ModifiedOWLQN (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.math.optimization.ModifiedOWLQN

All Implemented Interfaces:

Optimizer2
```
public class ModifiedOWLQN
extends Object
implements Optimizer2
```
This implements the Modified Orthant-Wise Limited memory Quasi-Newton(mOWL-QN) optimizer. This algorithm is an extension of LBFGS, and solves minimization problems of the form: f(x) + λ ||x||₁. It requires the function and it's gradient to work.

See:
- Gong, P., & Ye, J. (2015). A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis. In The 32nd International Conference on Machine Learning (Vol. 37).
- Andrew, G., & Gao, J. (2007). Scalable training of L1 -regularized log-linear models. In Proceedings of the 24th international conference on Machine learning - ICML ’07 (pp. 33–40). New York, New York, USA: ACM Press. doi:10.1145/1273496.1273501
Author:

Edward Raff

Constructor Summary

Constructors
Modifier	Constructor and Description
	`ModifiedOWLQN()` Creates a new mOWL-QN optimizer with no regularization penalty
	`ModifiedOWLQN(double lambda)` Creates a new mOWL-QN optimizer
`protected`	`ModifiedOWLQN(ModifiedOWLQN toCopy)` copy constructor

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`ModifiedOWLQN`	`clone()`
`double`	`getBeta()`
`double`	`getEps()`
`Vec`	`getLambdaMultipler()`
`int`	`getM()` Returns the number of history items that will be used
`int`	`getMaximumIterations()` Returns the maximum number of iterations to perform
`void`	`optimize(double tolerance, Vec w, Vec x0, Function f, FunctionVec fp, FunctionVec fpp)` Attempts to optimize the given function by finding the value of `w` that will minimize the value returned by `f(w)`, using w = x₀ as an initial starting point.
`void`	`optimize(double tolerance, Vec w, Vec x0, Function f, FunctionVec fp, FunctionVec fpp, ExecutorService ex)` Attempts to optimize the given function by finding the value of `w` that will minimize the value returned by `f(w)`, using w = x₀ as an initial starting point.
`void`	`setBeta(double beta)` Sets the shrinkage term used for the line search.
`void`	`setEps(double eps)` Sets the epsilon term that helps control when the gradient descent step is taken instead of the normal Quasi-Newton step.
`void`	`setLambda(double lambda)` Sets the regularization term for the optimizer
`void`	`setLambdaMultipler(Vec lambdaMultipler)` This method sets a vector that will contain a separate multiplier for `lambda` for each dimension of the problem.
`void`	`setM(int m)` Sets the number of history items to keep that are used to approximate the Hessian of the problem
`void`	`setMaximumIterations(int iterations)` Sets the maximum number of iterations allowed for the optimization method

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - ModifiedOWLQN
```
public ModifiedOWLQN()
```
    Creates a new mOWL-QN optimizer with no regularization penalty
  - ModifiedOWLQN
```
public ModifiedOWLQN(double lambda)
```
    Creates a new mOWL-QN optimizer
    
    Parameters:
    
    lambda - the regularization penalty to use
  - ModifiedOWLQN
```
protected ModifiedOWLQN(ModifiedOWLQN toCopy)
```
    copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setLambda
```
public void setLambda(double lambda)
```
    Sets the regularization term for the optimizer
    
    Parameters:
    
    lambda - the regularization penalty
  - setLambdaMultipler
```
public void setLambdaMultipler(Vec lambdaMultipler)
```
    This method sets a vector that will contain a separate multiplier for lambda for each dimension of the problem. This allows for each dimension to have a different regularization penalty.
    
    If set to null, all dimensions will simply use λ as their regularization value.
    
    Parameters:
    
    lambdaMultipler - the per-dimension regularization multiplier, or null.
  - getLambdaMultipler
```
public Vec getLambdaMultipler()
```
  - setM
```
public void setM(int m)
```
    Sets the number of history items to keep that are used to approximate the Hessian of the problem
    
    Parameters:
    
    m - the number of history items to keep
  - getM
```
public int getM()
```
    Returns the number of history items that will be used
    
    Returns:
    
    the number of history items that will be used
  - setEps
```
public void setEps(double eps)
```
    Sets the epsilon term that helps control when the gradient descent step is taken instead of the normal Quasi-Newton step. Larger values cause more GD steps. You shouldn't need to alter this variable
    
    Parameters:
    
    eps - tolerance term for GD steps
  - getEps
```
public double getEps()
```
  - setBeta
```
public void setBeta(double beta)
```
    Sets the shrinkage term used for the line search.
    
    Parameters:
    
    beta - the line search shrinkage term
  - getBeta
```
public double getBeta()
```
  - optimize
```
public void optimize(double tolerance,
                     Vec w,
                     Vec x0,
                     Function f,
                     FunctionVec fp,
                     FunctionVec fpp)
```
    Description copied from interface: Optimizer2
    
    Attempts to optimize the given function by finding the value of w that will minimize the value returned by f(w), using w = x₀ as an initial starting point.
    
    Specified by:
    
    optimize in interface Optimizer2
    
    Parameters:
    
    tolerance - the value that the gradient norm must be less than to consider converged
    
    w - the the location to store the final solution
    
    x0 - the initial guess for the solution. This value will not be changed, and intermediate matrices will be created as the same type.
    
    f - the objective function to minimizer
    
    fp - the derivative of the objective function, may be null depending on the optimizer
    
    fpp - the Hessian of the objective function, may be null depending on the optimizer
  - optimize
```
public void optimize(double tolerance,
                     Vec w,
                     Vec x0,
                     Function f,
                     FunctionVec fp,
                     FunctionVec fpp,
                     ExecutorService ex)
```
    Description copied from interface: Optimizer2
    
    Attempts to optimize the given function by finding the value of w that will minimize the value returned by f(w), using w = x₀ as an initial starting point.
    
    Specified by:
    
    optimize in interface Optimizer2
    
    Parameters:
    
    tolerance - the value that the gradient norm must be less than to consider converged
    
    w - the the location to store the final solution
    
    x0 - the initial guess for the solution. This value will not be changed, and intermediate matrices will be created as the same type.
    
    f - the objective function to minimizer
    
    fp - the derivative of the objective function, may be null depending on the optimizer
    
    fpp - the Hessian of the objective function, may be null depending on the optimizer
    
    ex - the source of threads for parallel computation, may be null to perform serial computation.
  - setMaximumIterations
```
public void setMaximumIterations(int iterations)
```
    Description copied from interface: Optimizer2
    
    Sets the maximum number of iterations allowed for the optimization method
    
    Specified by:
    
    setMaximumIterations in interface Optimizer2
    
    Parameters:
    
    iterations - the maximum number of iterations to perform
  - getMaximumIterations
```
public int getMaximumIterations()
```
    Description copied from interface: Optimizer2
    
    Returns the maximum number of iterations to perform
    
    Specified by:
    
    getMaximumIterations in interface Optimizer2
    
    Returns:
    
    the maximum number of iterations to perform
  - clone
```
public ModifiedOWLQN clone()
```
    Specified by:
    
    clone in interface Optimizer2
    
    Overrides:
    
    clone in class Object

Class ModifiedOWLQN

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

ModifiedOWLQN

ModifiedOWLQN

ModifiedOWLQN

Method Detail

setLambda

setLambdaMultipler

getLambdaMultipler

setM

getM

setEps

getEps

setBeta

getBeta

optimize

optimize

setMaximumIterations

getMaximumIterations

clone