{"id":"https://openalex.org/W2962731416","doi":"https://doi.org/10.1109/allerton.2017.8262782","title":"Curvature-aided incremental aggregated gradient method","display_name":"Curvature-aided incremental aggregated gradient method","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2962731416","doi":"https://doi.org/10.1109/allerton.2017.8262782","mag":"2962731416"},"language":"en","primary_location":{"id":"doi:10.1109/allerton.2017.8262782","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton.2017.8262782","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","raw_type":"proceedings-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/A5055166851","display_name":"Hoi-To Wai","orcid":"https://orcid.org/0000-0003-4796-4483"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hoi-To Wai","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102745656","display_name":"Wei Shi","orcid":"https://orcid.org/0000-0002-9511-3838"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Shi","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110629459","display_name":"Angelia Nedi\u0107","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Angelia Nedic","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029881017","display_name":"Anna Scaglione","orcid":"https://orcid.org/0000-0002-8892-3680"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anna Scaglione","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5055166851"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.9604,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74948055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"12","issue":null,"first_page":"526","last_page":"532"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9991999864578247,"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/T10057","display_name":"Face and Expression Recognition","score":0.9944999814033508,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hessian-matrix","display_name":"Hessian matrix","score":0.889275848865509},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.6023078560829163},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5585574507713318},{"id":"https://openalex.org/keywords/curvature","display_name":"Curvature","score":0.5481234192848206},{"id":"https://openalex.org/keywords/frank\u2013wolfe-algorithm","display_name":"Frank\u2013Wolfe algorithm","score":0.5365756750106812},{"id":"https://openalex.org/keywords/convex-function","display_name":"Convex function","score":0.5243818163871765},{"id":"https://openalex.org/keywords/gradient-method","display_name":"Gradient method","score":0.519462525844574},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4845772981643677},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.47293972969055176},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4645163118839264},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4634544551372528},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.44666701555252075},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.441032737493515},{"id":"https://openalex.org/keywords/proximal-gradient-methods","display_name":"Proximal Gradient Methods","score":0.4207615852355957},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.4175006151199341},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.32756704092025757},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.302051842212677},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17364054918289185},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13692623376846313},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.12795951962471008}],"concepts":[{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.889275848865509},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.6023078560829163},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5585574507713318},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.5481234192848206},{"id":"https://openalex.org/C122268817","wikidata":"https://www.wikidata.org/wiki/Q2020318","display_name":"Frank\u2013Wolfe algorithm","level":5,"score":0.5365756750106812},{"id":"https://openalex.org/C145446738","wikidata":"https://www.wikidata.org/wiki/Q319913","display_name":"Convex function","level":3,"score":0.5243818163871765},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.519462525844574},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4845772981643677},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.47293972969055176},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4645163118839264},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4634544551372528},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44666701555252075},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.441032737493515},{"id":"https://openalex.org/C10494615","wikidata":"https://www.wikidata.org/wiki/Q17086765","display_name":"Proximal Gradient Methods","level":4,"score":0.4207615852355957},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.4175006151199341},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.32756704092025757},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.302051842212677},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17364054918289185},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13692623376846313},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.12795951962471008},{"id":"https://openalex.org/C49870271","wikidata":"https://www.wikidata.org/wiki/Q193657","display_name":"Convex set","level":4,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/allerton.2017.8262782","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton.2017.8262782","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1506295250","https://openalex.org/W1988795359","https://openalex.org/W1994616650","https://openalex.org/W2076428552","https://openalex.org/W2105875671","https://openalex.org/W2107438106","https://openalex.org/W2134130436","https://openalex.org/W2153635508","https://openalex.org/W2276974598","https://openalex.org/W2436943375","https://openalex.org/W2517259856","https://openalex.org/W2559397378","https://openalex.org/W2804140211","https://openalex.org/W2952215077","https://openalex.org/W2962913067","https://openalex.org/W3104398353","https://openalex.org/W3141595720","https://openalex.org/W4254313632","https://openalex.org/W4300547822","https://openalex.org/W6675761068","https://openalex.org/W6676105031","https://openalex.org/W6680008295","https://openalex.org/W6680196509","https://openalex.org/W6718506159"],"related_works":["https://openalex.org/W3123504125","https://openalex.org/W3147739796","https://openalex.org/W2951335402","https://openalex.org/W1983212821","https://openalex.org/W3000303000","https://openalex.org/W2364728921","https://openalex.org/W4288301891","https://openalex.org/W4385452046","https://openalex.org/W2959146022","https://openalex.org/W2763066689"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,25,28,189],"new":[3],"algorithm":[4],"for":[5,27,176,180],"finite":[6],"sum":[7],"optimization":[8],"which":[9],"we":[10,140],"call":[11],"the":[12,21,32,45,59,66,73,84,91,107,115,121,130,147,151,155,163,168,181,185,192,200,208,214,234],"curvature-aided":[13],"incremental":[14,51,74,97,196],"aggregated":[15,52,86],"gradient":[16,53,93,165],"(CIAG)":[17],"method.":[18,77,203],"Motivated":[19],"by":[20,72],"problem":[22,235],"of":[23,34,68,146,162],"training":[24,35],"classifier":[26],"d-dimensional":[29],"problem,":[30],"where":[31],"number":[33],"data":[36],"is":[37,81,170,237],"m":[38,40],"and":[39,178,199,211,225],"\u226b":[41,43],"d":[42],"1,":[44],"CIAG":[46,148,177,186,215],"method":[47,149,187,216],"seeks":[48],"to":[49,82,89,124,127,133,136,154],"accelerate":[50],"(IAG)":[54],"methods":[55,198],"using":[56],"aids":[57],"from":[58],"curvature":[60],"(or":[61],"Hessian)":[62],"information,":[63],"while":[64,167],"avoiding":[65],"evaluation":[67],"matrix":[69,88],"inverses":[70],"required":[71],"Newton":[75],"(IN)":[76],"Specifically,":[78],"our":[79],"idea":[80],"exploit":[83],"incrementally":[85],"Hessian":[87],"trace":[90],"full":[92,164,182],"vector":[94],"at":[95],"every":[96],"step,":[98],"therefore":[99],"achieving":[100],"an":[101],"improved":[102],"linear":[103,117],"convergence":[104,118],"rate":[105,119],"over":[106],"state-of-the-art":[108],"IAG":[109,202],"methods.":[110],"For":[111],"strongly":[112],"convex":[113],"problems,":[114],"fast":[116],"requires":[120,226],"objective":[122],"function":[123],"be":[125,134],"close":[126,135],"quadratic,":[128],"or":[129],"initial":[131],"point":[132],"optimal":[137],"solution.":[138],"Importantly,":[139],"show":[141,212],"that":[142,213],"running":[143,159,229],"one":[144,160],"iteration":[145,161],"yields":[150],"same":[152],"improvement":[153],"optimality":[156],"gap":[157],"as":[158],"method,":[166],"complexity":[169,195],"O(d":[171],"<sup":[172],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[173],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[174],")":[175],"O(md)":[179],"gradient.":[183],"Overall,":[184],"strikes":[188],"balance":[190],"between":[191],"high":[193],"computation":[194],"Newton-type":[197],"slow":[201],"Our":[204],"numerical":[205],"results":[206],"support":[207],"theoretical":[209],"findings":[210],"often":[217],"converges":[218],"with":[219],"much":[220,227],"fewer":[221],"iterations":[222],"than":[223,231],"IAG,":[224],"shorter":[228],"time":[230],"IN":[232],"when":[233],"dimension":[236],"high.":[238]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}