benchmarkfcns.multifidelity.gano

benchmarkfcns.multifidelity.gano(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 Gano function. SCORES = gano(X) computes the value of the Gano function at point X. multifidelity.gano accepts a matrix of size M-by-2 and returns a matrix SCORES of size M-by-4 (HF obj, HF constr, LF obj, LF constr).

Mathematical Definition

\[\begin{aligned}\]

f_{hf}(x_1, x_2) &= 4x_1^2 + x_2^3 + x_1x_2 \ g_{hf}(x_1, x_2) &= frac{1}{x_1} + frac{1}{x_2} - 2 le 0 \ f_{lf}(x_1, x_2) &= 4(x_1 + 0.1)^2 + (x_2 - 0.1)^3 + x_1x_2 + 0.1 \ g_{lf}(x_1, x_2) &= frac{1}{x_1} + frac{1}{x_2 + 0.1} - 2.001 le 0 end{aligned}

Visualization

No visualization available for this function.