Optimizers_BranchAndBound.h

//
// Created by henri on 13/03/23.
//
 
#ifndef IDOL_OPTIMIZERS_BRANCHANDBOUND_H
#define IDOL_OPTIMIZERS_BRANCHANDBOUND_H
 
#include "idol/mixed-integer/optimizers/branch-and-bound/nodes/NodeSet.h"
#include "idol/general/optimizers/Algorithm.h"
#include "idol/mixed-integer/optimizers/branch-and-bound/branching-rules/factories/BranchingRuleFactory.h"
#include "idol/mixed-integer/optimizers/branch-and-bound/node-selection-rules/factories/NodeSelectionRuleFactory.h"
#include "idol/mixed-integer/optimizers/branch-and-bound/branching-rules/impls/BranchingRule.h"
#include "idol/mixed-integer/optimizers/branch-and-bound/node-selection-rules/impls/NodeSelectionRule.h"
#include "idol/mixed-integer/optimizers/branch-and-bound/nodes/Node.h"
#include "idol/mixed-integer/optimizers/branch-and-bound/nodes/NodeUpdator.h"
#include "idol/mixed-integer/optimizers/branch-and-bound/callbacks/AbstractBranchAndBoundCallbackI.h"
#include "idol/general/optimizers/OptimizerFactory.h"
#include "idol/mixed-integer/modeling/models/Model.h"
#include "idol/mixed-integer/optimizers/branch-and-bound/logs/Factory.h"
 
#include <memory>
#include <cassert>
#include <fstream>
 
namespace idol::Optimizers {
    template<class NodeInfoT> class BranchAndBound;
}
 
template<class NodeInfoT>
class idol::Optimizers::BranchAndBound : public Algorithm {
    using TreeNode = Node<NodeInfoT>;
    using SetOfActiveNodes = NodeSet<Node<NodeInfoT>>;
 
    std::unique_ptr<OptimizerFactory> m_relaxation_optimizer_factory;
 
    unsigned int m_n_threads = 1;
    std::vector<std::unique_ptr<Model>> m_relaxations;
    std::vector<std::unique_ptr<NodeUpdator<NodeInfoT>>> m_node_updators;
 
    std::unique_ptr<BranchingRule<NodeInfoT>> m_branching_rule;
    std::unique_ptr<NodeSelectionRule<NodeInfoT>> m_node_selection_rule;
    std::unique_ptr<typename Logs::BranchAndBound::Factory<NodeInfoT>::Strategy> m_logger;
 
    std::unique_ptr<AbstractBranchAndBoundCallbackI<NodeInfoT>> m_callback;
 
    bool m_has_integer_objective = false;
    std::vector<unsigned int> m_steps = { std::numeric_limits<unsigned int>::max(), 0, 0 };
    unsigned int m_n_created_nodes = 0;
    unsigned int m_n_solved_nodes = 0;
    unsigned int m_n_active_nodes = 0;
    double m_root_node_best_bound = -Inf;
    double m_root_node_best_obj = +Inf;
 
    std::optional<TreeNode> m_incumbent;
protected:
    void build() override;
    void hook_before_optimize() override;
    void hook_optimize() override;
    void hook_after_optimize() override;
    void add(const Var &t_var) override;
    void add(const Ctr &t_ctr) override;
    void add(const QCtr &t_ctr) override;
    void remove(const Var &t_var) override;
    void remove(const Ctr &t_ctr) override;
    void remove(const QCtr &t_ctr) override;
    void update() override;
    void write(const std::string &t_name) override;
 
    void detect_integer_objective();
    void create_relaxations();
    Node<NodeInfoT> create_root_node();
    void explore(TreeNode& t_node, unsigned int t_relaxation_id, SetOfActiveNodes& t_active_nodes, unsigned int t_step);
    void analyze(const TreeNode& t_node, unsigned int t_relaxation_id, bool* t_explore_children_flag, bool* t_reoptimize_flag);
    Node<NodeInfoT> select_node_for_branching(SetOfActiveNodes& t_active_nodes);
    std::vector<TreeNode> create_child_nodes(const TreeNode& t_node);
    void backtrack(SetOfActiveNodes& t_actives_nodes, SetOfActiveNodes& t_sub_active_nodes);
    void set_as_incumbent(const TreeNode& t_node);
    [[nodiscard]] bool gap_is_closed() const;
    void prune_nodes_by_bound(SetOfActiveNodes& t_active_nodes);
    void update_lower_bound(const SetOfActiveNodes& t_active_nodes);
    bool is_valid(const TreeNode& t_node) const;
 
    void log_node_after_solve(const Node<NodeInfoT>& t_node);
    void log_after_termination();
 
    SideEffectRegistry call_callbacks(CallbackEvent t_event, const TreeNode& t_node, unsigned int t_relaxation_id);
    void update_obj_sense() override;
    void update_obj() override;
    void update_rhs() override;
    void update_obj_constant() override;
    void update_mat_coeff(const Ctr &t_ctr, const Var &t_var) override;
    void update_ctr_type(const Ctr &t_ctr) override;
    void update_ctr_rhs(const Ctr &t_ctr) override;
    void update_var_type(const Var &t_var) override;
    void update_var_lb(const Var &t_var) override;
    void update_var_ub(const Var &t_var) override;
 
    void update_var_obj(const Var &t_var) override;
    void set_status(SolutionStatus t_status) override;
    void set_reason(SolutionReason t_reason) override;
    void set_best_bound(double t_value) override;
    void set_best_obj(double t_value) override;
public:
    explicit BranchAndBound(const Model& t_model,
                              const OptimizerFactory& t_node_optimizer,
                              const BranchingRuleFactory<NodeInfoT>& t_branching_rule_factory,
                              const NodeSelectionRuleFactory<NodeInfoT>& t_node_selection_rule_factory,
                              AbstractBranchAndBoundCallbackI<NodeInfoT>* t_callback,
                              const Logs::BranchAndBound::Factory<NodeInfoT>& t_logger_factory);
 
    [[nodiscard]] std::string name() const override { return "Branch-and-Bound"; }
 
    void solve(TreeNode& t_node, unsigned int t_relaxation_id) const;
 
    virtual void set_subtree_depth(unsigned int t_depth) { m_steps.at(1) = t_depth; }
 
    [[nodiscard]] unsigned int subtree_depth() const { return m_steps.at(1); }
 
    virtual void add_callback(BranchAndBoundCallback<NodeInfoT>* t_cb);
 
    void submit_heuristic_solution(NodeInfoT* t_info);
 
    void submit_lower_bound(double t_lower_bound);
 
    [[nodiscard]] unsigned int n_created_nodes() const { return m_n_created_nodes; }
 
    [[nodiscard]] unsigned int n_solved_nodes() const { return m_n_solved_nodes; }
 
    [[nodiscard]] unsigned int n_active_nodes() const { return m_n_active_nodes; }
 
    [[nodiscard]] const Model& relaxation() const { return *m_relaxations.front(); }
 
    [[nodiscard]] Model& relaxation() { return *m_relaxations.front(); }
 
    [[nodiscard]] double root_node_best_bound() const { return m_root_node_best_bound; }
 
    [[nodiscard]] double root_node_best_obj() const { return m_root_node_best_obj; }
 
    BranchingRule<NodeInfoT>& branching_rule() { return *m_branching_rule; }
 
    const BranchingRule<NodeInfoT>& branching_rule() const { return *m_branching_rule; }
 
    [[nodiscard]] bool has_incumbent() const { return m_incumbent.has_value(); }
 
    const TreeNode& incumbent() const { return m_incumbent.value(); }
 
    [[nodiscard]] double get_var_primal(const Var &t_var) const override;
 
    [[nodiscard]] double get_var_reduced_cost(const Var &t_var) const override;
 
    [[nodiscard]] double get_var_ray(const Var &t_var) const override;
 
    [[nodiscard]] double get_ctr_dual(const Ctr &t_ctr) const override;
 
    [[nodiscard]] double get_ctr_farkas(const Ctr &t_ctr) const override;
 
    [[nodiscard]] unsigned int get_n_solutions() const override;
 
    [[nodiscard]] unsigned int get_solution_index() const override;
 
    void set_solution_index(unsigned int t_index) override;
 
    void terminate() override;
};
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::remove(const idol::QCtr &t_ctr) {
    for (auto& relaxation : m_relaxations) {
        relaxation->remove(t_ctr);
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::add(const idol::QCtr &t_ctr) {
    for (auto& relaxation : m_relaxations) {
        relaxation->add(t_ctr);
    }
}
 
template<class NodeInfoT>
bool idol::Optimizers::BranchAndBound<NodeInfoT>::is_valid(const TreeNode &t_node) const {
    bool result;
#pragma omp critical
    result = m_branching_rule->is_valid(t_node);
    return result;
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::terminate() {
#pragma omp critical
    Optimizer::terminate();
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::detect_integer_objective() {
    const auto& src_model = parent();
    const auto& objective = src_model.get_obj_expr();
 
    if (!is_integer(objective.affine().constant(), Tolerance::Integer)) {
        m_has_integer_objective = false;
        return;
    }
 
    for (const auto& [var, val] : objective.affine().linear()) {
        if (src_model.get_var_type(var) == Continuous || !is_integer(val, Tolerance::Integer)) {
            m_has_integer_objective = false;
            return;
        }
    }
 
    m_has_integer_objective = true;
}
 
template<class NodeInfoT>
double idol::Optimizers::BranchAndBound<NodeInfoT>::get_var_reduced_cost(const idol::Var &t_var) const {
    if (m_n_solved_nodes > 1) {
        throw Exception("Reduced cost not available.");
    }
    return m_relaxations.front()->get_var_reduced_cost(t_var);
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::log_after_termination() {
 
    if (!get_param_logs()) {
        return;
    }
 
    if (m_callback) {
        m_callback->log_after_termination();
    }
 
    m_logger->log_after_termination();
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::log_node_after_solve(const idol::Node<NodeInfoT> &t_node) {
    m_logger->log_node_after_solve(t_node);
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::set_solution_index(unsigned int t_index) {
    if (t_index != 0) {
        throw Exception("Solution index out of bounds.");
    }
}
 
template<class NodeInfoT>
unsigned int idol::Optimizers::BranchAndBound<NodeInfoT>::get_solution_index() const {
    return 0;
}
 
template<class NodeInfoT>
unsigned int idol::Optimizers::BranchAndBound<NodeInfoT>::get_n_solutions() const {
    return m_incumbent.has_value();
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_obj() {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_obj_expr(parent().get_obj_expr());
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_rhs() {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_rhs_expr(parent().get_rhs_expr());
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_obj_constant() {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_obj_const(parent().get_obj_expr().affine().constant());
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_mat_coeff(const Ctr &t_ctr, const Var &t_var) {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_mat_coeff(t_ctr, t_var, parent().get_mat_coeff(t_ctr, t_var));
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_ctr_type(const Ctr &t_ctr) {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_ctr_type(t_ctr, parent().get_ctr_type(t_ctr));
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_ctr_rhs(const Ctr &t_ctr) {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_ctr_rhs(t_ctr, parent().get_ctr_rhs(t_ctr));
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_var_type(const Var &t_var) {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_var_type(t_var, parent().get_var_type(t_var));
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_var_lb(const Var &t_var) {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_var_lb(t_var, parent().get_var_lb(t_var));
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_var_ub(const Var &t_var) {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_var_ub(t_var, parent().get_var_ub(t_var));
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_var_obj(const Var &t_var) {
    for (auto& relaxation : m_relaxations) {
        relaxation->set_var_obj(t_var, parent().get_var_obj(t_var));
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_obj_sense() {
    throw Exception("Not implemented");
}
 
template<class NodeInfoT>
double idol::Optimizers::BranchAndBound<NodeInfoT>::get_ctr_farkas(const Ctr &t_ctr) const {
    if (m_n_solved_nodes > 1) {
        throw Exception("Accessing Farkas certificate for MIP is not available after the root node.");
    }
    return m_relaxations.front()->get_ctr_farkas(t_ctr);
}
 
template<class NodeInfoT>
double idol::Optimizers::BranchAndBound<NodeInfoT>::get_ctr_dual(const Ctr &t_ctr) const {
    if (m_n_solved_nodes > 1) {
        throw Exception("Accessing dual values for MIP is not available after the root node.");
    }
    return m_relaxations.front()->get_ctr_dual(t_ctr);
}
 
template<class NodeInfoT>
double idol::Optimizers::BranchAndBound<NodeInfoT>::get_var_ray(const Var &t_var) const {
    if (m_n_solved_nodes > 1) {
        throw Exception("Ray not available.");
    }
    return m_relaxations.front()->get_var_ray(t_var);
}
 
template<class NodeInfoT>
double idol::Optimizers::BranchAndBound<NodeInfoT>::get_var_primal(const Var &t_var) const {
 
    if (!m_incumbent.has_value()){
        throw Exception("Trying to access primal values while no incumbent was found.");
    }
 
    return m_incumbent->info().primal_solution().get(t_var);
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::submit_lower_bound(double t_lower_bound) {
    if (t_lower_bound > get_best_obj()) {
        set_status(Fail);
        set_reason(NotSpecified);
        terminate();
        return;
    }
    if (t_lower_bound > get_best_bound()) {
        set_best_bound(t_lower_bound);
        // New best LB
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::submit_heuristic_solution(NodeInfoT* t_info) {
 
    auto t_node = Node<NodeInfoT>::create_detached_node(t_info);
 
    if (t_node.info().objective_value() < get_best_obj()) {
 
        //if (m_branching_rule->is_valid(t_node)) {
            set_as_incumbent(t_node);
            // New incumbent by submission
        //} else {
        //    idol_Log(Trace, "Ignoring submitted heuristic solution, solution is not valid.");
        //}
 
    } else {
        // idol_Log(Trace, "Ignoring submitted heuristic solution, objective value is " << t_node.info().objective_value() << " while best obj is " << get_best_obj() << '.');
    }
 
}
 
template<class NodeInfoT>
idol::SideEffectRegistry
idol::Optimizers::BranchAndBound<NodeInfoT>::call_callbacks(CallbackEvent t_event, const Optimizers::BranchAndBound<NodeInfoT>::TreeNode &t_node, unsigned int t_relaxation_id) {
 
    if (!m_callback) {
        return {};
    }
 
    return m_callback->operator()(this, t_event, t_node, m_relaxations[t_relaxation_id].get());
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::add_callback(BranchAndBoundCallback<NodeInfoT> *t_cb) {
    m_callback->add_callback(t_cb);
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::build() {
 
}
 
template<class NodeInfoT>
idol::Optimizers::BranchAndBound<NodeInfoT>::BranchAndBound(const Model &t_model,
                                                      const OptimizerFactory& t_node_optimizer,
                                                      const BranchingRuleFactory<NodeInfoT>& t_branching_rule_factory,
                                                      const NodeSelectionRuleFactory<NodeInfoT>& t_node_selection_rule_factory,
                                                      AbstractBranchAndBoundCallbackI<NodeInfoT>* t_callback,
                                                      const Logs::BranchAndBound::Factory<NodeInfoT>& t_logger_factory)
    : Algorithm(t_model),
      m_relaxation_optimizer_factory(t_node_optimizer.clone()),
      m_branching_rule(t_branching_rule_factory(*this)),
      m_node_selection_rule(t_node_selection_rule_factory(*this)),
      m_callback(t_callback),
      m_logger(t_logger_factory.operator()(*this)) {
 
    create_relaxations();
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::create_relaxations() {
 
    const auto &original_model = parent();
 
    m_relaxations.clear();
    m_relaxations.reserve(m_n_threads);
 
    m_node_updators.clear();
    m_node_updators.reserve(m_n_threads);
 
    for (unsigned int i = 0 ; i < m_n_threads ; ++i) {
        auto* relaxation = original_model.clone();
        relaxation->use(*m_relaxation_optimizer_factory);
        m_relaxations.emplace_back(relaxation);
        m_node_updators.emplace_back(dynamic_cast<NodeUpdator<NodeInfoT>*>(NodeInfoT::create_updator(parent(), *relaxation)));
    }
 
}
 
template<class NodeInfoT>
idol::Node<NodeInfoT> idol::Optimizers::BranchAndBound<NodeInfoT>::create_root_node() {
 
    auto root_node = Node<NodeInfoT>::create_root_node();
    assert(root_node.id() == 0);
    ++m_n_created_nodes;
 
    return root_node;
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::hook_before_optimize() {
    Algorithm::hook_before_optimize();
 
    // Reset solution
    set_best_bound(std::max(-Inf, get_param_best_obj_stop()));
    set_best_obj(std::min(+Inf, get_param_best_bound_stop()));
    m_incumbent.reset();
 
    detect_integer_objective();
 
    m_n_created_nodes = 0;
    m_n_solved_nodes = 0;
    m_n_active_nodes = 0;
 
    m_root_node_best_bound = -Inf;
    m_root_node_best_obj = Inf;
 
    m_branching_rule->initialize();
    for (auto& node_updator : m_node_updators) {
        node_updator->initialize();
    }
    m_callback->initialize(this);
 
    m_logger->initialize();
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::hook_optimize() {
 
    auto root_node = create_root_node();
 
    SetOfActiveNodes active_nodes;
 
    explore(root_node, 0, active_nodes, 0);
 
    if (active_nodes.empty()) {
        set_best_bound(get_best_obj());
    }
 
    m_node_updators[0]->clear();
 
    if (get_status() == Fail) {
        return;
    }
 
    if (!m_incumbent.has_value()) {
 
        if (is_pos_inf(get_best_obj())) {
            set_status(Infeasible);
            return;
        }
 
        if (is_neg_inf(get_best_obj())) {
            set_status(Unbounded);
            return;
        }
 
    }
 
    set_status(Feasible);
 
    if (gap_is_closed()) {
        set_status(Optimal);
        set_reason(Proved);
        return;
    }
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::hook_after_optimize() {
 
    Optimizer::hook_after_optimize();
 
    log_after_termination();
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::explore(
                                                    TreeNode &t_node,
                                                    unsigned int t_relaxation_id,
                                                    SetOfActiveNodes & t_active_nodes,
                                                    unsigned int t_step) {
 
    if (is_terminated() || gap_is_closed()) { return; }
 
    bool reoptimize_flag;
    bool explore_children_flag;
 
    do {
 
        solve(t_node, t_relaxation_id);
 
        analyze(t_node, t_relaxation_id, &explore_children_flag, &reoptimize_flag);
 
        log_node_after_solve(t_node);
 
    } while (reoptimize_flag);
 
#pragma omp atomic
    ++m_n_solved_nodes;
 
    if (is_terminated() || gap_is_closed()) { return; }
 
    if (!explore_children_flag) { return; }
 
    t_active_nodes.emplace(t_node);
 
    const unsigned int max_levels = m_steps.at(t_step);
 
    for (unsigned int level = 0 ; level < max_levels ; ++level) {
 
        prune_nodes_by_bound(t_active_nodes);
 
        if (t_step == 0) {
            update_lower_bound(t_active_nodes);
            m_n_active_nodes = t_active_nodes.size();
        }
 
        if (t_active_nodes.empty()) { break; }
        
        if (is_terminated() || gap_is_closed()) { break; }
 
        auto selected_node = select_node_for_branching(t_active_nodes);
 
        auto children = create_child_nodes(selected_node);
 
        const unsigned int n_children = children.size();
 
        std::vector<SetOfActiveNodes> active_nodes(n_children);
 
        if (m_n_threads > 1 && t_step == 0) {
 
            std::vector<std::vector<unsigned int>> to_explore(m_n_threads);
 
            for (unsigned int q = 0 ; q < n_children ; ++q) {
                const unsigned int relaxation_id = q % m_n_threads;
                to_explore[relaxation_id].emplace_back(q);
            }
 
#pragma omp parallel for num_threads(m_n_threads)
            for (unsigned int k = 0 ; k < m_n_threads ; ++k) {
                for (unsigned int j = 0, n_tasks = to_explore[k].size() ; j < n_tasks ; ++j) {
                    const unsigned int q = to_explore[k][j];
                    explore(children[q], k, active_nodes[q], t_step + 1);
                }
            }
 
        } else {
 
            for (unsigned int q = 0 ; q < n_children ; ++q) {
                explore(children[q], t_relaxation_id, active_nodes[q], t_step + 1);
            }
 
        }
 
        for (unsigned int q = 0 ; q < n_children ; ++q) {
            backtrack(t_active_nodes, active_nodes[q]);
        }
 
    }
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::solve(TreeNode& t_node,
                                                        unsigned int t_relaxation_id) const {
 
    auto& node_updator = *m_node_updators[t_relaxation_id];
    auto& relaxation = *m_relaxations[t_relaxation_id];
 
    relaxation.optimizer().set_param_best_bound_stop(std::min(get_best_obj(), get_param_best_bound_stop()));
    relaxation.optimizer().set_param_time_limit(get_remaining_time());
 
    node_updator.prepare(t_node);
 
    /*
    for (const auto& var : parent().vars()) {
        const double lb = relaxation.get_var_lb(var);
        const double ub = relaxation.get_var_ub(var);
        if (lb > ub + Tolerance::Integer) {
            std::cerr << "Inconsistent bounds for variable " << var << ": " << lb << " > " << ub << std::endl;
        }
    }
     */
 
    relaxation.optimize();
 
    t_node.info().save(parent(), relaxation);
 
    m_branching_rule->on_node_solved(t_node);
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::analyze(const BranchAndBound::TreeNode &t_node, unsigned int t_relaxation_id, bool* t_explore_children_flag, bool* t_reoptimize_flag) {
 
    *t_explore_children_flag = false;
    *t_reoptimize_flag = false;
 
    if (get_best_bound() > get_best_obj()) {
        set_status(Fail);
        set_reason(NotSpecified);
        terminate();
        return;
    }
 
    const auto& status = t_node.info().status();
    const auto& reason = t_node.info().reason();
 
    if (get_remaining_time() == 0 || reason == TimeLimit) {
        set_reason(TimeLimit);
        terminate();
        return;
    }
 
    if (status == Unbounded) {
        set_reason(Proved);
        set_best_obj(-Inf);
        terminate();
        return;
    }
 
    if (status == Infeasible || status == InfOrUnbnd) {
 
        if (t_node.level() == 0) {
            set_status(status);
        }
 
        call_callbacks(PrunedSolution, t_node, t_relaxation_id);
        return;
    }
 
    if (status == Feasible && reason == ObjLimit) {
        call_callbacks(PrunedSolution, t_node, t_relaxation_id);
        return;
    }
 
    if (status == Fail || status == Loaded) {
 
        set_status(Fail);
        set_reason(NotSpecified);
        terminate();
        call_callbacks(PrunedSolution, t_node, t_relaxation_id);
        return;
    }
 
    if (t_node.level() == 0) {
        m_root_node_best_bound = t_node.info().objective_value();
    }
 
    if (t_node.info().objective_value() < get_best_obj()) {
 
        if (is_valid(t_node)) {
 
            auto side_effects = call_callbacks(IncumbentSolution, t_node, t_relaxation_id);
 
            if (side_effects.n_added_user_cuts > 0 || side_effects.n_added_lazy_cuts > 0) {
                *t_reoptimize_flag = true;
                return;
            }
 
            set_as_incumbent(t_node);
            return;
 
        }
 
    } else {
 
        call_callbacks(PrunedSolution, t_node, t_relaxation_id);
        return;
 
    }
 
    auto side_effects = call_callbacks(InvalidSolution, t_node, t_relaxation_id);
 
    if (t_node.level() == 0) {
        m_root_node_best_obj = get_best_obj();
    }
 
    if (side_effects.n_added_lazy_cuts > 0 || side_effects.n_added_user_cuts > 0 || side_effects.n_added_local_variable_branching > 0) {
        *t_reoptimize_flag = true;
        return;
    }
 
    *t_explore_children_flag = true;
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::set_as_incumbent(const BranchAndBound::TreeNode &t_node) {
#pragma omp critical
    m_incumbent = t_node;
    set_best_obj(t_node.info().objective_value());
    set_status(Feasible);
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update_lower_bound(const BranchAndBound::SetOfActiveNodes &t_active_nodes) {
 
    if (t_active_nodes.empty()) { return; }
 
    auto& lowest_node = *t_active_nodes.by_objective_value().begin();
    const double raw_lower_bound = lowest_node.info().objective_value();
    const double lower_bound = m_has_integer_objective && !is_integer(raw_lower_bound, Tolerance::Integer) ? std::ceil(raw_lower_bound) : raw_lower_bound;
    if (lower_bound > get_best_bound()) {
        set_best_bound(lower_bound);
    }
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::prune_nodes_by_bound(BranchAndBound::SetOfActiveNodes& t_active_nodes) {
 
    const double upper_bound = get_best_obj();
 
    auto it = t_active_nodes.by_objective_value().begin();
    auto end = t_active_nodes.by_objective_value().end();
 
    while (it != end) {
 
        const auto& node = *it;
        const double raw_lower_bound = node.info().objective_value();
        const double lower_bound = m_has_integer_objective && !is_integer(raw_lower_bound, Tolerance::Integer) ? std::ceil(raw_lower_bound) : raw_lower_bound;
 
        if (lower_bound >= upper_bound - get_tol_mip_absolute_gap()) {
            it = t_active_nodes.erase(it);
            end = t_active_nodes.by_objective_value().end();
        } else {
            break;
        }
 
    }
 
}
 
template<class NodeInfoT>
idol::Node<NodeInfoT>
idol::Optimizers::BranchAndBound<NodeInfoT>::select_node_for_branching(BranchAndBound::SetOfActiveNodes &t_active_nodes) {
    auto iterator = m_node_selection_rule->operator()(t_active_nodes);
    auto result = *iterator;
    t_active_nodes.erase(iterator);
    return result;
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::write(const std::string &t_name) {
    m_relaxations.front()->write(t_name);
}
 
template<class NodeInfoT>
std::vector<idol::Node<NodeInfoT>> idol::Optimizers::BranchAndBound<NodeInfoT>::create_child_nodes(const BranchAndBound::TreeNode &t_node) {
 
    auto children_info = m_branching_rule->create_child_nodes(t_node);
 
    std::vector<Node<NodeInfoT>> result;
    result.reserve(children_info.size());
 
    for (auto* ptr_to_info : children_info) {
        result.emplace_back(ptr_to_info, m_n_created_nodes++, t_node);
    }
 
    m_branching_rule->on_nodes_have_been_created();
 
    return result;
}
 
template<class NodeInfoT>
bool idol::Optimizers::BranchAndBound<NodeInfoT>::gap_is_closed() const {
    return get_relative_gap() <= get_tol_mip_relative_gap()
           || get_absolute_gap() <= get_tol_mip_absolute_gap();
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::backtrack(BranchAndBound::SetOfActiveNodes &t_actives_nodes,
                                                      SetOfActiveNodes &t_sub_active_nodes) {
 
    t_actives_nodes.merge(std::move(t_sub_active_nodes));
 
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::update() {
    for (auto& relaxation : m_relaxations) {
        relaxation->update();
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::remove(const Ctr &t_ctr) {
    for (auto& relaxation : m_relaxations) {
        relaxation->remove(t_ctr);
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::remove(const Var &t_var) {
    for (auto& relaxation : m_relaxations) {
        relaxation->remove(t_var);
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::add(const Ctr &t_ctr) {
    for (auto& relaxation : m_relaxations) {
        relaxation->add(t_ctr);
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::add(const Var &t_var) {
    for (auto& relaxation : m_relaxations) {
        relaxation->add(t_var);
    }
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::set_best_obj(double t_value) {
#pragma omp critical
    Algorithm::set_best_obj(t_value);
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::set_best_bound(double t_value) {
#pragma omp critical
    Algorithm::set_best_bound(t_value);
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::set_reason(idol::SolutionReason t_reason) {
#pragma omp critical
    Algorithm::set_reason(t_reason);
}
 
template<class NodeInfoT>
void idol::Optimizers::BranchAndBound<NodeInfoT>::set_status(idol::SolutionStatus t_status) {
#pragma omp critical
    Algorithm::set_status(t_status);
}
 
#endif //IDOL_OPTIMIZERS_BRANCHANDBOUND_H