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Crosslisted article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering
Gal Bloch, Ariel Gera, Matan Orbach, Ohad Eytan, Assaf Toledo
https://arxiv.org/abs/2606.10896 https://mastoxiv.page/@arXiv_csLG_bot/116724924444449706
- Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA
Vinamra Sharma, Xingjian Fu, Jude Haris, Jos\'e Cano
https://arxiv.org/abs/2606.11117 https://mastoxiv.page/@arXiv_csAR_bot/116724770690893031
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Improved Approximation Guarantees for Groupwise Maximin Share Fairness
Georgios Amanatidis, Anna Korfiati, Evangelos Markakis, Christodoulos Santorinaios
https://arxiv.org/abs/2606.04731 https://arxiv.org/pdf/2606.04731 https://arxiv.org/html/2606.04731
arXiv:2606.04731v1 Announce Type: new
Abstract: We study the problem of fairly allocating a set of indivisible goods to a set of $n$ agents with additive valuation functions. We focus on the very demanding notion of \textit{groupwise maximin share fairness} (GMMS), which requires that each agent $i$ receives value comparable to their maximin share, where the latter is computed \textit{with respect to any subset of agents that contains $i$}. We show that it is possible to compute $(\phi-1)$-approximate GMMS allocations in polynomial time, where $\phi \approx 1.618$ is the golden ratio). This improves on the previously known guarantee of $4/7$ of Chaudhury et al. [SICOMP; 2021] and Amanatidis et al. [TCS; 2020]. We propose a simple algorithm that maintains the same main properties as the Draft-and-Eliminate algorithm of Amanatidis et al. [TCS, 2020] and we improve on the approximation guarantee analysis by carefully bounding the relevant value within any subinstance induced by the restriction of our allocation to a subset of agents. Our analysis is asymptotically tight for algorithms that share these properties and has the additional benefit of giving improved guarantees for restricted settings; in particular, when the agents agree on the top $n$ goods or when the number of agents is small. To illustrate the challenges of going beyond the guarantees of our algorithm, we also present a variant with an improved approximation of $(\sqrt{10}-1)/3 \approx 0.72$ for the case of three agents. To achieve this improvement we partially characterize the maximin share guarantees of short picking sequences for a small number of goods.
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