,
+        max_cuts_per_round = 500,
-#     )
+    )
-#     knn = KnnDualGmiComponent(
+    knn = KnnDualGmiComponent(
-#         extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
+        extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
-#         k = 2,
+        k = 2,
-#     )
+    )
-#     knn.fit([h5_filename, h5_filename])
+    knn.fit([h5_filename, h5_filename])
-#     solver = LearningSolver(
+    solver = LearningSolver(
-#         components = [
+        components = [
-#             ExpertPrimalComponent(action = SetWarmStart()),
+            ExpertPrimalComponent(action = SetWarmStart()),
-#             knn,
+            knn,
-#         ],
+        ],
-#         skip_lp = true,
+        skip_lp = true,
-#     )
+    )
-#     solver.optimize(mps_filename, build_model)
+    solver.optimize(mps_filename, build_model)
-# end
+end
 
-# @setup_workload begin
+@setup_workload begin
-#     using SCIP
+    using SCIP
-#     using HiGHS
+    using HiGHS
-#     using MIPLearn.Cuts
+    using MIPLearn.Cuts
-#     using PrecompileTools: @setup_workload, @compile_workload
+    using PrecompileTools: @setup_workload, @compile_workload
 
-#     __init__()
+    __init__()
-#     Cuts.__init__()
+    Cuts.__init__()
 
-#     @compile_workload begin
+    @compile_workload begin
-#         __precompile_cuts__()
+        __precompile_cuts__()
-#     end
+    end
-# end
+end
 
 end # module