added trid objective function #378
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apaleyes
approved these changes
Oct 19, 2021
docs/refs.bib
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| @misc{ssurjano2021, | ||
| author = {Surjanovic, S. and Bingham, D.}, | ||
| title = {Virtual Library of Simulation Experiments: Test Functions and Datasets}, | ||
| howpublished = {Retrieved October 15, 2021, from \url{http://www.sfu.ca/~ssurjano}} | ||
| } |
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Other citations have two spaces indent
| def trid(x: TensorType, d: int = 10) -> TensorType: | ||
| """ | ||
| The Trid function over :math:`[-d^2, d^2]` for all i=1,...,d. Dimensionality is determined | ||
| by the parameter ``d`` and it has a global minimum. This functions has large variation in |
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| by the parameter ``d`` and it has a global minimum. This functions has large variation in | |
| by the parameter ``d`` and it has a global minimum. This function has large variation in |
| output which makes it challenging for Bayesian optimisation with vanilla Gaussian processes | ||
| with non-stationary kernels. Models that can deal with non-stationarities, such as deep | ||
| Gaussian processes, can be useful for modelling these functions. See :cite:`hebbal2019bayesian` | ||
| and https://www.sfu.ca/~ssurjano/trid.html for details. |
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Maybe insert citation instead of an url here?
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url specifically points to trid part of the website
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objective function from Hebbal et al 2019 (https://arxiv.org/abs/1905.03350), vanilla GPs seem to perform poorly, while DGPs well
might also be good testing ground for data transformation/normalisation (if and when we come to that)