Weird warning in birkhoff example

examples/birkhoff.jl

@@ -14,8 +14,8 @@ import HiGHS
 
 # For bug hunting:
 seed = rand(UInt64)
+seed = 0x6a330cff07fe872a
 @show seed
-seed = 0x3eb09305cecf69f0 
 Random.seed!(seed)
 
 
@@ -33,7 +33,7 @@ Random.seed!(seed)
 # The variables are ordered (Y, X, theta) in the MOI model
 # the objective only uses the last n^2 variables
 # Small dimensions since the size of the problem grows quickly (2 k n^2 + k variables)
-n = 3
+n = 5
 k = 2
 
 # generate random doubly stochastic matrix

src/callbacks.jl

@@ -83,4 +83,4 @@ function build_FW_callback(tree, min_number_lower, check_rounding_value::Bool, f
 
         return true
     end
-end
+end

src/custom_bonobo.jl

@@ -30,7 +30,10 @@ function Bonobo.optimize!(tree::Bonobo.BnBTree{<:FrankWolfeNode}; callback=(args
     #println("OWN OPTIMIZE")
     while !Bonobo.terminated(tree)
         node = Bonobo.get_next_node(tree, tree.options.traverse_strategy)
+        println("get_next_node")
+        @show node.id
         lb, ub = Bonobo.evaluate_node!(tree, node)
+        println("evaluate_node")
         # if the problem was infeasible we simply close the node and continue
         if isnan(lb) && isnan(ub)
             Bonobo.close_node!(tree, node)
@@ -39,6 +42,7 @@ function Bonobo.optimize!(tree::Bonobo.BnBTree{<:FrankWolfeNode}; callback=(args
         end
 
         Bonobo.set_node_bound!(tree.sense, node, lb, ub)
+        println("set_node_bound")
 
         # if the evaluated lower bound is worse than the best incumbent -> close and continue
         if node.lb >= tree.incumbent
@@ -56,16 +60,21 @@ function Bonobo.optimize!(tree::Bonobo.BnBTree{<:FrankWolfeNode}; callback=(args
         @assert p_lb <= tree.lb
 
         updated = Bonobo.update_best_solution!(tree, node)
+        println("update_best_solution")
         if updated
             Bonobo.bound!(tree, node.id)
+            println("bound")
             if isapprox(tree.incumbent, tree.lb; atol=tree.options.atol, rtol=tree.options.rtol)
                 break
             end
         end
 
         Bonobo.close_node!(tree, node)
+        println("close_node")
         Bonobo.branch!(tree, node)
+        println("branch")
         callback(tree, node)
+        println("callback\n")
     end
     Bonobo.sort_solutions!(tree.solutions, tree.sense)
 end

src/node.jl

@@ -108,9 +108,12 @@ function Bonobo.evaluate_node!(tree::Bonobo.BnBTree, node::FrankWolfeNode)
         restart_active_set(node, tree.root.problem.lmo.lmo, tree.root.problem.nvars)
     end
 
+    if node.id == 41
+        nothing
+    end
     # time tracking FW 
     time_ref = Dates.now()
-
+    println("BEFORE evaluation")
     # call blended_pairwise_conditional_gradient
     x,_,primal,dual_gap,_ , active_set = FrankWolfe.blended_pairwise_conditional_gradient(
         tree.root.problem.f,
@@ -126,7 +129,7 @@ function Bonobo.evaluate_node!(tree::Bonobo.BnBTree, node::FrankWolfeNode)
         callback=tree.root.options[:callback],
         lazy=true,
     ) 
-
+    println("AFTER evaluation")
     node.fw_time = Dates.now() - time_ref
 
     # update active set of the node