{"id":"https://openalex.org/W2337153280","doi":"https://doi.org/10.1080/10556788.2018.1425860","title":"Two stochastic optimization algorithms for convex optimization with fixed point constraints","display_name":"Two stochastic optimization algorithms for convex optimization with fixed point constraints","publication_year":2018,"publication_date":"2018-01-26","ids":{"openalex":"https://openalex.org/W2337153280","doi":"https://doi.org/10.1080/10556788.2018.1425860","mag":"2337153280"},"language":"en","primary_location":{"id":"doi:10.1080/10556788.2018.1425860","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10556788.2018.1425860","pdf_url":null,"source":{"id":"https://openalex.org/S103047102","display_name":"Optimization methods & software","issn_l":"1026-7670","issn":["1026-7670","1029-4937","1055-6788"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Optimization Methods and Software","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1604.04713","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016609320","display_name":"Hideaki Iiduka","orcid":"https://orcid.org/0000-0001-9173-6723"},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"H. Iiduka","raw_affiliation_strings":["Department of Computer Science, Meiji University, Kanagawa, Japan","[Department of Computer Science, Meiji University, Kanagawa, Japan]"],"raw_orcid":"https://orcid.org/0000-0001-9173-6723","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Meiji University, Kanagawa, Japan","institution_ids":["https://openalex.org/I16656306"]},{"raw_affiliation_string":"[Department of Computer Science, Meiji University, Kanagawa, Japan]","institution_ids":["https://openalex.org/I16656306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5016609320"],"corresponding_institution_ids":["https://openalex.org/I16656306"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01128858,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":"4","first_page":"731","last_page":"757"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10545","display_name":"Optimization and Variational Analysis","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10545","display_name":"Optimization and Variational Analysis","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10963","display_name":"Advanced Optimization Algorithms Research","score":0.9980000257492065,"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"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9932000041007996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6653704047203064},{"id":"https://openalex.org/keywords/fixed-point","display_name":"Fixed point","score":0.6212279200553894},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5702796578407288},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5668188333511353},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46599259972572327},{"id":"https://openalex.org/keywords/proximal-gradient-methods","display_name":"Proximal Gradient Methods","score":0.43781131505966187},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.43642640113830566},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.4345332384109497},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.36999040842056274},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.06606784462928772}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6653704047203064},{"id":"https://openalex.org/C61445026","wikidata":"https://www.wikidata.org/wiki/Q217608","display_name":"Fixed point","level":2,"score":0.6212279200553894},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5702796578407288},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5668188333511353},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46599259972572327},{"id":"https://openalex.org/C10494615","wikidata":"https://www.wikidata.org/wiki/Q17086765","display_name":"Proximal Gradient Methods","level":4,"score":0.43781131505966187},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.43642640113830566},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4345332384109497},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.36999040842056274},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.06606784462928772},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"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/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1080/10556788.2018.1425860","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10556788.2018.1425860","pdf_url":null,"source":{"id":"https://openalex.org/S103047102","display_name":"Optimization methods & software","issn_l":"1026-7670","issn":["1026-7670","1029-4937","1055-6788"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Optimization Methods and Software","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1604.04713","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1604.04713","pdf_url":"https://arxiv.org/pdf/1604.04713","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"mag:2337153280","is_oa":true,"landing_page_url":"https://www.arxiv.org/pdf/1604.04713v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.1604.04713","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1604.04713","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1604.04713","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1604.04713","pdf_url":"https://arxiv.org/pdf/1604.04713","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"id":"https://openalex.org/G8791501049","display_name":"Distributed Fixed Point Subgradient Methods for Solving Large-scale, Complicated Network Resource Allocation Problems","funder_award_id":"15K04763","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2337153280.pdf","grobid_xml":"https://content.openalex.org/works/W2337153280.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W156127673","https://openalex.org/W205960364","https://openalex.org/W1493782845","https://openalex.org/W1546168022","https://openalex.org/W1791808123","https://openalex.org/W1946620893","https://openalex.org/W1973754217","https://openalex.org/W1977075399","https://openalex.org/W1977111935","https://openalex.org/W1987083649","https://openalex.org/W1987609385","https://openalex.org/W1992208280","https://openalex.org/W1995878217","https://openalex.org/W2003492573","https://openalex.org/W2012180727","https://openalex.org/W2019569173","https://openalex.org/W2020532300","https://openalex.org/W2021902942","https://openalex.org/W2023610498","https://openalex.org/W2024484010","https://openalex.org/W2029463628","https://openalex.org/W2030618034","https://openalex.org/W2031658331","https://openalex.org/W2037124030","https://openalex.org/W2038497950","https://openalex.org/W2045744861","https://openalex.org/W2047605837","https://openalex.org/W2047873339","https://openalex.org/W2053487511","https://openalex.org/W2067881676","https://openalex.org/W2068605444","https://openalex.org/W2073750241","https://openalex.org/W2076428552","https://openalex.org/W2092507976","https://openalex.org/W2115158696","https://openalex.org/W2115321886","https://openalex.org/W2126296286","https://openalex.org/W2126776700","https://openalex.org/W2162287622","https://openalex.org/W2168909589","https://openalex.org/W2266856284","https://openalex.org/W2295489065","https://openalex.org/W2346898445","https://openalex.org/W2747060285","https://openalex.org/W2765202580","https://openalex.org/W2798579099","https://openalex.org/W2963264932","https://openalex.org/W2963470657","https://openalex.org/W2963692017","https://openalex.org/W2964029293","https://openalex.org/W4229650096","https://openalex.org/W4243772471"],"related_works":["https://openalex.org/W2949370480","https://openalex.org/W1791808123","https://openalex.org/W2284813724","https://openalex.org/W3111223774","https://openalex.org/W2020532300","https://openalex.org/W2190814664","https://openalex.org/W1932668325","https://openalex.org/W2970404488","https://openalex.org/W2130045969","https://openalex.org/W2197814125","https://openalex.org/W289745705","https://openalex.org/W2129247866","https://openalex.org/W2799315304","https://openalex.org/W2991309288","https://openalex.org/W3037023674","https://openalex.org/W3129044570","https://openalex.org/W2338154915","https://openalex.org/W2327959774","https://openalex.org/W1493782845","https://openalex.org/W2754227567"],"abstract_inverted_index":{"Two":[0],"optimization":[1,170],"algorithms":[2,74],"are":[3],"proposed":[4],"for":[5,11],"solving":[6],"a":[7,44,76,80,85,95,110],"stochastic":[8,96,111],"programming":[9],"problem":[10],"which":[12,60],"the":[13,19,22,28,34,57,61,66,100,116,142,152,156,165],"objective":[14],"function":[15,78],"is":[16,31,107],"given":[17],"in":[18,43,59],"form":[20],"of":[21,24,36,40,50,56,65,141,155,168],"expectation":[23],"convex":[25,77,127,169],"functions":[26],"and":[27,79,115,164],"constraint":[29,67],"set":[30,154],"defined":[32],"by":[33,84,145],"intersection":[35],"fixed":[37,51,102,118,172],"point":[38,52,103,113,119,140,173],"sets":[39,68,174],"nonexpansive":[41,81],"mappings":[42],"real":[45],"Hilbert":[46],"space.":[47],"This":[48],"setting":[49],"constraints":[53],"enables":[54],"consideration":[55],"case":[58],"projection":[62],"onto":[63],"each":[64,90],"cannot":[69],"be":[70,123],"computed":[71],"efficiently.":[72],"Both":[73],"use":[75],"mapping":[82],"determined":[83],"certain":[86,134],"probabilistic":[87],"process":[88],"at":[89],"iteration.":[91],"One":[92],"algorithm":[93,114,147],"blends":[94],"gradient":[97],"method":[98],"with":[99],"Halpern":[101,117],"algorithm.":[104],"The":[105],"other":[106],"based":[108],"on":[109],"proximal":[112],"algorithm;":[120],"it":[121],"can":[122],"applied":[124],"to":[125,151],"nonsmooth":[126],"optimization.":[128],"Convergence":[129,158],"analysis":[130,160],"showed":[131],"that,":[132],"under":[133],"assumptions,":[135],"any":[136],"weak":[137],"sequential":[138],"cluster":[139],"sequence":[143],"generated":[144],"either":[146],"almost":[148],"surely":[149],"belongs":[150],"solution":[153],"problem.":[157],"rate":[159],"illustrated":[161],"their":[162,176],"efficiency,":[163],"numerical":[166],"results":[167],"over":[171],"demonstrated":[175],"effectiveness.":[177]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
