Add CallbackNodeStatus (#162)

* Add CallbackNodeStatus

* Update Project.toml

Author: odow

Project.toml

@@ -1,7 +1,7 @@
 name = "GLPK"
 uuid = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
 repo = "https://github.com/jump-dev/GLPK.jl.git"
-version = "0.14.3"
+version = "0.14.4"
 
 [deps]
 BinaryProvider = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
@@ -14,7 +14,7 @@ MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 BinaryProvider = "~0.5"
 CEnum = "0.3, 0.4"
 GLPK_jll = "~4.64.0"
-MathOptInterface = "~0.9.15"
+MathOptInterface = "~0.9.19"
 julia = "1"
 
 [extras]

src/MOI_wrapper/MOI_callbacks.jl

@@ -50,6 +50,45 @@ function MOI.get(
     return glp_get_col_prim(subproblem, _info(model, x).column)
 end
 
+function MOI.get(model::Optimizer, attr::MOI.CallbackNodeStatus{CallbackData})
+    reason = glp_ios_reason(attr.callback_data.tree)
+    if reason == GLP_ISELECT
+        return MOI.CALLBACK_NODE_STATUS_UNKNOWN
+    elseif reason == GLP_IPREPRO
+        return MOI.CALLBACK_NODE_STATUS_UNKNOWN
+    elseif reason == GLP_IROWGEN
+        # From the the GLPK documentation:
+        #
+        # The callback routine is called with the reason code GLP_IROWGEN if LP
+        # relaxation of the current subproblem has just been solved to
+        # optimality and its objective value is better than the best known
+        # integer feasible solution.
+        #
+        # This can mean the solution is integer _or_ fractional, so we need to
+        # check.
+        subproblem = glp_ios_get_prob(attr.callback_data.tree)
+        for info in values(model.variable_info)
+            if info.type == CONTINUOUS
+                continue
+            end
+            x = glp_get_col_prim(subproblem, info.column)
+            if abs(x - round(Int, x)) > 1e-7
+                return MOI.CALLBACK_NODE_STATUS_FRACTIONAL
+            end
+        end
+        return MOI.CALLBACK_NODE_STATUS_INTEGER
+    elseif reason == GLP_IHEUR
+        return MOI.CALLBACK_NODE_STATUS_FRACTIONAL
+    elseif reason == GLP_ICUTGEN
+        return MOI.CALLBACK_NODE_STATUS_FRACTIONAL
+    elseif reason == GLP_IBRANCH
+        return MOI.CALLBACK_NODE_STATUS_FRACTIONAL
+    else
+        @assert reason == GLP_IBINGO
+        return MOI.CALLBACK_NODE_STATUS_INTEGER
+    end
+end
+
 # ==============================================================================
 #    MOI.LazyConstraint
 # ==============================================================================

test/MOI_callbacks.jl

@@ -48,6 +48,12 @@ end
                 x_val = MOI.get(model, MOI.CallbackVariablePrimal(cb_data), x)
                 y_val = MOI.get(model, MOI.CallbackVariablePrimal(cb_data), y)
                 @test MOI.supports(model, MOI.LazyConstraint(cb_data))
+                status = MOI.get(model, MOI.CallbackNodeStatus(cb_data))
+                if [x_val, y_val] ≈ round.(Int, [x_val, y_val]) atol=1e-7
+                    @test status == MOI.CALLBACK_NODE_STATUS_INTEGER
+                else
+                    @test status == MOI.CALLBACK_NODE_STATUS_FRACTIONAL
+                end
                 if y_val - x_val > 1 + 1e-6
                     MOI.submit(
                         model,
@@ -145,6 +151,8 @@ end
                     end
                 end
                 @test MOI.supports(model, MOI.UserCut(cb_data))
+                status = MOI.get(model, MOI.CallbackNodeStatus(cb_data))
+                @test status == MOI.CALLBACK_NODE_STATUS_FRACTIONAL
                 if accumulated > 10.0
                     MOI.submit(
                         model,
@@ -221,6 +229,8 @@ end
                 ) == MOI.HEURISTIC_SOLUTION_REJECTED
                     solution_rejected = true
                 end
+                status = MOI.get(model, MOI.CallbackNodeStatus(cb_data))
+                @test status == MOI.CALLBACK_NODE_STATUS_FRACTIONAL
             end)
             @test MOI.supports(model, MOI.HeuristicCallback())
             MOI.optimize!(model)
@@ -296,6 +306,12 @@ end
                 end
                 x_val = MOI.get(model, MOI.CallbackVariablePrimal(cb_data), x)
                 y_val = MOI.get(model, MOI.CallbackVariablePrimal(cb_data), y)
+                status = MOI.get(model, MOI.CallbackNodeStatus(cb_data))
+                if [x_val, y_val] ≈ round.(Int, [x_val, y_val]) atol=1e-7
+                    @test status == MOI.CALLBACK_NODE_STATUS_INTEGER
+                else
+                    @test status == MOI.CALLBACK_NODE_STATUS_FRACTIONAL
+                end
                 if y_val - x_val > 1 + 1e-6
                     MOI.submit(model, MOI.LazyConstraint(cb_data),
                         MOI.ScalarAffineFunction{Float64}(
@@ -333,6 +349,8 @@ end
                 reason = GLPK.glp_ios_reason(cb_data.tree)
                 push!(cb_calls, reason)
                 if reason != GLPK.GLP_ICUTGEN
+                    status = MOI.get(model, MOI.CallbackNodeStatus(cb_data))
+                    @test status !== nothing
                     return
                 end
                 terms = MOI.ScalarAffineTerm{Float64}[]
@@ -352,6 +370,8 @@ end
                     )
                     user_cut_submitted = true
                 end
+                status = MOI.get(model, MOI.CallbackNodeStatus(cb_data))
+                @test status == MOI.CALLBACK_NODE_STATUS_FRACTIONAL
             end)
             MOI.optimize!(model)
             @test user_cut_submitted
@@ -385,6 +405,8 @@ end
                 ) == MOI.HEURISTIC_SOLUTION_REJECTED
                     solution_rejected = true
                 end
+                status = MOI.get(model, MOI.CallbackNodeStatus(cb_data))
+                @test status == MOI.CALLBACK_NODE_STATUS_FRACTIONAL
             end)
             MOI.optimize!(model)
             @test solution_accepted