发表机构
Google Research; Yale University; Google DeepMind（谷歌研究; 耶鲁大学; 谷歌DeepMind）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单一可加买家，证明分开销售或整体捆绑销售中较优者能达到最优收益的3.52倍以内，改进了先前5.2的近似因子，缩小了与下界2的差距。
AI 中文摘要
我们研究在单一可加买家且物品价值相互独立的设定下，卖家若仅采用分开销售或整体捆绑销售，其收益可能损失多少。尽管收益最优机制可能需要彩票和无限菜单，但Babaioff、Immorlica、Lucier和Weinberg证明，这两种简单格式中较优者总能达到最优收益的常数比例。我们证明$\nmathrm{OPT} \le 3.52 \max\{\mathrm{SREV}, \mathrm{BREV}\}$，其中$\mathrm{SREV}$和$\mathrm{BREV}$分别是分开销售和整体捆绑销售的最优收益。这改进了Ma和Simchi-Levi先前已知的最佳近似因子$5.2$，并缩小了与已知下界$2$之间的差距。
英文摘要
We study how much revenue a seller can lose by restricting attention to selling separately or grand bundling, in the setting of a single additive buyer with independent item values. Although revenue-optimal mechanisms can require lotteries and infinite menus, Babaioff, Immorlica, Lucier, and Weinberg showed that the better of these two simple formats always achieves a constant fraction of optimal revenue. We prove that $\mathrm{OPT} \le 3.52 \max\{\mathrm{SREV}, \mathrm{BREV}\}$, where $\mathrm{SREV}$ and $\mathrm{BREV}$ are the optimal revenues from selling separately and grand bundling, respectively. This improves the previous best-known approximation factor of $5.2$ due to Ma and Simchi-Levi and narrows the gap to the known lower bound of $2$.
