benchmarkfcns.booth

benchmarkfcns.booth(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 Booth benchmark function. SCORES = booth(X) computes the value of the Booth’s function at point X. booth 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: 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 visit: benchmarkfcns.info/doc/boothfcn

Mathematical Definition

Visualization

booth landscape