New proximal type algorithms for convex minimization and its application to image deblurring
In this work, we are interested in solving a convex minimization problem in real Hilbert spaces. We propose a new modified proximal algorithm using the inertial extrapolation and the linesearch technique. Its weak convergence theorems are established under mild conditions. Numerical experiments are...
Gespeichert in:
| Veröffentlicht in: | Computational & applied mathematics 2022-10, Vol.41 (7), Article 333 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 7 |
| container_start_page | |
| container_title | Computational & applied mathematics |
| container_volume | 41 |
| creator | Kesornprom, Suparat Cholamjiak, Prasit Park, Choonkil |
| description | In this work, we are interested in solving a convex minimization problem in real Hilbert spaces. We propose a new modified proximal algorithm using the inertial extrapolation and the linesearch technique. Its weak convergence theorems are established under mild conditions. Numerical experiments are presented to illustrate the performance of the proposed algorithm in image deblurring. |
| doi_str_mv | 10.1007/s40314-022-02042-7 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2719732814</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2719732814</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-d219932fb0e41319e199fea7eb1baca2dcec66b01daa203f853587729e8ab4d63</originalsourceid><addsrcrecordid>eNp9UE1LxDAQDaLguvoHPAU8Vycf27RHWfyCRS96E0LaTtcsbVOTVnf99Wat4M3DMMzw3pt5j5BzBpcMQF0FCYLJBDiPBZIn6oDMWAYqAQH8kMw4F1kiUhDH5CSEDYBQTMoZeX3ET9p7t7Wtaeiw65GaZu28Hd7aQGvnaem6D9zS1na2tV9msK6jpquoHQI1fd_YctoNjkaNNdIKi2b03nbrU3JUmybg2W-fk5fbm-flfbJ6untYXq-SUrB8SCrO8lzwugCULG4wjjUahQUrTGl4VWKZpgWwyhgOos4WYpEpxXPMTCGrVMzJxaQbjbyPGAa9caPv4knNFcuV4BmTEcUnVOldCB5r3fv4sd9pBnqfop5S1DFF_ZOiVpEkJlLo947Q_0n_w_oGlf12eg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2719732814</pqid></control><display><type>article</type><title>New proximal type algorithms for convex minimization and its application to image deblurring</title><source>Springer Nature - Complete Springer Journals</source><creator>Kesornprom, Suparat ; Cholamjiak, Prasit ; Park, Choonkil</creator><creatorcontrib>Kesornprom, Suparat ; Cholamjiak, Prasit ; Park, Choonkil</creatorcontrib><description>In this work, we are interested in solving a convex minimization problem in real Hilbert spaces. We propose a new modified proximal algorithm using the inertial extrapolation and the linesearch technique. Its weak convergence theorems are established under mild conditions. Numerical experiments are presented to illustrate the performance of the proposed algorithm in image deblurring.</description><identifier>ISSN: 2238-3603</identifier><identifier>EISSN: 1807-0302</identifier><identifier>DOI: 10.1007/s40314-022-02042-7</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Algorithms ; Applications of Mathematics ; Applied physics ; Computational mathematics ; Computational Mathematics and Numerical Analysis ; Hilbert space ; Mathematical Applications in Computer Science ; Mathematical Applications in the Physical Sciences ; Mathematics ; Mathematics and Statistics ; Optimization</subject><ispartof>Computational & applied mathematics, 2022-10, Vol.41 (7), Article 333</ispartof><rights>The Author(s) under exclusive licence to Sociedade Brasileira de Matemática Aplicada e Computacional 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-d219932fb0e41319e199fea7eb1baca2dcec66b01daa203f853587729e8ab4d63</citedby><cites>FETCH-LOGICAL-c319t-d219932fb0e41319e199fea7eb1baca2dcec66b01daa203f853587729e8ab4d63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40314-022-02042-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40314-022-02042-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Kesornprom, Suparat</creatorcontrib><creatorcontrib>Cholamjiak, Prasit</creatorcontrib><creatorcontrib>Park, Choonkil</creatorcontrib><title>New proximal type algorithms for convex minimization and its application to image deblurring</title><title>Computational & applied mathematics</title><addtitle>Comp. Appl. Math</addtitle><description>In this work, we are interested in solving a convex minimization problem in real Hilbert spaces. We propose a new modified proximal algorithm using the inertial extrapolation and the linesearch technique. Its weak convergence theorems are established under mild conditions. Numerical experiments are presented to illustrate the performance of the proposed algorithm in image deblurring.</description><subject>Algorithms</subject><subject>Applications of Mathematics</subject><subject>Applied physics</subject><subject>Computational mathematics</subject><subject>Computational Mathematics and Numerical Analysis</subject><subject>Hilbert space</subject><subject>Mathematical Applications in Computer Science</subject><subject>Mathematical Applications in the Physical Sciences</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Optimization</subject><issn>2238-3603</issn><issn>1807-0302</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9UE1LxDAQDaLguvoHPAU8Vycf27RHWfyCRS96E0LaTtcsbVOTVnf99Wat4M3DMMzw3pt5j5BzBpcMQF0FCYLJBDiPBZIn6oDMWAYqAQH8kMw4F1kiUhDH5CSEDYBQTMoZeX3ET9p7t7Wtaeiw65GaZu28Hd7aQGvnaem6D9zS1na2tV9msK6jpquoHQI1fd_YctoNjkaNNdIKi2b03nbrU3JUmybg2W-fk5fbm-flfbJ6untYXq-SUrB8SCrO8lzwugCULG4wjjUahQUrTGl4VWKZpgWwyhgOos4WYpEpxXPMTCGrVMzJxaQbjbyPGAa9caPv4knNFcuV4BmTEcUnVOldCB5r3fv4sd9pBnqfop5S1DFF_ZOiVpEkJlLo947Q_0n_w_oGlf12eg</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Kesornprom, Suparat</creator><creator>Cholamjiak, Prasit</creator><creator>Park, Choonkil</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20221001</creationdate><title>New proximal type algorithms for convex minimization and its application to image deblurring</title><author>Kesornprom, Suparat ; Cholamjiak, Prasit ; Park, Choonkil</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-d219932fb0e41319e199fea7eb1baca2dcec66b01daa203f853587729e8ab4d63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Applications of Mathematics</topic><topic>Applied physics</topic><topic>Computational mathematics</topic><topic>Computational Mathematics and Numerical Analysis</topic><topic>Hilbert space</topic><topic>Mathematical Applications in Computer Science</topic><topic>Mathematical Applications in the Physical Sciences</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kesornprom, Suparat</creatorcontrib><creatorcontrib>Cholamjiak, Prasit</creatorcontrib><creatorcontrib>Park, Choonkil</creatorcontrib><collection>CrossRef</collection><jtitle>Computational & applied mathematics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kesornprom, Suparat</au><au>Cholamjiak, Prasit</au><au>Park, Choonkil</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>New proximal type algorithms for convex minimization and its application to image deblurring</atitle><jtitle>Computational & applied mathematics</jtitle><stitle>Comp. Appl. Math</stitle><date>2022-10-01</date><risdate>2022</risdate><volume>41</volume><issue>7</issue><artnum>333</artnum><issn>2238-3603</issn><eissn>1807-0302</eissn><abstract>In this work, we are interested in solving a convex minimization problem in real Hilbert spaces. We propose a new modified proximal algorithm using the inertial extrapolation and the linesearch technique. Its weak convergence theorems are established under mild conditions. Numerical experiments are presented to illustrate the performance of the proposed algorithm in image deblurring.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s40314-022-02042-7</doi></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2238-3603 |
| ispartof | Computational & applied mathematics, 2022-10, Vol.41 (7), Article 333 |
| issn | 2238-3603 1807-0302 |
| language | eng |
| recordid | cdi_proquest_journals_2719732814 |
| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Applications of Mathematics Applied physics Computational mathematics Computational Mathematics and Numerical Analysis Hilbert space Mathematical Applications in Computer Science Mathematical Applications in the Physical Sciences Mathematics Mathematics and Statistics Optimization |
| title | New proximal type algorithms for convex minimization and its application to image deblurring |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T15%3A22%3A45IST&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=New%20proximal%20type%20algorithms%20for%20convex%20minimization%20and%20its%20application%20to%20image%20deblurring&rft.jtitle=Computational%20&%20applied%20mathematics&rft.au=Kesornprom,%20Suparat&rft.date=2022-10-01&rft.volume=41&rft.issue=7&rft.artnum=333&rft.issn=2238-3603&rft.eissn=1807-0302&rft_id=info:doi/10.1007/s40314-022-02042-7&rft_dat=%3Cproquest_cross%3E2719732814%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2719732814&rft_id=info:pmid/&rfr_iscdi=true |