The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Shiri Chechik - One of the best experts on this subject based on the ideXlab platform.
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Incremental topological sort and cycle detection in equation expected total time
Symposium on Discrete Algorithms, 2018Co-Authors: Aaron Bernstein, Shiri ChechikAbstract:In the Incremental cycle detection problem edges are inserted to a directed graph (initially empty) and the algorithm has to report once a directed cycle is formed in the graph. A closely related problem to the Incremental cycle detection is that of the Incremental topological sort problem, in which edges are inserted to an acyclic graph and the algorithm has to maintain a valid topological sort on the vertices at all times. Both Incremental cycle detection and Incremental topological sort have a long history. The state of the art is a recent breakthrough of Bender, Fineman, Gilbert and Tarjan [TALG 2016], with two different algorithms with respective total update times of O(n2) and O(m · min{m1/2, n2/3}). The two algorithms work for both Incremental cycle detection and Incremental topological sort. In this paper we introduce a novel technique that allows us to improve upon the state of the art for a wide range of graph sparsity. Our algorithms has a total expected update time of [Equation] for both the Incremental cycle detection and the topological sort problems.
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SODA - Incremental topological sort and cycle detection in [Equation] expected total time
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, 2018Co-Authors: Aaron Bernstein, Shiri ChechikAbstract:In the Incremental cycle detection problem edges are inserted to a directed graph (initially empty) and the algorithm has to report once a directed cycle is formed in the graph. A closely related problem to the Incremental cycle detection is that of the Incremental topological sort problem, in which edges are inserted to an acyclic graph and the algorithm has to maintain a valid topological sort on the vertices at all times. Both Incremental cycle detection and Incremental topological sort have a long history. The state of the art is a recent breakthrough of Bender, Fineman, Gilbert and Tarjan [TALG 2016], with two different algorithms with respective total update times of O(n2) and O(m · min{m1/2, n2/3}). The two algorithms work for both Incremental cycle detection and Incremental topological sort. In this paper we introduce a novel technique that allows us to improve upon the state of the art for a wide range of graph sparsity. Our algorithms has a total expected update time of [Equation] for both the Incremental cycle detection and the topological sort problems.
Aaron Bernstein - One of the best experts on this subject based on the ideXlab platform.
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Incremental topological sort and cycle detection in equation expected total time
Symposium on Discrete Algorithms, 2018Co-Authors: Aaron Bernstein, Shiri ChechikAbstract:In the Incremental cycle detection problem edges are inserted to a directed graph (initially empty) and the algorithm has to report once a directed cycle is formed in the graph. A closely related problem to the Incremental cycle detection is that of the Incremental topological sort problem, in which edges are inserted to an acyclic graph and the algorithm has to maintain a valid topological sort on the vertices at all times. Both Incremental cycle detection and Incremental topological sort have a long history. The state of the art is a recent breakthrough of Bender, Fineman, Gilbert and Tarjan [TALG 2016], with two different algorithms with respective total update times of O(n2) and O(m · min{m1/2, n2/3}). The two algorithms work for both Incremental cycle detection and Incremental topological sort. In this paper we introduce a novel technique that allows us to improve upon the state of the art for a wide range of graph sparsity. Our algorithms has a total expected update time of [Equation] for both the Incremental cycle detection and the topological sort problems.
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SODA - Incremental topological sort and cycle detection in [Equation] expected total time
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, 2018Co-Authors: Aaron Bernstein, Shiri ChechikAbstract:In the Incremental cycle detection problem edges are inserted to a directed graph (initially empty) and the algorithm has to report once a directed cycle is formed in the graph. A closely related problem to the Incremental cycle detection is that of the Incremental topological sort problem, in which edges are inserted to an acyclic graph and the algorithm has to maintain a valid topological sort on the vertices at all times. Both Incremental cycle detection and Incremental topological sort have a long history. The state of the art is a recent breakthrough of Bender, Fineman, Gilbert and Tarjan [TALG 2016], with two different algorithms with respective total update times of O(n2) and O(m · min{m1/2, n2/3}). The two algorithms work for both Incremental cycle detection and Incremental topological sort. In this paper we introduce a novel technique that allows us to improve upon the state of the art for a wide range of graph sparsity. Our algorithms has a total expected update time of [Equation] for both the Incremental cycle detection and the topological sort problems.
B S Panda - One of the best experts on this subject based on the ideXlab platform.
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ICDCN - Boruvka-Incremental Power Greedy Heuristic for Strong Minimum Energy Topology in Wireless Sensor Networks
Proceedings of the 2015 International Conference on Distributed Computing and Networking, 2015Co-Authors: B S Panda, B. K. Bhatta, Deepak MishraAbstract:Given a set of sensors, the strong minimum energy topology (SMET) problem is to assign transmission range to each sensor node so that the sum of the transmission range for all the sensor is minimum subject to the constraint that the network is strongly connected (there is a directed path between every pair of nodes in the Network). This problem is known to be NP-hard. As this problem has lots of practical applications, several approximation algorithms and heuristics have been proposed. In this paper, we propose a new heuristic called Boruvka-Incremental power greedy heuristic based on the Boruvka algorithm for the minimum spanning tree (MST) problem for solving the SMET problem. We compare the performance of the Boruvka-Incremental power greedy heuristic with Kruskal-Incremental power greedy heuristic and Prim-Incremental power greedy heuristic. Extensive simulation results illustrate that Boruvka heuristic outperforms the Kruskal-Incremental power greedy heuristic and Prim-Incremental power greedy heuristic. We have also proved that apart from providing significant improvement in terms of average power savings, Boruvka Incremental power greedy heuristic takes O(n) time for planar graphs as compared to O(n log n) time taken by Kruskal-Incremental power greedy heuristic and O(n2) time taken by Prim-Incremental power greedy heuristic, where n is the number of nodes in the network.
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an Incremental power greedy heuristic for strong minimum energy topology in wireless sensor networks
International Conference on Distributed Computing and Internet Technology, 2011Co-Authors: B S Panda, Pushparaj D ShettyAbstract:Given a set of sensors in the plane, the strong minimum energy topology (SMET) problem is to assign transmit power to each sensor such that the resulting topology containing only bidirectional links is strongly connected. This problemis known to be NP-hard. As this problem is verymuch significant from application point of view, several heuristic algorithms have been proposed. In this paper, we propose a new Incremental power greedy heuristic for SMET problem, called Kruskal-Incremental power greedy heuristic. We compare Kruskal-Incremental power greedy heuristic with Prim-Incremental power greedy heuristic, one of the most popular heuristics available in the literature, through extensive simulation. The simulation results suggest that Kruskal-Incremental power greedy heuristic outperforms on an average the Prim-Incremental power greedy heuristic.
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ICDCIT - An Incremental power greedy heuristic for strong minimum energy topology in wireless sensor networks
Distributed Computing and Internet Technology, 2011Co-Authors: B S Panda, D. Pushparaj ShettyAbstract:Given a set of sensors in the plane, the strong minimum energy topology (SMET) problem is to assign transmit power to each sensor such that the resulting topology containing only bidirectional links is strongly connected. This problemis known to be NP-hard. As this problem is verymuch significant from application point of view, several heuristic algorithms have been proposed. In this paper, we propose a new Incremental power greedy heuristic for SMET problem, called Kruskal-Incremental power greedy heuristic. We compare Kruskal-Incremental power greedy heuristic with Prim-Incremental power greedy heuristic, one of the most popular heuristics available in the literature, through extensive simulation. The simulation results suggest that Kruskal-Incremental power greedy heuristic outperforms on an average the Prim-Incremental power greedy heuristic.
D. Pushparaj Shetty - One of the best experts on this subject based on the ideXlab platform.
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ICDCIT - An Incremental power greedy heuristic for strong minimum energy topology in wireless sensor networks
Distributed Computing and Internet Technology, 2011Co-Authors: B S Panda, D. Pushparaj ShettyAbstract:Given a set of sensors in the plane, the strong minimum energy topology (SMET) problem is to assign transmit power to each sensor such that the resulting topology containing only bidirectional links is strongly connected. This problemis known to be NP-hard. As this problem is verymuch significant from application point of view, several heuristic algorithms have been proposed. In this paper, we propose a new Incremental power greedy heuristic for SMET problem, called Kruskal-Incremental power greedy heuristic. We compare Kruskal-Incremental power greedy heuristic with Prim-Incremental power greedy heuristic, one of the most popular heuristics available in the literature, through extensive simulation. The simulation results suggest that Kruskal-Incremental power greedy heuristic outperforms on an average the Prim-Incremental power greedy heuristic.
Pushparaj D Shetty - One of the best experts on this subject based on the ideXlab platform.
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an Incremental power greedy heuristic for strong minimum energy topology in wireless sensor networks
International Conference on Distributed Computing and Internet Technology, 2011Co-Authors: B S Panda, Pushparaj D ShettyAbstract:Given a set of sensors in the plane, the strong minimum energy topology (SMET) problem is to assign transmit power to each sensor such that the resulting topology containing only bidirectional links is strongly connected. This problemis known to be NP-hard. As this problem is verymuch significant from application point of view, several heuristic algorithms have been proposed. In this paper, we propose a new Incremental power greedy heuristic for SMET problem, called Kruskal-Incremental power greedy heuristic. We compare Kruskal-Incremental power greedy heuristic with Prim-Incremental power greedy heuristic, one of the most popular heuristics available in the literature, through extensive simulation. The simulation results suggest that Kruskal-Incremental power greedy heuristic outperforms on an average the Prim-Incremental power greedy heuristic.