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Space Efficient Algorithms for Breadth-Depth Search

机译:广度搜索的空间高效算法

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Continuing the recent trend, in this article we design several space-efficient algorithms for two well-known graph search methods. Both these search methods share the same name breadth-depth search (henceforth BDS), although they work entirely in different fashion. The classical implementation for these graph search methods takes 0(m + n) time and O(n lg n) bits of space in the standard word RAM model (with word size being Ө(lg n) bits), where m and n denotes the number of edges and vertices of the input graph respectively. Our goal here is to beat the space bound of the classical implementations, and design o(n lg n) space algorithms for these search methods by paying little to no penalty in the running time. Note that our space bounds (i.e., with o(n lg n) bits of space) do not even allow us to explicitly store the required information to implement the classical algorithms, yet our algorithms visits and reports all the vertices of the input graph in correct order.
机译:延续最近的趋势,在本文中,我们为两种著名的图搜索方法设计了几种节省空间的算法。尽管这两种搜索方法完全以不同的方式工作,但它们具有相同的名称广度深度搜索(以下简称BDS)。这些图搜索方法的经典实现在标准字RAM模型中占用0(m + n)时间和O(n lg n)位空间(字大小为Ө(lg n)位),其中m和n表示输入图的边和顶点数。我们的目标是突破经典实现的空间限制,并通过在运行时间上付出很少甚至不付出任何代价来设计用于这些搜索方法的o(n lg n)空间算法。请注意,我们的空间边界(即具有o(n lg n)位空间)甚至不允许我们显式存储实现经典算法所需的信息,但是我们的算法会在以下位置访问并报告输入图的所有顶点:正确的顺序。

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