snof function

Helper function to create numeric single-objective optimization test function.

This is a simplifying wrapper around makeSingleObjectiveFunction. It can be used if the function to generte is purely numeric to save some lines of code.

snof(
  name = NULL,
  id = NULL,
  par.len = NULL,
  par.id = "x",
  par.lower = NULL,
  par.upper = NULL,
  description = NULL,
  fn,
  vectorized = FALSE,
  noisy = FALSE,
  fn.mean = NULL,
  minimize = TRUE,
  constraint.fn = NULL,
  tags = character(0),
  global.opt.params = NULL,
  global.opt.value = NULL,
  local.opt.params = NULL,
  local.opt.values = NULL
)

Arguments

• name: [character(1)]

  Function name. Used for the title of plots for example.

• id: [character(1) | NULL]

  Optional short function identifier. If provided, this should be a short name without whitespaces and now special characters beside the underscore. Default is NULL, which means no ID at all.

• par.len: [integer(1)]

  Length of parameter vector.

• par.id: [character(1)]

  Optional name of parameter vector. Default is x .

• par.lower: [numeric]

  Vector of lower bounds. A single value of length 1 is automatically replicated to n.pars. Default is -Inf.

• par.upper: [numeric]

  Vector of upper bounds. A singe value of length 1 is automatically replicated to n.pars. Default is Inf.

• description: [character(1) | NULL]

  Optional function description.

• fn: [function]

  Objective function.

• vectorized: [logical(1)]

  Can the objective function handle vector input, i.~e., does it accept matrix of parameters? Default is FALSE.

• noisy: [logical(1)]

  Is the function noisy? Defaults to FALSE.

• fn.mean: [function]

  Optional true mean function in case of a noisy objective function. This functions should have the same mean as fn.

• minimize: [logical(1)]

  Set this to TRUE if the function should be minimized and to FALSE otherwise. The default is TRUE.

• constraint.fn: [function | NULL]

  Function which returns a logical vector indicating whether certain conditions are met or not. Default is NULL, which means, that there are no constraints beside possible box constraints defined via the par.set argument.

• tags: [character]

  Optional character vector of tags or keywords which characterize the function, e.~g. unimodal , separable . See getAvailableTags for a character vector of allowed tags.

• global.opt.params: [list | numeric | data.frame | matrix | NULL]

  Default is NULL which means unknown. Passing a numeric vector will be the most frequent case (numeric only functions). In this case there is only a single global optimum. If there are multiple global optima, passing a numeric matrix is the best choice. Passing a list or a data.frame

  is necessary if your function is mixed, e.g., it expects both numeric and discrete parameters. Internally, however, each representation is casted to a data.frame

  for reasons of consistency.

• global.opt.value: [numeric(1) | NULL]

  Global optimum value if known. Default is NULL, which means unknown. If only the global.opt.params are passed, the value is computed automatically.

• local.opt.params: [list | numeric | data.frame | matrix | NULL]

  Default is NULL, which means the function has no local optima or they are unknown. For details see the description of global.opt.params.

• local.opt.values: [numeric | NULL]

  Value(s) of local optima. Default is NULL, which means unknown. If only the local.opt.params are passed, the values are computed automatically.

Examples

# first we generate the 10d sphere function the long way
fn = makeSingleObjectiveFunction(
  name = "Testfun",
  fn = function(x) sum(x^2),
  par.set = makeNumericParamSet(
    len = 10L, id = "a",
    lower = rep(-1.5, 10L), upper = rep(1.5, 10L)
  )
)

# ... and now the short way
fn = snof(
 name = "Testfun",
 fn = function(x) sum(x^2),
 par.len = 10L, par.id = "a", par.lower = -1.5, par.upper = 1.5
)