benchmarkfcns.wolfe

benchmarkfcns.wolfe(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 Wolfe function. SCORES = wolfe(X) computes the value of the Wolfe function at point X. wolfe accepts a matrix of size M-by-3 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: (0, 0, …, 0)

  • Number of dimensions: 3 (usually, though 2D versions exist)

  • Recommended domain: x_i ∈ [0, 2]

  • Number of local minima: 0 (It is unimodal in a sense, but has “pathological”

ridges).

  • Number of global minima: 1

  • Convexity: Non-convex

  • Separability: Non-separable

  • Modality: Unimodal

  • Differentiable: No

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

Mathematical Definition

Visualization

wolfe landscape