benchmarkfcns.multifidelity.friedman

benchmarkfcns.multifidelity.friedman(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 multi-fidelity Friedman function. SCORES = friedman(X) computes the value of the Friedman function at point X. multifidelity.friedman accepts a matrix of size M-by-5 and returns a matrix SCORES of size M-by-2. Properties (High-fidelity):

• Dimensions: 5

• Recommended domain: [0, 1]^5

Mathematical Definition

\[\begin{aligned}\]

f_{hf}(mathbf{x}) &= 10 sin(pi x_1 x_2) + 20(x_3 - 0.5)^2 + 10x_4 + 5x_5 \ f_{lf}(mathbf{x}) &= 10 sin(pi x_1 x_2) + 20(x_3 - 0.5)^2 + 5x_4 + 2.5x_5 end{aligned}

Visualization

No visualization available for this function.