• Restored strong branching output (#1123)

• Correction for global bounds output when strong branching is used
• Output information every time an improving primal solution is found

src/Algorithm/branching/branchingalgo.jl

@@ -279,6 +279,10 @@ function Branching.eval_child_of_candidate!(child, phase::Branching.AbstractStro
         restore_from_records!(units_to_restore, child.records)
         conquer_input = ConquerInputFromSb(global_primal_handler, child, units_to_restore)
         child.conquer_output = run!(Branching.get_conquer(phase), env, reform, conquer_input)
+        child.ip_dual_bound = get_lp_dual_bound(child.conquer_output)
+        for sol in get_ip_primal_sols(child.conquer_output)
+            store_ip_primal_sol!(global_primal_handler, sol)
+        end
         TreeSearch.set_records!(child, create_records(reform))
     end
 

src/Algorithm/branching/printer.jl

@@ -30,9 +30,9 @@ function new_phase_context(
     return PhasePrinter(inner_ctx, phase_index)
 end
 
-function Branching.perform_branching_phase!(candidates, cand_children, phase::PhasePrinter, sb_state, env, reform, input)
+function Branching.perform_branching_phase!(candidates, cand_children, phase::PhasePrinter, env, reform, input)
     println("**** Strong branching phase ", phase.phase_index, " is started *****");
-    scores = Branching.perform_branching_phase_inner!(cand_children, phase, sb_state, env, reform, input)
+    scores = Branching.perform_branching_phase_inner!(cand_children, phase, env, reform, input)
     for (candidate, children, score) in Iterators.zip(candidates, cand_children, scores)
         @printf "SB phase %i branch on %+10s" phase.phase_index  Branching.getdescription(candidate)
         @printf " (lhs=%.4f) : [" Branching.get_lhs(candidate)

src/Algorithm/colgen/printer.jl

@@ -55,7 +55,10 @@ function ColGen.update_master_constrs_dual_vals!(ctx::ColGenPrinterContext, mast
 end
 
 ColGen.check_primal_ip_feasibility!(mast_primal_sol, ctx::ColGenPrinterContext, phase, env) = ColGen.check_primal_ip_feasibility!(mast_primal_sol, ctx.inner, phase, env)
-ColGen.update_inc_primal_sol!(ctx::ColGenPrinterContext, ip_primal_sol, new_ip_primal_sol) = ColGen.update_inc_primal_sol!(ctx.inner, ip_primal_sol, new_ip_primal_sol)
+function ColGen.update_inc_primal_sol!(ctx::ColGenPrinterContext, ip_primal_sol, new_ip_primal_sol) 
+    @info "Improving primal solution with value $(ColunaBase.getvalue(new_ip_primal_sol)) is found during column generation"
+    ColGen.update_inc_primal_sol!(ctx.inner, ip_primal_sol, new_ip_primal_sol)
+end
 
 ColGen.get_subprob_var_orig_costs(ctx::ColGenPrinterContext) = ColGen.get_subprob_var_orig_costs(ctx.inner)
 ColGen.get_subprob_var_coef_matrix(ctx::ColGenPrinterContext) = ColGen.get_subprob_var_coef_matrix(ctx.inner)

src/Algorithm/conquer.jl

@@ -92,6 +92,7 @@ struct ColCutGenConquer <: AbstractConquerAlgorithm
     max_nb_cut_rounds::Int # TODO : tailing-off ?
     opt_atol::Float64# TODO : force this value in an init() method
     opt_rtol::Float64 # TODO : force this value in an init() method
+    verbose::Bool 
 end
 
 ColCutGenConquer(;
@@ -103,7 +104,8 @@ ColCutGenConquer(;
         cutgen = CutCallbacks(),
         max_nb_cut_rounds = 3,
         opt_atol = AlgoAPI.default_opt_atol(),
-        opt_rtol = AlgoAPI.default_opt_rtol()
+        opt_rtol = AlgoAPI.default_opt_rtol(), 
+        verbose = true
 ) = ColCutGenConquer(
     colgen, 
     primal_heuristics, 
@@ -113,7 +115,8 @@ ColCutGenConquer(;
     cutgen, 
     max_nb_cut_rounds, 
     opt_atol, 
-    opt_rtol
+    opt_rtol,
+    verbose
 )
 
 # ColCutGenConquer does not use any storage unit for the moment, therefore 
@@ -261,11 +264,18 @@ function run_heuristics!(ctx::ColCutGenContext, heuristics, env, reform, input,
             records = create_records(reform)
         end
 
+        prev_primal_bound = get_ip_primal_bound(conquer_output)
+
         heuristic_output = run!(heuristic.algorithm, env, reform, conquer_output)
-        update!(conquer_output, heuristic_output)
-        # for sol in Heuristic.get_primal_sols(output)
-        #     store_ip_primal_sol!(get_global_primal_handler(input), sol)
-        # end
+        add_ip_primal_sols!(conquer_output, get_ip_primal_sols(heuristic_output)...)
+        update_ip_dual_bound!(conquer_output, get_ip_dual_bound(heuristic_output))
+
+        if ctx.params.verbose 
+            curr_primal_bound = get_ip_primal_bound(conquer_output)
+            if curr_primal_bound != prev_primal_bound
+                @info "Heuristic $(heuristic.name) found improving primal solution with value $(curr_primal_bound.value)"
+            end
+        end
 
         if ismanager(heuristic.algorithm) 
             restore_from_records!(input.units_to_restore, records)

src/Algorithm/heuristic/restricted_master.jl

@@ -21,12 +21,6 @@ function get_child_algorithms(algo::RestrictedMasterHeuristic, reform::Reformula
     return child_algs
 end
 
-# struct RestrictedMasterHeuristicOutput <: Heuristic.AbstractHeuristicOutput
-#     ip_primal_sols::Vector{PrimalSolution}
-# end
-
-# Heuristic.get_primal_sols(o::RestrictedMasterHeuristicOutput) = o.ip_primal_sols
-
 function run!(algo::RestrictedMasterHeuristic, env, reform, input::OptimizationState)
     master = getmaster(reform)
     ip_form_output = run!(algo.solve_ip_form_alg, env, master, input)

src/ColGen/ColGen.jl

@@ -137,8 +137,7 @@ function run_colgen_iteration!(context, phase, stage, env, ip_primal_sol, stab)
 
     mast_dual_sol = get_dual_sol(mast_result)
     if isnothing(mast_dual_sol)
-        error("Cannot continue")
-        # TODO: user friendly error message.
+        error("Column generation interrupted: LP solver did not return an optimal dual solution")
     end
 
     # Stores dual solution in the constraint. This is used when the pricing solver supports