---

ObjCryst::MonteCarloObj Class Reference

Base object for Monte-Carlo Global Optimization methods. More...

Inheritance diagram for ObjCryst::MonteCarloObj:

List of all members.

Public Methods
	MonteCarloObj (const string name="")
	Constructor.
	MonteCarloObj (const bool internalUseOnly)
	Constructor.
virtual	~MonteCarloObj ()
	Destructor.
void	SetAlgorithmSimulAnnealing (const AnnealingSchedule scheduleTemp, const REAL tMax, const REAL tMin, const AnnealingSchedule scheduleMutation=ANNEALING_CONSTANT, const REAL mutMax=16., const REAL mutMin=.125, const long nbTrialRetry=0, const REAL minCostRetry=0., const long maxNbTrialSinceBest=0)
	Set the refinement method to simulated Annealing. Note that Parellel Tempering is more efficient to get out of local minima, so you sould rather use that method.
void	SetAlgorithmParallTempering (const AnnealingSchedule scheduleTemp, const REAL tMax, const REAL tMin, const AnnealingSchedule scheduleMutation=ANNEALING_CONSTANT, const REAL mutMax=16., const REAL mutMin=.125)
	Set the refinement method to Parallel Tempering.
virtual void	Optimize (long &nbSteps, const bool silent=false, const REAL finalcost=0, const REAL maxTime=-1)
	Launch optimization for N steps.
virtual void	XMLOutput (ostream &os, int indent=0) const
	Output a description of the object in XML format to a stream.
virtual void	XMLInput (istream &is, const XMLCrystTag &tag)
	Input in XML format from a stream, restoring the set of refined objects and the associated cost functions. Note that the corresponding objects must have been loaded in memory before, else shit happens.
virtual const string	GetClassName () const
	Get the name for this class type.
Protected Methods
virtual void	NewConfiguration (const RefParType *type=gpRefParTypeObjCryst)
	Make a random change in the configuration.
virtual void	InitOptions ()
	Initialization of options.
Protected Attributes
RefObjOpt	mGlobalOptimType
	Method used for the global optimization.
REAL	mCurrentCost
	Current value of the cost function.
RefObjOpt	mSaveDetailledHistory
	Option to save the evolution of all optimized parameters (for testing only ! this is very slow...).
string	mHistorySaveFileName
	Save the evolution of refined parameters after optimization ?
long	mLastParSavedSetIndex
	Index of the 'last' parameter set.
REAL	mTemperature
	Current temperature for annealing.
REAL	mTemperatureMax
	Beginning temperature for annealing.
REAL	mTemperatureMin
	Lower temperature.
RefObjOpt	mAnnealingScheduleTemp
	Schedule for the annealing.
REAL	mMutationAmplitude
	Mutation amplitude.
REAL	mMutationAmplitudeMax
	Mutation amplitude at the beginning of the optimization.
REAL	mMutationAmplitudeMin
	Mutation amplitude at the end of the optimization.
RefObjOpt	mAnnealingScheduleMutation
	Schedule for the annealing.
long	mNbTrialRetry
	Number of trials before testing if we are below the given minimum cost.
REAL	mMinCostRetry
	Cost to reach unless an automatic randomization and retry is done.
long	mMaxNbTrialSinceBest
	If more than mMaxNbTrialSinceBest trials have been made since the best configuration has been found, then revert to the best configuration.

---

Detailed Description

Base object for Monte-Carlo Global Optimization methods.

The algorithm is quite simple, whith two type of optimizations, either simulated Annealing or Parallel Tempering, the latter being recommanded for most real-world optimizations.

---

Constructor & Destructor Documentation

ObjCryst::MonteCarloObj::MonteCarloObj (  const bool    internalUseOnly )

Constructor. Using internalUseOnly=true will avoid registering the the object to any registry, and thus (for example) no display will be created, nor will this object be automatically be saved.

---

Member Function Documentation

virtual void ObjCryst::MonteCarloObj::NewConfiguration (  const RefParType *    type = gpRefParTypeObjCryst )  [protected, virtual]

Make a random change in the configuration. For internal use only. This just generates a new configuration with random changes (according to current parameters). The old config is stored in mRefParList as the last config (index mLastParSavedSetIndex). The new one is not tested in this function vs temperature: this should be done in the OptimizationObj::Optimize() function, which also chooses whether to revert to the previous configuration. Random moves are made by the objects and not by this function, because the new configuration can be specific (like, for example, permutations between some of the parameters (atoms)). Parameters: type:  can be used to restrict the move to a given category of parameters.

virtual void ObjCryst::MonteCarloObj::Optimize (  long &    nbSteps, const bool    silent = false, const REAL    finalcost = 0, const REAL    maxTime = -1 )  [virtual]

Launch optimization for N steps. Parameters: nbSteps:  the number of steps to go. This number is modified (decreases!) as the refinement goes on. silent  : if true, absolutely no message should be printed (except debugging) finalcost:  the optimization will stop if overall cost fallse below this value maxTime:  the optimization will stop after the given number of seconds has been spent optimizing (ignored if <0). Implements ObjCryst::OptimizationObj.

void ObjCryst::MonteCarloObj::SetAlgorithmParallTempering (  const AnnealingSchedule    scheduleTemp, const REAL    tMax, const REAL    tMin, const AnnealingSchedule    scheduleMutation = ANNEALING_CONSTANT, const REAL    mutMax = 16., const REAL    mutMin = .125 )

Set the refinement method to Parallel Tempering. The refinement begins at max and finishes at min temperature. Parameters: scheduleTemp:  temperature schedule tMax,tMin:  Max and Min temperatures. See AnnealingSchedule. scheduleMutation:  the mutation schedule. For each new configuration, the variation of each refinable parameter is less than its RefinablePar::GlobalOptimStep(), multiplied by the current mutation amplitude. By default this mutation is equal to 1., but making bigger steps can be a good idea at the beginning of the refinement. Thus you can choose a schedule for the amplitude, exactly like for the temperature. mutMax,mutMin:  Max and Min mutation amplitudes. Warning: do not use the 'smart' option for the temperature schedule, it is not yet implemented. Later it will be used to set the temperatures as a function of the amplitude schedule, so that we keep accepted move between 30% and 70%. Note: this will be removed when we separate the different algorithms in different classes.

void ObjCryst::MonteCarloObj::SetAlgorithmSimulAnnealing (  const AnnealingSchedule    scheduleTemp, const REAL    tMax, const REAL    tMin, const AnnealingSchedule    scheduleMutation = ANNEALING_CONSTANT, const REAL    mutMax = 16., const REAL    mutMin = .125, const long    nbTrialRetry = 0, const REAL    minCostRetry = 0., const long    maxNbTrialSinceBest = 0 )

Set the refinement method to simulated Annealing. Note that Parellel Tempering is more efficient to get out of local minima, so you sould rather use that method. The refinement begins at max and finishes at min temperature. Parameters: scheduleTemp:  temperature schedule. See AnnealingSchedule. tMax,tMin:  Max and Min temperatures. scheduleMutation:  the mutation schedule. For each new configuration, the variation of each refinable parameter is less than its RefinablePar::GlobalOptimStep(), multiplied by the current mutation amplitude. By default this mutation is equal to 1., but making bigger steps is a good idea at the beginning of the refinement (for higher temperatures). See AnnealingSchedule. See AnnealingSchedule. mutMax,mutMin:  Max and Min mutation amplitudes. minCostRetry, nbTrialRetry:  if after nbTrialRetry, the cost function is still above minCostRetry, then start again from a random configuration. No randomization is made if nbTrialRetry <= 0. maxNbTrialSinceBest:  if more than maxNbTrialSinceBest trials have been made since the best configuration was recorded, then revert to that configuration. This should be large enough to have an ergodic search (the default is never to revert..) Warning: do not use the 'smart' option for the temperature schedule, it is not yet implemented. Later it will be used to set the temperatures as a function of the amplitude schedule, so that we accept between 30% and 70% moves. Note: this will be removed when we separate the different algorithms in different classes.

virtual void ObjCryst::MonteCarloObj::XMLOutput (  ostream &    os, int    indent = 0 )  const [virtual]

Output a description of the object in XML format to a stream. This saves the list of refined object and the cost functions, as well as options for the refinement. The refined objects are not saved, so this must be done somewhere else (they must be reloaded before this object). Implements ObjCryst::OptimizationObj.

---

Member Data Documentation

RefObjOpt ObjCryst::MonteCarloObj::mGlobalOptimType [protected]

Method used for the global optimization. Should be removed when we switch to using several classes for different algorithms.

long ObjCryst::MonteCarloObj::mMaxNbTrialSinceBest [protected]

If more than mMaxNbTrialSinceBest trials have been made since the best configuration has been found, then revert to the best configuration. If <=0, then this is ignored. This must be large enough to have an ergodic algorithm (more strictly, should not be used ?)

REAL ObjCryst::MonteCarloObj::mMutationAmplitude [protected]

Mutation amplitude. From .25 to 64. Random moves will have a maximum amplitude equal to this amplitude multiplied by the Global optimization step defined for each RefinablePar. Large amplitude should be used at the beginning of the refinement (high temeratures).

long ObjCryst::MonteCarloObj::mNbTrialRetry [protected]

Number of trials before testing if we are below the given minimum cost. If <=0, this will be ignored.

---

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

• GlobalOptimObj.h

---