Simplification of the divide output (#1080)

src/Algorithm/branching/branchingalgo.jl

@@ -23,7 +23,7 @@ Divide algorithm that does nothing. It does not generate any child.
 struct NoBranching <: AlgoAPI.AbstractDivideAlgorithm end
 
 function run!(::NoBranching, ::Env, reform::Reformulation, ::Branching.AbstractDivideInput)
-    return DivideOutput([], OptimizationState(getmaster(reform)))
+    return DivideOutput([])
 end
 
 ############################################################################################
@@ -76,11 +76,6 @@ Branching.get_int_tol(ctx::BranchingContext) = ctx.int_tol
 Branching.get_selection_criterion(ctx::BranchingContext) = ctx.selection_criterion
 Branching.get_rules(ctx::BranchingContext) = ctx.rules
 
-function Branching.new_ip_primal_sols_pool(ctx::BranchingContext, reform::Reformulation, input)
-    # Optimization state with no information.
-    return OptimizationState(getmaster(reform))
-end
-
 function _is_integer(sol::PrimalSolution)
     for (varid, val) in sol
         integer_val = abs(val - round(val)) < 1e-5
@@ -130,8 +125,8 @@ function Branching.why_no_candidate(reform::Reformulation, input, extended_sol,
     return _why_no_candidate(master, reform, input, extended_sol, original_sol)
 end
 
-Branching.new_divide_output(children::Vector{SbNode}, optimization_state) = DivideOutput(children, optimization_state)
-Branching.new_divide_output(::Nothing, optimization_state) = DivideOutput(SbNode[], optimization_state)
+Branching.new_divide_output(children::Vector{SbNode}) = DivideOutput(children)
+Branching.new_divide_output(::Nothing) = DivideOutput(SbNode[])
 
 ############################################################################################
 # Branching API implementation for the strong branching
@@ -272,38 +267,24 @@ function new_context(
     )
 end
 
-function Branching.eval_child_of_candidate!(child, phase::Branching.AbstractStrongBrPhaseContext, ip_primal_sols_found, env, reform, input)    
+function Branching.eval_child_of_candidate!(child, phase::Branching.AbstractStrongBrPhaseContext, env, reform, input)    
     child_state = OptimizationState(getmaster(reform))
     child.conquer_output = child_state
 
-    # In the `ip_primal_sols_found`, we maintain all the primal solutions found during the 
-    # strong branching procedure but also the best primal bound found so far (in the whole optimization).
-    update_ip_primal_bound!(child_state, get_ip_primal_bound(ip_primal_sols_found))
-
+    global_primal_handler = Branching.get_global_primal_handler(input)
+    update_ip_primal_bound!(child_state, get_global_primal_bound(global_primal_handler))
+
     if !ip_gap_closed(child_state)
         units_to_restore = Branching.get_units_to_restore_for_conquer(phase)
         restore_from_records!(units_to_restore, child.records)
-        conquer_input = ConquerInputFromSb(Branching.get_global_primal_handler(input), child, units_to_restore)
+        conquer_input = ConquerInputFromSb(global_primal_handler, child, units_to_restore)
         child.conquer_output = run!(Branching.get_conquer(phase), env, reform, conquer_input)
         TreeSearch.set_records!(child, create_records(reform))
     end
 
     # Store new primal solutions found during the evaluation of the child.
-    add_ip_primal_sols!(ip_primal_sols_found, get_ip_primal_sols(child_state)...)
     for sol in get_ip_primal_sols(child_state)
-        store_ip_primal_sol!(Branching.get_global_primal_handler(input), sol)
+        store_ip_primal_sol!(global_primal_handler, sol)
     end
     return
 end
-
-function Branching.new_ip_primal_sols_pool(ctx::StrongBranchingContext, reform, input)
-    # Optimization state with copy of bounds only (except lp_primal_bound).
-    # Only the ip primal bound is used to avoid inserting integer solutions that are not
-    # better than the incumbent.
-    # We also use the primal bound to init candidate nodes in the strong branching procedure.
-    input_opt_state = Branching.get_conquer_opt_state(input)
-    return OptimizationState(
-        getmaster(reform);
-        ip_primal_bound = get_ip_primal_bound(input_opt_state),
-    )
-end

src/Algorithm/branching/interface.jl

@@ -1,10 +1,8 @@
 struct DivideOutput{N} <: Branching.AbstractDivideOutput
     children::Vector{N}
-    optstate::Union{Nothing,OptimizationState}
 end
 
 Branching.get_children(output::DivideOutput) = output.children
-#Branching.get__opt_state(output::DivideOutput) = output.optstate
 
 function get_extended_sol(reform, opt_state)
     return get_best_lp_primal_sol(opt_state)

src/Algorithm/branching/printer.jl

@@ -7,7 +7,6 @@ Branching.get_int_tol(ctx::BranchingPrinter) = Branching.get_int_tol(ctx.inner)
 Branching.get_selection_criterion(ctx::BranchingPrinter) = Branching.get_selection_criterion(ctx.inner)
 Branching.get_selection_nb_candidates(ctx::BranchingPrinter) = Branching.get_selection_nb_candidates(ctx.inner)
 Branching.get_phases(ctx::BranchingPrinter) = Branching.get_phases(ctx.inner)
-Branching.new_ip_primal_sols_pool(ctx::BranchingPrinter, reform, input) = Branching.new_ip_primal_sols_pool(ctx.inner, reform, input)
 
 struct PhasePrinter{PhaseContext<:Branching.AbstractStrongBrPhaseContext} <: Branching.AbstractStrongBrPhaseContext
     inner::PhaseContext

src/Branching/Branching.jl

@@ -65,15 +65,14 @@ abstract type AbstractDivideContext end
 
 # branching output
 """
-    new_divide_output(children::Union{Vector{N}, Nothing}, ip_primal_sols_found{C, Nothing}) where {N, C} -> AbstractDivideOutput
+    new_divide_output(children::Union{Vector{N}, Nothing}) where {N} -> AbstractDivideOutput
 
 where:
 - `N` is the type of nodes generated by the branching algorithm.
-- `C` is the type of the collection that stores all ip primal solutions found by the branching algorithm.
 
-If no nodes nor ip primal solutions are found, the generic implementation may provide `nothing`.
+If no nodes are found, the generic implementation may provide `nothing`.
 """
-@mustimplement "BranchingOutput" new_divide_output(children, ip_primal_sols_found) = nothing
+@mustimplement "BranchingOutput" new_divide_output(children) = nothing
 
 # Default implementations.
 "Candidates selection for branching algorithms."
@@ -89,7 +88,7 @@ abstract type AbstractBranchingContext <: AbstractDivideContext end
 
 function advanced_select!(ctx::AbstractBranchingContext, candidates, env, reform, input::AbstractDivideInput)
     children = generate_children!(first(candidates), env, reform, input)
-    return new_divide_output(children, nothing)
+    return new_divide_output(children)
 end
 
 ############################################################################################
@@ -124,13 +123,8 @@ strong branching phase.
 "Returns the maximum number of candidates kept at the end of a given strong branching phase."
 @mustimplement "StrongBranching" get_max_nb_candidates(::AbstractStrongBrPhaseContext) = nothing
 
-""
-@mustimplement "StrongBranchingOptState" new_ip_primal_sols_pool(ctx, reform, input) = nothing
-
 function advanced_select!(ctx::Branching.AbstractStrongBrContext, candidates, env, reform, input::Branching.AbstractDivideInput)
     return perform_strong_branching!(ctx, env, reform, input, candidates)
-    #children = get_children(first(candidates))
-    #return new_divide_output(children, ip_primal_sols_found)
 end
 
 function perform_strong_branching!(
@@ -142,10 +136,6 @@ end
 function perform_strong_branching_inner!(
     ctx::AbstractStrongBrContext, env, reform, input::Branching.AbstractDivideInput, candidates::Vector{C}
 ) where {C<:AbstractBranchingCandidate}
-    # We will store all the new ip primal solution found during the strong branching in the
-    # following data structure.
-    ip_primal_sols_found = new_ip_primal_sols_pool(ctx, reform, input)
-
     cand_children = [generate_children!(candidate, env, reform, input) for candidate in candidates]
 
     phases = get_phases(ctx)
@@ -163,7 +153,7 @@ function perform_strong_branching_inner!(
             nb_candidates_for_next_phase = min(nb_candidates_for_next_phase, length(cand_children))
         end
 
-        scores = perform_branching_phase!(candidates, cand_children, current_phase, ip_primal_sols_found, env, reform, input)
+        scores = perform_branching_phase!(candidates, cand_children, current_phase, env, reform, input)
 
         perm = sortperm(scores, rev=true)
         permute!(cand_children, perm)
@@ -178,15 +168,15 @@ function perform_strong_branching_inner!(
         resize!(cand_children, nb_candidates_for_next_phase)
         resize!(candidates, nb_candidates_for_next_phase)
     end
-    return new_divide_output(first(cand_children), ip_primal_sols_found)
+    return new_divide_output(first(cand_children))
 end
 
-function perform_branching_phase!(candidates, cand_children, phase, ip_primal_sols_found, env, reform, input)
-    return perform_branching_phase_inner!(cand_children, phase, ip_primal_sols_found, env, reform, input)
+function perform_branching_phase!(candidates, cand_children, phase, env, reform, input)
+    return perform_branching_phase_inner!(cand_children, phase, env, reform, input)
 end
 
 "Performs a branching phase."
-function perform_branching_phase_inner!(cand_children, phase, ip_primal_sols_found, env, reform, input)
+function perform_branching_phase_inner!(cand_children, phase, env, reform, input)
 
     return map(cand_children) do children
         # TODO; I don't understand why we need to sort the children here.
@@ -205,24 +195,24 @@ function perform_branching_phase_inner!(cand_children, phase, ip_primal_sols_fou
         #     by = child -> get_lp_primal_bound(child)
         # )
 
-        return eval_candidate!(children, phase, ip_primal_sols_found, env, reform, input)
+        return eval_candidate!(children, phase, env, reform, input)
     end
 end
 
-function eval_candidate!(children, phase::AbstractStrongBrPhaseContext, ip_primal_sols_found, env, reform, input)
-    return eval_candidate_inner!(children, phase, ip_primal_sols_found, env, reform, input)
+function eval_candidate!(children, phase::AbstractStrongBrPhaseContext, env, reform, input)
+    return eval_candidate_inner!(children, phase, env, reform, input)
 end
 
 "Evaluates a candidate."
-function eval_candidate_inner!(children, phase::AbstractStrongBrPhaseContext, ip_primal_sols_found, env, reform, input)
+function eval_candidate_inner!(children, phase::AbstractStrongBrPhaseContext, env, reform, input)
     for child in children
-        eval_child_of_candidate!(child, phase, ip_primal_sols_found, env, reform, input)
+        eval_child_of_candidate!(child, phase, env, reform, input)
     end
     return compute_score(get_score(phase), children, input)
 end
 
 "Evaluate children of a candidate."
-@mustimplement "StrongBranching" eval_child_of_candidate!(child, phase, ip_primal_sols_found, env, reform, input) = nothing
+@mustimplement "StrongBranching" eval_child_of_candidate!(child, phase, env, reform, input) = nothing
 
 @mustimplement "Branching" isroot(node) = nothing
 
@@ -302,7 +292,7 @@ function run_branching!(ctx, env, reform, input::Branching.AbstractDivideInput,
     if length(candidates) == 0
         @warn "No candidate generated. No children will be generated. However, the node is not conquered."
         why_no_candidate(reform, input, extended_sol, original_sol)
-        return new_divide_output(nothing, nothing)
+        return new_divide_output(nothing)
     end
     return advanced_select!(ctx, candidates, env, reform, input)
 end

test/unit/TreeSearch/treesearch.jl

@@ -133,7 +133,7 @@ function Coluna.Algorithm.run!(alg::DeterministicDivide, ::Coluna.Env, ::Coluna.
     for c in children                             ## update flag
         push!(alg.nodes_created_by_divide, c.id)
     end
-    return Coluna.Algorithm.DivideOutput(children, nothing) 
+    return Coluna.Algorithm.DivideOutput(children) 
 end
 
 # constructs a real node from a LightNode, used in new_children to built real children from the minimal information contained in LightNode