Space Efficient Linear Time Algorithms for BFS, DFS and Applications

Banerjee, Niranka; Chakraborty, Sankardeep; Raman, Venkatesh; Satti, Srinivasa Rao

doi:10.1007/s00224-017-9841-2

Cited by 23 publications

(19 citation statements)

References 48 publications

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“…Compared with the traditional BFS (breadth-first search) and DFS (depth-first search) (Banerjee et al 2018) blind search algorithms, A* algorithm is more efficient because it is a heuristic search algorithm. The A * algorithm has a similar procedure as the Dijkstra except that the evaluation function f(n) would be used as the label, instead of g(n).…”

Section: Improved A* Algorithmmentioning

confidence: 99%

Efficient Path Planning Method of USV for Intelligent Target Search

Zhang

Liu

et al. 2019

J geovis spat anal

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In order to accomplish a target search task safely and efficiently and make full use of prior information and real-time information, a path planning method of unmanned surface vehicle (USV) for intelligent target search is proposed. The overall strategy is divided into three parts: global path planning based on prior information, local path planning based on real-time information, and improved A* obstacle avoidance algorithm. Before the start of the task, the global path planning is carried out based on prior information such as the initial position of USV, the predicted position of the target and range of search area. After the start of the task, if USV finds suspicious targets, in order to further approach these suspicious targets, it will enter different local path planning modes according to the characteristics of these targets. During task execution, if obstacles are encountered, an improved A* obstacle avoidance algorithm is adopted. The simulation results show that the proposed method can improve the efficiency of target recognition and reduce the turning cost of USV when encountering obstacles. So, for USV intelligent target search, the proposed path planning method can save resources and improve search efficiency.

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Section: Improved A* Algorithmmentioning

confidence: 99%

Efficient Path Planning Method of USV for Intelligent Target Search

Zhang

Liu

et al. 2019

J geovis spat anal

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“…In what follows, we illustrate a bit more on the inner working details of BDS j with the help of an example. Following the convention, as in the recent papers [1,2], here also in BDS j we output the vertices as and when they are expanded (note that, if reporting in any other order is required, it can be done so with straighforward modification in our algorithms). Hence the root will be output at the very first step, followed by its rightmost child and so on.…”

Section: Breadth-depth Search Of Jiangmentioning

confidence: 99%

“…In this section, we show how one can obtain linear bits and linear time algorithm for BDS j for sparse graphs. For this we use the following lemma from [2]. Lemma 1.…”

Section: Using O(n Lg(m/n)) Bits and O(m + N) Timementioning

confidence: 99%

Space Efficient Algorithms for Breadth-Depth Search

Chakraborty

Mukherjee

Satti

2019

Fundamentals of Computation Theory

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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 O(m + n) time and O(n lg n) bits of space in the standard word RAM model (with word size being Θ(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.

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“…Banerjee, Chakraborty, Raman and Satti [3] indicated a slew of 3-color BFS algorithms with the following resource pairs:…”

Section: Recent Work and Our Contributionmentioning

confidence: 99%