R: Optimization via Non-linear Optimization

mlr_optimizers_nloptr {bbotk} | R Documentation

## Optimization via Non-linear Optimization

### Description

`OptimizerNLoptr` class that implements non-linear optimization. Calls `nloptr::nloptr()` from package nloptr.

### Parameters

`algorithm`

  `character(1)`

`eval_g_ineq`

  `function()`

`xtol_rel`

  `numeric(1)`

`xtol_abs`

  `numeric(1)`

`ftol_rel`

  `numeric(1)`

`ftol_abs`

  `numeric(1)`

`start_values`

  `character(1)`
  Create `random` start values or based on `center` of search space? In the latter case, it is the center of the parameters before a trafo is applied.

For the meaning of the control parameters, see `nloptr::nloptr()` and `nloptr::nloptr.print.options()`.

The termination conditions `stopval`, `maxtime` and `maxeval` of `nloptr::nloptr()` are deactivated and replaced by the Terminator subclasses. The x and function value tolerance termination conditions (`xtol_rel = 10^-4`, `xtol_abs = rep(0.0, length(x0))`, `ftol_rel = 0.0` and `ftol_abs = 0.0`) are still available and implemented with their package defaults. To deactivate these conditions, set them to `-1`.

### Progress Bars

`$optimize()` supports progress bars via the package progressr combined with a Terminator. Simply wrap the function in `progressr::with_progress()` to enable them. We recommend to use package progress as backend; enable with `progressr::handlers("progress")`.

### Super class

`bbotk::Optimizer` -> `OptimizerNLoptr`

### Methods

#### Public methods

- `OptimizerNLoptr$new()`

- `OptimizerNLoptr$clone()`

Inherited methods

- `bbotk::Optimizer$format()`

- `bbotk::Optimizer$optimize()`

- `bbotk::Optimizer$print()`

#### Method `new()`

Creates a new instance of this R6 class.

##### Usage

    OptimizerNLoptr$new()

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    OptimizerNLoptr$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### Source

Johnson, G S (2020). “The NLopt nonlinear-optimization package.” https://github.com/stevengj/nlopt.

### Examples

    if (requireNamespace("nloptr")) {

      search_space = domain = ps(x = p_dbl(lower = -1, upper = 1))

      codomain = ps(y = p_dbl(tags = "minimize"))

      objective_function = function(xs) {
        list(y = as.numeric(xs)^2)
      }

      objective = ObjectiveRFun$new(
        fun = objective_function,
        domain = domain,
        codomain = codomain)

      # We use the internal termination criterion xtol_rel
      terminator = trm("none")
      instance = OptimInstanceSingleCrit$new(
        objective = objective,
        search_space = search_space,
        terminator = terminator)

      optimizer = opt("nloptr", algorithm = "NLOPT_LN_BOBYQA")

      # Modifies the instance by reference
      optimizer$optimize(instance)

      # Returns best scoring evaluation
      instance$result

      # Allows access of data.table of full path of all evaluations
      as.data.table(instance$archive)
    }

[Package bbotk version 0.5.0 Index]