{"id":"https://openalex.org/W4392461759","doi":"https://doi.org/10.3390/sym16030303","title":"Semi-Proximal ADMM for Primal and Dual Robust Low-Rank Matrix Restoration from Corrupted Observations","display_name":"Semi-Proximal ADMM for Primal and Dual Robust Low-Rank Matrix Restoration from Corrupted Observations","publication_year":2024,"publication_date":"2024-03-05","ids":{"openalex":"https://openalex.org/W4392461759","doi":"https://doi.org/10.3390/sym16030303"},"language":"en","primary_location":{"id":"doi:10.3390/sym16030303","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym16030303","pdf_url":"https://www.mdpi.com/2073-8994/16/3/303/pdf?version=1709613037","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/16/3/303/pdf?version=1709613037","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021349926","display_name":"Weiwei Ding","orcid":"https://orcid.org/0000-0002-9944-2356"},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Ding","raw_affiliation_strings":["School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China","institution_ids":["https://openalex.org/I167383011"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120393203","display_name":"Youlin Shang","orcid":"https://orcid.org/0000-0002-9859-4040"},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youlin Shang","raw_affiliation_strings":["School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China","institution_ids":["https://openalex.org/I167383011"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101725733","display_name":"Zheng\u2010Fen Jin","orcid":"https://orcid.org/0000-0001-9501-1500"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]},{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhengfen Jin","raw_affiliation_strings":["LMIB of the Ministry of Education, School of Mathematical Sciences, Beihang University, Beijing 100191, China","School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LMIB of the Ministry of Education, School of Mathematical Sciences, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China","institution_ids":["https://openalex.org/I167383011"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113185194","display_name":"Yibao Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibao Fan","raw_affiliation_strings":["School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China","institution_ids":["https://openalex.org/I167383011"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101725733"],"corresponding_institution_ids":["https://openalex.org/I167383011","https://openalex.org/I82880672"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03152007,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":"3","first_page":"303","last_page":"303"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.6670328378677368},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6124393939971924},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5943436622619629},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.573334813117981},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5173351168632507},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.5167545676231384},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.5001354217529297},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.493783563375473},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44641363620758057},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.4446978271007538},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4397057592868805},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35883891582489014}],"concepts":[{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.6670328378677368},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6124393939971924},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5943436622619629},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.573334813117981},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5173351168632507},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5167545676231384},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.5001354217529297},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.493783563375473},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44641363620758057},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.4446978271007538},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4397057592868805},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35883891582489014},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/sym16030303","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym16030303","pdf_url":"https://www.mdpi.com/2073-8994/16/3/303/pdf?version=1709613037","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9deb7fec901f488291a3df162a34bc8d","is_oa":false,"landing_page_url":"https://doaj.org/article/9deb7fec901f488291a3df162a34bc8d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 16, Iss 3, p 303 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/sym16030303","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym16030303","pdf_url":"https://www.mdpi.com/2073-8994/16/3/303/pdf?version=1709613037","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5}],"awards":[{"id":"https://openalex.org/G2363803437","display_name":null,"funder_award_id":"12131004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2416943596","display_name":null,"funder_award_id":"12071112","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6542365803","display_name":null,"funder_award_id":"12101195","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8634726384","display_name":null,"funder_award_id":"61972133","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392461759.pdf","grobid_xml":"https://content.openalex.org/works/W4392461759.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W282757658","https://openalex.org/W1967073510","https://openalex.org/W1999905147","https://openalex.org/W2007437396","https://openalex.org/W2011359124","https://openalex.org/W2032363154","https://openalex.org/W2045079045","https://openalex.org/W2053332507","https://openalex.org/W2056145308","https://openalex.org/W2066459185","https://openalex.org/W2072536590","https://openalex.org/W2074471762","https://openalex.org/W2079558799","https://openalex.org/W2079654589","https://openalex.org/W2081933987","https://openalex.org/W2085013758","https://openalex.org/W2103972604","https://openalex.org/W2109240917","https://openalex.org/W2113075591","https://openalex.org/W2134332047","https://openalex.org/W2144730813","https://openalex.org/W2145962650","https://openalex.org/W2161374719","https://openalex.org/W2339666411","https://openalex.org/W2400319222","https://openalex.org/W2598601363","https://openalex.org/W2611328865","https://openalex.org/W2955107073","https://openalex.org/W2963862965","https://openalex.org/W2963881204","https://openalex.org/W2964338073","https://openalex.org/W4206519735","https://openalex.org/W4244393449","https://openalex.org/W4249667877","https://openalex.org/W4381512658","https://openalex.org/W4390853492","https://openalex.org/W6677964101","https://openalex.org/W6690347758","https://openalex.org/W6690352536","https://openalex.org/W6703697878"],"related_works":["https://openalex.org/W2594023433","https://openalex.org/W2106005123","https://openalex.org/W1969698720","https://openalex.org/W2788826952","https://openalex.org/W4302315572","https://openalex.org/W3038662035","https://openalex.org/W2964006653","https://openalex.org/W2289262545","https://openalex.org/W2585415590","https://openalex.org/W4300005947"],"abstract_inverted_index":{"The":[0],"matrix":[1],"nuclear":[2,36,56],"norm":[3,37,57],"minimization":[4,38,58],"problem":[5,59,136],"has":[6,65],"been":[7,66],"extensively":[8],"researched":[9],"in":[10,18,68],"recent":[11],"years":[12],"due":[13],"to":[14,132,155],"its":[15,134],"widespread":[16],"applications":[17],"control":[19],"design,":[20],"signal":[21],"and":[22,30,116,163],"image":[23],"restoration,":[24],"machine":[25],"learning,":[26],"big":[27],"data":[28],"problems,":[29],"more.":[31],"One":[32],"popular":[33],"model":[34,162],"is":[35,46],"with":[39,52,60,74,77],"the":[40,61,75,93,96,100,120,140,144,157,160,164,167],"l2-norm":[41],"fidelity":[42,63],"term,":[43],"but":[44,82],"it":[45,90,106,118],"only":[47,79],"effective":[48],"for":[49,95,143],"those":[50],"problems":[51,76],"Gaussian":[53,84],"noise.":[54],"A":[55],"l1-norm":[62],"term":[64],"studied":[67],"this":[69],"paper,":[70],"which":[71],"can":[72],"deal":[73],"not":[78],"non-Gaussian":[80],"noise":[81,85],"also":[83,91],"or":[86],"their":[87],"mixture.":[88],"Moreover,":[89],"keeps":[92],"efficiency":[94],"noiseless":[97],"case.":[98],"Given":[99],"nonsmooth":[101],"proposed":[102,161],"model,":[103],"we":[104,129],"transform":[105],"into":[107],"a":[108],"separated":[109],"form":[110],"by":[111,119,137],"introducing":[112],"an":[113],"auxiliary":[114],"variable":[115],"solve":[117,133],"semi-proximal":[121],"alternating":[122],"direction":[123],"method":[124],"of":[125,159,166],"multipliers":[126],"(sPADMM).":[127],"Furthermore,":[128],"first":[130],"attempt":[131],"dual":[135],"sPADMM.":[138],"Then,":[139],"convergence":[141],"guarantees":[142],"aforementioned":[145],"algorithms":[146],"are":[147,153],"given.":[148],"Finally,":[149],"some":[150],"numerical":[151],"studies":[152],"dedicated":[154],"show":[156],"robustness":[158],"effectiveness":[165],"presented":[168],"algorithms.":[169]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
