smoof: snof – R documentation

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snof

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

Description

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.

Usage

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
)

smoof

Single and Multi-Objective Optimization Test Functions

v1.6.0.2

BSD_2_clause + file LICENSE

Authors

Jakob Bossek [aut, cre], Pascal Kerschke [ctb]

Initial release

2020-02-17