Relevant bibliographies by topics / Greedy Algorithms

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  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: 7 February 2022

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Journal articles on the topic "Greedy Algorithms"

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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 (October 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 reached. The algorithm then switches to Cylinder routing. Cluster-Cylinder elects a dominating set for all nodes and then uses this set for projection and routing. The fourth algorithm Greedy-cluster-Cylinder is a combination between Greedy-Cylinder and Cluster-Cylinder. The authors evaluate their new algorithms and compare them with many classical known algorithms. The simulation outcomes show the substantial enhancement in delivery rate over other algorithms.
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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 (August 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 (March 15, 2019): 804–7. http://dx.doi.org/10.1111/1365-2656.12963.

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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 (May 2, 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 algorithms is demonstrated in detail by stochastic simulation experiments. Second, two new heuristic formulas are presented, and their scale factors are determined. Third, an example is used to show how the two new algorithms work, and four real-world D-matrices are employed to validate their universality and stability. Finally, the application scope of the Mix1 and Mix2 algorithms is determined based on stochastic simulation experiments, and the two greedy algorithms are also used to improve a multistep look-ahead heuristic algorithm. The Mix1 and Mix2 algorithms can obtain good results in a reasonable time and have a wide application scope, which also can be used to improve the multistep look-ahead heuristic algorithm.
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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 (June 16, 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 Heuristic (CIH) and Greedy algorithm can provide almost the same optimization results. The difference was only the selection of the journey. The strength of the Greedy algorithm was that the calculation steps are simpler than the Cheapest Insertion Heuristic (CIH) algorithm. While the disadvantage of the Greedy algorithm was that it is inappropriate to find the shortest route with a relatively large number of places visited. The advantage of the Cheapest Insertion Heuristic (CIH) algorithm was that this algorithm is still stable, used for the relatively large number of places visited. While the lack of Cheapest Insertion Heuristic (CIH) algorithm was a complicated principle of calculation and was relatively longer than the Greedy algorithm.
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Ni, Qiufen, Chuanhe Huang, Panos M. Pardalos, Jia Ye, and Bin Fu. "Different Approximation Algorithms for Channel Scheduling in Wireless Networks." Mobile Information Systems 2020 (November 16, 2020): 1–13. http://dx.doi.org/10.1155/2020/8836517.

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We introduce a new two-side approximation method for the channel scheduling problem, which controls the accuracy of approximation in two sides by a pair of parameters f , g . We present a series of simple and practical-for-implementation greedy algorithms which give constant factor approximation in both sides. First, we propose four approximation algorithms for the weighted channel allocation problem: 1. a greedy algorithm for the multichannel with fixed interference radius scheduling problem is proposed and an one side O 1 -IS-approximation is obtained; 2. a greedy O 1 , O 1 -approximation algorithm for single channel with fixed interference radius scheduling problem is presented; 3. we improve the existing algorithm for the multichannel scheduling and show an E O d / ε time 1 − ϵ -approximation algorithm; 4. we speed up the polynomial time approximation scheme for single-channel scheduling through merging two algorithms and show a 1 − ϵ , O 1 -approximation algorithm. Next, we study two polynomial time constant factor greedy approximation algorithms for the unweighted channel allocation with variate interference radius. A greedy O 1 -approximation algorithm for the multichannel scheduling problem and an O 1 , O 1 -approximation algorithm for single-channel scheduling problem are developed. At last, we do some experiments to verify the effectiveness of our proposed methods.
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WANG, LIN. "FAST ALGORITHMS FOR THERMAL-AWARE FLOORPLANNING." Journal of Circuits, Systems and Computers 23, no. 07 (June 2, 2014): 1450098. http://dx.doi.org/10.1142/s0218126614500984.

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Thermal-aware floorplanning is an effective way to solve the thermal problem in modern integrated circuit (IC) designs. Existing thermal-aware floorplanning methods are all based on simulated annealing (SA), genetic algorithms (GAs) or linear programming (LP), which are quite time-consuming. In this paper, we propose two fast algorithms for thermal-aware floorplanning, a greedy algorithm based on the less-flexibility-first (LFF) principle and a hybrid algorithm combining the greedy algorithm and an SA-based refinement. The greedy algorithm can fast obtain a locally optimized floorplan with reduced area and temperature. The hybrid method can get similar results compared with pure SA-based approaches but it is still much faster.
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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 (February 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 path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra's algorithm in this case is better than the other two algorithms because it always gets optimal results.
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Li, Jonathan, Rohan Potru, and Farhad Shahrokhi. "A Performance Study of Some Approximation Algorithms for Computing a Small Dominating Set in a Graph." Algorithms 13, no. 12 (December 14, 2020): 339. http://dx.doi.org/10.3390/a13120339.

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We implement and test the performances of several approximation algorithms for computing the minimum dominating set of a graph. These algorithms are the standard greedy algorithm, the recent Linear programming (LP) rounding algorithms and a hybrid algorithm that we design by combining the greedy and LP rounding algorithms. Over the range of test data, all algorithms perform better than anticipated in theory, and have small performance ratios, measured as the size of output divided by the LP objective lower bound. However, each have advantages over the others. For instance, LP rounding algorithm normally outperforms the other algorithms on sparse real-world graphs. On a graph with 400,000+ vertices, LP rounding took less than 15 s of CPU time to generate a solution with performance ratio 1.011, while the greedy and hybrid algorithms generated solutions of performance ratio 1.12 in similar time. For synthetic graphs, the hybrid algorithm normally outperforms the others, whereas for hypercubes and k-Queens graphs, greedy outperforms the rest. Another advantage of the hybrid algorithm is to solve very large problems that are suitable for application of LP rounding (sparse graphs) but LP formulations become formidable in practice and LP solvers crash, as we observed on a real-world graph with 7.7 million+ vertices and a planar graph on 1,000,000 vertices.
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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 nodes want to estimate a power spectral density. The data measured by different sensors in such networks are typically correlated. Another type are multiple processor networks of computational nodes that cooperate to solve problems too difficult for the nodes to solve individually. In this thesis, we mainly consider greedy algorithms for distributed CS. To this end, we begin with a review of current knowledge in the field. Here, we also introduce signal models to model correlation and network models for simulation of network. We proceed by considering two applications; power spectrum density estimation and distributed reconstruction algorithms for multiple processor networks. Then, we delve deeper into the greedy algorithms with the objective to improve reconstruction performance; this naturally comes at the expense of increased computational complexity. The main objective of the thesis is to design greedy algorithms for distributed CS that exploit data correlation in sensor networks to improve performance. We develop several such algorithms, where a key element is to use intuitive democratic voting principles. Finally, we show the merit of such voting principles by probabilistic analysis based on a new input/output system model of greedy algorithms in CS. By comparing the new single sensor algorithms to well known greedy pursuit algorithms already present in the literature, we see that the goal of improved performance is achieved. We compare complexity using big-O analysis where the increased complexity is characterized. Using simulations we verify the performance and confirm complexity claims. The complexity of distributed algorithms is typically harder to analyze since it depends on the specific problem and network topology. However, when analysis is not possible, we provide extensive simulation results. No distributed algorithms based on the signal-models used in this thesis were so far available in the literature. Therefore, we compare our algorithms to standard single-sensor algorithms, and our results can then easily be used as benchmarks for future research. Compared to the stand-alone case, the new distributed algorithms provide significant performance gains. Throughout the thesis, we strive to present the work in a smooth flow of algorithm design, simulation results and analysis.
Compressed sensing (CS) är en nyutvecklad teknik som utnyttjar gleshet i stora undersamplade signaler. Många intressanta signaler besitter dessa glesa egenskaper. Utifrån en undersamplad vektor återskapar CS-algoritmer hela den sökta signalen. En klass av rekonstruktionsalgoritmer är de så kallade giriga algoritmerna, som blivit populära tack vare låg komplexitet och god prestanda. CS kan användas i vissa typer av nätverk för att detektera eller estimera stora signaler. En typ av nätverk där detta kan göras är i sensornätverk för kognitiv radio, där man använder sensorer för att estimera effektspektrum. Datan som samplas av de olika sensorerna i sådana nätverk är typiskt korrelerad. En annan typ av nätverk är multiprocessornätverk bestående av distribuerade beräkningsnoder, där noderna genom samarbete kan lösa svårare problem än de kan göra ensamma. Avhandlingen kommer främst att behandla giriga algoritmer för distribuerade CS-problem. Vi börjar med en överblick av nuvarande kunskap inom området. Här introducerar vi signalmodeller för korrelation och nätverksmodeller som används för simulering i nätverk. Vi fortsätter med att studera två tillämpningar; estimering av effektspektrum och en distribuerad återskapningsalgoritm för multiprocessornätverk. Därefter tar vi ett djupare steg i studien av giriga algoritmer, där vi utvecklar nya algoritmer med förbättrad prestanda, detta till priset av ökad beräkningskomplexitet. Huvudmålet med avhandlingen är giriga algoritmer för distribuerad CS, där algoritmerna utnyttjar datakorrelationen i sensornätverk. Vi utvecklar flera sådana algoritmer, där en huvudingrediens är att använda demokratiska röstningsalgoritmer. Vi analyserar sedan denna typ av röstningsalgoritmer genom att introducera en ingång/utgångs modell. Analysen visar att algoritmerna ger bra resultat. Genom att jämföra algoritmer för enskilda sensorer med redan befintliga algoritmer i litteraturen ser vi att målet med ökad prestanda uppnås. Vi karaktäriserar också komplexiteten. Genom simulationer verifierar vi både prestandan och komplexiteten. Att analysera komplexitet hos distribuerade algoritmer är generellt svårare eftersom den beror på specifik signalrealisation, nätverkstopologi och andra parametrar. I de fall där vi inte kan göra analys presenterar vi istället genomgående simuleringsresultat. Vi jämför våra algoritmer med de vanligaste algoritmerna för enskilda sensorsystem, och våra resultat kan därför enkelt användas som referens för framtida forskning. Jämfört med prestandan för enskilda sensorer visar de nya distribuerade algoritmerna markant förbättring.
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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 performance of simultaneous orthogonal matching pursuit (SOMP), a greedy support recovery algorithm tailored to multiple measurement vector signal models. Chapters 4 and 5 detail novel improvements in understanding the performance of SOMP. Chapter 4 presents analyses of SOMP for noiseless measurements; using those analyses, Chapter 5 extensively studies the performance of SOMP in the noisy case. A second challenge consists in optimally weighting the impact of each measurement vector on the decisions of SOMP. If measurement vectors feature unequal signal-to-noise ratios, properly weighting their impact improves the performance of SOMP. Chapter 6 introduces a novel weighting strategy from which SOMP benefits. The chapter describes the novel weighting strategy, derives theoretically optimal weights for it, and presents both theoretical and numerical evidence that the strategy improves the performance of SOMP. Finally, Chapter 7 deals with the tendency for support recovery algorithms to pick support indices solely for mapping a particular noise realization. To ensure that such algorithms pick all the correct support indices, researchers often make the algorithms pick more support indices than the number strictly required. Chapter 7 presents a support reduction technique, that is, a technique removing from a support the supernumerary indices solely mapping noise. The advantage of the technique, which relies on cross-validation, is that it is universal, in that it makes no assumption regarding the support recovery algorithm generating the support. Theoretical results demonstrate that the technique is reliable. Furthermore, numerical evidence proves that the proposed technique performs similarly to orthogonal matching pursuit with cross-validation (OMP-CV), a state-of-the-art algorithm for support reduction.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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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 representative of Gibbs distribution via a greedy strategy. 2) Searching for a set of diverse and high-quality bounding boxes. We formulate this problem as the constrained maximization of a monotonic sub-modular function such that there exists a greedy algorithm having near-optimal guarantee. 3) Fill-in-the-blank. The goal is to generate missing words conditioned on context given an image. We extend Beam Search, a greedy algorithm applicable on unidirectional expansion, to bidirectional neural models when both past and future information have to be considered. We test our proposed approaches on a series of Computer Vision and Natural Language Processing benchmarks and show that they are effective and efficient.
Ph. D.
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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 certain restrictions the problem still remains NP-complete, and show how the problem can be solved in deterministic polynomial time if the restrictions imposed become more severe. Let H be a fixed undirected graph. An H-colouring of an undirected graph G is a homomorphism from G to H. In the second part of the thesis, we show that, if the vertices of G are partially ordered then the complexity of deciding whether a given vertex of G is in a lexicographically first maximal H-colourable subgraph of G is NP-complete, if H is bipartite, and Sp2-complete, if H is non-bipartite. We then show that if the vertices of G are linearly, as opposed to partially, ordered then the complexity of deciding whether a given vertex of G is in the lexicographically first maximal H-colourable subgraph of G is P-complete, if H is bipartite, and DP2-complete, if H is non-bipartite. In the final part of the thesis we show that the results obtained can be paralleled in the setting of graphs where orders are given by degrees of the vertices.
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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 surveyed are refinements of standard greedy algorithm.
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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.
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 for the aforementioned graph invariants by means of two different approaches, one using level greedy trees and majorization, while the other one is somewhat more direct. Finally, we prove some new results on greedy trees for additive parameters with specific toll functions.
AFRIKAANSE OPSOMMING: ’n Gulsige boom word vanuit ’n gegewe graadry deur middel van ’n eenvoudige gulsige algoritme gebou, wat die hoogste graad aan die wortel toewys, die tweede-, derde-, . . . , -hoogste graad aan die wortel se bure, ens. Hierdie spesifieke boom is die oplossing van ’n groot aantal ekstremale probleme onder al die bome met gegewe graadry. In hierdie tesis beskou ons ’n versameling van resultate oor afstand-gebaseerde grafiekinvariante, die aantal subbome en die spektraalstraal waarin gulsige bome ’n belangrike rol speel. Ons bewys dat gulsige bome ekstremaal vir die bogenoemde grafiekinvariante is deur van twee verskillende tegnieke gebruik te maak: een met behulp van vlak-gulsige bome en majorering, en ’n ander metode wat effens meer direk is. Laastens bewys ons sommige nuwe resultate oor gulsige bome vir additiewe parameters met spesifieke tolfunksies.
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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.
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"

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Bednorz, Witold. Greedy algorithms. Rijek, Crotia: InTech, 2008.

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

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

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

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

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Sotiris, Nikoletseas, ed. Experimental and efficient algorithms: 4th International Workshop, WEA 2005, Santorini Island, Greece, May 10-13, 2005 : proceedings. Berlin: Springer, 2005.

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Roderick, Michael J. Channel and switchbox routing using a greedy based channel algorithm with outward scanning technique. Monterey, Calif: 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. Berlin: Springer, 1995.

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F, Makedon, ed. VLSI algorithms and architectures: Aegean Workshop on Computing, Loutraki, Greece, July 8-11, 1986 : proceedings. Berlin: Springer-Verlag, 1986.

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Aegean Workshop on Computing: VLSI Algorithms and Architectures (1986 Loutráki, Greece). VLSI Algorithms and Architectures: Aegean Workshop on Computing, Loutraki, Greece, July 8-11, 1986 : proceedings. Berlin: Springer-Verlag, 1986.

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Book chapters on the topic "Greedy Algorithms"

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Majumdar, Angshul. "Greedy Algorithms." In Compressed Sensing for Engineers, 9–28. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis, [2019] | Series: Devices, circuits, and systems: 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, 611–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_295.

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Aldous, Joan M., and Robin J. Wilson. "Greedy Algorithms." In Graphs and Applications, 182–201. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0467-4_8.

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Stinson, Douglas R. "Greedy Algorithms." In Techniques for Designing and Analyzing Algorithms, 171–214. Boca Raton: 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, 143–74. Boca Raton: 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, 169–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54099-7_12.

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Wang, Feng, and Weili Wu. "Greedy Approximation Algorithms." In Encyclopedia of Algorithms, 376–79. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_174.

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Wu, Weili, and Feng Wang. "Greedy Approximation Algorithms." In Encyclopedia of Algorithms, 882–86. New York, NY: 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, 1–7. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_174-2.

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Wan, Peng-Jun. "Greedy Approximation Algorithms." In Handbook of Combinatorial Optimization, 1599–629. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-7997-1_48.

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Conference papers on the topic "Greedy Algorithms"

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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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Dickerson, Matthew T., Robert L. Scot Drysdale, Scott A. McElfresh, and Emo Welzl. "Fast greedy triangulation algorithms." In the tenth annual symposium. New York, New York, USA: 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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Baburaj, Maya K., Sooraj K. Ambat, and V. S. Sheeba. "Compressive beamforming using greedy algorithms." In 2016 International Conference on Communication Systems and Networks (ComNet). IEEE, 2016. http://dx.doi.org/10.1109/csn.2016.7823991.

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

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

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Kumar, Ravi, Benjamin Moseley, Sergei Vassilvitskii, and Andrea Vattani. "Fast greedy algorithms in mapreduce and streaming." In SPAA '13: 25th ACM Symposium on Parallelism in Algorithms and Architectures. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2486159.2486168.

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Julstrom, Bryant A. "Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem." In the 2005 conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068111.

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Smorodkina, Ekaterina, and Daniel Tauritz. "Greedy Population Sizing for Evolutionary Algorithms." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424742.

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Gao, Wanru, Tobias Friedrich, Frank Neumann, and Christian Hercher. "Randomized greedy algorithms for covering problems." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205455.3205542.

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Reports on the topic "Greedy Algorithms"

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

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Leviatan, D., and V. N. Temlyakov. Simultaneous Approximation by Greedy Algorithms. Fort Belvoir, VA: Defense Technical Information Center, January 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. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada637114.

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Temlyakov, V. N. A Criterion for Convergence of Weak Greedy Algorithms. Fort Belvoir, VA: Defense Technical Information Center, January 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. Fort Belvoir, VA: Defense Technical Information Center, May 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. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada554134.

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Dilworth, S. J., N. J. Kalton, D. Kutzarova, and V. N. Temlyakov. The Thresholding Greedy Algorithm, Greedy Bases and Duality. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada640677.

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