磁盘上的加权分组搜索和改进的优先疏散下界

Konstantinos Georgiou, Xin Wang
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摘要

我们考虑的是(emph{磁盘上的加权分组搜索},这是一个涉及 2 个具有单位速度的移动代理的搜索类型问题。这两个代理开始时是共同定位的,它们的目标是到达一个(隐藏的)目标,该目标位于未知位置,已知距离恰好为 1(即搜索域为单位圆盘)。代理在所谓的 \emph{无线}模型中运行,该模型允许它们即时了解彼此的发现。代理轨迹的终止成本是每个代理到达目标所花费时间的最坏情况加权平均值(我们用参数 $w$ 来量化),这也是问题名称的由来。我们的工作沿袭了搜索和疏散方面的长期研究成果,但更重要的是,它分别是对两个已被充分研究的问题的变体和扩展。已知的变体是搜索域为线段的问题,其最优解是已知的。我们的问题也是所谓的 \emph{priorityevacuation} 的扩展,通过设置权重参数 $w$ 为 $0$,我们得到了它。对于后一个问题,已知的最佳上/下限差距很大。我们对磁盘上加权分组搜索的贡献有三点:textit{首先},我们推导出了加权平均值 $w$ 整个频谱的上界。我们的算法是在已知技术的基础上改进而来的,但分析的技术含量更高。\第二,我们的主要贡献是推导出了所有加权平均数的下界。这源于一个基于线性规划并受度量嵌入松弛启发的证明组合搜索问题下界的新框架。\第三,我们将我们的框架应用于优先疏散问题,将之前已知的最佳下限从$4.38962$提高到$4.56798$,从而将上下限差距从$0.42892$缩小到$0.25056$。
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Weighted Group Search on the Disk & Improved Lower Bounds for Priority Evacuation
We consider \emph{weighted group search on a disk}, which is a search-type problem involving 2 mobile agents with unit-speed. The two agents start collocated and their goal is to reach a (hidden) target at an unknown location and a known distance of exactly 1 (i.e., the search domain is the unit disk). The agents operate in the so-called \emph{wireless} model that allows them instantaneous knowledge of each others findings. The termination cost of agents' trajectories is the worst-case \emph{arithmetic weighted average}, which we quantify by parameter $w$, of the times it takes each agent to reach the target, hence the name of the problem. Our work follows a long line of research in search and evacuation, but quite importantly it is a variation and extension of two well-studied problems, respectively. The known variant is the one in which the search domain is the line, and for which an optimal solution is known. Our problem is also the extension of the so-called \emph{priority evacuation}, which we obtain by setting the weight parameter $w$ to $0$. For the latter problem the best upper/lower bound gap known is significant. Our contributions for weighted group search on a disk are threefold. \textit{First}, we derive upper bounds for the entire spectrum of weighted averages $w$. Our algorithms are obtained as a adaptations of known techniques, however the analysis is much more technical. \textit{Second}, our main contribution is the derivation of lower bounds for all weighted averages. This follows from a \emph{novel framework} for proving lower bounds for combinatorial search problems based on linear programming and inspired by metric embedding relaxations. \textit{Third}, we apply our framework to the priority evacuation problem, improving the previously best lower bound known from $4.38962$ to $4.56798$, thus reducing the upper/lower bound gap from $0.42892$ to $0.25056$.
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