IMP  2.0.1
The Integrative Modeling Platform
IMP::core::MonteCarloWithBasinHopping Class Reference

This variant of Monte Carlo uses basis hopping. More...

#include <IMP/core/MonteCarlo.h>

+ Inheritance diagram for IMP::core::MonteCarloWithBasinHopping:

Public Member Functions

 MonteCarloWithBasinHopping (Optimizer *opt, unsigned int ns)
 
- Public Member Functions inherited from IMP::core::MonteCarloWithLocalOptimization
 MonteCarloWithLocalOptimization (Optimizer *opt, unsigned int steps)
 
Optimizerget_local_optimizer () const
 
unsigned int get_number_of_steps () const
 
- Public Member Functions inherited from IMP::core::MonteCarlo
 MonteCarlo (Model *m=nullptr)
 
double get_best_accepted_energy () const
 
double get_last_accepted_energy () const
 
double get_maximum_difference () const
 
void set_maximum_difference (double d)
 
void set_return_best (bool tf)
 
void set_kt (Float t)
 
Float get_kt () const
 
unsigned int get_number_of_forward_steps () const
 Return how many times the optimizer has succeeded in taking a step.
 
unsigned int get_number_of_upward_steps () const
 Return how many times the optimizer has stepped to higher energy.
 
void remove_mover (MonteCarloMover *d)
 
void remove_movers (const MonteCarloMovers &d)
 
void set_movers (const MonteCarloMovers &ps)
 
void set_movers_order (const MonteCarloMovers &objs)
 
unsigned int add_mover (MonteCarloMover *obj)
 
void add_movers (const MonteCarloMovers &objs)
 
void clear_movers ()
 
unsigned int get_number_of_movers () const
 
bool get_has_movers ()
 
MonteCarloMoverget_mover (unsigned int i) const
 
MonteCarloMovers get_movers () const
 
void reserve_movers (unsigned int sz)
 
void set_incremental_scoring_function (IncrementalScoringFunction *isf)
 
bool get_use_incremental_scoring_function () const
 
IncrementalScoringFunctionget_incremental_scoring_function () const
 
- Public Member Functions inherited from IMP::kernel::Optimizer
 Optimizer (Model *m, std::string name="Optimizer %1%")
 
double get_last_score () const
 Return the score found in the last evaluate.
 
Modelget_model () const
 Get the model being optimized.
 
ScoringFunctionget_scoring_function () const
 Return the scoring function that is being used.
 
bool get_stop_on_good_score () const
 
double optimize (unsigned int max_steps)
 Optimize the model for up to max_steps iterations. More...
 
void set_model (Model *m)
 Set the model being optimized. More...
 
virtual void set_scoring_function (ScoringFunctionAdaptor sf)
 
void set_stop_on_good_score (bool tf)
 
virtual void show (std::ostream &out=std::cout) const
 Print info about the optimizer state. More...
 
void remove_optimizer_state (OptimizerState *d)
 
void remove_optimizer_states (const OptimizerStates &d)
 
void set_optimizer_states (const OptimizerStates &ps)
 
void set_optimizer_states_order (const OptimizerStates &objs)
 
unsigned int add_optimizer_state (OptimizerState *obj)
 
void add_optimizer_states (const OptimizerStates &objs)
 
void clear_optimizer_states ()
 
unsigned int get_number_of_optimizer_states () const
 
bool get_has_optimizer_states ()
 
OptimizerStateget_optimizer_state (unsigned int i) const
 
OptimizerStates get_optimizer_states () const
 
void reserve_optimizer_states (unsigned int sz)
 
- Public Member Functions inherited from IMP::base::Object
virtual void clear_caches ()
 
virtual IMP::base::VersionInfo get_version_info () const =0
 Get information about the module and version of the object.
 
void set_check_level (CheckLevel l)
 
void set_log_level (LogLevel l)
 Set the logging level used in this object. More...
 
void set_was_used (bool tf) const
 
void show (std::ostream &out=std::cout) const
 
const std::string & get_name () const
 
void set_name (std::string name)
 

Protected Member Functions

virtual void do_step ()
 a class that inherits from this should override this method
 

Additional Inherited Members

Detailed Description

Basin hopping is where, after a move, a local optimizer is used to relax the model before the energy computation. However, the pre-relaxation state of the model is used as the starting point for the next step. The idea is that models are accepted or rejected based on the score of the nearest local minima, but they can still climb the barriers in between as the model is not reset to the minima after each step.

Definition at line 233 of file MonteCarlo.h.


The documentation for this class was generated from the following file: