benchmarkfcns.holdertable

benchmarkfcns.holdertable(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 Holder table benchmark function. SCORES = holdertable(X) computes the value of the Holder table function at point X. holdertable accepts a matrix of size M-by-2 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: -19.2085

  • Location of global minimum: There are 4 global minima: (±8.05502, ±9.66459)

  • Number of dimensions: 2

  • Recommended domain: x ∈ [-10, 10], y ∈ [-10, 10]

  • Number of local minima: Numerous (Many smaller ripples leading to the “legs” of

    the table)

  • Number of global minima: 4

  • Convexity: Non-convex

  • Separability: Non-separable

  • Modality: Highly Multimodal

  • Symmetry: Symmetric (Across both axes and the origin)

  • Differentiable: No

For more information, please visit: benchmarkfcns.info/doc/holdertablefcn

Mathematical Definition

Visualization

holdertable landscape