benchmarkfcns.levin13

benchmarkfcns.levin13(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 Levi N. 13 benchmark function. SCORES = levin13(X) computes the value of the Levi N. 13 function at point X. levin13 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, 1)

  • Number of dimensions: 2

  • Recommended domain: [-10, 10]^2

  • Number of local minima: Approximately 100 (depending on the search range)

  • Number of global minima: 1

  • Convexity: Non-convex

  • Separability: Non-separable

  • Modality: Highly Multimodal

  • Symmetry: Non-symmetric (though it appears somewhat periodic)

  • Differentiable: Yes

For more information, please visit: benchmarkfcns.info/doc/levin13fcn

Mathematical Definition

Visualization

levin13 landscape