{"id":"https://openalex.org/W4416799401","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11249336","title":"Large Sparse Covariance Matrix Estimation via Dual Proximal Gradient Method","display_name":"Large Sparse Covariance Matrix Estimation via Dual Proximal Gradient Method","publication_year":2025,"publication_date":"2025-10-22","ids":{"openalex":"https://openalex.org/W4416799401","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11249336"},"language":null,"primary_location":{"id":"doi:10.1109/apsipaasc65261.2025.11249336","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11249336","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015581947","display_name":"Fengpei Li","orcid":"https://orcid.org/0000-0003-0904-4723"},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengpei Li","raw_affiliation_strings":["ShanghaiTech University,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ShanghaiTech University,Shanghai,China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100675627","display_name":"Ziping Zhao","orcid":"https://orcid.org/0000-0001-7431-6444"},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziping Zhao","raw_affiliation_strings":["ShanghaiTech University,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ShanghaiTech University,Shanghai,China","institution_ids":["https://openalex.org/I30809798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I30809798"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17296599,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1247","last_page":"1252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.5746999979019165,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.5746999979019165,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.3206000030040741,"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/T12303","display_name":"Tensor decomposition and applications","score":0.02370000071823597,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.692799985408783},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.682200014591217},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6571000218391418},{"id":"https://openalex.org/keywords/positive-definiteness","display_name":"Positive definiteness","score":0.6074000000953674},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.5656999945640564},{"id":"https://openalex.org/keywords/positive-definite-matrix","display_name":"Positive-definite matrix","score":0.51910001039505},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4490000009536743},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.44609999656677246}],"concepts":[{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.692799985408783},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.682200014591217},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6571000218391418},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6498000025749207},{"id":"https://openalex.org/C2778265155","wikidata":"https://www.wikidata.org/wiki/Q7233276","display_name":"Positive definiteness","level":4,"score":0.6074000000953674},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5917999744415283},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.5656999945640564},{"id":"https://openalex.org/C49712288","wikidata":"https://www.wikidata.org/wiki/Q77601250","display_name":"Positive-definite matrix","level":3,"score":0.51910001039505},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4490000009536743},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4212999939918518},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.41940000653266907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4147999882698059},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C10494615","wikidata":"https://www.wikidata.org/wiki/Q17086765","display_name":"Proximal Gradient Methods","level":4,"score":0.31200000643730164},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C181243257","wikidata":"https://www.wikidata.org/wiki/Q1693522","display_name":"Sample mean and sample covariance","level":3,"score":0.30390000343322754},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.2953000068664551},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2912999987602234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.273499995470047},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C205555498","wikidata":"https://www.wikidata.org/wiki/Q505588","display_name":"CMA-ES","level":4,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc65261.2025.11249336","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11249336","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W216325278","https://openalex.org/W1990512452","https://openalex.org/W2017911295","https://openalex.org/W2040373108","https://openalex.org/W2045079045","https://openalex.org/W2057535756","https://openalex.org/W2057624533","https://openalex.org/W2097515331","https://openalex.org/W2118800758","https://openalex.org/W2119963163","https://openalex.org/W2126051190","https://openalex.org/W2130351130","https://openalex.org/W2135046866","https://openalex.org/W2165408259","https://openalex.org/W2582533304","https://openalex.org/W2787894218","https://openalex.org/W2964003745","https://openalex.org/W2964044082","https://openalex.org/W4206406546","https://openalex.org/W4238253035","https://openalex.org/W4292363360","https://openalex.org/W4385694596","https://openalex.org/W4386590847"],"related_works":[],"abstract_inverted_index":{"Covariance":[0],"matrix":[1],"estimation":[2,43],"in":[3,10,75],"high":[4],"dimensions":[5],"is":[6,89],"a":[7,71],"central":[8],"problem":[9],"data":[11],"science,":[12],"signal":[13],"processing,":[14],"and":[15,31,47,57,88],"machine":[16],"learning,":[17],"yet":[18],"it":[19],"remains":[20],"challenging":[21],"due":[22],"to":[23,26],"the":[24,39,63,76,86,97,100],"need":[25],"ensure":[27],"both":[28],"statistical":[29],"accuracy":[30],"computational":[32],"efficiency.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37],"revisit":[38],"positive":[40,83],"definite":[41],"covariance":[42,111],"framework":[44],"of":[45,99],"[1]":[46],"develop":[48],"an":[49],"efficient":[50],"primal-dual":[51],"algorithm":[52,80,102],"that":[53,62],"alternately":[54],"updates":[55],"primal":[56],"dual":[58,77],"variables.":[59],"We":[60],"demonstrate":[61],"proposed":[64,101],"method":[65],"can":[66],"be":[67],"interpreted":[68],"equivalently":[69],"as":[70],"proximal":[72],"gradient":[73],"scheme":[74],"domain.":[78],"The":[79],"inherently":[81],"preserves":[82],"definiteness":[84],"throughout":[85],"iterations":[87],"provably":[90],"globally":[91],"linearly":[92],"convergent.":[93],"Numerical":[94],"experiments":[95],"establish":[96],"superiority":[98],"over":[103],"state-of-the-art":[104],"methods,":[105],"highlighting":[106],"its":[107],"effectiveness":[108],"for":[109],"high-dimensional":[110],"estimation.":[112]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-11-28T00:00:00"}
