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Nesterovs momentum optimizer

OptimizerNAG.Rd

This class defines Nesterovs momentum using Nesterov accelerated gradient (NAG).

Super class

vistool::Optimizer -> OptimizerNAG

Active bindings

momentum

(`numeric(1)`) Momentum of the algorithm.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this [R6][R6::R6Class] class.

Usage

OptimizerNAG$new(
  objective,
  x_start,
  lr = 0.01,
  momentum = 0.9,
  id = "NAG",
  print_trace = TRUE
)

Arguments

objective

(`Objective`)
The objective to optimize.

x_start

(`numeric()`)
Start value of the optimization. Note, after the first call of `$optimize()` the last value is used to continue optimization. Get this value with `$x`.

lr

(`numeric(1)`)
Step size with which the update is multiplied.

momentum

(`numeric(1)`)
Momentum value.

id

(`character(1)`)
Id of the object.

print_trace

(`logical(1)`)
Indicator whether to print the status of `$optimize()`.

Method optimize()

Optimize `steps` iteration.

Usage

OptimizerNAG$optimize(
  steps = 1L,
  stepSizeControl = function(x, u, obj, opt) {
     return(1)
 },
  minimize = NULL
)

Arguments

steps

(`integer(1)`)
Number of steps/iterations.

stepSizeControl

(`function()`) A function with arguments `x` (the old input value), `u` (the update generated by `$update()`), and `obj` (the objective object).

minimize

(`logical(1)`) Indicator to whether minimize or optimize the objective. The default (`NULL`) uses the option defined in `objective$minimize`.

Method update()

Calculate the update for `x`

Usage

OptimizerNAG$update(lr, mom, minimize)

Arguments

lr

(`numeric(1)`) The learning rate.

mom

(`numeric(1)`) The momentum.

minimize

(`logical(1)`) Indicator to whether minimize or optimize the objective (default = `TRUE`).

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerNAG$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.