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