PCF Learned Sort: a Learning Augmented Sort Algorithm with $O(n \log\log n)$ Expected Complexity
<|reference_start|>PCF Learned Sort: a Learning Augmented Sort Algorithm with $O(n \log\log n)$ Expected Complexity: Sorting is one of the most fundamental algorithms in computer science. Recently, Learned Sorts, which use machine learning to improve sorting speed, have attracted attention. While existing studies show that Learned Sort is experimentally faster than classical sorting algorithms, they do not provide theoretical guarantees about its computational complexity. We propose PCF Learned Sort, a theoretically guaranteed Learned Sort algorithm. We prove that the expected complexity of PCF Learned Sort is $O(n \log \log n)$ under mild assumptions on the data distribution. We also confirm experimentally that PCF Learned Sort has a computational complexity of $O(n \log \log n)$ on both synthetic and real datasets. This is the first study to theoretically support the experimental success of Learned Sort, and provides evidence for why Learned Sort is fast.<|reference_end|>
@article{sato2024pcf,
    title={PCF Learned Sort: a Learning Augmented Sort Algorithm with $O(n \log\log
  n)$ Expected Complexity},
    author={Atsuki Sato and Yusuke Matsui},
    journal={arXiv preprint arXiv:2405.07122},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07122},
    primaryClass={cs.DS cs.CC}
}