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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 $δ> 0$.

A Nearly Quadratic-Time FPTAS for Knapsack

TL;DR

This work presents a fully polynomial-time approximation scheme for Knapsack with running time , outperforming the previous bound. The authors fuse proximity results—showing that optimal solutions largely use high-efficiency items—with a functional approximation framework that computes for all capacities via -convolution and grouping by profits. A carefully designed reduction RP, 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 -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 time. This improves upon the -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 -convolution has no truly subquadratic-time algorithm, since this conjecture implies that Knapsack has no -time FPTAS for any constant .
Paper Structure (25 sections, 25 theorems, 35 equations, 3 figures, 1 table)

This paper contains 25 sections, 25 theorems, 35 equations, 3 figures, 1 table.

Key Result

Theorem 1

There is an FPTAS for Knapsack that runs in $\widetilde{O}(n + \frac{1}{\varepsilon^2})$ time.

Figures (3)

  • Figure 1: $I_H$ is superior to $b$ and $I_L$ is inferior to $b$ by $\tau$ distnct profits
  • Figure 2: Partition of $I_{\mathrm{tail}}$
  • Figure 3: A Partition of $I_{\mathrm{head}}$ for $[t_j, t_{j+1}]$

Theorems & Definitions (45)

  • Theorem 1
  • Lemma 2
  • proof
  • Lemma 3: Chan18
  • Lemma 4
  • Lemma 5
  • Lemma 6
  • proof : Proof of Lemma \ref{['lem:proximity']}
  • Definition 7: BW21
  • Definition 8: BW21
  • ...and 35 more