RProp (Resilient Back Propagation)

This is an implementation of the Resilient Backpropagation algorithm. See the wikipedia article for reference.

Usage

e["Solver"]["Type"] = "Optimizer/Rprop"

Results

These are the results produced by this solver:

Best Gradient(x)

  • Usage: e[“Results”][“Best Gradient(x)”] = List of real number

  • Description: Values of dF(x) for the x parameters that produced the best F(x) found so far.

Best F(x)

  • Usage: e[“Results”][“Best F(x)”] = real number

  • Description: Optimal value of F(x) found so far.

Best Parameters

  • Usage: e[“Results”][“Best Parameters”] = List of real number

  • Description: Value for the x parameters that produced the best F(x).

Variable-Specific Settings

These are settings required by this module that are added to each of the experiment’s variables when this module is selected.

Lower Bound

  • Usage: e[“Variables”][index][“Lower Bound”] = real number

  • Description: [Hint] Lower bound for the variable’s value.

Upper Bound

  • Usage: e[“Variables”][index][“Upper Bound”] = real number

  • Description: [Hint] Upper bound for the variable’s value.

Initial Value

  • Usage: e[“Variables”][index][“Initial Value”] = real number

  • Description: [Hint] Initial value at or around which the algorithm shall start looking for an optimum.

Initial Mean

  • Usage: e[“Variables”][index][“Initial Mean”] = real number

  • Description: [Hint] Initial mean for the proposal distribution. This value must be defined between the variable’s Mininum and Maximum settings (by default, this value is given by the center of the variable domain).

Initial Standard Deviation

  • Usage: e[“Variables”][index][“Initial Standard Deviation”] = real number

  • Description: [Hint] Initial standard deviation of the proposal distribution for a variable (by default, this value is given by 30% of the variable domain width).

Minimum Standard Deviation Update

  • Usage: e[“Variables”][index][“Minimum Standard Deviation Update”] = real number

  • Description: [Hint] Lower bound for the standard deviation updates of the proposal distribution for a variable. Korali increases the scaling factor sigma if this value is undershot.

Values

  • Usage: e[“Variables”][index][“Values”] = List of real number

  • Description: [Hint] Locations to evaluate the Objective Function.

Configuration

These are settings required by this module.

Delta0

  • Usage: e[“Solver”][“Delta0”] = real number

  • Description: Initial Delta.

Delta Min

  • Usage: e[“Solver”][“Delta Min”] = real number

  • Description: Minimum Delta, parameter for step size calibration.

Delta Max

  • Usage: e[“Solver”][“Delta Max”] = real number

  • Description: Maximum Delta, parameter for step size calibration.

Eta Minus

  • Usage: e[“Solver”][“Eta Minus”] = real number

  • Description: Parameter for step size calibration.

Eta Plus

  • Usage: e[“Solver”][“Eta Plus”] = real number

  • Description: Parameter for step size calibration.

Termination Criteria

These are the customizable criteria that indicates whether the solver should continue or finish execution. Korali will stop when at least one of these conditions are met. The criteria is expressed in C++ since it is compiled and evaluated as seen here in the engine.

Max Gradient Norm

  • Usage: e[“Solver”][“Max Gradient Norm”] = real number

  • Description: Maximum value of the norm of the gradient.

  • Criteria: _normPreviousGradient < _maxGradientNorm

Max Stall Generations

  • Usage: e[“Solver”][“Max Stall Generations”] = unsigned integer

  • Description: Maximum times stalled with function evaluation bigger than the best one.

  • Criteria: :code:` _maxStallCounter >= _maxStallGenerations`

Parameter Relative Tolerance

  • Usage: e[“Solver”][“Parameter Relative Tolerance”] = real number

  • Description: Relative tolerance in parameter difference between generations.

  • Criteria: _xDiff<_parameterRelativeTolerance && _xDiff>0

Max Value

  • Usage: e[“Solver”][“Max Value”] = real number

  • Description: Specifies the maximum target fitness to stop maximization.

  • Criteria: _k->_currentGeneration > 1 && (+_bestEverValue > _maxValue)

Min Value Difference Threshold

  • Usage: e[“Solver”][“Min Value Difference Threshold”] = real number

  • Description: Specifies the minimum fitness differential between two consecutive generations before stopping execution.

  • Criteria: _k->_currentGeneration > 1 && (fabs(_currentBestValue - _previousBestValue) < _minValueDifferenceThreshold)

Max Model Evaluations

  • Usage: e[“Solver”][“Max Model Evaluations”] = unsigned integer

  • Description: Specifies the maximum allowed evaluations of the computational model.

  • Criteria: _maxModelEvaluations <= _modelEvaluationCount

Max Generations

  • Usage: e[“Solver”][“Max Generations”] = unsigned integer

  • Description: Determines how many solver generations to run before stopping execution. Execution can be resumed at a later moment.

  • Criteria: _k->_currentGeneration > _maxGenerations

Default Configuration

These following configuration will be assigned by default. Any settings defined by the user will override the given settings specified in these defaults.

{
"Delta Max": 50,
"Delta Min": 1e-06,
"Delta0": 0.1,
"Eta Minus": 0.5,
"Eta Plus": 1.2,
"Model Evaluation Count": 0,
"Termination Criteria": {
    "Max Generations": 10000000000,
    "Max Gradient Norm": 0.0,
    "Max Model Evaluations": 1000000000,
    "Max Stall Generations": 20,
    "Max Value": Infinity,
    "Min Value Difference Threshold": -Infinity,
    "Parameter Relative Tolerance": 0.0001
    },
"Variable Count": 0
}

Variable Defaults

These following configuration will be assigned to each of the experiment variables by default. Any settings defined by the user will override the given settings specified in these defaults.

{
"Initial Mean": NaN,
"Initial Standard Deviation": NaN,
"Initial Value": NaN,
"Lower Bound": -Infinity,
"Minimum Standard Deviation Update": 0.0,
"Upper Bound": Infinity,
"Values": []
}