{"id":"https://openalex.org/W1751687266","doi":"https://doi.org/10.1137/110830629","title":"Hybrid Deterministic-Stochastic Methods for Data Fitting","display_name":"Hybrid Deterministic-Stochastic Methods for Data Fitting","publication_year":2012,"publication_date":"2012-01-01","ids":{"openalex":"https://openalex.org/W1751687266","doi":"https://doi.org/10.1137/110830629","mag":"1751687266"},"language":"en","primary_location":{"id":"doi:10.1137/110830629","is_oa":false,"landing_page_url":"https://doi.org/10.1137/110830629","pdf_url":null,"source":{"id":"https://openalex.org/S165512578","display_name":"SIAM Journal on Scientific Computing","issn_l":"1064-8275","issn":["1064-8275","1095-7197"],"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 Scientific Computing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1104.2373","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Michael P. Friedlander","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Michael P. Friedlander","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Mark Schmidt","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mark Schmidt","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":8.4694,"has_fulltext":false,"cited_by_count":243,"citation_normalized_percentile":{"value":0.97486259,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"34","issue":"3","first_page":"A1380","last_page":"A1405"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":1.0,"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":1.0,"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":0.9995999932289124,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.670199990272522},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4668000042438507},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.42660000920295715},{"id":"https://openalex.org/keywords/numerical-analysis","display_name":"Numerical analysis","score":0.39660000801086426},{"id":"https://openalex.org/keywords/gradient-method","display_name":"Gradient method","score":0.3921000063419342},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.35679998993873596}],"concepts":[{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.670199990272522},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5745999813079834},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5317999720573425},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5098000168800354},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4668000042438507},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.42660000920295715},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4124000072479248},{"id":"https://openalex.org/C48753275","wikidata":"https://www.wikidata.org/wiki/Q11216","display_name":"Numerical analysis","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3935999870300293},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C184389593","wikidata":"https://www.wikidata.org/wiki/Q603159","display_name":"Curve fitting","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2605000138282776},{"id":"https://openalex.org/C2987595161","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Optimization algorithm","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1137/110830629","is_oa":false,"landing_page_url":"https://doi.org/10.1137/110830629","pdf_url":null,"source":{"id":"https://openalex.org/S165512578","display_name":"SIAM Journal on Scientific Computing","issn_l":"1064-8275","issn":["1064-8275","1095-7197"],"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 Scientific Computing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1104.2373","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1104.2373","pdf_url":"https://arxiv.org/pdf/1104.2373","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"},{"id":"pmh:oai:HAL:inria-00626571v1","is_oa":false,"landing_page_url":"https://inria.hal.science/inria-00626571","pdf_url":null,"source":{"id":"https://openalex.org/S4406922466","display_name":"SPIRE - Sciences Po Institutional REpository","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SIAM Journal on Scientific Computing, 2012, 34 (3), pp.A1380-A1405. ⟨10.1137/110830629⟩","raw_type":"Journal articles"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1104.2373","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1104.2373","pdf_url":"https://arxiv.org/pdf/1104.2373","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W28766783","https://openalex.org/W1970997001","https://openalex.org/W1988795359","https://openalex.org/W1991083751","https://openalex.org/W1992208280","https://openalex.org/W2010522529","https://openalex.org/W2016384870","https://openalex.org/W2035079355","https://openalex.org/W2093417350","https://openalex.org/W2098269688","https://openalex.org/W2121210949","https://openalex.org/W2125838338"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2376932109","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W1596801655","https://openalex.org/W2387147530","https://openalex.org/W2373011076","https://openalex.org/W1532762289","https://openalex.org/W2277606789"],"abstract_inverted_index":{"Many":[0],"structured":[1],"data-fitting":[2],"applications":[3],"require":[4],"the":[5,32,35,60,64,78,89,101],"solution":[6],"of":[7,19,31,62,80,105],"an":[8,93],"optimization":[9],"problem":[10],"involving":[11],"a":[12,15,29,50,110],"sum":[13],"over":[14],"potentially":[16],"large":[17],"number":[18],"measurements.":[20],"Incremental":[21],"gradient":[22,68],"algorithms":[23],"offer":[24],"inexpensive":[25],"iterations":[26],"by":[27,87],"sampling":[28],"subset":[30],"terms":[33],"in":[34,92],"sum;":[36],"these":[37],"methods":[38,55,75],"can":[39],"make":[40],"great":[41],"progress":[42],"initially,":[43],"but":[44],"often":[45],"slow":[46],"as":[47],"they":[48],"approach":[49],"solution.":[51],"In":[52],"contrast,":[53],"full-gradient":[54,106],"achieve":[56],"steady":[57,102],"convergence":[58,103],"at":[59],"expense":[61],"evaluating":[63],"full":[65],"objective":[66],"and":[67],"on":[69,115],"each":[70],"iteration.":[71],"We":[72,108],"explore":[73],"hybrid":[74],"that":[76,86],"exhibit":[77],"benefits":[79],"both":[81],"approaches.":[82],"Rate-of-convergence":[83],"analysis":[84],"shows":[85],"controlling":[88],"sample":[90],"size":[91],"incremental-gradient":[94],"algorithm,":[95],"it":[96],"is":[97],"possible":[98],"to":[99],"maintain":[100],"rates":[104],"methods.":[107],"detail":[109],"practical":[111],"quasi-Newton":[112],"implementation":[113],"based":[114],"this":[116],"approach.":[117],"Numerical":[118],"experiments":[119],"illustrate":[120],"its":[121],"potential":[122],"benefits.":[123]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":41},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":18},{"year":2017,"cited_by_count":13},{"year":2016,"cited_by_count":10},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":10},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2016-06-24T00:00:00"}