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Stochastic Coordinate descent is a technique for minimizing a function by doing a line search along a single direction at the current point in the iteration. More...
Public Member Functions  
SCD (const double stepSize=0.01, const size_t maxIterations=100000, const double tolerance=1e5, const size_t updateInterval=1e3, const DescentPolicyType descentPolicy=DescentPolicyType())  
Construct the SCD optimizer with the given function and parameters. More...  
DescentPolicyType  DescentPolicy () const 
Get the descent policy. More...  
DescentPolicyType &  DescentPolicy () 
Modify the descent policy. 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 ResolvableFunctionType >  
double  Optimize (ResolvableFunctionType &function, arma::mat &iterate) 
Optimize the given function using stochastic coordinate descent. 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...  
size_t  UpdateInterval () const 
Get the update interval for reporting objective. More...  
size_t &  UpdateInterval () 
Modify the update interval for reporting objective. More...  
Detailed Description
template<typenameDescentPolicyType=RandomDescent>
class mlpack::optimization::SCD< DescentPolicyType >
Stochastic Coordinate descent is a technique for minimizing a function by doing a line search along a single direction at the current point in the iteration.
The direction (or "coordinate") can be chosen cyclically, randomly or in a greedy fashion(depending on the DescentPolicy).
This optimizer is useful for problems with a smooth multivariate function where computing the entire gradient for an update is infeasable. CD method typically significantly outperform GD, especially on sparse problems with a very large number variables/coordinates.
For more information, see the following.
For SCD to work, the class must implement the following functions:
size_t NumFeatures(); double Evaluate(const arma::mat& coordinates); void PartialGradient(const arma::mat& coordinates, const size_t j, arma::sp_mat& gradient);
NumFeatures() should return the number of features in the decision variable. Evaluate gives the value of the loss function at the current decision variable and PartialGradient is used to evaluate the partial gradient with respect to the jth feature.
 Template Parameters

DescentPolicy Descent policy to decide the order in which the coordinate for descent is selected.
Constructor & Destructor Documentation
◆ SCD()
SCD  (  const double  stepSize = 0.01 , 
const size_t  maxIterations = 100000 , 

const double  tolerance = 1e5 , 

const size_t  updateInterval = 1e3 , 

const DescentPolicyType  descentPolicy = DescentPolicyType() 

) 
Construct the SCD optimizer with the given function and parameters.
The default value here are not necessarily good for every problem, so it is suggested that the values used are tailored for the task at hand. The maximum number of iterations refers to the maximum number of "descents" the algorithm does (in one iteration, the algorithm updates the decision variable numFeatures times).
 Parameters

stepSize Step size for each iteration. maxIterations Maximum number of iterations allowed (0 means to limit). tolerance Maximum absolute tolerance to terminate the algorithm. updateInterval The interval at which the objective is to be reported and checked for convergence. descentPolicy The policy to use for picking up the coordinate to descend on.
Member Function Documentation
◆ DescentPolicy() [1/2]

inline 
◆ DescentPolicy() [2/2]

inline 
◆ MaxIterations() [1/2]

inline 
◆ MaxIterations() [2/2]

inline 
◆ Optimize()
double Optimize  (  ResolvableFunctionType &  function, 
arma::mat &  iterate  
) 
Optimize the given function using stochastic coordinate descent.
The given starting point will be modified to store the finishing point of the optimization, and the final objective value is returned.
 Template Parameters

ResolvableFunctionType Type of the function to be optimized.
 Parameters

function Function to optimize. iterate Starting point (will be modified).
 Returns
 Objective value at the final point.
◆ StepSize() [1/2]
◆ StepSize() [2/2]
◆ Tolerance() [1/2]

inline 
◆ Tolerance() [2/2]

inline 
◆ UpdateInterval() [1/2]

inline 
◆ UpdateInterval() [2/2]

inline 
The documentation for this class was generated from the following file:
 /var/www/www.mlpack.org/mlpackgit/src/mlpack/core/optimizers/scd/scd.hpp
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