Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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TL;DR: The proposed algorithm can find the core nodes of different communities in the network through the label propagation process and has low time complexity, which makes it applicable to large-scale networks.
82 citations
TL;DR: The algorithm is a recursive greedy algorithm adapted from the greedy algorithm for the directed Steiner tree problem and gives an O((log Σi|gi|1+eċ log m) approximation in polynomial time) for every fixed constant e > 0.
82 citations
TL;DR: A distributed heuristic algorithm allowing a sensor to determine how to synchronize itself based on its neighbourhood information only is developed, and the proposed protocol gives consistent performance under different conditions with its performance comparable to that of the centralized algorithm.
Abstract: Recently, a time synchronization algorithm called pairwise broadcast synchronization (PBS) is proposed. With PBS, a sensor can be synchronized by overhearing synchronization packet exchange among its neighbouring sensors without sending out any packet itself. In an one-hop sensor network where every node is a neighbour of each other, a single PBS message exchange between two nodes would facilitate all nodes to synchronize. However, in a multi-hop sensor network, PBS message exchanges in several node pairs are needed in order to achieve network-wide synchronization. To reduce the number of message exchanges, these node pairs should be carefully chosen. In this paper, we investigate how to choose these ldquoappropriaterdquo sensors aiming at reducing the number of PBS message exchanges while allowing every node to synchronize. This selection problem is shown to be NP-complete, for which the greedy heuristic is a good polynomial-time approximation algorithm. Nevertheless, a centralized algorithm is not suitable for wireless sensor networks. Therefore, we develop a distributed heuristic algorithm allowing a sensor to determine how to synchronize itself based on its neighbourhood information only. The protocol is tested through extensive simulations. The simulation results reveal that the proposed protocol gives consistent performance under different conditions with its performance comparable to that of the centralized algorithm.
82 citations
28 Jan 1996
TL;DR: The answer to the first question is that the known lower bound is tight, and the second question is answered in the affirmative by using a slight modification of anO(nlogn) algorithm for the greedy triangulation.
Abstract: This article settles the following two longstanding open problems:?What is the worst case approximation ratio between the greedy triangulation and the minimum weight triangulation??Is there a polynomial time algorithm that always produces a triangulation whose length is within a constant factor from the minimum?The answer to the first question is that the known lower bound is tight. The second question is answered in the affirmative by using a slight modification of anO(nlogn) algorithm for the greedy triangulation. We also derive some other interesting results. For example, we show that a constant-factor approximation of the minimum weight convex partition can be obtained within the same time bounds.
82 citations
TL;DR: The authors achieve near-optimal filling for flat layouts with respect to each of these objectives, and indicate that the hybrid hierarchical filling approach is efficient, scalable, accurate, and highly competitive with existing methods for hierarchical layouts.
Abstract: Chemical-mechanical polishing (CMP) and other manufacturing steps in very deep submicron very large scale integration have varying effects on device and interconnect features, depending on local characteristics of the layout. To improve manufacturability and performance predictability, the authors seek to make a layout uniform with respect to prescribed density criteria, by inserting "area fill" geometries into the layout. In this paper, they make the following contributions. First, the authors define the flat, hierarchical, and multiple-layer filling problems, along with a unified density model description. Secondly, for the flat filling problem, they summarize current linear programming approaches with two different objectives, i.e., the Min-Var and Min-Fill objectives. They then propose several new Monte Carlo-based filling methods with fast dynamic data structures. Thirdly, they give practical iterated methods for layout density control for CMP uniformity based on linear programming, Monte Carlo, and greedy algorithms. Fourthly, to address the large data volume and inherent lack of scalability of flat layout density control, the authors propose practical methods for hierarchical layout density control. These methods smoothly trade off runtime, solution quality, and output data volume. Finally, they extend the linear programming approaches and present new Monte Carlo-based methods for the multiple-layer filling problem. Comparisons with previous filling methods show the advantages of the new iterated Monte Carlo and iterated greedy methods for both flat and hierarchical layouts and for both density models (spatial density and effective density). The authors achieve near-optimal filling for flat layouts with respect to each of these objectives. Their experiments indicate that the hybrid hierarchical filling approach is efficient, scalable, accurate, and highly competitive with existing methods (e.g., linear programming-based techniques) for hierarchical layouts.
82 citations