Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
Papers published on a yearly basis
Papers
More filters
20 Jun 2011
TL;DR: A near-optimal algorithm based on dynamic programming which runs in time linear in the number of objects andlinear in the sequence length is given which results in state-of-the-art performance.
Abstract: We analyze the computational problem of multi-object tracking in video sequences. We formulate the problem using a cost function that requires estimating the number of tracks, as well as their birth and death states. We show that the global solution can be obtained with a greedy algorithm that sequentially instantiates tracks using shortest path computations on a flow network. Greedy algorithms allow one to embed pre-processing steps, such as nonmax suppression, within the tracking algorithm. Furthermore, we give a near-optimal algorithm based on dynamic programming which runs in time linear in the number of objects and linear in the sequence length. Our algorithms are fast, simple, and scalable, allowing us to process dense input data. This results in state-of-the-art performance.
904 citations
20 Jun 2011
TL;DR: An efficient greedy algorithm for superpixel segmentation is developed by exploiting submodular and mono-tonic properties of the objective function and proving an approximation bound of ½ for the optimality of the solution.
Abstract: We propose a new objective function for superpixel segmentation This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes We present a novel graph construction for images and show that this construction induces a matroid — a combinatorial structure that generalizes the concept of linear independence in vector spaces The segmentation is then given by the graph topology that maximizes the objective function under the matroid constraint By exploiting submodular and mono-tonic properties of the objective function, we develop an efficient greedy algorithm Furthermore, we prove an approximation bound of ½ for the optimality of the solution Extensive experiments on the Berkeley segmentation benchmark show that the proposed algorithm outperforms the state of the art in all the standard evaluation metrics
894 citations
20 Aug 2002
TL;DR: It is proved that DISCOVER finds without redundancy all relevant candidate networks, whose size can be data bound, by exploiting the structure of the schema and the selection of the optimal execution plan (way to reuse common subexpressions) is NP-complete.
Abstract: DISCOVER operates on relational databases and facilitates information discovery on them by allowing its user to issue keyword queries without any knowledge of the database schema or of SQL. DISCOVER returns qualified joining networks of tuples, that is, sets of tuples that are associated because they join on their primary and foreign keys and collectively contain all the keywords of the query. DISCOVER proceeds in two steps. First the Candidate Network Generator generates all candidate networks of relations, that is, join expressions that generate the joining networks of tuples. Then the Plan Generator builds plans for the efficient evaluation of the set of candidate networks, exploiting the opportunities to reuse common subexpressions of the candidate networks.
We prove that DISCOVER finds without redundancy all relevant candidate networks, whose size can be data bound, by exploiting the structure of the schema. We prove that the selection of the optimal execution plan (way to reuse common subexpressions) is NP-complete. We provide a greedy algorithm and we show that it provides near-optimal plan execution time cost. Our experimentation also provides hints on tuning the greedy algorithm.
892 citations
01 Jan 1997
TL;DR: It turns out that the new rank based ant system can compete with the other methods in terms of average behavior, and shows even better worst case behavior.
Abstract: The ant system is a new meta-heuristic for hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP), but has been also successfully applied to problems such as quadratic assignment, job-shop scheduling, vehicle routing and graph coloring.In this paper we introduce a new rank based version of the ant system and present results of a computational study, where we compare the ant system with simulated annealing and a genetic algorithm on several TSP instances. It turns out that our rank based ant system can compete with the other methods in terms of average behavior, and shows even better worst case behavior. (author's abstract)
881 citations
TL;DR: Linear-algebra rank is the solution to an especially tractable optimization problem which are linear programs relative to certain derived polyhedra.
Abstract: Linear-algebra rank is the solution to an especially tractable optimization problem This tractability is viewed abstractly, and extended to certain more general optimization problems which are linear programs relative to certain derived polyhedra
831 citations