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What's the algorithm complexity of DYCORS with CubicRBF? #13
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Is it |
pySOT is currently solving the entire RBF system from scratch each time, so each iteration is O(n^3). This adds up to O(n^4). It's possible to do each iteration in O(n^2) by updating the LU-factorization of the RBF system incrementally and that's something that I can easily add to the code. This gives you the desired O(n^3) complexity. |
Then why is Kriging slower?
Gaussian processes are generally considered slow since the inference time grows cubically in the number of observations, as it necessitates the inversion of a dense covariance matrix. But if RBF is also cubic, I don't see where is the difference. Can you please explain to me? Thanks |
Good question. I'm not that familiar with Kriging and I'm not sure if I can be very helpful without looking at the Kriging implementation that we rely on: https://github.com/capaulson/pyKriging. From numerical tests it looks like Kriging is at least an order of magnitude slower than RBFs. |
Thanks
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