{"id":"https://openalex.org/W3083116065","doi":"https://doi.org/10.1137/20m1364862","title":"Communication-Efficient Distributed Eigenspace Estimation","display_name":"Communication-Efficient Distributed Eigenspace Estimation","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3083116065","doi":"https://doi.org/10.1137/20m1364862","mag":"3083116065"},"language":"en","primary_location":{"id":"doi:10.1137/20m1364862","is_oa":true,"landing_page_url":"https://doi.org/10.1137/20m1364862","pdf_url":"https://epubs.siam.org/doi/pdf/10.1137/20M1364862","source":{"id":"https://openalex.org/S4210229561","display_name":"SIAM Journal on Mathematics of Data Science","issn_l":"2577-0187","issn":["2577-0187"],"is_oa":true,"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 Mathematics of Data Science","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://epubs.siam.org/doi/pdf/10.1137/20M1364862","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027183969","display_name":"Vasileios Charisopoulos","orcid":"https://orcid.org/0000-0002-3717-0236"},"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":"Vasileios Charisopoulos","raw_affiliation_strings":["cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"cornell University","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009319874","display_name":"Austin R. Benson","orcid":"https://orcid.org/0000-0001-6110-1583"},"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":"Austin R. Benson","raw_affiliation_strings":["cornell University"],"raw_orcid":"https://orcid.org/0000-0001-6110-1583","affiliations":[{"raw_affiliation_string":"cornell University","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015625725","display_name":"Anil Damle","orcid":"https://orcid.org/0000-0002-1711-128X"},"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":"Anil Damle","raw_affiliation_strings":["cornell University"],"raw_orcid":"https://orcid.org/0000-0002-1711-128X","affiliations":[{"raw_affiliation_string":"cornell University","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027183969"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.1781,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.4205276,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":"4","first_page":"1067","last_page":"1092"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9965000152587891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6265060901641846},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.6064385771751404},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.572592556476593},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5469777584075928},{"id":"https://openalex.org/keywords/orthonormal-basis","display_name":"Orthonormal basis","score":0.5312464237213135},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5276797413825989},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.4882395565509796},{"id":"https://openalex.org/keywords/invariant-subspace","display_name":"Invariant subspace","score":0.46552011370658875},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.46153175830841064},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3731960654258728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2306881546974182},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2285882532596588},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.15932872891426086}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6265060901641846},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6064385771751404},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.572592556476593},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5469777584075928},{"id":"https://openalex.org/C5806529","wikidata":"https://www.wikidata.org/wiki/Q2365325","display_name":"Orthonormal basis","level":2,"score":0.5312464237213135},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5276797413825989},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.4882395565509796},{"id":"https://openalex.org/C2777059694","wikidata":"https://www.wikidata.org/wiki/Q2706744","display_name":"Invariant subspace","level":3,"score":0.46552011370658875},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.46153175830841064},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3731960654258728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2306881546974182},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2285882532596588},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.15932872891426086},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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":4,"locations":[{"id":"doi:10.1137/20m1364862","is_oa":true,"landing_page_url":"https://doi.org/10.1137/20m1364862","pdf_url":"https://epubs.siam.org/doi/pdf/10.1137/20M1364862","source":{"id":"https://openalex.org/S4210229561","display_name":"SIAM Journal on Mathematics of Data Science","issn_l":"2577-0187","issn":["2577-0187"],"is_oa":true,"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 Mathematics of Data Science","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2009.02436","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.02436","pdf_url":"https://arxiv.org/pdf/2009.02436","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":"","raw_type":null},{"id":"mag:3083116065","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2009.02436.pdf","pdf_url":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2009.02436","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2009.02436","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1137/20m1364862","is_oa":true,"landing_page_url":"https://doi.org/10.1137/20m1364862","pdf_url":"https://epubs.siam.org/doi/pdf/10.1137/20M1364862","source":{"id":"https://openalex.org/S4210229561","display_name":"SIAM Journal on Mathematics of Data Science","issn_l":"2577-0187","issn":["2577-0187"],"is_oa":true,"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 Mathematics of Data Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2687707333","display_name":null,"funder_award_id":"DMS-1830274","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4553646684","display_name":null,"funder_award_id":"W911NF19-1-0057","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320307774","display_name":"JPMorgan Chase and Company","ror":"https://ror.org/01x3kkr08"},{"id":"https://openalex.org/F4320333591","display_name":"Multidisciplinary University Research Initiative","ror":null},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3083116065.pdf","grobid_xml":"https://content.openalex.org/works/W3083116065.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W1485437584","https://openalex.org/W1626317705","https://openalex.org/W1694091468","https://openalex.org/W1854214752","https://openalex.org/W1855339086","https://openalex.org/W1946315329","https://openalex.org/W1988152444","https://openalex.org/W2003516452","https://openalex.org/W2009537245","https://openalex.org/W2044212084","https://openalex.org/W2091382115","https://openalex.org/W2095846217","https://openalex.org/W2108475251","https://openalex.org/W2116612304","https://openalex.org/W2132914434","https://openalex.org/W2133105246","https://openalex.org/W2142949395","https://openalex.org/W2148087609","https://openalex.org/W2151148091","https://openalex.org/W2162576315","https://openalex.org/W2229238337","https://openalex.org/W2271840356","https://openalex.org/W2387462954","https://openalex.org/W2530417694","https://openalex.org/W2541884796","https://openalex.org/W2614254310","https://openalex.org/W2755092149","https://openalex.org/W2798909945","https://openalex.org/W2945656780","https://openalex.org/W2962676598","https://openalex.org/W2962741697","https://openalex.org/W2963058819","https://openalex.org/W2963250364","https://openalex.org/W2963583445","https://openalex.org/W2963759574","https://openalex.org/W2963861706","https://openalex.org/W2963865165","https://openalex.org/W2963879412","https://openalex.org/W2963901142","https://openalex.org/W2963992805","https://openalex.org/W2964091758","https://openalex.org/W2964231067","https://openalex.org/W2964242436","https://openalex.org/W2966291568","https://openalex.org/W2969215180","https://openalex.org/W2970483056","https://openalex.org/W2982674132","https://openalex.org/W2989361410","https://openalex.org/W3014128115","https://openalex.org/W3016897523","https://openalex.org/W3036710239","https://openalex.org/W3080407530","https://openalex.org/W3099514962","https://openalex.org/W3101665129","https://openalex.org/W3102206315","https://openalex.org/W3103741939","https://openalex.org/W4205648292"],"related_works":["https://openalex.org/W3043454293","https://openalex.org/W2958392238","https://openalex.org/W2964026016","https://openalex.org/W3011893207","https://openalex.org/W1522126829","https://openalex.org/W1570678512","https://openalex.org/W3173221963","https://openalex.org/W2914820002","https://openalex.org/W1592300041","https://openalex.org/W2946351580","https://openalex.org/W3008274461","https://openalex.org/W194329854","https://openalex.org/W2980889341","https://openalex.org/W1545407741","https://openalex.org/W2905225656","https://openalex.org/W2753671491","https://openalex.org/W3081315316","https://openalex.org/W2786453881","https://openalex.org/W2510632979","https://openalex.org/W2337072805"],"abstract_inverted_index":{"Distributed":[0],"computing":[1,131],"is":[2,28,48],"a":[3,42,94,126,137,143,156,162,182,189],"standard":[4],"way":[5],"to":[6,15,49,119,186],"scale":[7],"up":[8,118],"machine":[9,58],"learning":[10],"and":[11,59,121,155,159,223],"data":[12,112,138,222],"science":[13],"algorithms":[14],"process":[16],"large":[17],"amounts":[18],"of":[19,39,72,96,104,109,136,165,171,188,199],"data.":[20],"In":[21],"such":[22,97,216],"settings,":[23],"avoiding":[24,46],"communication":[25,47],"amongst":[26],"machines":[27],"paramount":[29],"for":[30,45,130,203,220,226],"achieving":[31],"high":[32],"performance.":[33],"Rather":[34],"than":[35],"distribute":[36],"the":[37,62,85,105,132,149,168,197],"computation":[38],"existing":[40],"algorithms,":[41],"common":[43],"practice":[44],"compute":[50],"local":[51,73,86,153],"solutions":[52,74,87,100,154,212],"or":[53],"parameter":[54],"estimates":[55],"on":[56],"each":[57],"then":[60],"combine":[61],"results;":[63],"in":[64],"many":[65],"convex":[66],"optimization":[67],"problems,":[68,98],"even":[69],"simple":[70],"averaging":[71],"can":[75],"work":[76,83],"well.":[77],"However,":[78],"these":[79],"schemes":[80],"do":[81],"not":[82,89],"when":[84],"are":[88,93,101,115],"unique.":[90],"Spectral":[91],"methods":[92],"collection":[95],"where":[99,211],"orthonormal":[102],"bases":[103],"leading":[106,133],"invariant":[107,134],"subspace":[108,135],"an":[110],"associated":[111],"matrix,":[113],"which":[114],"only":[116,160],"unique":[117],"rotation":[120],"reflections.":[122],"Here,":[123],"we":[124,176],"develop":[125],"communication-efficient":[127],"distributed":[128,204],"algorithm":[129,141,180,202],"matrix.":[139],"Our":[140],"uses":[142],"novel":[144],"alignment":[145],"scheme":[146],"that":[147,178,187],"minimizes":[148],"Procrustean":[150],"distance":[151],"between":[152],"reference":[157],"solution,":[158],"requires":[161],"single":[163],"round":[164],"communication.":[166],"For":[167],"important":[169],"case":[170],"principal":[172],"component":[173],"analysis":[174],"(PCA),":[175],"show":[177],"our":[179,200],"achieves":[181],"similar":[183],"error":[184],"rate":[185],"centralized":[190],"estimator.":[191],"We":[192],"present":[193],"numerical":[194],"experiments":[195],"demonstrating":[196],"efficacy":[198],"proposed":[201],"PCA,":[205],"as":[206,208,217],"well":[207],"other":[209],"problems":[210],"exhibit":[213],"rotational":[214],"symmetry,":[215],"node":[218],"embeddings":[219],"graph":[221],"spectral":[224],"initialization":[225],"quadratic":[227],"sensing.":[228]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
