benchmarkfcns.watson

benchmarkfcns.watson(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 Watson benchmark function. SCORES = watson(X) computes the value of the Watson function at point X. watson accepts a matrix of size M-by-6 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: approx 0.002288

  • Location of global minimum: (0, 0.8, …, 0)

  • Number of dimensions: 6

  • Recommended domain: [-5, 5]^6

  • Number of local minima: 0 (unimodal in the search space)

  • Number of global minima: 1

  • Convexity: non-convex

  • Separability: non-separable

  • Modality: unimodal

  • Differentiable: Yes

Mathematical Definition

Visualization

watson landscape