1 of 8

What is the preferred and efficient approach for interpolating multidimensional data?

What is the preferred and efficient approach for interpolating multidimensional data?

Things I'm worried about:

  1. performance for construction, single/batch evaluation
  2. handling dimensions from 1 to 6
  3. linear or higher-order
  4. ability to obtain gradients (if not linear)
  5. regular vs scattered grid
  6. using as Interpolating Function, e.g. to find roots

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, RBF, IDW, etc.

Here is what I found so far on this topic:

http://stackoverflow.com/questions/14119892

http://stackoverflow.com/questions/16217995

http://stackoverflow.com/questions/16983843

http://stackoverflow.com/questions/6238250

http://stackoverflow.com/questions/592026