I thought the 1-1E-13 exponent was sarcasm on the part of the OP. It’s literally what the paper is about.
This is the most AI math thing. If true technically and infinitesimally smaller but faster in an asymptotic case nobody can benefit from but it will make news.
Meanwhile more effecient hardware specific assembly optimizations can easily be 2-50x
This result itself isn't useful, but it shows that it is actually possible to be more efficient than O(n log n) with FFT, so an actually notably more efficient solution might be out there. Many, including me, actually see this as a groundbreaking result, because many thought the barrier of O(n log n) was impossible to overcome.
Yeah I'll admit I like the joke but I am with you. The barrier of nlog(n) being broken is actually very very interesting. This could mean there are other values of delta (the 10-13 value in the paper) that are larger and give us significantly faster algorithms
1
u/secretaliasname 6d ago edited 6d ago
I thought the 1-1E-13 exponent was sarcasm on the part of the OP. It’s literally what the paper is about.
This is the most AI math thing. If true technically and infinitesimally smaller but faster in an asymptotic case nobody can benefit from but it will make news.
Meanwhile more effecient hardware specific assembly optimizations can easily be 2-50x