Faster sorting algorithms discovered using deep reinforcement learning
3 years ago by bici to c/programming
I've not yet had time for reading the full article but the topic is really interesting. This is for sure one great and good application of deep learning. Anyone with some insights on this topic?
Wouldn't this still be pretty massive at large scales like datacenters?
I'm afraid it would take a lot of time for me to fully understand this paper, and I know a thing or two about Computer Science.
I'm positive you can't get any better than n log (n) when sorting a vector of size n using comparisons, and the most widely used algorithm, QuickSort, gets very close to that performance. I guess the improvement here only involves some constant factor, but I can't be sure without actually reading the paper. Everyone, feel free to correct me ofc c:
I gave it a quick skim. It seems the improvements are on specific sort tasks. There may also be fitting to the distribution of sequences. It won’t beat n log(n) for all cases, but it might do better for common common situations in the data set.
It’s still neat they made it work.
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This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.
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At first glance this doesn't seem to be as impactful as you might think...
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