benchmarkfcns.multifidelity.brown¶
- benchmarkfcns.multifidelity.brown(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 Brown function. SCORES = brown(X) computes the value of the Brown function at point X. multifidelity.brown accepts a matrix of size M-by-N and returns a matrix SCORES of size M-by-2.
Mathematical Definition
\[f(\textbf{x}) = \sum_{i=1}^{n-1}(x_i^2)^{(x_{i+1}^{2}+1)}+(x_{i+1}^2)^{(x_{i}^{2}+1)}\]
Visualization
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