BB: Remove duplication in SB/RB

src/bb/varbranch/reliability.jl

@@ -19,6 +19,50 @@ Base.@kwdef mutable struct ReliabilityBranching <: VariableBranchingRule
     aggregation::Symbol = :prod
 end
 
+function _strong_branch_score(;
+    node::Node,
+    pool::NodePool,
+    var::Variable,
+    x::Float64,
+    side_effect::Bool,
+    max_iterations::Int,
+    aggregation::Symbol,
+)::Tuple{Float64,Int}
+
+    # Find current variable lower and upper bounds
+    offset = findfirst(isequal(var), node.mip.int_vars)
+    var_lb = node.mip.int_vars_lb[offset]
+    var_ub = node.mip.int_vars_ub[offset]
+    for (offset, v) in enumerate(node.branch_vars)
+        if v == var
+            var_lb = max(var_lb, node.branch_lb[offset])
+            var_ub = min(var_ub, node.branch_ub[offset])
+        end
+    end
+
+    obj_up, obj_down = 0, 0
+    obj_up, obj_down = probe(node.mip, var, x, var_lb, var_ub, max_iterations)
+    obj_change_up = obj_up - node.obj
+    obj_change_down = obj_down - node.obj
+    if side_effect
+        _update_var_history(
+            pool = pool,
+            var = var,
+            x = x,
+            obj_change_down = obj_change_down,
+            obj_change_up = obj_change_up,
+        )
+    end
+    if aggregation == :prod
+        return (obj_change_up * obj_change_down, var.index)
+    elseif aggregation == :min
+        sense = node.mip.sense
+        return (min(sense * obj_up, sense * obj_down), var.index)
+    else
+        error("Unknown aggregation: $aggregation")
+    end
+end
+
 function find_branching_var(
     rule::ReliabilityBranching,
     node::Node,

src/bb/varbranch/strong.jl

@@ -10,8 +10,6 @@ using Random
 Branching strategy that selects a subset of fractional variables
 as candidates (according to pseudocosts) the solves two linear
 programming problems for each candidate.
-
-
 """
 Base.@kwdef struct StrongBranching <: VariableBranchingRule
     look_ahead::Int = 10
@@ -22,85 +20,13 @@ Base.@kwdef struct StrongBranching <: VariableBranchingRule
 end
 
 function find_branching_var(rule::StrongBranching, node::Node, pool::NodePool)::Variable
-    nfrac = length(node.fractional_variables)
+    rb_rule = ReliabilityBranching(
-    pseudocost_scores = [
+        min_samples=typemax(Int),
-        _pseudocost_score(
+        max_sb_calls=rule.max_calls,
-            node,
+        look_ahead=rule.look_ahead,
-            pool,
+        side_effect=rule.side_effect,
-            node.fractional_variables[j],
+        max_iterations=rule.max_iterations,
-            node.fractional_values[j],
+        aggregation=rule.aggregation,
-        ) for j = 1:nfrac
+    )
-    ]
+    return find_branching_var(rb_rule, node, pool)
-    σ = sortperm(pseudocost_scores, rev = true)
-    sorted_vars = node.fractional_variables[σ]
-    _set_node_bounds(node)
-    no_improv_count, call_count = 0, 0
-    max_score, max_var = (-Inf, -Inf), sorted_vars[1]
-    for (i, var) in enumerate(sorted_vars)
-        call_count += 1
-        score = _strong_branch_score(
-            node = node,
-            pool = pool,
-            var = var,
-            x = node.fractional_values[σ[i]],
-            side_effect = rule.side_effect,
-            max_iterations = rule.max_iterations,
-            aggregation = rule.aggregation,
-        )
-        # @show name(node.mip, var), round(score[1], digits=2)
-        if score > max_score
-            max_score, max_var = score, var
-            no_improv_count = 0
-        else
-            no_improv_count += 1
-        end
-        no_improv_count <= rule.look_ahead || break
-        call_count <= rule.max_calls || break
-    end
-    _unset_node_bounds(node)
-    return max_var
-end
-
-function _strong_branch_score(;
-    node::Node,
-    pool::NodePool,
-    var::Variable,
-    x::Float64,
-    side_effect::Bool,
-    max_iterations::Int,
-    aggregation::Symbol,
-)::Tuple{Float64,Int}
-
-    # Find current variable lower and upper bounds
-    offset = findfirst(isequal(var), node.mip.int_vars)
-    var_lb = node.mip.int_vars_lb[offset]
-    var_ub = node.mip.int_vars_ub[offset]
-    for (offset, v) in enumerate(node.branch_vars)
-        if v == var
-            var_lb = max(var_lb, node.branch_lb[offset])
-            var_ub = min(var_ub, node.branch_ub[offset])
-        end
-    end
-
-    obj_up, obj_down = 0, 0
-    obj_up, obj_down = probe(node.mip, var, x, var_lb, var_ub, max_iterations)
-    obj_change_up = obj_up - node.obj
-    obj_change_down = obj_down - node.obj
-    if side_effect
-        _update_var_history(
-            pool = pool,
-            var = var,
-            x = x,
-            obj_change_down = obj_change_down,
-            obj_change_up = obj_change_up,
-        )
-    end
-    if aggregation == :prod
-        return (obj_change_up * obj_change_down, var.index)
-    elseif aggregation == :min
-        sense = node.mip.sense
-        return (min(sense * obj_up, sense * obj_down), var.index)
-    else
-        error("Unknown aggregation: $aggregation")
-    end
 end