A Nearly Quadratic-Time FPTAS for Knapsack
Lin Chen, Jiayi Lian, Yuchen Mao, Guochuan Zhang
TL;DR
This work presents a fully polynomial-time approximation scheme for Knapsack with running time $\\tilde{O}(n + (1/\\varepsilon)^2)$, outperforming the previous $\\tilde{O}(n + (1/\\varepsilon)^{11/5})$ bound. The authors fuse proximity results—showing that optimal solutions largely use high-efficiency items—with a functional approximation framework that computes $f_I(x)$ for all capacities via $\\max, +$-convolution and grouping by profits. A carefully designed reduction RP$(\\varepsilon, \\alpha)$, together with tail/head decomposition and additive-combinatorics-based proximity bounds, yields an efficient algorithm that achieves the target time up to polylogarithmic factors, conditional on hardness of $\\min, +$-convolution. The result advances the state of the art for Knapsack, supports near-quadratic-time FPTAS lower bounds under a standard conjecture, and motivates further exploration of the boundary between exact, approximate, and weakly-approximate regimes in combinatorial optimization.
Abstract
We investigate the classic Knapsack problem and propose a fully polynomial-time approximation scheme (FPTAS) that runs in $\widetilde{O}(n + (1/\varepsilon)^2)$ time. This improves upon the $\widetilde{O}(n + (1/\varepsilon)^{11/5})$-time algorithm by Deng, Jin, and Mao [\textit{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 $O((n + 1/\varepsilon)^{2-δ})$-time FPTAS for any constant $δ> 0$.
