{"id":"https://openalex.org/W2474669789","doi":"https://doi.org/10.1137/140966587","title":"IMRO: A Proximal Quasi-Newton Method for Solving $\\ell_1$-Regularized Least Squares Problems","display_name":"IMRO: A Proximal Quasi-Newton Method for Solving $\\ell_1$-Regularized Least Squares Problems","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2474669789","doi":"https://doi.org/10.1137/140966587","mag":"2474669789"},"language":"en","primary_location":{"id":"doi:10.1137/140966587","is_oa":false,"landing_page_url":"https://doi.org/10.1137/140966587","pdf_url":null,"source":{"id":"https://openalex.org/S928796702","display_name":"SIAM Journal on Optimization","issn_l":"1052-6234","issn":["1052-6234","1095-7189"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Optimization","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1401.4220","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sahar Karimi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sahar Karimi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Stephen Vavasis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stephen Vavasis","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6616,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.8756898,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"27","issue":"2","first_page":"583","last_page":"615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.5645999908447266,"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":0.5645999908447266,"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":0.41920000314712524,"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":0.005499999970197678,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hessian-matrix","display_name":"Hessian matrix","score":0.717199981212616},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.5884000062942505},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5131000280380249},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5117999911308289},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.44519999623298645},{"id":"https://openalex.org/keywords/proximal-gradient-methods","display_name":"Proximal Gradient Methods","score":0.40299999713897705},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.3725000023841858},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.3560999929904938}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7912999987602234},{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.717199981212616},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.5884000062942505},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5131000280380249},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5117999911308289},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5110999941825867},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4348999857902527},{"id":"https://openalex.org/C10494615","wikidata":"https://www.wikidata.org/wiki/Q17086765","display_name":"Proximal Gradient Methods","level":4,"score":0.40299999713897705},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3725000023841858},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3714999854564667},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.3379000127315521},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C48753275","wikidata":"https://www.wikidata.org/wiki/Q11216","display_name":"Numerical analysis","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C126090379","wikidata":"https://www.wikidata.org/wiki/Q6094424","display_name":"Iteratively reweighted least squares","level":4,"score":0.33169999718666077},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.28119999170303345},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.27639999985694885}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1137/140966587","is_oa":false,"landing_page_url":"https://doi.org/10.1137/140966587","pdf_url":null,"source":{"id":"https://openalex.org/S928796702","display_name":"SIAM Journal on Optimization","issn_l":"1052-6234","issn":["1052-6234","1095-7189"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Optimization","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1401.4220","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1401.4220","pdf_url":"https://arxiv.org/pdf/1401.4220","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1401.4220","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1401.4220","pdf_url":"https://arxiv.org/pdf/1401.4220","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1967020502","https://openalex.org/W1973734200","https://openalex.org/W1986737463","https://openalex.org/W1996287810","https://openalex.org/W2000218110","https://openalex.org/W2000359198","https://openalex.org/W2006262045","https://openalex.org/W2009702064","https://openalex.org/W2010286849","https://openalex.org/W2022611944","https://openalex.org/W2023722580","https://openalex.org/W2028349405","https://openalex.org/W2028912194","https://openalex.org/W2048742402","https://openalex.org/W2076605490","https://openalex.org/W2083042020","https://openalex.org/W2100556411","https://openalex.org/W2104266187","https://openalex.org/W2108005121","https://openalex.org/W2109449402","https://openalex.org/W2110505738","https://openalex.org/W2119058682","https://openalex.org/W2119667497","https://openalex.org/W2126607811","https://openalex.org/W2129638195","https://openalex.org/W2145096794","https://openalex.org/W2151568819","https://openalex.org/W2167732364","https://openalex.org/W2963173886","https://openalex.org/W3022380717","https://openalex.org/W4250955649"],"related_works":[],"abstract_inverted_index":{"We":[0,79],"present":[1],"a":[2,82],"proximal":[3,35],"quasi-Newton":[4],"method":[5],"in":[6,23,47,53],"which":[7],"the":[8,11,14,34,65],"approximation":[9],"of":[10,17,77,87],"Hessian":[12],"has":[13],"special":[15],"format":[16],"\u201cidentity":[18],"minus":[19],"rank":[20],"one\u201d":[21],"(IMRO)":[22],"each":[24],"iteration.":[25],"The":[26,37],"proposed":[27,66],"structure":[28],"enables":[29],"us":[30],"to":[31,41],"effectively":[32],"recover":[33],"point.":[36],"algorithm":[38],"is":[39],"applied":[40],"$\\ell_1$-regularized":[42],"least":[43],"squares":[44],"problems":[45],"arising":[46],"many":[48],"applications":[49],"including":[50],"sparse":[51],"recovery":[52],"compressive":[54],"sensing,":[55],"machine":[56],"learning,":[57],"and":[58],"statistics.":[59],"Our":[60],"numerical":[61],"experiment":[62],"suggests":[63],"that":[64,90],"technique":[67],"competes":[68],"favorably":[69],"with":[70],"other":[71],"state-of-the-art":[72],"solvers":[73],"for":[74,85],"this":[75],"class":[76],"problems.":[78],"also":[80],"provide":[81],"complexity":[83],"analysis":[84],"variants":[86],"IMRO,":[88],"showing":[89],"it":[91],"matches":[92],"known":[93],"best":[94],"bounds.":[95]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2016-07-22T00:00:00"}
