Relevant bibliographies by topics / Greedy Algorithms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Greedy Algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Greedy Algorithms"

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Wang, Yizhun. "Review on greedy algorithm." Theoretical and Natural Science 14, no. 1 (2023): 233–39. http://dx.doi.org/10.54254/2753-8818/14/20241041.

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The greedy algorithm is a commonly used algorithm design idea that can provide efficient solutions to many practical problems. This paper aims to review and summarize the basic ideas, characteristics and application fields of greedy algorithms, and discuss their advantages and limitations. Firstly, the basic concepts of greedy algorithms are introduced, including the greedy selection properties and optimal substructures. Then, some classic greedy algorithms such as the backpack problem, the activity selection problem, and the minimum spanning tree problem are introduced, and the concept of tim
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Abdallah, Alaa E., Mohammad Bsoul, Emad E. Abdallah, Ibrahim Al–Oqily, and George Kao. "Cluster-Based Online Routing Protocols for Ad Hoc Network." International Journal of Information Technology and Web Engineering 9, no. 4 (2014): 54–66. http://dx.doi.org/10.4018/ijitwe.2014100105.

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In geographical routing algorithms, mobile nodes rely on geographical position to make routing judgments. Researchers frequently discuss such routing algorithms in (2D) space. However, in reality, mobile nodes spread in (3D) space. In this paper the authors present four new 3D geographical-based routing algorithms Cylinder, Greedy-Cylinder, Cluster-Cylinder, and Greedy-cluster-Cylinder. In Cylinder routing, the nodes are locally projected on the inner surface of a cylinder, perimeter routing is executed after that. Greedy-Cylinder starts with Greedy routing algorithm until a local minimum is r
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Chen, Xi. "Comparison of different algorithms in Reversi AI." Applied and Computational Engineering 32, no. 1 (2024): 99–105. http://dx.doi.org/10.54254/2755-2721/32/20230190.

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Minimax and alpha-beta pruning have been widely applied in AI for various strategic board games, the utilization of greedy algorithms in this context has received less attention. The Greedy algorithms aim to make locally optimal choices at each step, exploiting immediate gains. This research aims to reveal the potential benefits and limitations of applying greedy algorithms in Reversi gaming AI, specifically through a comparison with the Minimax algorithm. A series of AI versus AI matches were conducted to evaluate and compare the performance of the three different AI algorithms. The objective
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Tian, Heng, Fuhai Duan, Yong Sang, and Liang Fan. "Novel algorithms for sequential fault diagnosis based on greedy method." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 6 (2020): 779–92. http://dx.doi.org/10.1177/1748006x20914498.

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Test sequencing for binary systems is a nondeterministic polynomial-complete problem, where greedy algorithms have been proposed to find the solution. The traditional greedy algorithms only extract a single kind of information from the D-matrix to search the optimal test sequence, so their application scope is limited. In this study, two novel greedy algorithms that combine the weight index for fault detection with the information entropy are introduced for this problem, which are defined as the Mix1 algorithm and the Mix2 algorithm. First, the application scope for the traditional greedy algo
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Hignasari, L. Virginayoga. "Komparasi Algoritma Cheapest Insertion Heuristic (CIH) Dan Greedy Dalam Optimasi Rute Pendistribusian Barang." Jurnal Ilmiah Vastuwidya 2, no. 2 (2020): 31–39. http://dx.doi.org/10.47532/jiv.v2i2.87.

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This study was aimed to compare algorithms that can effectively provide better solutions related to the problem of determining the shortest route in the distribution of goods. This research was a qualitative research. The object of research was the route of shipping goods of a business that is engaged in printing and convection. The algorithms compared in this study were Cheapest Insertion Heuristic (CIH) and Greedy algorithms. Both algorithms have advantages and disadvantages in finding the shortest route. From the results of the analysis using these two algorithms, the Cheapest Insertion Heu
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Ochkov, V. F., A. O. Ivanova, and M. D. Alekseev. "THREE "GREEDY" ALGORITHMS." Informatics in school, no. 9 (December 20, 2018): 34–42. http://dx.doi.org/10.32517/2221-1993-2018-17-9-34-42.

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The article considers three logistic tasks (transportation problem, traveling salesman problem, pursuit problem), by the example of which the essence and features of “greedy” algorithms are shown. For the frst time, a solution was given to a transportation problem in the Mathcad Prime environment using the matrix method using units of measure. Two new applications of the traveling salesman problem have been proposed. The difference scheme for solving the pursuit problem is described.
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Lutoborski, Adam, and Vladimir N. Temlyakov. "Vector greedy algorithms." Journal of Complexity 19, no. 4 (2003): 458–73. http://dx.doi.org/10.1016/s0885-064x(03)00026-8.

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Simmons, Benno I., Christoph Hoeppke, and William J. Sutherland. "Beware greedy algorithms." Journal of Animal Ecology 88, no. 5 (2019): 804–7. http://dx.doi.org/10.1111/1365-2656.12963.

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Ichikawa, Koji, Shinji Ito, Daisuke Hatano, et al. "New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12708–16. http://dx.doi.org/10.1609/aaai.v38i11.29166.

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We consider the sparse contextual bandit problem where arm feature affects reward through the inner product of sparse parameters. Recent studies have developed sparsity-agnostic algorithms based on the greedy arm selection policy. However, the analysis of these algorithms requires strong assumptions on the arm feature distribution to ensure that the greedily selected samples are sufficiently diverse; One of the most common assumptions, relaxed symmetry, imposes approximate origin-symmetry on the distribution, which cannot allow distributions that has origin-asymmetric support. In this paper, w
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Wayahdi, Muhammad Rhifky, Subhan Hafiz Nanda Ginting, and Dinur Syahputra. "Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path." International Journal of Advances in Data and Information Systems 2, no. 1 (2021): 45–52. http://dx.doi.org/10.25008/ijadis.v2i1.1206.

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The problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a
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Dissertations / Theses on the topic "Greedy Algorithms"

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Sundman, Dennis. "Greedy Algorithms for Distributed Compressed Sensing." Doctoral thesis, KTH, Kommunikationsteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-144907.

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Compressed sensing (CS) is a recently invented sub-sampling technique that utilizes sparsity in full signals. Most natural signals possess this sparsity property. From a sub-sampled vector, some CS reconstruction algorithm is used to recover the full signal. One class of reconstruction algorithms is formed by the greedy pursuit, or simply greedy, algorithms, which is popular due to low complexity and good performance. Meanwhile, in sensor networks, sensor nodes monitor natural data for estimation or detection. One application of sensor networking is in cognitive radio networks, where sensor no
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Beis, Michail. "Greedy algorithms for random regular graphs." Thesis, University of Liverpool, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427021.

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Determe, Jean-François. "Greedy algorithms for multi-channel sparse recovery." Doctoral thesis, Universite Libre de Bruxelles, 2018. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/265808.

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During the last decade, research has shown compressive sensing (CS) to be a promising theoretical framework for reconstructing high-dimensional sparse signals. Leveraging a sparsity hypothesis, algorithms based on CS reconstruct signals on the basis of a limited set of (often random) measurements. Such algorithms require fewer measurements than conventional techniques to fully reconstruct a sparse signal, thereby saving time and hardware resources. This thesis addresses several challenges. The first is to theoretically understand how some parameters—such as noise variance—affect the performanc
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Puricella, Antonio. "The complexity of greedy algorithms on ordered graphs." Thesis, University of Leicester, 2002. http://hdl.handle.net/2381/30518.

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Let p be any fixed polynomial time testable, non-trivial, hereditary property of graphs. Suppose that the vertices of a graph G are not necessarily linearly ordered but partially ordered, where we think of this partial order as a collection of (possibly exponentially many) linear orders in the natural way. In the first part of this thesis, we prove that the problem of deciding whether a lexicographically first maximal (with respect to one of these linear orders) subgraph of G satisfying p, contains a specified vertex is NP-complete. For some of these properties p we then show that by applying
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Sun, Qing. "Greedy Inference Algorithms for Structured and Neural Models." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/81860.

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A number of problems in Computer Vision, Natural Language Processing, and Machine Learning produce structured outputs in high-dimensional space, which makes searching for the global optimal solution extremely expensive. Thus, greedy algorithms, making trade-offs between precision and efficiency, are widely used. %Unfortunately, they in general lack theoretical guarantees. In this thesis, we prove that greedy algorithms are effective and efficient to search for multiple top-scoring hypotheses from structured (neural) models: 1) Entropy estimation. We aim to find deterministic samples that are
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Dutta, Himanshu Shekhar. "Survey of Approximation Algorithms for Set Cover Problem." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc12118/.

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In this thesis, I survey 11 approximation algorithms for unweighted set cover problem. I have also implemented the three algorithms and created a software library that stores the code I have written. The algorithms I survey are: 1. Johnson's standard greedy; 2. f-frequency greedy; 3. Goldsmidt, Hochbaum and Yu's modified greedy; 4. Halldorsson's local optimization; 5. Dur and Furer semi local optimization; 6. Asaf Levin's improvement to Dur and Furer; 7. Simple rounding; 8. Randomized rounding; 9. LP duality; 10. Primal-dual schema; and 11. Network flow technique. Most of the algorithms surve
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Oglic, Dino [Verfasser]. "Constructive Approximation and Learning by Greedy Algorithms / Dino Oglic." Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1170777910/34.

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Yuen, Chi-kan. "A double-track greedy algorithm for VLSI channel routing /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19656373.

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Razanajatovo, Misanantenaina Valisoa. "Properties of greedy trees." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95909.

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Thesis (MSc)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: A greedy tree is constructed from a given degree sequence using a simple greedy algorithm that assigns the highest degree to the root, the second, the third, . . . , -highest degree to the root’s neighbours, etc. This particular tree is the solution to numerous extremal problems among all trees with given degree sequence. In this thesis, we collect results for some distancebased graph invariants, the number of subtrees and the spectral radius in which greedy trees play a major role. We show that greedy trees are extremal
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Ortiz, John E. "Absolute position measurement for automated guided vehicles using the Greedy DeBruijn Sequence." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FOrtiz.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2006.<br>Thesis Advisor(s): Harold M. Fredricksen, Jon T. Butler. "September 2006." Includes bibliographical references (p. 149). Also available in print.
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Books on the topic "Greedy Algorithms"

1

Bednorz, Witold. Greedy algorithms. InTech, 2008.

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Hirai, Hiroshi. Greedy fans: A geometric approach to dual greedy algorithms. Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2005.

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Sano, Yoshio. The greedy algorithm for strict cg-matroids. Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2007.

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Faigle, Ulrich. A general model for matroids and the greedy algorithm. Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2007.

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Roderick, Michael J. Channel and switchbox routing using a greedy based channel algorithm with outward scanning technique. Naval Postgraduate School, 1988.

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ESA, '95 (1995 Corfu Greece). Algorithms - ESA '95: Third Annual European Symposium, Corfu, Greece, September 25-27, 1995 : proceedings. Springer, 1995.

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1955-, Spirakis P. G., ed. Algorithms - ESA '95: Third Annual European Symposium, Corfu, Greece, September 25-27, 1995 : proceedings. Springer, 1995.

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1953-, Toueg S., Spirakis P. G. 1955-, Kirousis L. 1951-, and International Workshop on Distributed Algorithms (5th : 1991 : Delphoi, Greece), eds. Distributed algorithms: 5th international workshop, WDAG '91, Delphi, Greece, October 7-9, 1991 : proceedings. Springer-Verlag, 1992.

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Vangelis, Markakis, Milis Ioannis, Paschos Vangelis Th, and SpringerLink (Online service), eds. Combinatorial Optimization: Second International Symposium, ISCO 2012, Athens, Greece, April 19-21, 2012, Revised Selected Papers. Springer Berlin Heidelberg, 2012.

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DISC '98 (1998 Andros, Greece). Distributed computing: 12th international symposium, DISC '98, Andros, Greece, September 24-26, 1998 : proceedings. Springer, 1998.

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Book chapters on the topic "Greedy Algorithms"

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Aldous, Joan M., and Robin J. Wilson. "Greedy Algorithms." In Graphs and Applications. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0467-4_8.

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Izadkhah, Habib. "Greedy Algorithms." In Problems on Algorithms. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17043-0_12.

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Bosc, Patrick, Marc Guyomard, and Laurent Miclet. "Greedy algorithms." In Algorithm Design: A Methodological Approach - 150 problems and detailed solutions. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/b23251-7.

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Majumdar, Angshul. "Greedy Algorithms." In Compressed Sensing for Engineers. CRC Press, 2018. http://dx.doi.org/10.1201/9781351261364-2.

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Temlyakov, Vladimir. "Greedy Algorithms." In Encyclopedia of Applied and Computational Mathematics. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_295.

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Stinson, Douglas R. "Greedy Algorithms." In Techniques for Designing and Analyzing Algorithms. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429277412-5.

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Mohanty, Sachi Nandan, Pabitra Kumar Tripathy, and Suneeta Satpathy. "Greedy Algorithms." In The Art of Algorithm Design. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003093886-5.

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Cidon, Israel, Shay Kutten, Yishay Mansour, and David Peleg. "Greedy packet scheduling." In Distributed Algorithms. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54099-7_12.

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Wu, Weili, and Feng Wang. "Greedy Approximation Algorithms." In Encyclopedia of Algorithms. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_174.

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Wu, Weili, and Feng Wang. "Greedy Approximation Algorithms." In Encyclopedia of Algorithms. Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_174-2.

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Conference papers on the topic "Greedy Algorithms"

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Blasingame, Zander W., and Chen Liu. "Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs." In 2024 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2024. http://dx.doi.org/10.1109/ijcb62174.2024.10744517.

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QiHui, Miao, Lin Hao, Peng YuRun, and Liu Hao. "Hybrid compressed lightweight epilepsy detection network based on greedy strategy." In 2025 4th Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2025. https://doi.org/10.1109/cacml64929.2025.11010964.

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Noonari, Nooruddin, Kaleem Arshid, Shamaila Fardous, Javed Karim, and Sathishkumar Duraisamy. "Implementation of Maximum Weighted Independent Set through Brute Force and Greedy Heuristics Approaches." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721981.

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Liu, Wei, Chunyi Chen, Yuxin Wei, and Zhuo Shao. "New Parallelized Greedy Soup Algorithm in the End-to-End Automatic Speech Recognition." In 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI). IEEE, 2024. https://doi.org/10.1109/acai63924.2024.10899738.

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Kodaganallur, Viswanathan, and Anup K. Sen. "Greedy by Chance - Stochastic Greedy Algorithms." In 2010 Sixth International Conference on Autonomic and Autonomous Systems (ICAS). IEEE, 2010. http://dx.doi.org/10.1109/icas.2010.32.

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Ayanzadeh, Ramin, John Dorband, Milton Halem, and Tim Finin. "Quantum-Assisted Greedy Algorithms." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884795.

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Dickerson, Matthew T., Robert L. Scot Drysdale, Scott A. McElfresh, and Emo Welzl. "Fast greedy triangulation algorithms." In the tenth annual symposium. ACM Press, 1994. http://dx.doi.org/10.1145/177424.177649.

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Song, Yunlong, and Min Liu. "Greedy-based distributed algorithms for green traffic routing." In 2014 23rd International Conference on Computer Communication and Networks (ICCCN). IEEE, 2014. http://dx.doi.org/10.1109/icccn.2014.6911771.

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Gupta, Anupam, and Amit Kumar. "Greedy Algorithms for Steiner Forest." In STOC '15: Symposium on Theory of Computing. ACM, 2015. http://dx.doi.org/10.1145/2746539.2746590.

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Lucier, Brendan, and Vasilis Syrgkanis. "Greedy Algorithms Make Efficient Mechanisms." In EC '15: ACM Conference on Economics and Computation. ACM, 2015. http://dx.doi.org/10.1145/2764468.2764506.

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Reports on the topic "Greedy Algorithms"

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Temlyakov, V. N. Greedy Algorithms in Banach Spaces. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada637095.

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Leviatan, D., and V. N. Temlyakov. Simultaneous Approximation by Greedy Algorithms. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada619364.

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Livshitz, E. D., and V. N. Temlyakov. On Convergence of Weak Greedy Algorithms. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada637114.

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Temlyakov, V. N. A Criterion for Convergence of Weak Greedy Algorithms. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada637117.

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Binev, Peter, Albert Cohen, Wolfgang Dahmen, Ronald DeVore, Guergana Petrova, and Przemyslaw Wojtaszczyk. Convergence Rates for Greedy Algorithms in Reduced Basis Methods. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada640047.

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Hesthaven, Jan S., Benjamin Stamm, and Shun Zhang. Efficient Greedy Algorithms for High-Dimensional Parameter Spaces with Applications to Empirical Interpolation and Reduced Basis Methods. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada554134.

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Kularatne, Dhanushka N., Subhrajit Bhattacharya, and M. Ani Hsieh. Computing Energy Optimal Paths in Time-Varying Flows. Drexel University, 2016. http://dx.doi.org/10.17918/d8b66v.

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Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration
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Tipton, Kelley, Brian F. Leas, Emilia Flores, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), 2023. http://dx.doi.org/10.23970/ahrqepccer268.

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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-se
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Dilworth, S. J., N. J. Kalton, D. Kutzarova, and V. N. Temlyakov. The Thresholding Greedy Algorithm, Greedy Bases and Duality. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada640677.

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SHinkevich, A. I., F. F. Galimulina, N. V. Barsegyan, and I. A. YUmankin. Algorithm for predicting a «green» business model based on discriminant analysis. Ailamazyan Program Systems Institute of Russian Academy of Sciences, 2025. https://doi.org/10.12731/ofernio.2025.25455.

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