#ifndef LEMON_SIMANN_H

 #define LEMON_SIMANN_H

 /// \ingroup experimental

 /// \file

 /// \brief Simulated annealing framework.

 /// \author Akos Ladanyi

 #include <cstdlib>

 #include <cmath>

 #include <lemon/time_measure.h>

 namespace lemon {

 /// \addtogroup experimental

 /// @{

   /*! \brief A base class for controllers. */

   class ControllerBase {

     friend class SimAnnBase;

   public:

     /*! \brief Pointer to the simulated annealing base class. */

     SimAnnBase *simann;

     /*! \brief Initializes the controller. */

     virtual void init() {}

     /*! \brief This is called when a neighbouring state gets accepted. */

     virtual void acceptEvent() {}

     /*! \brief This is called when the accepted neighbouring state's cost is

      *  less than the best found one's.

      */

     virtual void improveEvent() {}

     /*! \brief This is called when a neighbouring state gets rejected. */

     virtual void rejectEvent() {}

     /*! \brief Decides whether to continue the annealing process or not. */

     virtual bool next() = 0;

     /*! \brief Decides whether to accept the current solution or not. */

     virtual bool accept() = 0;

   };

   /*! \brief Skeleton of an entity class. */

   class EntityBase {

   public:

     /*! \brief Makes a minor change to the entity.

      *  \return the new cost

      */

     virtual double mutate() = 0;

     /*! \brief Restores the entity to its previous state i.e. reverts the

      *  effects of the last mutate().

      */

     virtual void revert() = 0;

     /*! \brief Makes a copy of the entity. */

     virtual EntityBase* clone() = 0;

     /*! \brief Makes a major change to the entity. */

     virtual void randomize() = 0;

   };

   /*! \brief Simulated annealing base class. */

   class SimAnnBase {

   private:

     /*! Pointer to the controller. */

     ControllerBase *controller;

     /*! \brief Cost of the current solution. */

     double curr_cost;

     /*! \brief Cost of the best solution. */

     double best_cost;

     /*! \brief Cost of the previous solution. */

     double prev_cost;

     /*! \brief Cost of the solution preceding the previous one. */

     double prev_prev_cost;

     /*! \brief Number of iterations. */

     long iter;

     /*! \brief Number of iterations which did not improve the solution since

      *  the last improvement. */

     long last_impr;

   protected:

     /*! \brief Step to a neighbouring state. */

     virtual double mutate() = 0;

     /*! \brief Reverts the last mutate(). */

     virtual void revert() = 0;

     /*! \brief Saves the current solution as the best one. */

     virtual void saveAsBest() = 0;

     /*! \brief Does initializations before each run. */

     virtual void init() {

       controller->init();

       curr_cost = prev_cost = prev_prev_cost = best_cost =

         std::numeric_limits<double>::infinity();

       iter = last_impr = 0;

     }

   public:

     /*! \brief Sets the controller class to use. */

     void setController(ControllerBase &_controller) {

       controller = &_controller;

       controller->simann = this;

     }

     /*! \brief Returns the cost of the current solution. */

     double getCurrCost() const { return curr_cost; }

     /*! \brief Returns the cost of the previous solution. */

     double getPrevCost() const { return prev_cost; }

     /*! \brief Returns the cost of the best solution. */

     double getBestCost() const { return best_cost; }

     /*! \brief Returns the number of iterations. */

     long getIter() const { return iter; }

     /*! \brief Returns the number of the last iteration when the solution was

      *  improved.

      */

     long getLastImpr() const { return last_impr; }

     /*! \brief Performs one iteration. */

     bool step() {

       iter++;

       prev_prev_cost = prev_cost;

       prev_cost = curr_cost;

       curr_cost = mutate();

       if (controller->accept()) {

         controller->acceptEvent();

         last_impr = iter;

         if (curr_cost < best_cost) {

           best_cost = curr_cost;

           saveAsBest();

           controller->improveEvent();

         }

       }

       else {

         revert();

         curr_cost = prev_cost;

         prev_cost = prev_prev_cost;

         controller->rejectEvent();

       }

       return controller->next();

     }

     /*! \brief Performs a given number of iterations.

      *  \param n the number of iterations

      */

     bool step(int n) {

       for(; n > 0 && step(); --n) ;

       return !n;

     }

     /*! \brief Starts the annealing process. */

     void run() {

       init();

       do { } while (step());

     }

   };

   /*! \brief Simulated annealing class. */

   class SimAnn : public SimAnnBase {

   private:

     /*! \brief Pointer to the current entity. */

     EntityBase *curr_ent;

     /*! \brief Pointer to the best entity. */

     EntityBase *best_ent;

     /*! \brief Does initializations before each run. */

     void init() {

       SimAnnBase::init();

       if (best_ent) delete best_ent;

       best_ent = NULL;

       curr_ent->randomize();

     }

   public:

     /*! \brief Constructor. */

     SimAnn() : curr_ent(NULL), best_ent(NULL) {}

     /*! \brief Destructor. */

     virtual ~SimAnn() {

       if (best_ent) delete best_ent;

     }

     /*! \brief Step to a neighbouring state. */

     double mutate() {

       return curr_ent->mutate();

     }

     /*! \brief Reverts the last mutate(). */

     void revert() {

       curr_ent->revert();

     }

     /*! \brief Saves the current solution as the best one. */

     void saveAsBest() { 

       if (best_ent) delete best_ent;

       best_ent = curr_ent->clone();

     }

     /*! \brief Sets the current entity. */

     void setEntity(EntityBase &_ent) {

       curr_ent = &_ent;

     }

     /*! \brief Returns a copy of the best found entity. */

     EntityBase* getBestEntity() { return best_ent->clone(); }

   };

   /*! \brief A simple controller for the simulated annealing class. */

   class SimpleController : public ControllerBase {

   public:

     /*! \brief Maximum number of iterations. */

     long max_iter;

     /*! \brief Maximum number of iterations which do not improve the

      *  solution. */

     long max_no_impr;

     /*! \brief Temperature. */

     double temp;

     /*! \brief Annealing factor. */

     double ann_fact;

     /*! \brief Constructor.

      *  \param _max_iter maximum number of iterations

      *  \param _max_no_impr maximum number of consecutive iterations which do

      *         not yield a better solution

      *  \param _temp initial temperature

      *  \param _ann_fact annealing factor

      */

     SimpleController(long _max_iter = 500000, long _max_no_impr = 20000,

     double _temp = 1000.0, double _ann_fact = 0.9999) : max_iter(_max_iter),

       max_no_impr(_max_no_impr), temp(_temp), ann_fact(_ann_fact)

     {

       srand48(time(0));

     }

     /*! \brief This is called when a neighbouring state gets accepted. */

     void acceptEvent() {}

     /*! \brief This is called when the accepted neighbouring state's cost is

      *  less than the best found one's.

      */

     void improveEvent() {}

     /*! \brief This is called when a neighbouring state gets rejected. */

     void rejectEvent() {}

     /*! \brief Decides whether to continue the annealing process or not. Also

      *  decreases the temperature. */

     bool next() {

       temp *= ann_fact;

       bool quit = (simann->getIter() > max_iter) ||

         (simann->getIter() - simann->getLastImpr() > max_no_impr);

       return !quit;

     }

     /*! \brief Decides whether to accept the current solution or not. */

     bool accept() {

       double cost_diff = simann->getPrevCost() - simann->getCurrCost();

       return (drand48() <= exp(cost_diff / temp));

     }

   };

   /*! \brief A controller with preset running time for the simulated annealing

    *  class.

    *

    *  With this controller you can set the running time of the annealing

    *  process in advance. It works the following way: the controller measures

    *  a kind of divergence. The divergence is the difference of the average

    *  cost of the recently found solutions the cost of the best found one. In

    *  case this divergence is greater than a given threshold, then we decrease

    *  the annealing factor, that is we cool the system faster. In case the

    *  divergence is lower than the threshold, then we increase the temperature.

    *  The threshold is a function of the elapsed time which reaches zero at the

    *  desired end time.

    */

   class AdvancedController : public ControllerBase {

   private:

     Timer timer;

     /*! \param time the elapsed time in seconds */

     virtual double threshold(double time) {

       return (-1.0) * start_threshold / end_time * time + start_threshold;

     }

   public:

     double alpha;

     double beta;

     double gamma;

     /*! \brief The time at the end of the algorithm. */

     double end_time;

     /*! \brief The time at the start of the algorithm. */

     double start_time;

     /*! \brief Starting threshold. */

     double start_threshold;

     /*! \brief Average cost of recent solutions. */

     double avg_cost;

     /*! \brief Temperature. */

     double temp;

     /*! \brief Annealing factor. */

     double ann_fact;

     /*! \brief Initial annealing factor. */

     double init_ann_fact;

     bool warmup;

     /*! \brief Constructor.

      *  \param _end_time running time in seconds

      *  \param _alpha parameter used to calculate the running average

      *  \param _beta parameter used to decrease the annealing factor

      *  \param _gamma parameter used to increase the temperature

      *  \param _ann_fact initial annealing factor

      */

     AdvancedController(double _end_time, double _alpha = 0.2,

     double _beta = 0.9, double _gamma = 1.6, double _ann_fact = 0.9999) :

     alpha(_alpha), beta(_beta), gamma(_gamma), end_time(_end_time),

     ann_fact(_ann_fact), init_ann_fact(_ann_fact), warmup(true)

     {

       srand48(time(0));

     }

     void init() {

       avg_cost = simann->getCurrCost();

     }

     /*! \brief This is called when a neighbouring state gets accepted. */

     void acceptEvent() {

       avg_cost = alpha * simann->getCurrCost() + (1.0 - alpha) * avg_cost;

       if (warmup) {

         static int cnt = 0;

         cnt++;

         if (cnt >= 100) {

           // calculate starting threshold and starting temperature

           start_threshold = 5.0 * fabs(simann->getBestCost() - avg_cost);

           temp = 10000.0;

           warmup = false;

           timer.reset();

         }

       }

     }

     /*! \brief Decides whether to continue the annealing process or not. */

     bool next() {

       if (warmup) {

         return true;

       }

       else {

         double elapsed_time = timer.getRealTime();

         if (fabs(avg_cost - simann->getBestCost()) > threshold(elapsed_time)) {

           // decrease the annealing factor

           ann_fact *= beta;

         }

         else {

           // increase the temperature

           temp *= gamma;

           // reset the annealing factor

           ann_fact = init_ann_fact;

         }

         temp *= ann_fact;

         return elapsed_time < end_time;

       }

     }

     /*! \brief Decides whether to accept the current solution or not. */

     bool accept() {

       if (warmup) {

         // we accept eveything during the "warm up" phase

         return true;

       }

       else {

         double cost_diff = simann->getPrevCost() - simann->getCurrCost();

         if (cost_diff < 0.0) {

           return (drand48() <= exp(cost_diff / temp));

         }

         else {

           return true;

         }

       }

     }

   };

 /// @}

 }

 #endif