benchmarkfcns.multifidelity.booth

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

Computes the value of the Booth function at different fidelity levels. SCORES = multifidelity.booth(X) computes the value of the Booth function at point X. multifidelity.booth accepts a matrix of size M-by-2 and returns a vector SCORES of size M-by-2 in which each column contains the function value for each row of X and each column contains the function value for the corresponding fidelity level. Properties (High-fidelity):

  • Global minimum: 0

  • Location of global minimum: (1, 3)

  • Number of dimensions: 2

  • Recommended domain: [-10, 10]^2

  • Number of local minima: 0

  • Number of global minima: 1

  • Convexity: convex

  • Separability: non-separable

  • Modality: unimodal

  • Symmetry: non-symmetric

  • Differentiable: Yes

For more information, please refer to: Dong, H., Song, B., Wang, P. et al. Multi-fidelity information fusion based on prediction of kriging. Struct Multidisc Optim 51, 1267-1280 (2015) doi:10.1007/s00158-014-1213-9

Mathematical Definition

Visualization

booth landscape