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Sparse Grid Interpolation

version 2.0 (35.6 KB) by Alexander
Employs Smolyak styled grids for sparse interpolation which helps with the curse of dimensionality.

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Updated 26 Jul 2017

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Employs Smolyak styled grids for polynomial and linear interpolation. The depth of grids is adaptive, and with adp=1 is dimension-adaptive. Several features are employed, so that the user can make sure the pairwise dimensions do not outgrow each other (syme, see in file), coefficients that are not relevant get dropped (purge), a title can be added (title), nonvectorized functions can be called up (non_vec), costly functions are not called up twice for the same points (memoize). Error criteria can be adapted.
For full functionality (to see the progress of a nonvectorized job run in parallel) download parfor_progress. And you need to set non_vec=2 and dsp>0. Also, the function afclean which can also be found in the exchange helps to keep the interpolation functions light.

Cite As

Alexander (2021). Sparse Grid Interpolation (https://www.mathworks.com/matlabcentral/fileexchange/54289-sparse-grid-interpolation), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (1)

Toby Driscoll

I get errors in the provided Examples.m. Typical output (version 2017b):

Error using 'range' with input argument of type 'double'.
Check for missing argument or incorrect argument data type.

Error in obtain_errors (line 38)
perc_err=max(abs_err/max(range(Y),ups));

Error in Smolyak_fit (line 452)
=obtain_errors(model,obj_trans,mu_d,para,BASE,BASE_base,maxY,minY,max_no_coef);

Error in Examples (line 8)
Smolyak_poly_1d=Smolyak_fit(obj);

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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