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We have implemented a faster 1D convolutional operator. It is extremely fast for VERY large input arrays with comparable dimensions.
If the dimensions of the two vectors are quite different the best way to work is to compare execution times (in general the speed improvement depends by their sizes).
For smaller input vectors MATLAB implementation results more efficient.
Example
a=floor(10000*rand(200000,1));
b=floor(10000*rand(200000,1));
Matlab conv(a,b) -----> requires 1616 secs.
Our mcgo_conv(a,b) -->requires 639 secs.
Cite As
Luigi Rosa (2026). A Faster Convolutional 1D Operator (https://au.mathworks.com/matlabcentral/fileexchange/4056-a-faster-convolutional-1d-operator), MATLAB Central File Exchange. Retrieved .
General Information
- Version 1.0.0.0 (3.69 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.0 | A faster implementation optimizing memory management. |
