But there is a practical problem. Fomel and I have made the decision to clutter up the code somewhat to save a great deal of memory. This should be important to people interested in solving multidimensional problems with big data sets. Normally, the number of filter coefficients is many fewer than the number of data points, but here we have very many more. Indeed, there are na times more.
Variable filters require na times more memory than the data itself.
To make the nonstationary helix code more practical, we now require the filters to be constant in patches. Every filter output point is to be assigned to a patch. All filters of a given patch number will be the same filter. Finally, we are ready for the convolution operator. Because of the massive increase in the number of filter coefficients, allowing these many filters takes us from overdetermined to very undetermined.