{"id":"https://openalex.org/W2100864539","doi":"https://doi.org/10.1109/tsp.2011.2161292","title":"The In-Crowd Algorithm for Fast Basis Pursuit Denoising","display_name":"The In-Crowd Algorithm for Fast Basis Pursuit Denoising","publication_year":2011,"publication_date":"2011-07-07","ids":{"openalex":"https://openalex.org/W2100864539","doi":"https://doi.org/10.1109/tsp.2011.2161292","mag":"2100864539"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2011.2161292","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2011.2161292","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-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/A5071073936","display_name":"P. Gill","orcid":"https://orcid.org/0000-0002-2920-888X"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Patrick R. Gill","raw_affiliation_strings":["School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA","Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA#TAB#","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034208427","display_name":"Albert Wang","orcid":"https://orcid.org/0000-0002-0581-5765"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Albert Wang","raw_affiliation_strings":["School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA","Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA#TAB#","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009855846","display_name":"Alyosha Molnar","orcid":"https://orcid.org/0000-0001-7674-1874"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alyosha Molnar","raw_affiliation_strings":["School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA","Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA#TAB#","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071073936"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":7.5795,"has_fulltext":false,"cited_by_count":144,"citation_normalized_percentile":{"value":0.9779693,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"59","issue":"10","first_page":"4595","last_page":"4605"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9987000226974487,"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/basis-pursuit","display_name":"Basis pursuit","score":0.6534560918807983},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6239413619041443},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.5395471453666687},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.49898862838745117},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49208658933639526},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.45043444633483887},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44822436571121216},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41119977831840515},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3703007996082306},{"id":"https://openalex.org/keywords/matching-pursuit","display_name":"Matching pursuit","score":0.2977107763290405},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.22597944736480713},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.0821719765663147}],"concepts":[{"id":"https://openalex.org/C99217422","wikidata":"https://www.wikidata.org/wiki/Q4867576","display_name":"Basis pursuit","level":4,"score":0.6534560918807983},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6239413619041443},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.5395471453666687},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.49898862838745117},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49208658933639526},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.45043444633483887},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44822436571121216},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41119977831840515},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3703007996082306},{"id":"https://openalex.org/C156872377","wikidata":"https://www.wikidata.org/wiki/Q6786281","display_name":"Matching pursuit","level":3,"score":0.2977107763290405},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.22597944736480713},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0821719765663147},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2011.2161292","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2011.2161292","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W291003596","https://openalex.org/W560409768","https://openalex.org/W1601798363","https://openalex.org/W1640678802","https://openalex.org/W1965494568","https://openalex.org/W1976709621","https://openalex.org/W2015292809","https://openalex.org/W2021473911","https://openalex.org/W2023722580","https://openalex.org/W2029816571","https://openalex.org/W2047820699","https://openalex.org/W2049157665","https://openalex.org/W2056775112","https://openalex.org/W2063978378","https://openalex.org/W2065169398","https://openalex.org/W2069959554","https://openalex.org/W2074376560","https://openalex.org/W2078204800","https://openalex.org/W2080709627","https://openalex.org/W2082949838","https://openalex.org/W2083042020","https://openalex.org/W2105464873","https://openalex.org/W2109449402","https://openalex.org/W2115314650","https://openalex.org/W2120164586","https://openalex.org/W2127271355","https://openalex.org/W2135046866","https://openalex.org/W2139904615","https://openalex.org/W2151608460","https://openalex.org/W2153663612","https://openalex.org/W2160979406","https://openalex.org/W2171534739","https://openalex.org/W2289917018","https://openalex.org/W2296616510","https://openalex.org/W3022380717","https://openalex.org/W3124114587","https://openalex.org/W4250955649","https://openalex.org/W4253840677","https://openalex.org/W6677518489","https://openalex.org/W7046143278"],"related_works":["https://openalex.org/W2056822213","https://openalex.org/W2039479881","https://openalex.org/W2140609183","https://openalex.org/W3028969993","https://openalex.org/W2950144003","https://openalex.org/W4298044324","https://openalex.org/W2542178727","https://openalex.org/W2593618608","https://openalex.org/W2020154758","https://openalex.org/W2368432573"],"abstract_inverted_index":{"We":[0,48,68,110],"introduce":[1],"a":[2,22],"fast":[3],"method,":[4],"the":[5,10,30,34,53,59,72,78,112],"\u201cin-crowd\u201d":[6],"algorithm,":[7],"for":[8,90],"finding":[9],"exact":[11],"solution":[12,36,62],"to":[13,27,58,63],"basis":[14,64],"pursuit":[15,65],"denoising":[16,66],"problems.":[17,47,67],"The":[18],"in-crowd":[19,54,73,113],"algorithm":[20,55,74],"discovers":[21],"sequence":[23],"of":[24,33,37],"subspaces":[25],"guaranteed":[26],"arrive":[28],"at":[29],"support":[31],"set":[32],"final":[35],"<i":[38,91],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[39,42,92,95],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">l</i>":[40,93],"<sub":[41,94],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[43,96],"-regularized":[44],"least":[45],"squares":[46],"provide":[49],"theorems":[50],"showing":[51],"that":[52,71],"always":[56],"converges":[57],"correct":[60],"global":[61],"show":[69],"empirically":[70],"is":[75],"faster":[76],"than":[77,107],"best":[79],"alternative":[80],"solvers":[81],"(homotopy,":[82],"fixed":[83],"point":[84],"continuation":[85],"and":[86,101,118,128],"spectral":[87],"projected":[88],"gradient":[89],"minimization)":[97],"on":[98,124],"certain":[99],"well-":[100],"ill-conditioned":[102],"sparse":[103],"problems":[104],"with":[105],"more":[106,125],"1000":[108],"unknowns.":[109],"compare":[111],"algorithm's":[114],"performance":[115,123],"in":[116],"high-":[117],"low-noise":[119],"regimes,":[120],"demonstrate":[121],"its":[122,132],"dense":[126],"problems,":[127],"derive":[129],"expressions":[130],"giving":[131],"computational":[133],"complexity.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":14},{"year":2017,"cited_by_count":19},{"year":2016,"cited_by_count":14},{"year":2015,"cited_by_count":15},{"year":2014,"cited_by_count":10},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}