12-03-2020, 05:39 PM

Hi All,

I posted a new article titled “The Implicit Shammas Interpolation” on my web site. Instead of actually developing a new interpolation algorithm, my approach is to apply small powers (like 0.5 and 0.1) to the x data and the interpolating x values. The results are making the data more linear and yielding more accurate interpolated y values. The study shows how I apply this approach to a dozen interpolation algorithms, including Lagrangian interpolation, Newton divided-difference interpolation, and Neville interpolation.

My interpolation preconditioning scheme works if the interpolated function becomes less curved after applying lower powers to x.

You can find the web page here. Look up the last item in the list of articles.

Namir

I posted a new article titled “The Implicit Shammas Interpolation” on my web site. Instead of actually developing a new interpolation algorithm, my approach is to apply small powers (like 0.5 and 0.1) to the x data and the interpolating x values. The results are making the data more linear and yielding more accurate interpolated y values. The study shows how I apply this approach to a dozen interpolation algorithms, including Lagrangian interpolation, Newton divided-difference interpolation, and Neville interpolation.

My interpolation preconditioning scheme works if the interpolated function becomes less curved after applying lower powers to x.

You can find the web page here. Look up the last item in the list of articles.

Namir