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New algorithms enhance online knapsack problem with item removal

By PulseAugur Editorial · [1 sources] ·

This paper introduces new algorithms for the online unweighted knapsack problem, focusing on scenarios where items can be removed after being accepted. The research explores improvements to competitive ratios by incorporating knowledge of item size bounds and the ability to reinsert previously rejected items. Additionally, the paper presents adaptive online algorithms that achieve comparable performance without requiring prior knowledge of item size bounds. AI

IMPACT This research advances theoretical computer science, potentially impacting future optimization algorithms used in AI systems.

RANK_REASON The cluster contains an academic paper published on arXiv detailing new algorithms for a specific computational problem. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithms enhance online knapsack problem with item removal

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17 / 100
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The cluster contains an academic paper published on arXiv detailing new algorithms for a specific computational problem. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Dimitris Fotakis, Laurent Gourv\`es, Aris Pagourtzis, Panagiotis Patsilinakos ·

    Leveraging Recourse and Size Bounds for OnlineKnapsack with Removable Items

    arXiv:2610.40290v2 Announce Type: replace-cross Abstract: In the online unweighted knapsack problem, items arrive sequentially and must be accepted or rejected immediately for a bounded-capacity knapsack, aiming to maximize the total size of accepted items. When decisions are irr…

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