Loading…

Fast predictor-corrector approach for the tempered fractional differential equations

The tempered evolution equation describes the trapped dynamics, widely appearing in nature, e.g., the motion of living particles in viscous liquid. This paper proposes the fast predictor-corrector approach for the tempered fractional ordinary differential equations by digging out the potential ‘very...

Full description

Saved in:
Bibliographic Details
Published in:Numerical algorithms 2017-03, Vol.74 (3), p.717-754
Main Authors: Deng, Jingwei, Zhao, Lijing, Wu, Yujiang
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c316t-96c6d1b97b20622183077388bb58d6720e9b50ef2a2d5869d6b3c90b62f73ce43
cites cdi_FETCH-LOGICAL-c316t-96c6d1b97b20622183077388bb58d6720e9b50ef2a2d5869d6b3c90b62f73ce43
container_end_page 754
container_issue 3
container_start_page 717
container_title Numerical algorithms
container_volume 74
creator Deng, Jingwei
Zhao, Lijing
Wu, Yujiang
description The tempered evolution equation describes the trapped dynamics, widely appearing in nature, e.g., the motion of living particles in viscous liquid. This paper proposes the fast predictor-corrector approach for the tempered fractional ordinary differential equations by digging out the potential ‘very’ short memory principle. Algorithms based on the idea of equidistributing are detailedly described. Error estimates for the proposed schemes are derived; and the effectiveness and low computation cost, being linearly increasing with time t , are numerically demonstrated.
doi_str_mv 10.1007/s11075-016-0169-9
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2918492036</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2918492036</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-96c6d1b97b20622183077388bb58d6720e9b50ef2a2d5869d6b3c90b62f73ce43</originalsourceid><addsrcrecordid>eNp1UE1LAzEUDKJgrf4Abwueo-8l3XwcpVgVCl7qOWSzid3SdrdJevDfm2UFTx6G98HMMAwh9wiPCCCfEiLImgKKEZrqCzLDWjKqmagvyw4oKXKtrslNSjuAomJyRjYrm3I1RN92LveRuj5GP26VHYbYW7etQjny1lfZHwZfiFWI1uWuP9p91XYhlN8xd-Xwp7Md_-mWXAW7T_7ud87J5-pls3yj64_X9-XzmjqOIlMtnGix0bJhIBhDxUFKrlTT1KoVkoHXTQ0-MMvaWgndioY7DY1gQXLnF3xOHibfkvR09imbXX-OJVcyTKNaaAZcFBZOLBf7lKIPZojdwcZvg2DG8sxUninFjdBGFw2bNKlwj18-_jn_L_oBgsxyGw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2918492036</pqid></control><display><type>article</type><title>Fast predictor-corrector approach for the tempered fractional differential equations</title><source>Springer Link</source><creator>Deng, Jingwei ; Zhao, Lijing ; Wu, Yujiang</creator><creatorcontrib>Deng, Jingwei ; Zhao, Lijing ; Wu, Yujiang</creatorcontrib><description>The tempered evolution equation describes the trapped dynamics, widely appearing in nature, e.g., the motion of living particles in viscous liquid. This paper proposes the fast predictor-corrector approach for the tempered fractional ordinary differential equations by digging out the potential ‘very’ short memory principle. Algorithms based on the idea of equidistributing are detailedly described. Error estimates for the proposed schemes are derived; and the effectiveness and low computation cost, being linearly increasing with time t , are numerically demonstrated.</description><identifier>ISSN: 1017-1398</identifier><identifier>EISSN: 1572-9265</identifier><identifier>DOI: 10.1007/s11075-016-0169-9</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algebra ; Algorithms ; Applied mathematics ; Approximation ; Calculus ; Computer Science ; Differential equations ; Fractional calculus ; Mathematical analysis ; Numeric Computing ; Numerical Analysis ; Ordinary differential equations ; Original Paper ; Predictor-corrector methods ; Theory of Computation</subject><ispartof>Numerical algorithms, 2017-03, Vol.74 (3), p.717-754</ispartof><rights>Springer Science+Business Media New York 2016</rights><rights>Springer Science+Business Media New York 2016.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-96c6d1b97b20622183077388bb58d6720e9b50ef2a2d5869d6b3c90b62f73ce43</citedby><cites>FETCH-LOGICAL-c316t-96c6d1b97b20622183077388bb58d6720e9b50ef2a2d5869d6b3c90b62f73ce43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids></links><search><creatorcontrib>Deng, Jingwei</creatorcontrib><creatorcontrib>Zhao, Lijing</creatorcontrib><creatorcontrib>Wu, Yujiang</creatorcontrib><title>Fast predictor-corrector approach for the tempered fractional differential equations</title><title>Numerical algorithms</title><addtitle>Numer Algor</addtitle><description>The tempered evolution equation describes the trapped dynamics, widely appearing in nature, e.g., the motion of living particles in viscous liquid. This paper proposes the fast predictor-corrector approach for the tempered fractional ordinary differential equations by digging out the potential ‘very’ short memory principle. Algorithms based on the idea of equidistributing are detailedly described. Error estimates for the proposed schemes are derived; and the effectiveness and low computation cost, being linearly increasing with time t , are numerically demonstrated.</description><subject>Algebra</subject><subject>Algorithms</subject><subject>Applied mathematics</subject><subject>Approximation</subject><subject>Calculus</subject><subject>Computer Science</subject><subject>Differential equations</subject><subject>Fractional calculus</subject><subject>Mathematical analysis</subject><subject>Numeric Computing</subject><subject>Numerical Analysis</subject><subject>Ordinary differential equations</subject><subject>Original Paper</subject><subject>Predictor-corrector methods</subject><subject>Theory of Computation</subject><issn>1017-1398</issn><issn>1572-9265</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp1UE1LAzEUDKJgrf4Abwueo-8l3XwcpVgVCl7qOWSzid3SdrdJevDfm2UFTx6G98HMMAwh9wiPCCCfEiLImgKKEZrqCzLDWjKqmagvyw4oKXKtrslNSjuAomJyRjYrm3I1RN92LveRuj5GP26VHYbYW7etQjny1lfZHwZfiFWI1uWuP9p91XYhlN8xd-Xwp7Md_-mWXAW7T_7ud87J5-pls3yj64_X9-XzmjqOIlMtnGix0bJhIBhDxUFKrlTT1KoVkoHXTQ0-MMvaWgndioY7DY1gQXLnF3xOHibfkvR09imbXX-OJVcyTKNaaAZcFBZOLBf7lKIPZojdwcZvg2DG8sxUninFjdBGFw2bNKlwj18-_jn_L_oBgsxyGw</recordid><startdate>20170301</startdate><enddate>20170301</enddate><creator>Deng, Jingwei</creator><creator>Zhao, Lijing</creator><creator>Wu, Yujiang</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20170301</creationdate><title>Fast predictor-corrector approach for the tempered fractional differential equations</title><author>Deng, Jingwei ; Zhao, Lijing ; Wu, Yujiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-96c6d1b97b20622183077388bb58d6720e9b50ef2a2d5869d6b3c90b62f73ce43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algebra</topic><topic>Algorithms</topic><topic>Applied mathematics</topic><topic>Approximation</topic><topic>Calculus</topic><topic>Computer Science</topic><topic>Differential equations</topic><topic>Fractional calculus</topic><topic>Mathematical analysis</topic><topic>Numeric Computing</topic><topic>Numerical Analysis</topic><topic>Ordinary differential equations</topic><topic>Original Paper</topic><topic>Predictor-corrector methods</topic><topic>Theory of Computation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Deng, Jingwei</creatorcontrib><creatorcontrib>Zhao, Lijing</creatorcontrib><creatorcontrib>Wu, Yujiang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Database (Proquest)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Engineering Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><jtitle>Numerical algorithms</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Deng, Jingwei</au><au>Zhao, Lijing</au><au>Wu, Yujiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fast predictor-corrector approach for the tempered fractional differential equations</atitle><jtitle>Numerical algorithms</jtitle><stitle>Numer Algor</stitle><date>2017-03-01</date><risdate>2017</risdate><volume>74</volume><issue>3</issue><spage>717</spage><epage>754</epage><pages>717-754</pages><issn>1017-1398</issn><eissn>1572-9265</eissn><abstract>The tempered evolution equation describes the trapped dynamics, widely appearing in nature, e.g., the motion of living particles in viscous liquid. This paper proposes the fast predictor-corrector approach for the tempered fractional ordinary differential equations by digging out the potential ‘very’ short memory principle. Algorithms based on the idea of equidistributing are detailedly described. Error estimates for the proposed schemes are derived; and the effectiveness and low computation cost, being linearly increasing with time t , are numerically demonstrated.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11075-016-0169-9</doi><tpages>38</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1017-1398
ispartof Numerical algorithms, 2017-03, Vol.74 (3), p.717-754
issn 1017-1398
1572-9265
language eng
recordid cdi_proquest_journals_2918492036
source Springer Link
subjects Algebra
Algorithms
Applied mathematics
Approximation
Calculus
Computer Science
Differential equations
Fractional calculus
Mathematical analysis
Numeric Computing
Numerical Analysis
Ordinary differential equations
Original Paper
Predictor-corrector methods
Theory of Computation
title Fast predictor-corrector approach for the tempered fractional differential equations
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-29T20%3A29%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fast%20predictor-corrector%20approach%20for%20the%20tempered%20fractional%20differential%20equations&rft.jtitle=Numerical%20algorithms&rft.au=Deng,%20Jingwei&rft.date=2017-03-01&rft.volume=74&rft.issue=3&rft.spage=717&rft.epage=754&rft.pages=717-754&rft.issn=1017-1398&rft.eissn=1572-9265&rft_id=info:doi/10.1007/s11075-016-0169-9&rft_dat=%3Cproquest_cross%3E2918492036%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c316t-96c6d1b97b20622183077388bb58d6720e9b50ef2a2d5869d6b3c90b62f73ce43%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2918492036&rft_id=info:pmid/&rfr_iscdi=true