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SARAHType< UpdatePolicyType > Class Template Reference

StochAstic Recusive gRadient algoritHm (SARAH). More...

Public Member Functions

 SARAHType (const double stepSize=0.01, const size_t batchSize=32, const size_t maxIterations=1000, const size_t innerIterations=0, const double tolerance=1e-5, const bool shuffle=true, const UpdatePolicyType &updatePolicy=UpdatePolicyType())
 Construct the SARAH optimizer with the given function and parameters. More...

 
size_t BatchSize () const
 Get the batch size. More...

 
size_t & BatchSize ()
 Modify the batch size. More...

 
size_t InnerIterations () const
 Get the maximum number of iterations (0 indicates default n / b). More...

 
size_t & InnerIterations ()
 Modify the maximum number of iterations (0 indicates default n / b). More...

 
size_t MaxIterations () const
 Get the maximum number of iterations (0 indicates no limit). More...

 
size_t & MaxIterations ()
 Modify the maximum number of iterations (0 indicates no limit). More...

 
template
<
typename
DecomposableFunctionType
>
double Optimize (DecomposableFunctionType &function, arma::mat &iterate)
 Optimize the given function using SARAH. More...

 
bool Shuffle () const
 Get whether or not the individual functions are shuffled. More...

 
bool & Shuffle ()
 Modify whether or not the individual functions are shuffled. More...

 
double StepSize () const
 Get the step size. More...

 
double & StepSize ()
 Modify the step size. More...

 
double Tolerance () const
 Get the tolerance for termination. More...

 
double & Tolerance ()
 Modify the tolerance for termination. More...

 
const UpdatePolicyType & UpdatePolicy () const
 Get the update policy. More...

 
UpdatePolicyType & UpdatePolicy ()
 Modify the update policy. More...

 

Detailed Description


template
<
typename
UpdatePolicyType
=
SARAHUpdate
>

class mlpack::optimization::SARAHType< UpdatePolicyType >

StochAstic Recusive gRadient algoritHm (SARAH).

is a variance reducing stochastic recursive gradient algorithm for minimizing a function which can be expressed as a sum of other functions.

For more information, see the following.

@article{Nguyen2017,
author = {{Nguyen}, L.~M. and {Liu}, J. and {Scheinberg},
K. and {Tak{\'a}{\v c}}, M.},
title = {SARAH: A Novel Method for Machine Learning Problems Using
Stochastic Recursive Gradient},
journal = {ArXiv e-prints},
url = {https://arxiv.org/abs/1703.00102}
year = 2017,
}

For SARAH to work, a DecomposableFunctionType template parameter is required. This class must implement the following function:

size_t NumFunctions(); double Evaluate(const arma::mat& coordinates, const size_t i, const size_t batchSize); void Gradient(const arma::mat& coordinates, const size_t i, arma::mat& gradient, const size_t batchSize);

NumFunctions() should return the number of functions ( $n$), and in the other two functions, the parameter i refers to which individual function (or gradient) is being evaluated. So, for the case of a data-dependent function, such as NCA (see mlpack::nca::NCA), NumFunctions() should return the number of points in the dataset, and Evaluate(coordinates, 0) will evaluate the objective function on the first point in the dataset ( is held internally in the DecomposableFunctionType).

Template Parameters
UpdatePolicyTypeupdate policy used by SARAHType during the iterative update process.

Definition at line 66 of file sarah.hpp.

Constructor & Destructor Documentation

◆ SARAHType()

SARAHType ( const double  stepSize = 0.01,
const size_t  batchSize = 32,
const size_t  maxIterations = 1000,
const size_t  innerIterations = 0,
const double  tolerance = 1e-5,
const bool  shuffle = true,
const UpdatePolicyType &  updatePolicy = UpdatePolicyType() 
)

Construct the SARAH optimizer with the given function and parameters.

The defaults here are not necessarily good for the given problem, so it is suggested that the values used be tailored to the task at hand. The maximum number of iterations refers to the maximum number of points that are processed (i.e., one iteration equals one point; one iteration does not equal one pass over the dataset).

Parameters
stepSizeStep size for each iteration.
batchSizeBatch size to use for each step.
maxIterationsMaximum number of iterations allowed (0 means no limit).
innerIterationsThe number of inner iterations allowed (0 means n / batchSize). Note that the full gradient is only calculated in the outer iteration.
toleranceMaximum absolute tolerance to terminate algorithm.
shuffleIf true, the function order is shuffled; otherwise, each function is visited in linear order.
updatePolicyInstantiated update policy used to adjust the given parameters.

Member Function Documentation

◆ BatchSize() [1/2]

size_t BatchSize ( ) const
inline

Get the batch size.

Definition at line 117 of file sarah.hpp.

◆ BatchSize() [2/2]

size_t& BatchSize ( )
inline

Modify the batch size.

Definition at line 119 of file sarah.hpp.

◆ InnerIterations() [1/2]

size_t InnerIterations ( ) const
inline

Get the maximum number of iterations (0 indicates default n / b).

Definition at line 127 of file sarah.hpp.

◆ InnerIterations() [2/2]

size_t& InnerIterations ( )
inline

Modify the maximum number of iterations (0 indicates default n / b).

Definition at line 129 of file sarah.hpp.

◆ MaxIterations() [1/2]

size_t MaxIterations ( ) const
inline

Get the maximum number of iterations (0 indicates no limit).

Definition at line 122 of file sarah.hpp.

◆ MaxIterations() [2/2]

size_t& MaxIterations ( )
inline

Modify the maximum number of iterations (0 indicates no limit).

Definition at line 124 of file sarah.hpp.

◆ Optimize()

double Optimize ( DecomposableFunctionType &  function,
arma::mat &  iterate 
)

Optimize the given function using SARAH.

The given starting point will be modified to store the finishing point of the algorithm, and the final objective value is returned.

Template Parameters
DecomposableFunctionTypeType of the function to be optimized.
Parameters
functionFunction to optimize.
iterateStarting point (will be modified).
Returns
Objective value of the final point.

◆ Shuffle() [1/2]

bool Shuffle ( ) const
inline

Get whether or not the individual functions are shuffled.

Definition at line 137 of file sarah.hpp.

◆ Shuffle() [2/2]

bool& Shuffle ( )
inline

Modify whether or not the individual functions are shuffled.

Definition at line 139 of file sarah.hpp.

◆ StepSize() [1/2]

double StepSize ( ) const
inline

Get the step size.

Definition at line 112 of file sarah.hpp.

◆ StepSize() [2/2]

double& StepSize ( )
inline

Modify the step size.

Definition at line 114 of file sarah.hpp.

◆ Tolerance() [1/2]

double Tolerance ( ) const
inline

Get the tolerance for termination.

Definition at line 132 of file sarah.hpp.

◆ Tolerance() [2/2]

double& Tolerance ( )
inline

Modify the tolerance for termination.

Definition at line 134 of file sarah.hpp.

◆ UpdatePolicy() [1/2]

const UpdatePolicyType& UpdatePolicy ( ) const
inline

Get the update policy.

Definition at line 142 of file sarah.hpp.

◆ UpdatePolicy() [2/2]

UpdatePolicyType& UpdatePolicy ( )
inline

Modify the update policy.

Definition at line 144 of file sarah.hpp.


The documentation for this class was generated from the following file:
  • /var/www/www.mlpack.org/mlpack-git/src/mlpack/core/optimizers/sarah/sarah.hpp