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: A new greedy algorithm for surface reconstruction from unorganized point sets that achieves topologically correct reconstruction in most cases and can handle surfaces with complex topology, boundaries, and nonuniform sampling.
Abstract: In this paper, we present a new greedy algorithm for surface reconstruction from unorganized point sets. Starting from a seed facet, a piecewise linear surface is grown by adding Delaunay triangles one by one. The most plausible triangles are added first and in such a way as to prevent the appearance of topological singularities. The output is thus guaranteed to be a piecewise linear orientable manifold, possibly with boundary. Experiments show that this method is very fast and achieves topologically correct reconstruction in most cases. Moreover, it can handle surfaces with complex topology, boundaries, and nonuniform sampling.
133 citations
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TL;DR: In this paper, an effective general search strategy for the optimization of various objective functions for community detection purposes is presented, and applied to modularity, on both real-world and synthetic networks, the search strategy substantially outperforms the best existing algorithms in terms of final scores of the objective function.
Abstract: networks. Most of them are based upon the optimization of objective functions, among which modularity is the most common, though a number of alternatives have been suggested in the scientific literature. We present here an effective general search strategy for the optimization of various objective functions for community detection purposes. When applied to modularity, on both real-world and synthetic networks, our search strategy substantially outperforms the best existing algorithms in terms of final scores of the objective function. In terms of execution time for modularity optimization this approach also outperforms most of the alternatives present in literature with the exception of fastest but usually less efficient greedy algorithms. The networks of up to 30000 nodes can be analyzed in time spans ranging from minutes to a few hours on average workstations, making our approach readily applicable to tasks not limited by strict time constraints but requiring the quality of partitioning to be as high as possible. Some examples are presented in order to demonstrate how this quality could be affected by even relatively small changes in the modularity score stressing the importance of optimization accuracy.
133 citations
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01 Dec 2009TL;DR: This paper shows that reformulating that step as a constrained flow optimization problem results in a convex problem that can be solved using standard linear programming techniques and yields excellent results on the PETS 2009 data set.
Abstract: Multi-object tracking can be achieved by detecting objects in individual frames and then linking detections across frames. Such an approach can be made very robust to the occasional detection failure: If an object is not detected in a frame but is in previous and following ones, a correct trajectory will nevertheless be produced. By contrast, a false-positive detection in a few frames will be ignored. However, when dealing with a multiple target problem, the linking step results in a difficult optimization problem in the space of all possible families of trajectories. This is usually dealt with by sampling or greedy search based on variants of Dynamic Programming, which can easily miss the global optimum. In this paper, we show that reformulating that step as a constrained flow optimization problem results in a convex problem that can be solved using standard Linear Programming techniques. In addition, this new approach is far simpler formally and algorithmically than existing techniques and yields excellent results on the PETS 2009 data set.
133 citations
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TL;DR: A new and efficient composite heuristic is proposed for the pickup and delivery traveling salesman problem, which is composed of two phases: a solution construction phase including a local optimization component and a deletion and re-insertion improvement phase.
133 citations
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TL;DR: This paper develops several placement algorithms to make informed placement decisions, which can be used to maximize the reliability of SDN, since network failures could easily cause disconnections between the control and forwarding planes.
133 citations