,
Anita Dürr
,
Adam Polak
Creative Commons Attribution 4.0 International license
We present a pseudopolynomial-time algorithm for the Knapsack problem that has running time Õ(n + t√{p_{max}}), where n is the number of items, t is the knapsack capacity, and p_{max} is the maximum item profit. This improves over the Õ(n + t p_{max})-time algorithm based on the convolution and prediction technique by Bateni et al. (STOC 2018). Moreover, we give some evidence, based on a strengthening of the Min-Plus Convolution Hypothesis, that our running time might be optimal.
Our algorithm uses two new technical tools, which might be of independent interest. First, we generalize the Õ(n^{1.5})-time algorithm for bounded monotone min-plus convolution by Chi et al. (STOC 2022) to the rectangular case where the range of entries can be different from the sequence length. Second, we give a reduction from general knapsack instances to balanced instances, where all items have nearly the same profit-to-weight ratio, up to a constant factor.
Using these techniques, we can also obtain algorithms that run in time Õ(n + OPT√{w_{max}}), Õ(n + (nw_{max}p_{max})^{1/3}t^{2/3}), and Õ(n + (nw_{max}p_{max})^{1/3} OPT^{2/3}), where OPT is the optimal total profit and w_{max} is the maximum item weight.
@InProceedings{bringmann_et_al:LIPIcs.ESA.2024.33,
author = {Bringmann, Karl and D\"{u}rr, Anita and Polak, Adam},
title = {{Even Faster Knapsack via Rectangular Monotone Min-Plus Convolution and Balancing}},
booktitle = {32nd Annual European Symposium on Algorithms (ESA 2024)},
pages = {33:1--33:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-338-6},
ISSN = {1868-8969},
year = {2024},
volume = {308},
editor = {Chan, Timothy and Fischer, Johannes and Iacono, John and Herman, Grzegorz},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2024.33},
URN = {urn:nbn:de:0030-drops-211047},
doi = {10.4230/LIPIcs.ESA.2024.33},
annote = {Keywords: 0-1-Knapsack problem, bounded monotone min-plus convolution, fine-grained complexity}
}