A Nearly Quadratic-Time FPTAS for Knapsack
Lin Chen, Jiayi Lian, Yuchen Mao, Guochuan Zhang
2024年份
4被引次数
10顶会引用
摘要
We investigate the classic Knapsack problem and propose a fully polynomial-time approximation scheme (FPTAS) that runs in O(n+(1/ε) 2 ) time. This improves upon the O(n+(1/ε) 11/5 )time algorithm by Deng, Jin, and Mao [Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms, 2023 ]. Our algorithm is the best possible (up to a polylogarithmic factor) conditioned on the conjecture that (min, +)-convolution has no truly subquadratic-time algorithm, since this conjecture implies that Knapsack has no O((n + 1/ε) 2-δ )-time FPTAS for any constant δ > 0.
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引用它的顶会 Paper10
- 0-1 Knapsack in Nearly Quadratic TimeCe JinSTOC 2024 · 被引用 6 次
- A Poisson Process for Submodular MaximizationAmit Ganz Rozenman, Ariel Kulik, Roy Schwartz, Mohit SinghSTOC 2026 · 被引用 5 次
- Faster Algorithms for Text-to-Pattern Hamming DistancesTimothy M. Chan, Ce Jin, Virginia Vassilevska Williams, Yinzhan XuFOCS 2023 · 被引用 3 次
- Approximating Partition in Near-Linear TimeLin Chen, Jiayi Lian, Yuchen Mao, Guochuan ZhangSTOC 2024 · 被引用 3 次
- Universe Reduction for APSP: Equivalence of Three Fine-Grained HypothesesNick FischerSTOC 2026 · 被引用 2 次
它引用的顶会 Paper7
- On Near-Linear-Time Algorithms for Dense Subset SumKarl Bringmann, Philip WellnitzSODA 2021 · 被引用 19 次
- A Fine-Grained Perspective on Approximating Subset Sum and PartitionKarl Bringmann, Vasileios NakosSODA 2021 · 被引用 14 次
- Approximating Knapsack and Partition via Dense Subset SumsMingyang Deng, Ce Jin, Xiao MaoSODA 2023 · 被引用 10 次
- Faster Algorithms for Bounded Knapsack and Bounded Subset Sum Via Fine-Grained Proximity ResultsLin Chen, Jiayi Lian, Yuchen Mao, Guochuan ZhangSODA 2024 · 被引用 10 次
- Knapsack with Small Items in Near-Quadratic TimeKarl BringmannSTOC 2024 · 被引用 8 次
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