Make lazy constraints compatible with JuMP

src/MIPLearn.jl

@@ -13,6 +13,7 @@ include("extractors.jl")
 include("io.jl")
 include("problems/setcover.jl")
 include("problems/stab.jl")
+include("problems/tsp.jl")
 include("solvers/jump.jl")
 include("solvers/learning.jl")
 
@@ -23,6 +24,7 @@ function __init__()
     __init_io__()
     __init_problems_setcover__()
     __init_problems_stab__()
+    __init_problems_tsp__()
     __init_solvers_jump__()
     __init_solvers_learning__()
 end

src/problems/tsp.jl

@@ -0,0 +1,71 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using JuMP
+
+global TravelingSalesmanData = PyNULL()
+global TravelingSalesmanGenerator = PyNULL()
+
+function __init_problems_tsp__()
+    copy!(TravelingSalesmanData, pyimport("miplearn.problems.tsp").TravelingSalesmanData)
+    copy!(TravelingSalesmanGenerator, pyimport("miplearn.problems.tsp").TravelingSalesmanGenerator)
+end
+
+function build_tsp_model_jump(data::Any; optimizer=HiGHS.Optimizer)
+    nx = pyimport("networkx")
+
+    if data isa String
+        data = read_pkl_gz(data)
+    end
+    model = Model(optimizer)
+    edges = [(i, j) for i in 1:data.n_cities for j in (i+1):data.n_cities]
+    x = @variable(model, x[edges], Bin)
+    @objective(model, Min, sum(
+        x[(i, j)] * data.distances[i, j] for (i, j) in edges
+    ))
+
+    # Eq: Must choose two edges adjacent to each node
+    @constraint(
+        model,
+        eq_degree[i in 1:data.n_cities],
+        sum(x[(min(i, j), max(i, j))] for j in 1:data.n_cities if i != j) == 2
+    )
+
+    function lazy_separate(cb_data)
+        x_val = callback_value.(Ref(cb_data), x)
+        violations = []
+        selected_edges = [e for e in edges if x_val[e] > 0.5]
+        graph = nx.Graph()
+        graph.add_edges_from(selected_edges)
+        for component in nx.connected_components(graph)
+            if length(component) < data.n_cities
+                cut_edges = [
+                    [e[1], e[2]]
+                    for e in edges
+                    if (e[1] ∈ component && e[2] ∉ component)
+                    ||
+                    (e[1] ∉ component && e[2] ∈ component)
+                ]
+                push!(violations, cut_edges)
+            end
+        end
+        return violations
+    end
+
+    function lazy_enforce(violations)
+        @info "Adding $(length(violations)) subtour elimination eqs..."
+        for violation in violations
+            constr = @build_constraint(sum(x[(e[1], e[2])] for e in violation) >= 2)
+            submit(model, constr)
+        end
+    end
+
+    return JumpModel(
+        model,
+        lazy_enforce=lazy_enforce,
+        lazy_separate=lazy_separate,
+    )
+end
+
+export TravelingSalesmanData, TravelingSalesmanGenerator, build_tsp_model_jump

src/solvers/jump.jl

@@ -12,9 +12,12 @@ Base.@kwdef mutable struct _JumpModelExtData
     aot_cuts = nothing
     cb_data = nothing
     cuts = []
+    lazy = []
     where::Symbol = :WHERE_DEFAULT
     cuts_enforce::Union{Function,Nothing} = nothing
     cuts_separate::Union{Function,Nothing} = nothing
+    lazy_enforce::Union{Function,Nothing} = nothing
+    lazy_separate::Union{Function,Nothing} = nothing
 end
 
 function JuMP.copy_extension_data(
@@ -58,8 +61,10 @@ function submit(model::JuMP.Model, constr)
     ext = model.ext[:miplearn]
     if ext.where == :WHERE_CUTS
         MOI.submit(model, MOI.UserCut(ext.cb_data), constr)
+    elseif ext.where == :WHERE_LAZY
+        MOI.submit(model, MOI.LazyConstraint(ext.cb_data), constr)
     else
-        error("not implemented")
+        add_constraint(model, constr)
     end
 end
 
@@ -281,9 +286,10 @@ function _extract_after_mip(model::JuMP.Model, h5)
     slacks = abs.(lhs * x - rhs)
     h5.put_array("mip_constr_slacks", slacks)
 
-    # Cuts
+    # Cuts and lazy constraints
     ext = model.ext[:miplearn]
     h5.put_scalar("mip_cuts", JSON.json(ext.cuts))
+    h5.put_scalar("mip_lazy", JSON.json(ext.lazy))
 end
 
 function _fix_variables(model::JuMP.Model, var_names, var_values, stats)
@@ -318,6 +324,23 @@ function _optimize(model::JuMP.Model)
         set_attribute(model, MOI.UserCutCallback(), cut_callback)
     end
 
+    # Set up lazy constraint callbacks
+    ext.lazy = []
+    function lazy_callback(cb_data)
+        ext.cb_data = cb_data
+        ext.where = :WHERE_LAZY
+        violations = ext.lazy_separate(cb_data)
+        for v in violations
+            push!(ext.lazy, v)
+        end
+        if !isempty(violations)
+            ext.lazy_enforce(violations)
+        end
+    end
+    if ext.lazy_separate !== nothing
+        set_attribute(model, MOI.LazyConstraintCallback(), lazy_callback)
+    end
+
     # Optimize
     ext.where = :WHERE_DEFAULT
     optimize!(model)
@@ -363,12 +386,15 @@ function __init_solvers_jump__()
             inner;
             cuts_enforce::Union{Function,Nothing}=nothing,
             cuts_separate::Union{Function,Nothing}=nothing,
+            lazy_enforce::Union{Function,Nothing}=nothing,
+            lazy_separate::Union{Function,Nothing}=nothing,
         )
-            AbstractModel.__init__(self)
             self.inner = inner
             self.inner.ext[:miplearn] = _JumpModelExtData(
                 cuts_enforce=cuts_enforce,
                 cuts_separate=cuts_separate,
+                lazy_enforce=lazy_enforce,
+                lazy_separate=lazy_separate,
             )
         end
 
@@ -409,6 +435,10 @@ function __init_solvers_jump__()
         function set_cuts(self, cuts)
             self.inner.ext[:miplearn].aot_cuts = cuts
         end
+
+        function lazy_enforce(self, model, violations)
+            self.inner.ext[:miplearn].lazy_enforce(violations)
+        end
     end
     copy!(JumpModel, Class)
 end