{"id":"https://openalex.org/W4391607468","doi":"https://doi.org/10.1186/s13634-024-01114-6","title":"Lp quasi-norm minimization: algorithm and applications","display_name":"Lp quasi-norm minimization: algorithm and applications","publication_year":2024,"publication_date":"2024-02-07","ids":{"openalex":"https://openalex.org/W4391607468","doi":"https://doi.org/10.1186/s13634-024-01114-6"},"language":"en","primary_location":{"id":"doi:10.1186/s13634-024-01114-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-024-01114-6","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01114-6","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1110-8657","issn":["1110-8657","1687-0433","1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01114-6","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057473024","display_name":"Omar M. Sleem","orcid":"https://orcid.org/0000-0002-4994-0808"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Omar M. Sleem","raw_affiliation_strings":["Department of Electrical Engineering, Pennsylvania State University, State College, PA, 16802, USA"],"raw_orcid":"https://orcid.org/0000-0002-4994-0808","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Pennsylvania State University, State College, PA, 16802, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019223385","display_name":"M. E. Ashour","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087596","display_name":"Qualcomm (United States)","ror":"https://ror.org/002zrf773","country_code":"US","type":"company","lineage":["https://openalex.org/I4210087596"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. E. Ashour","raw_affiliation_strings":["Wireless R&D Department, Qualcomm Technologies, Inc, San Diego, CA, 92121, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wireless R&D Department, Qualcomm Technologies, Inc, San Diego, CA, 92121, USA","institution_ids":["https://openalex.org/I4210087596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010338155","display_name":"N. S. Aybat","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"N. S. Aybat","raw_affiliation_strings":["Department of Industrial and Manufacturing Engineering, Pennsylvania State University, State College, PA, 16802, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Manufacturing Engineering, Pennsylvania State University, State College, PA, 16802, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035668981","display_name":"Constantino Lagoa","orcid":"https://orcid.org/0000-0001-6871-3240"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Constantino M. Lagoa","raw_affiliation_strings":["Department of Electrical Engineering, Pennsylvania State University, State College, PA, 16802, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Pennsylvania State University, State College, PA, 16802, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5057473024"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":{"value":1790,"currency":"USD","value_usd":1790},"apc_paid":{"value":1790,"currency":"USD","value_usd":1790},"fwci":1.0109,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.66363494,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"2024","issue":"1","first_page":null,"last_page":null},"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/T10963","display_name":"Advanced Optimization Algorithms Research","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2612","display_name":"Numerical Analysis"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.7920961976051331},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5264005661010742},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.399814635515213},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33056026697158813}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.7920961976051331},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5264005661010742},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.399814635515213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33056026697158813}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13634-024-01114-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-024-01114-6","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01114-6","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1110-8657","issn":["1110-8657","1687-0433","1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d1a35b7bfff34e81892fda974729f89c","is_oa":true,"landing_page_url":"https://doaj.org/article/d1a35b7bfff34e81892fda974729f89c","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2024, Iss 1, Pp 1-28 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13634-024-01114-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-024-01114-6","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-024-01114-6","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1110-8657","issn":["1110-8657","1687-0433","1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G443048570","display_name":"Collaborative Research: Data driven control of switched systems with applications to human behavioral modification","funder_award_id":"1808266","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4635542383","display_name":"Phase 1 clinical trial to develop a personalized adaptive text message intervention using control systems engineering tools to increase physical activity in early adulthood","funder_award_id":"5r01hl142732-04","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6629274746","display_name":null,"funder_award_id":"R01 HL142732","funder_id":"https://openalex.org/F4320306080","funder_display_name":"Foundation for the National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306080","display_name":"Foundation for the National Institutes of Health","ror":"https://ror.org/00k86s890"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391607468.pdf","grobid_xml":"https://content.openalex.org/works/W4391607468.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1515750879","https://openalex.org/W1568307856","https://openalex.org/W1632601927","https://openalex.org/W1963665337","https://openalex.org/W1966096622","https://openalex.org/W1967138577","https://openalex.org/W1976709621","https://openalex.org/W1982452826","https://openalex.org/W2004544971","https://openalex.org/W2012961725","https://openalex.org/W2015058428","https://openalex.org/W2016384870","https://openalex.org/W2019479063","https://openalex.org/W2027982384","https://openalex.org/W2031906930","https://openalex.org/W2031993275","https://openalex.org/W2037964547","https://openalex.org/W2039050532","https://openalex.org/W2050968963","https://openalex.org/W2051306216","https://openalex.org/W2056145308","https://openalex.org/W2056201402","https://openalex.org/W2056532343","https://openalex.org/W2074471762","https://openalex.org/W2084983808","https://openalex.org/W2093347109","https://openalex.org/W2100556411","https://openalex.org/W2106568288","https://openalex.org/W2114748345","https://openalex.org/W2116148865","https://openalex.org/W2118297240","https://openalex.org/W2129131372","https://openalex.org/W2129732816","https://openalex.org/W2129812935","https://openalex.org/W2145096794","https://openalex.org/W2148232185","https://openalex.org/W2151693816","https://openalex.org/W2159405849","https://openalex.org/W2161374719","https://openalex.org/W2163232332","https://openalex.org/W2163293561","https://openalex.org/W2164278908","https://openalex.org/W2168745297","https://openalex.org/W2626376214","https://openalex.org/W2731651626","https://openalex.org/W2760945190","https://openalex.org/W2801029166","https://openalex.org/W2912522929","https://openalex.org/W2962853966","https://openalex.org/W3092255678","https://openalex.org/W3098600672","https://openalex.org/W3105393233","https://openalex.org/W3200762276","https://openalex.org/W4244393449","https://openalex.org/W4250955649","https://openalex.org/W4283662786","https://openalex.org/W4290704785","https://openalex.org/W4300819821"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Abstract":[0],"Sparsity":[1],"finds":[2],"applications":[3,176],"in":[4,100,213],"diverse":[5],"areas":[6],"such":[7],"as":[8,92],"statistics,":[9],"machine":[10],"learning,":[11],"and":[12,27,163,188],"signal":[13,184],"processing.":[14],"Computations":[15],"over":[16],"sparse":[17,39,183],"structures":[18],"are":[19],"less":[20,29],"complex":[21],"compared":[22,202],"to":[23,74,94,117,127,203],"their":[24],"dense":[25],"counterparts":[26],"need":[28],"storage.":[30],"This":[31],"paper":[32],"proposes":[33],"a":[34,88,96,146,153],"heuristic":[35],"method":[36],"for":[37,65,84],"retrieving":[38],"approximate":[40],"solutions":[41],"of":[42,87,103,136],"optimization":[43],"problems":[44],"via":[45],"minimizing":[46,66],"the":[47,67,85,101,119,133,159,166,178,194,199],"$$\\ell":[48,68,104,120,205],"_{p}$$":[49,69,121,206],"\u2113":[50,70,106,122,207],"p":[51,57,71,123,208],"quasi-norm,":[52],"where":[53,177],"$$0":[54],"0":[55],"<":[56,58],"1":[59,107],".":[60],"An":[61],"iterative":[62],"two-block":[63],"algorithm":[64,81,149,180,201],"quasi-norm":[72,124,209],"subject":[73,126],"convex":[75,129,160],"constraints":[76,130,137],"is":[77,150],"proposed.":[78,151],"The":[79,110,191],"proposed":[80,179,200],"requires":[82],"solving":[83],"roots":[86],"scalar":[89],"degree":[90],"polynomial":[91],"opposed":[93],"applying":[95],"soft":[97],"thresholding":[98],"operator":[99],"case":[102,135],"_{1}$$":[105],"norm":[108],"minimization.":[109],"algorithm\u2019s":[111,167],"merit":[112],"relies":[113],"on":[114],"its":[115,171],"ability":[116],"solve":[118],"minimization":[125],"any":[128],"set.":[131],"For":[132],"specific":[134],"defined":[138],"by":[139,198],"differentiable":[140],"functions":[141],"with":[142],"Lipschitz":[143],"continuous":[144],"gradient,":[145],"second,":[147],"faster":[148],"Using":[152],"proximal":[154],"gradient":[155],"step,":[156],"we":[157],"mitigate":[158],"projection":[161],"step":[162],"hence":[164],"enhance":[165],"speed":[168],"while":[169],"proving":[170],"convergence.":[172],"We":[173],"present":[174],"various":[175],"excels,":[181],"namely,":[182],"reconstruction,":[185],"system":[186],"identification,":[187],"matrix":[189],"completion.":[190],"results":[192],"demonstrate":[193],"significant":[195],"gains":[196],"obtained":[197],"other":[204],"based":[210],"methods":[211],"presented":[212],"previous":[214],"literature.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":3}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}