What is the preferred and efficient approach for interpolating multidimensional data?
Things I'm worried about:
- performance and memory for construction, single/batch evaluation
- handling dimensions from 1 to 6
- linear or higher-order
- ability to obtain gradients (if not linear)
- regular vs scattered grid
- using as Interpolating Function, e.g. to find roots or to minimize
- extrapolation capabilities
Is there efficient open-source implementation of this?
I had partial luck with scipy.interpolate and kriging from scikit-learn.
I did not try splines, Chebyshev polynomials, etc.
Here is what I found so far on this topic: