This is an introduction to the R package TestFunctions. It is available on CRAN and is maintained through Github.

Test functions are used whenever one needs to evaluate an algorithm. For example, an optimization algorithm should be tested on many different functions to make sure that it works and is robust. Thus many optimization test functions are very tricky, such as those with many local minima meant to make the global minimum harder to find.

Each of the test functions is called like any other function. The
first argument, `x`

, should be a vector representing one
point or a matrix that has points in its rows. This can cause problems
if you are using a 1-dimensional function and pass in a vector of
values. Instead you should pass them in as a matrix with a single
column, or vectorize the function.

The code below shows how the `branin`

function can be
used, taking in either a vector or a matrix.

`## [1] 9.476405`

```
## [1] 24.119600 71.180268 18.374071 9.839029 36.607437 72.884496
## [7] 196.302169 25.185022 13.059216 27.129463
```

A contour of the banana function is shown below.

The functions are all designed to be run by default in the \([0,1]^D\) unit cube. If you want to run the
function on the original input values, you can set
`scale_it=FALSE`

.

Independent Gaussian noise can be added to most functions by passing
the standard deviation of the noise as the `noise`

parameter.
The plots below show the original function, then what data from the
function with noise looks like.

The function `RFF_get`

will return a random wave function
with any given number of dimensions. The function is created by
combining many different one dimensional waves passing through the input
area with various directions, magnitude, and offset. The default is
composed of sine waves, but this can be changed to block or v waves.

Below is an example of a one dimensional wave.

Below is an example of a random wave in two dimensions.

There are some functions that modify other functions.

`add_linear_terms`

adds linear terms to a function.`add_noise`

adds random noise to a function.`add_null_dims`

adds extra dimensions that do not affect the function output.`add_zoom`

lets you zoom in on part of a function. Below are two examples of zooming in on the banana function.

Many of the functions, along with code implementing the function, can be found at https://www.sfu.ca/~ssurjano/. The code was not taken from this site, but some of my implementations were checked against theirs for consistency.

The R package `smoof`

also provides many optimization test functions.

Mike McCourtâ€™s GitHub repository evalset provides many test functions for Python.

HPOlib provides some benchmark functions for Python.

Hansen et al (2009) provide details for 24 benchmark functions.