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- W3023895800 abstract "Consider a generalization of the classical binary search problem in linearly sorted data to the graph-theoretic setting. The goal is to design an adaptive query algorithm, called a strategy, that identifies an initially unknown target vertex in a graph by asking queries. Each query is conducted as follows: the strategy selects a vertex $q$ and receives a reply $v$: if $q$ is the target, then $v=q$, and if $q$ is not the target, then $v$ is a neighbor of $q$ that lies on a shortest path to the target. Furthermore, there is a noise parameter $0leq p<frac{1}{2}$, which means that each reply can be incorrect with probability $p$. The optimization criterion to be minimized is the overall number of queries asked by the strategy, called the query complexity. The query complexity is well understood to be $O(varepsilon^{-2}log n)$ for general graphs, where $n$ is the order of the graph and $varepsilon=frac{1}{2}-p$. However, implementing such a strategy is computationally expensive, with each query requiring possibly $O(n^2)$ operations. In this work we propose two efficient strategies that keep the optimal query complexity. The first strategy achieves the overall complexity of $O(varepsilon^{-1}nlog n)$ per a single query. The second strategy is dedicated to graphs of small diameter $D$ and maximum degree $Delta$ and has the average complexity of $O(n+varepsilon^{-2}DDeltalog n)$ per query. We stress out that we develop an algorithmic tool of graph median approximation that is of independent interest: the median can be efficiently approximated by finding a vertex minimizing the sum of distances to a randomly sampled vertex subset of size $O(varepsilon^{-2}log n)$." @default.
- W3023895800 created "2020-05-13" @default.
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- W3023895800 date "2020-04-30" @default.
- W3023895800 modified "2023-09-27" @default.
- W3023895800 title "An Efficient Noisy Binary Search in Graphs via Median Approximation" @default.
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