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Faster Knapsack Algorithms via Bounded Monotone Min-Plus-Convolution

Authors Karl Bringmann, Alejandro Cassis

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Karl Bringmann
  • Universität des Saarlandes, Saarland Informatics Campus, Saarbrücken, Germany
  • Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
Alejandro Cassis
  • Universität des Saarlandes, Saarland Informatics Campus, Saarbrücken, Germany
  • Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany

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Karl Bringmann and Alejandro Cassis. Faster Knapsack Algorithms via Bounded Monotone Min-Plus-Convolution. In 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 229, pp. 31:1-31:21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)


We present new exact and approximation algorithms for 0-1-Knapsack and Unbounded Knapsack: - Exact Algorithm for 0-1-Knapsack: 0-1-Knapsack has known algorithms running in time Õ(n + min{n ⋅ OPT, n ⋅ W, OPT², W²}) [Bellman '57], where n is the number of items, W is the weight budget, and OPT is the optimal profit. We present an algorithm running in time Õ(n + (W + OPT)^{1.5}). This improves the running time in case n,W,OPT are roughly equal. - Exact Algorithm for Unbounded Knapsack: Unbounded Knapsack has known algorithms running in time Õ(n + min{n ⋅ p_max, n ⋅ w_max, p_max², w_max²}) [Axiotis, Tzamos '19, Jansen, Rohwedder '19, Chan, He '22], where n is the number of items, w_{max} is the largest weight of any item, and p_max is the largest profit of any item. We present an algorithm running in time Õ(n + (p_max + w_max)^{1.5}), giving a similar improvement as for 0-1-Knapsack. - Approximating Unbounded Knapsack with Resource Augmentation: Unbounded Knapsack has a known FPTAS with running time Õ(min{n/ε, n + 1/ε²}) [Jansen, Kraft '18]. We study weak approximation algorithms, which approximate the optimal profit but are allowed to overshoot the weight constraint (i.e. resource augmentation). We present the first approximation scheme for Unbounded Knapsack in this setting, achieving running time Õ(n + 1/ε^{1.5}). Along the way, we also give a simpler FPTAS with lower order improvement in the standard setting. For all of these problem settings the previously known results had matching conditional lower bounds. We avoid these lower bounds in the approximation setting by allowing resource augmentation, and in the exact setting by analyzing the time complexity in terms of weight and profit parameters (instead of only weight or only profit parameters). Our algorithms can be seen as reductions to Min-Plus-Convolution on monotone sequences with bounded entries. These structured instances of Min-Plus-Convolution can be solved in time O(n^1.5) [Chi, Duan, Xie, Zhang '22] (in contrast to the conjectured n^{2-o(1)} lower bound for the general case). We complement our results by showing reductions in the opposite direction, that is, we show that achieving our results with the constant 1.5 replaced by any constant < 2 implies subquadratic algorithms for Min-Plus-Convolution on monotone sequences with bounded entries.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
  • Knapsack
  • Approximation Schemes
  • Fine-Grained Complexity
  • Min-Plus Convolution


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