{"id":"https://openalex.org/W4393530403","doi":"https://doi.org/10.1080/01630563.2024.2333255","title":"A New Dai-Liao Conjugate Gradient Method based on Approximately Optimal Stepsize for Unconstrained Optimization","display_name":"A New Dai-Liao Conjugate Gradient Method based on Approximately Optimal Stepsize for Unconstrained Optimization","publication_year":2024,"publication_date":"2024-04-02","ids":{"openalex":"https://openalex.org/W4393530403","doi":"https://doi.org/10.1080/01630563.2024.2333255"},"language":"en","primary_location":{"id":"doi:10.1080/01630563.2024.2333255","doi":"https://doi.org/10.1080/01630563.2024.2333255","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01630563.2024.2333255","pdf_url":null,"source":{"id":"https://openalex.org/S27114296","display_name":"Numerical Functional Analysis and Optimization","issn_l":"0163-0563","issn":["0163-0563","1532-2467"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Numerical Functional Analysis and Optimization","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5100568654","display_name":"Yan Ni","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Ni","raw_affiliation_strings":["School of Mathematics and Statistics, Guizhou University, Guiyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Guizhou University, Guiyang, China","institution_ids":["https://openalex.org/I178232147"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5094328666","display_name":"Liu Zexian","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liu Zexian","raw_affiliation_strings":["School of Mathematics and Statistics, Guizhou University, Guiyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Guizhou University, Guiyang, China","institution_ids":["https://openalex.org/I178232147"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5094328666"],"corresponding_institution_ids":["https://openalex.org/I178232147"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03561334,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"45","issue":"4-6","first_page":"333","last_page":"354"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10963","display_name":"Advanced Optimization Algorithms Research","score":0.9771000146865845,"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"}},"topics":[{"id":"https://openalex.org/T10963","display_name":"Advanced Optimization Algorithms Research","score":0.9771000146865845,"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/T10792","display_name":"Matrix Theory and Algorithms","score":0.014800000004470348,"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/T10545","display_name":"Optimization and Variational Analysis","score":0.002199999988079071,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/conjugate-gradient-method","display_name":"conjugate gradient method","score":0.8199999928474426},{"id":"https://openalex.org/keywords/unconstrained-optimization","display_name":"unconstrained optimization","score":0.9639999866485596},{"id":"https://openalex.org/keywords/large-scale-optimization","display_name":"large-scale optimization","score":0.9819999933242798},{"id":"https://openalex.org/keywords/sufficient-descent-condition","display_name":"sufficient descent condition","score":0.8489999771118164},{"id":"https://openalex.org/keywords/global-convergence","display_name":"global convergence","score":0.9779999852180481},{"id":"https://openalex.org/keywords/uniformly-convex-functions","display_name":"uniformly convex functions","score":0.8539999723434448},{"id":"https://openalex.org/keywords/search-direction","display_name":"search direction","score":0.6359999775886536}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.8633384704589844},{"id":"https://openalex.org/C81184566","wikidata":"https://www.wikidata.org/wiki/Q1191895","display_name":"Conjugate gradient method","level":2,"score":0.8351081609725952},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4986851215362549},{"id":"https://openalex.org/C26362088","wikidata":"https://www.wikidata.org/wiki/Q17086453","display_name":"Nonlinear conjugate gradient method","level":4,"score":0.47195759415626526},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.42919033765792847},{"id":"https://openalex.org/C197336794","wikidata":"https://www.wikidata.org/wiki/Q5161150","display_name":"Conjugate","level":2,"score":0.42853862047195435},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.42316633462905884},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.27327924966812134},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.1748335063457489},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.08881819248199463},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.07347306609153748},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01630563.2024.2333255","doi":"https://doi.org/10.1080/01630563.2024.2333255","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01630563.2024.2333255","pdf_url":null,"source":{"id":"https://openalex.org/S27114296","display_name":"Numerical Functional Analysis and Optimization","issn_l":"0163-0563","issn":["0163-0563","1532-2467"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Numerical Functional Analysis and Optimization","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G8007440332","display_name":null,"funder_award_id":"12261019","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W124158035","https://openalex.org/W186175500","https://openalex.org/W1497566274","https://openalex.org/W1578471822","https://openalex.org/W1981529857","https://openalex.org/W1982067912","https://openalex.org/W1989818863","https://openalex.org/W2010647796","https://openalex.org/W2012231377","https://openalex.org/W2012341322","https://openalex.org/W2014851363","https://openalex.org/W2018215034","https://openalex.org/W2050096287","https://openalex.org/W2051247543","https://openalex.org/W2057565823","https://openalex.org/W2073938719","https://openalex.org/W2075313995","https://openalex.org/W2076605490","https://openalex.org/W2087256975","https://openalex.org/W2091549324","https://openalex.org/W2091874256","https://openalex.org/W2108560279","https://openalex.org/W2144331407","https://openalex.org/W2149454052","https://openalex.org/W2316564661","https://openalex.org/W2669391889","https://openalex.org/W2743352242","https://openalex.org/W2767809233","https://openalex.org/W2900576582"],"related_works":["https://openalex.org/W2900576582","https://openalex.org/W7131132576","https://openalex.org/W2990264654","https://openalex.org/W2934240244","https://openalex.org/W2736319691","https://openalex.org/W4413252549","https://openalex.org/W4387378532","https://openalex.org/W2087504876","https://openalex.org/W3006252800","https://openalex.org/W3206146670"],"abstract_inverted_index":{"Conjugate":[0],"gradient":[1,22,40,55,108],"methods":[2,10],"are":[3,79],"a":[4,18,88],"class":[5],"of":[6,69,90],"very":[7],"effective":[8],"iterative":[9],"for":[11,24,38,47,73],"large-scale":[12,26],"unconstrained":[13,27],"optimization.":[14],"In":[15],"this":[16],"paper,":[17],"new":[19,45],"Dai-Liao":[20,53],"conjugate":[21,54,107],"method":[23,72,101],"solving":[25],"optimization":[28],"problem":[29],"is":[30,102],"proposed.":[31],"Based":[32],"on":[33,87],"the":[34,39,48,61,66,70,94,99],"approximately":[35],"optimal":[36],"stepsize":[37],"method,":[41],"we":[42],"derive":[43],"three":[44],"choices":[46],"important":[49],"parameters":[50],"tk":[51],"in":[52],"method.":[56],"The":[57],"search":[58],"direction":[59],"satisfies":[60],"sufficient":[62],"descent":[63],"condition,":[64],"and":[65,76],"global":[67],"convergences":[68],"proposed":[71,100],"uniformly":[74],"convex":[75],"general":[77],"functions":[78],"proved":[80],"under":[81],"some":[82,105],"mild":[83],"conditions.":[84],"Numerical":[85],"experiments":[86],"set":[89],"test":[91],"problems":[92],"from":[93],"CUTEst":[95],"library":[96],"show":[97],"that":[98],"superior":[103],"to":[104],"well-known":[106],"methods.":[109]},"counts_by_year":[],"updated_date":"2026-08-15T07:11:24.734988","created_date":"2024-04-03T00:00:00"}