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About 10,000 papers for “greedy algorithm”
Xiaoxing Liu, Krishnan, Arun, Mondry, Adrian · Figshare · 2011
<b>Copyright information:</b>Taken from "An Entropy-based gene selection method for cancer classification using microarray data"BMC Bioinformatics 2005;6():76-76.Published online 24 Mar 2005PMCID:PMC1087831.Copyright © 2005 Liu et al; licensee BioMed Central Ltd.
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Abhishek Jain, Manjeet Saini, M. Rahul Kumar · Journal of Advance Research in Computer Science & Engineering (ISSN 2456-3552) · 2015
This paper describes the basic technological aspects of algorithm, algorithmic efficiency and Greedy algorithm. Algorithmic efficiency is the property of an algorithm which relate to the amount of resources use by the algorithm in computer sciences. An algorithm is considered efficient if its resource consumption (or computational cost) is at or below some acceptable level.
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Saul I. Gass, Michael C. Fu · 2013
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SpringerReference · 2011
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Xiaoxing Liu, Krishnan, Arun, Mondry, Adrian · Figshare · 2011
<b>Copyright information:</b>Taken from "An Entropy-based gene selection method for cancer classification using microarray data"BMC Bioinformatics 2005;6():76-76.Published online 24 Mar 2005PMCID:PMC1087831.Copyright © 2005 Liu et al; licensee BioMed Central Ltd.
Cited by 0Open access
2012
A dominating set of a graph G = (V, E) is a subset S ⊆ V of the nodes such that for all nodes v ∈ V, either v ∈ S or a neighbor u of v is in S. There are many distributed applications where computing a small dominating set of the network graph is important. It is well-known that computing a dominating set of minimal size is NP-hard. We therefore look for approximation algorithms, that is, algorithms which produce solutions which are optimal up to a certain factor. 10.1 Sequential Greedy Algorithm In order to understand the problem, we start with a very simple sequential algorithm. We start with S = ∅ and greedily add ‘good ’ nodes to S until S is a dominating set. We call nodes in S black, nodes which are covered (neighbors of nodes in S) grey, and all uncovered nodes white. By w(v), we denote the number of white nodes among the direct neighbors of v, including v itself. We call w(v) the span of v. The most natural greedy approach works as follows.
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andreagpons · Zenodo (CERN European Organization for Nuclear Research) · 2026
Jupyter notebook
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andreagpons · Zenodo (CERN European Organization for Nuclear Research) · 2026
Jupyter notebook
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Alexis Bénichou (19832361), Jean-Baptiste Masson (107354), Christian L. Vestergaard (14559912) · Figshare · 2024
<p>(A) Illustration of a single step of the greedy stochastic algorithm. The putative compression Δ<i>L</i>(<i>G</i>, <i>θ</i>, <i>s</i>) that would be obtained by contracting each of the subgraphs in the minibatch is calculated, and the subgraph contraction resulting in the highest compression is selected (highlighted in blue). (B) Example of motif set inferred in the connectome of the right hemisphere of the mushroom bodies (MB right) of the <i>Drosophila</i> larva. (C) Evolution of the codelength during a single algorithm run. The algorithm is continued until no more subgraphs can be contracted. The representation <i>θ</i>* = <i>θ</i><sub><i>t</i></sub> with the shortest codelength is selected; here, after the 31st iteration (indicated by a vertical black dashed line). The horizontal orange dashed line indicates the codelength of the corresponding simple graph model without motifs (see Motif-free reference codes). (D) The algorithm is run a hundred times for each dyadic base model and the most compressing model is selected. Histograms represent the codelengths of models with motifs after each run of the greedy algorithm; colors correspond to the different base models (blue: ER model, orange: configuration model, pink: reciprocal ER model, yellow: reciprocal configuration model, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012460#pcbi.1012460.g001" target="_blank">Fig 1B</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012460#pcbi.1012460.t001" target="_blank">Table 1</a>); vertical dashed lines represent the codelengths of models without motifs, and the black dashed line indicates the codelength of the shortest-codelength model—here the configuration model with motifs.</p>
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Guangquan Lu (3234408), Ying Xiong (245379), Chuan Ding (3234411) et al. · Figshare · 2016
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2008
is the first approximation algorithm for the $k $-PPSS. Furthermore, it is clear that we can let $at=1 $ if $f$ is symmetric, which implies the results of [6, 16, 18]. Several more applications will also be given. We next consider to approximate the $k $-PPSS via minimum 2, 3–partition computations. We will show some properties on the performance and use them to approximate the $k $-PPH by factor about $2-_{k}3 $. This extends our result [19] for the $k$-PPG and improves the previous best bound $2-\\frac{2}{k} $ (implied by the result $) $ for the &amp;-PP3S due to Queyranne [16]. Finally we extend our results to the target split problem in submodular systems (TSPSS), which for an additional given target set $T\\subseteq V(|T|\\geq k) $ asks to find aminimum $k $-partition $\\{V_{1}, V_{2}, \\ldots, V_{k}\\} $ such $V_{\\dot{1}} $ that each contains at least one target in $T $. Aspecial case in which $|T|=k$, $V $ and $f $ are respectively the vertex set and the cut function of graphs is called the rnultiteminal (or previously multiway) cut even problem, which is $\\mathrm{N}\\mathrm{P}$-hard for $k=3[3] $ , and can be approximated within factor $1.5- \\frac{1}{k}[1] $ , 1.3438 [7]. Clearly the TSPSS is ageneralization of the $k $-PPSS and the multiterminal cut problem. We note that Maeda, Nagamochi and Ibaraki [11] have considered the target split problem in graphs and shown that it can be approximated within factor $2-k2 $ in polynomial time. Our result will also give asimpler proof to show their result.
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Guangquan Lu (3234408), Ying Xiong (245379), Chuan Ding (3234411) et al. · Figshare · 2016
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Guangquan Lu (3234408), Ying Xiong (245379), Chuan Ding (3234411) et al. · Figshare · 2016
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П. А. Бородин, Sergeĭ Konyagin · Mathematical Notes · 2021
We introduce and study a new type of greedy algorithm, namely, projection greedy algorithms with respect to a given dictionary in a Hilbert space. We prove that these algorithms converge and estimate the rate of convergence for initial elements from the convex hull of the dictionary. Several specific examples of dictionaries are used to compare the introduced algorithms with orthogonal greedy algorithms.
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Yizhun Wang · Theoretical and Natural Science · 2023
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 time complexity is introduced. Next, the application of greedy algorithms in practical problems, such as scheduling problems, network routing, and graph generation, will be discussed. Finally, the advantages of the greedy algorithm and the limitation of the inability to obtain the global optimal solution will be evaluated, and the improvement direction combined with other algorithms will be proposed.
Cited by 24Open access
Vishisht Srihari Rao (22291042), Aounon Kumar (22291045), Himabindu Lakkaraju (14243828) et al. · Figshare · 2025
<div> The integrity of peer review is fundamental to scientific progress, but the rise of large language models (LLMs) has introduced concerns that some reviewers may rely on these tools to generate reviews rather than writing them independently. Although some venues have banned LLM-assisted reviewing, enforcement remains difficult as existing detection tools cannot reliably distinguish between fully generated reviews and those merely polished with AI assistance. In this work, we address the challenge of detecting LLM-generated reviews. We consider the approach of performing indirect prompt injection via the paper’s PDF, prompting the LLM to embed a covert watermark in the generated review, and subsequently testing for presence of the watermark in the review. We identify and address several pitfalls in naïve implementations of this approach. Our primary contribution is a rigorous watermarking and detection framework that offers strong statistical guarantees. Specifically, we introduce watermarking schemes and hypothesis tests that control the family-wise error rate across multiple reviews, achieving higher statistical power than standard corrections such as Bonferroni, while making no assumptions about the nature of human-written reviews. We explore multiple indirect prompt injection strategies–including font-based embedding and obfuscated prompts–and evaluate their effectiveness under various reviewer defense scenarios. Our experiments find high success rates in watermark embedding across various LLMs. We also empirically find that our approach is resilient to common reviewer defenses, and that the bounds on error rates in our statistical tests hold in practice. In contrast, we find that Bonferroni-style corrections are too conservative to be useful in this setting. </div>
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Xin He, Huaming Zhang · Lecture notes in computer science · 2010
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Amir Alavi (9569337), Ziv Bar-Joseph (19139) · Figshare · 2020
Algorithm S1: Greedy pair assignment algorithm.It takes as input the distance matrix D (S1 Fig) and algorithm hyperparameters and and returns the set of pairings S between the source batch of cells A and the target batch of cells B. The rows of the matrix D correspond to cells in A, the columns correspond to cells in B, and the value at D ij is the distance between cells d(A i , B j ) where d(, ) is the euclidean distance function.S is a set of tuples of indexers into sets A and B which will be chosen by the algorithm:The countOccurences(j, S) function below counts the number of times the jth target cell appears in the set of pairings S.Input: D distance matrix, source set match threshold, target set node match limit Result: S pair set S {}; P sortElementsAscending(D); foreach D ij in P do if i / S and countOccurences(j, S) then /* assign pair if i hasn't been matched yet, and j hasn't been matched more than times */ S S {(i, j)}; end if |S| |A| then /* stop if more than A cells have been assigned */ break; end end
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Timur Oikhberg · Journal of Approximation Theory · 2017
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Min Zeng · 2008
This paper relates the meaning of greedy algorithm ,explores the main ideas and implementation course,and finally discusses the specific application,features and problems by means of C Language with the help of some examples.
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