benchmarkfcns.step

benchmarkfcns.step(arg0: Annotated[numpy.typing.NDArray[numpy.float64], '[m, n]', 'flags.c_contiguous']) → Annotated[numpy.typing.NDArray[numpy.float64], '[m, 1]']

Computes the value of the Step benchmark function (De Jong N. 3). SCORES = step(X) computes the value of the Step function at point X. step accepts a matrix of size M-by-N and returns a vector SCORES of size M-by-1 in which each row contains the function value for the corresponding row of X. Properties:

  • Global minimum: 0

  • Location of global minimum: depends on domain (typically [0, 1]^n)

  • Number of dimensions: n

  • Recommended domain: [-5.12, 5.12]^n

  • Modality: unimodal (with plateaus)

  • Characteristic: zero gradient almost everywhere.

Mathematical Definition

Visualization

step landscape