Standard Benchmark Optimization Functions

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Documentation for package ‘optim.functions’ version 0.1

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optim.functions-package optim.functions: A collection of standard optimization functions along with a standard interface to call and sample those functions.
cartesian.sample The sampling functions take (n, k) where n is the number of samples and k is the number of dimensions. The sampling functions should return samples in a 0-1 hypercube. sampling.func <- function(n, k)
get_info Lookup information about a function by name
halton.sample The sampling functions take (n, k) where n is the number of samples and k is the number of dimensions. The sampling functions should return samples in a 0-1 hypercube. sampling.func <- function(n, k)
hexagonal.sample The sampling functions take (n, k) where n is the number of samples and k is the number of dimensions. The sampling functions should return samples in a 0-1 hypercube. sampling.func <- function(n, k)
lh.sample The sampling functions take (n, k) where n is the number of samples and k is the number of dimensions. The sampling functions should return samples in a 0-1 hypercube. sampling.func <- function(n, k)
optim.functions optim.functions: A collection of standard optimization functions along with a standard interface to call and sample those functions.
random.sample The sampling functions take (n, k) where n is the number of samples and k is the number of dimensions. The sampling functions should return samples in a 0-1 hypercube. sampling.func <- function(n, k)
sample.func Unified function sampling interface.
samplingf The sampling functions take (n, k) where n is the number of samples and k is the number of dimensions. The sampling functions should return samples in a 0-1 hypercube. sampling.func <- function(n, k)
sobol.sample The sampling functions take (n, k) where n is the number of samples and k is the number of dimensions. The sampling functions should return samples in a 0-1 hypercube. sampling.func <- function(n, k)
torus.sample The sampling functions take (n, k) where n is the number of samples and k is the number of dimensions. The sampling functions should return samples in a 0-1 hypercube. sampling.func <- function(n, k)