ResilientBackpropagation Class

Resilient Backpropagation method for unconstrained optimization.

Inheritance Hierarchy

SystemObject
  Accord.Math.OptimizationBaseOptimizationMethod
    Accord.Math.OptimizationBaseGradientOptimizationMethod
      Accord.Math.OptimizationResilientBackpropagation

Namespace: Accord.Math.Optimization
Assembly: Accord.Math (in Accord.Math.dll) Version: 3.8.0

Syntax

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public class ResilientBackpropagation : BaseGradientOptimizationMethod, 
	IGradientOptimizationMethod, IOptimizationMethod, IOptimizationMethod<double[], double>, 
	IGradientOptimizationMethod<double[], double>, IFunctionOptimizationMethod<double[], double>

Public Class ResilientBackpropagation
	Inherits BaseGradientOptimizationMethod
	Implements IGradientOptimizationMethod, IOptimizationMethod, IOptimizationMethod(Of Double(), Double), 
	IGradientOptimizationMethod(Of Double(), Double), IFunctionOptimizationMethod(Of Double(), Double)

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The ResilientBackpropagation type exposes the following members.

Constructors

	Name	Description
	ResilientBackpropagation(Int32)	Creates a new ResilientBackpropagation function optimizer.
	ResilientBackpropagation(NonlinearObjectiveFunction)	Creates a new ResilientBackpropagation function optimizer.
	ResilientBackpropagation(Int32, FuncDouble, Double, FuncDouble, Double)	Creates a new ResilientBackpropagation function optimizer.

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Properties

	Name	Description
	DecreaseFactor	Gets the decrease parameter, also referred as eta minus. Default is 0.5.
	Function	Gets or sets the function to be optimized. (Inherited from BaseOptimizationMethod.)
	Gradient	Gets or sets a function returning the gradient vector of the function to be optimized for a given value of its free parameters. (Inherited from BaseGradientOptimizationMethod.)
	IncreaseFactor	Gets the increase parameter, also referred as eta plus. Default is 1.2.
	Iterations	Gets or sets the maximum number of iterations performed by the learning algorithm.
	NumberOfVariables	Gets the number of variables (free parameters) in the optimization problem. (Inherited from BaseOptimizationMethod.)
	Solution	Gets the current solution found, the values of the parameters which optimizes the function. (Inherited from BaseOptimizationMethod.)
	Token	Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running. (Inherited from BaseOptimizationMethod.)
	Tolerance	Gets or sets the maximum change in the average log-likelihood after an iteration of the algorithm used to detect convergence.
	UpdateLowerBound	Gets or sets the minimum possible update step, also referred as delta max. Default is 1e-6.
	UpdateUpperBound	Gets or sets the maximum possible update step, also referred as delta min. Default is 50.
	Value	Gets the output of the function at the current Solution. (Inherited from BaseOptimizationMethod.)

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Methods

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Maximize	Finds the maximum value of a function. The solution vector will be made available at the Solution property. (Inherited from BaseGradientOptimizationMethod.)
	Maximize(Double)	Finds the maximum value of a function. The solution vector will be made available at the Solution property. (Inherited from BaseOptimizationMethod.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Minimize	Finds the minimum value of a function. The solution vector will be made available at the Solution property. (Inherited from BaseGradientOptimizationMethod.)
	Minimize(Double)	Finds the minimum value of a function. The solution vector will be made available at the Solution property. (Inherited from BaseOptimizationMethod.)
	OnNumberOfVariablesChanged	Called when the NumberOfVariables property has changed. (Overrides BaseOptimizationMethodOnNumberOfVariablesChanged(Int32).)
	OnProgressChanged	Raises the [E:ProgressChanged] event.
	Optimize	Implements the actual optimization algorithm. This method should try to minimize the objective function. (Overrides BaseOptimizationMethodOptimize.)
	Reset	Resets the current update steps using the given learning rate.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Events

	Name	Description
	ProgressChanged	Occurs when the current learning progress has changed.

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Reference

Accord.Math.Optimization Namespace

Accord.Math.OptimizationConjugateGradient

Accord.Math.OptimizationBoundedBroydenFletcherGoldfarbShanno

Accord.Math.OptimizationBroydenFletcherGoldfarbShanno

Accord.Math.OptimizationTrustRegionNewtonMethod