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About 645 papers for “shell sort”
Karl Achilles, Achim Stößer, Dietmar Herrmann et al. · 1982
ZusammenfassungBei dem von D.L. Shell 1959 vorgeschlagenen Verfahren werden die Elemente der zu sortierenden Liste über abnehmende Distanz verglichen und bei Fehlstand vertauscht. Für die Distanz-Steuerung gibt es mehrere Möglichkeiten : Bei dem folgenden Programm ist die Distanz anfangs gleich der halben Feldlänge und wird dann schrittweise ganzzahlig halbiert.
Cited by 0
2000
This chapter contains sections titled: The Algorithm Streamlined Stochastic Analysis Other Increment Sequences
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M. E., Macherki · Figshare · 2016
This code resumes the most famous sorting algorithm: the shell Sort. This method starts by sorting pairs of elements far apart from each other, then progressively reducing the gap between elements to be compared.The running time of Shell sort is heavily dependent on the gap sequence it uses.
Cited by 0Open access
M. E. Macherki · Figshare · 2016
This code resumes the most famous sorting algorithm: the shell Sort. This method starts by sorting pairs of elements far apart from each other, then progressively reducing the gap between elements to be compared.The running time of Shell sort is heavily dependent on the gap sequence it uses.
Cited by 0Open access
R. T. Smythe, Jon A. Wellner · Algorithmica · 2001
Cited by 6
Joseph B. Klerlein, Curtis Fullbright · 1988
This paper describes a sorting method obtained by modifying bubble sort. This new sort, while still inefficient, sets the stage for Shell sort. It also raises some interesting questions which students can investigate.
Cited by 3Open access
Arif Norhidayat, · Analisis Harga Pokok Produksi Rumah Pada (UIN Syarif Hidayatullah Jakarta) · 2014
Adanya kelebihan dan kekurangan pada algoritma pengurutan data (sorting) pada pengurutan data yang kecil \ndan besar membuat pengurutan data menjadi tidak efisien, terlebih lagi karena pada dasarnya suatu algoritma \ndibangun menggunakan komputasi serial yang membutuhkan waktu pemrosesan yang lama. Agar \nmenghasilkan algoritma pengurutan data (sorting) yang cepat dalam pengurutan data sedikit maupun data \nbanyak, maka dilakukan perbaikan, dengan cara mengkombinasikan (hybrid) algoritma satu dengan yang \nlain. Pada penelitian ini, algoritma Shell sort dengan Quick sort dikombinasikan (hybrid) dan \ndiimplementasikan ke dalam pemrograman paralel berbasis Message Passing Interface (MPI) menggunakan \ndua komputer. Dalam penelitian ini juga telah dilakukan analisis pendahuluan, rancang bangun jaringan fisik, \nrancang bangun kode program paralel dalam bahasa C, pengambilan data waktu eksekusi, pengolahan data \nwaktu eksekusi, analisis kecepatan pemrosesan (speed up) dan efisiensi penggunaan dua komputer. Dari \npengujian pada 5-5000 sampel data acak, dapat disimpulkan bahwa speed up program hybrid algoritma Shell \nsort dan Quick sort cenderung meningkat dan efisiensi masih dalam batas 50%-100%, hanya saja mengalami \npenurunan kecepatan pada 50-100 data, dengan nilai speed up 0,671201-0,784806 dan dengan nilai efisiensi \nterendah 34%-39%. Selain itu, program hybrid algoritma Shell sort dan Quick sort mengalami percepatan \nsuperlinier pada pengurutan 1000 data acak dengan nilai speed up 2,4909998 dan dengan nilai efisiensi \ntertinggi mencapai 125%. \nKata kunci : algoritma, bahasa C, sorting, Shell sort, Quick sort, hybrid, message passing interface, speed \nup, efisiensi.
Cited by 0Open access
dann s. washko · Bulletin of Miscellaneous Information (Royal Gardens Kew)
This episode of Linux in the Shell talks about the basics of the sort command. Remember, head on over to the website for the full write up and the audio file: http://www.linuxintheshell.org.
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Shengrong Hu · Theoretical Computer Science · 2025
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Mikhael Muhammad Luthfi Zulfa · Zenodo (CERN European Organization for Nuclear Research) · 2022
Sorting data is very useful because sorted data will be easier to check and correct if there are errors. There are various kinds of data sorting algorithms including bubble sort, merge sort, shell sort, radix sort, quicksort, and so on. Sorting data is the best treatment as it will be easier to check and correct if there are errors occurring. In this paper, bubble sort, shell sort, and quick sort are tested to sorting 100 lines of random integer with a value range of 0-100. The result shows that quick sort is the most efficient algorithm because it has the fewest steps, consume less memory, and take a little time while sorting.
Cited by 4Open access
Joon-Woo Lee, Young Sik Kim, Jong‐Seon No · IEEE Access · 2021
The Shell sort algorithm is one of the most practically effective in-place sorting algorithms. However, it is difficult to execute this algorithm with its intended running time complexity on data encrypted using fully homomorphic encryption (FHE), because the insertion sort in Shell sort has to be performed by considering the worst-case input data. In this paper, in order for the sorting algorithm to be used on the FHE data, we modify the Shell sort with an additional parameter α, allowing exponentially small sorting failure probability. For a gap sequence of powers of two, the modified Shell sort with input array length n is found to have the trade-off between the running time complexity of O(n3/2√α+loglogn) and the sorting failure probability of 2-α. Its running time complexity is close to the intended running time complexity of O(n3/2) and the sorting failure probability can be made very low with slightly increased running time. Further, the near-optimal window length of the modified Shell sort is also derived via convex optimization. The proposed analysis of the modified Shell sort is numerically confirmed by using randomly generated arrays. For the practical aspect, our modification can be applied to any gap sequence, and we show that Ciura’s gap sequence, which is known to have good practical performance, is also practically effective when our modified Shell sort is applied. We compare our modified Shell sort with other sorting algorithms with the FHE over the torus (TFHE) library, and it is shown that this modified Shell sort has the best performance in running time among in-place sorting algorithms on homomorphic encryption scheme.
Cited by 0Open access
Putra, Niko Adetya · Unika Repositor (Unika) · 2017
to sort data by using the computer program. Many students only understand \nabout basic understanding of the algorithm sorting without knowing how it works. \nThe purpose of visualization about how to sort algorithm works and the result is \nfor assisting students in studying the sorting algorithm. This visualization of \nsorting aimes to learn shell and exchange sort easily. \nThis program shows the working of shell and exchange sort. This sorting \nvisualization program displays results of each step from sorting progress. The \nvisualization sorting works with the random data. Then it sorts the data while \neach results of the sorting loop will appear on the screen. In the last Looping will \nappear the result of the sorting results. Inside this visualization program use \nvarious buttons there are input, random, and sort. \nThe sorting visualization is created with HTML canvas and javascript. \nWhere HTML canvas displays the number in boxes and javascript will moves the \nanimated boxes. \nKeywords: visualization, shell sort, exchange sort, HTML, javascript \niv
Cited by 0Open access
Ralph Mason Dreger · Educational and Psychological Measurement · 1989
The Shell-Metzner sort algorithm is a very fast sorting routine which surpasses other more well-known routines. The BASIC program written here allows for sorting data from a disk or diskette or from DATA statements.
Cited by 3
Irmantas Radavičius, Mykolas Baranauskas · Lietuvos matematikos rinkinys · 2013
We present an improved version of the Shell sort algorithm. Using the algorithm, we study various geometrical sequences and the performance of Shell sort. We demonstrate that neither number of assignments nor the number of comparisons is sufficient to properly evaluate Shell sort and pick the optimal gap sequence. We argue that one should count both operations, as well as measure actual running times.
Cited by 2Open access
Hill, J.G., Anderson, G.S., Simpson, B.C. · University of North Texas Digital Library (University of North Texas) · 1995
The Sort on Radioactive Waste Type (SORWT) Model is a method to categorize Hanford Site single-shell tanks (SSTS) into groups of tanks expected to exhibit similar chemical and physical characteristics based on their major waste types and processing histories. The model has identified 24 different waste-type groups encompassing 133 of the 149 SSTs and 93% of the total waste volume in SSTS. The remaining 16 SSTs and associated wastes could not be grouped. according to the established criteria and were placed in an ungrouped category. A detailed statistical verification study has been conducted that employs analysis of variance (ANOVA) and the core sample analysis data collected since 1989. These data cover eight tanks and five SORWT groups. The verification study showed that these five SORWT groups are highly statistically significant; they represent approximately 10% of the total waste volume and 26% of the total sludge volume in SSTS. Future sampling recommendations based on the SORWT Model results include 32 core samples from 16 tanks and 18 auger samples from six tanks. Combining these data with the existing body of information will form the basis for characterizing 98 SSTs (66%). These 98 SSTs represent 78% of the total waste volume, 61% of the total sludge volume, and 88 % of the salt cake volume.
Cited by 0Open access
J.G. Hill, B.C. Simpson · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1994
The Sort on Radioactive Waste Type (SORWT) model presents a method to categorize Hanford Site single-shell tanks (SSTs) into groups of tank expected to exhibit similar chemical and physical characteristics based on their major waste types and processing histories. This model has identified 29 different waste-type groups encompassing 135 of the 149 SSTs and 93% of the total waste volume in SSTs. The remaining 14 SSTs and associated wastes could not be grouped according to the established criteria and were placed in an ungrouped category. This letter report will detail the assumptions and methodologies used to develop the SORWT model and present the grouping results. In the near future, the validity of the predicted groups will be statistically tested using analysis of variance of characterization data obtained from recent (post-1989) core sampling and analysis activities. In addition, the SORWT model will be used to project the nominal waste characteristics of entire waste type groups that have some recent characterization data available. These subsequent activities will be documented along with these initial results in a comprehensive, formal PNL report cleared for public release by September 1994.
Cited by 38Open access
J.G. Hill, G.S. Anderson, B.C. Simpson · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1995
The SORWT model presents a methodology to group SSTs that is both simple to understand and logical in its assumptions and construction. The SORWT model has predicted the existence of 24 groups of SSTs ranging from 22 tanks per group to two tanks per group. These 24 groups encompass 133 tanks and 93% of the total waste contained in SSTs. The first 14 groups (i.e., those that contain four tanks per group or more) represent 109 tanks and 83% of the total waste volume. This demonstrates the potential for using the SORWT model to efficiently allocate resources and to maximize characterization information gained by a minimum number of sampling events. The verification study has shown that the SST groups predicted by the SORWT model are statistically significant and reduce the variability in the concentrations for all analytes examined. The SORWT model organizes a vast amount of information and presents clear options on which SSTs are more desirable to sample. The model is also simple and flexible in its ability to incorporate new parameters such as new SST analytical data, shifting programmatic needs, and/or risk assessment-oriented criteria. This report presents the nominal composition, inventory, and uncertainty for five of the 24 SORWT groups, representing 28 tanks, 10% of the total waste volume, and 29% of the total sludge volume in SSTs. Consequently, this document provides a logical beginning framework for tank waste characterization until further information becomes available or different programmatic needs are identified.
Cited by 7Open access
J.G. Hill, B.C. Simpson · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1994
The Sort on Radioactive Waste Type (SORWT) model presents a method to categorize Hanford Site single-shell tanks (SSTs) into groups of tanks expected to exhibit similar chemical and physical characteristics based on their major waste types and processing histories. This model has identified 29 different waste-type groups encompassing 135 of the 149 SSTs and 93% of the total waste volume in SSTs. The remaining 14 SSTs and associated wastes could not be grouped according to the established criteria and were placed in an ungrouped category. This letter report will detail the assumptions and methodologies used to develop the SORWT model and present the grouping results. Included with this report is a brief description and approximate compositions of the single-shell tank waste types. In the near future, the validity of the predicted groups will be statistically tested using analysis of variance of characterization data obtained from recent (post-1989) core sampling and analysis activities. In addition, the SORWT model will be used to project the nominal waste characteristics of entire waste type groups that have some recent characterization data available. These subsequent activities will be documented along with these initial results in a comprehensive, formal PNL report cleared for public release by September 1994.
Cited by 12Open access
Paul E. Black · 2022
These are historical notes, from those involved, on the names and origins of Shell sort, Bresenham's Algorithm, and the Chinese Postman Problem.
Cited by 0Open access
R. T. Smythe, J. Wellner · 2013
Abstract. We analyze the Shell Sort algorithm under the usual random permutation model. Using empirical distribution functions, we recover Louchard’s result that the running time of the 1-stage of (2, 1)-Shell Sort has a limiting distribution given by the area under the absolute Brownian bridge. The analysis extends to (h, 1)-Shell Sort where we find a limiting distribution given by the sum of areas under correlated absolute Brownian bridges. A variation of (h, 1)-Shell Sort which is slightly more efficient is presented and its asymptotic behavior analyzed.
Cited by 0