{"id":"https://openalex.org/W1993964048","doi":"https://doi.org/10.1137/s0363012901399751","title":"A Convex Optimization Approach to ARMA(<i>n</i>,<i>m</i>) Model Design from Covariance and Cepstral Data","display_name":"A Convex Optimization Approach to ARMA(<i>n</i>,<i>m</i>) Model Design from Covariance and Cepstral Data","publication_year":2004,"publication_date":"2004-01-01","ids":{"openalex":"https://openalex.org/W1993964048","doi":"https://doi.org/10.1137/s0363012901399751","mag":"1993964048"},"language":"en","primary_location":{"id":"doi:10.1137/s0363012901399751","is_oa":false,"landing_page_url":"https://doi.org/10.1137/s0363012901399751","pdf_url":null,"source":{"id":"https://openalex.org/S897311980","display_name":"SIAM Journal on Control and Optimization","issn_l":"0363-0129","issn":["0363-0129","1095-7138"],"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 Control and Optimization","raw_type":"journal-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/A5018917817","display_name":"Per Enqvist","orcid":"https://orcid.org/0000-0002-4635-3202"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"P. Enqvist","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5018917817"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.5619,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.97396515,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"43","issue":"3","first_page":"1011","last_page":"1036"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/cepstrum","display_name":"Cepstrum","score":0.8211640119552612},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.800531804561615},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.7692314386367798},{"id":"https://openalex.org/keywords/covariance-function","display_name":"Covariance function","score":0.4690214693546295},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.46495163440704346},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4530255198478699},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.4344669282436371},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.42952120304107666},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.41993623971939087},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3957078158855438},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3152620792388916},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.30346888303756714},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.173363596200943},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.16190233826637268},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.11591163277626038},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.06867766380310059}],"concepts":[{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.8211640119552612},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.800531804561615},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7692314386367798},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.4690214693546295},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.46495163440704346},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4530255198478699},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4344669282436371},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.42952120304107666},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.41993623971939087},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3957078158855438},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3152620792388916},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.30346888303756714},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.173363596200943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.16190233826637268},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.11591163277626038},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.06867766380310059},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1137/s0363012901399751","is_oa":false,"landing_page_url":"https://doi.org/10.1137/s0363012901399751","pdf_url":null,"source":{"id":"https://openalex.org/S897311980","display_name":"SIAM Journal on Control and Optimization","issn_l":"0363-0129","issn":["0363-0129","1095-7138"],"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 Control and Optimization","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W193819586","https://openalex.org/W1496923911","https://openalex.org/W1498710259","https://openalex.org/W1509685104","https://openalex.org/W1752175949","https://openalex.org/W1890883228","https://openalex.org/W1999955319","https://openalex.org/W2010730829","https://openalex.org/W2017754977","https://openalex.org/W2074488941","https://openalex.org/W2079665343","https://openalex.org/W2082979114","https://openalex.org/W2107561939","https://openalex.org/W2110429782","https://openalex.org/W2125368842","https://openalex.org/W2126509795","https://openalex.org/W2138056643","https://openalex.org/W2141601259","https://openalex.org/W2141757682","https://openalex.org/W2170060163","https://openalex.org/W2266946488","https://openalex.org/W2313953460"],"related_works":["https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097","https://openalex.org/W2787035864","https://openalex.org/W2373675101","https://openalex.org/W4385672897","https://openalex.org/W106160982","https://openalex.org/W2359140082","https://openalex.org/W2074132948","https://openalex.org/W2160511961"],"abstract_inverted_index":{"Methods":[0],"for":[1,199],"determining":[2],"ARMA(n,m)":[3,34],"filters":[4,198],"from":[5,63],"covariance":[6,48,91,160,201,248],"and":[7,18,37,50,96,107,116,124,161,183,202,223,249,255],"cepstral":[8,55,97,113,162,185,203,238,250],"estimates":[9],"are":[10,87,105,187],"proposed.":[11],"In":[12,80,191],"[C.":[13],"I.":[14],"Byrnes,":[15],"P.":[16],"Enqvist,":[17],"A.":[19],"Lindquist,":[20],"SIAM":[21],"J.":[22],"Control":[23],"Optim.,":[24],"41":[25],"(2002),":[26],"pp.":[27],"23--59],":[28],"we":[29],"have":[30],"shown":[31,70,120],"that":[32,121,129,149,219,225],"an":[33],"model":[35,59],"determines":[36],"is":[38,119,130,142,150,208,216,221],"uniquely":[39],"determined":[40,62],"by":[41,108],"a":[42,64,76,127,143,171,205],"window":[43],"r0":[44,93],",r1":[45,94],",...,rn":[46,95],"of":[47,54,75,84,102,111,139,145,173,262],"lags":[49,92,98],"c1":[51,99],",c2":[52,100],",...,cn":[53],"lags.":[56,239],"This":[57,214],"unique":[58,227],"can":[60],"be":[61,72],"convex":[65],"optimization":[66,147,212],"problem":[67,86],"which":[68,252],"was":[69],"to":[71,154,170,193,210],"the":[73,136,146,159,165,180,184,211,226,232,237,241,247,256,260,263],"dual":[74],"maximum":[77],"entropy":[78,261],"problem.":[79,213],"this":[81,85,140],"paper,":[82],"generalizations":[83],"analyzed.":[88],"Problems":[89],"with":[90,132,177],",...,cm":[101],"different":[103],"lengths":[104],"considered,":[106],"considering":[109],"differentcombinations":[110],"covariances,":[112],"parameters,":[114],"poles,":[115],"zeros,":[117],"it":[118],"only":[122,235],"zeros":[123,178],"covariances":[125,233],"give":[126],"parameterization":[128],"consistent":[131],"generic":[133,156],"data.":[134,157],"However,":[135],"main":[137],"contribution":[138],"paper":[141],"regularization":[144],"problems":[148],"proposed":[151],"in":[152],"order":[153,192],"handle":[155],"For":[158],"problem,":[163],"if":[164],"data":[166],"does":[167],"not":[168,188],"correspond":[169],"system":[172],"desired":[174],"order,":[175],"solutions":[176],"on":[179,246],"boundary":[181],"occur":[182],"coefficients":[186],"interpolated":[189],"exactly.":[190],"achieve":[194],"strictly":[195],"minimum":[196],"phase":[197],"estimated":[200],"data,":[204,251],"barrier-like":[206],"term":[207,215,258],"introduced":[209],"chosen":[217],"so":[218,224],"convexity":[220],"maintained":[222],"solution":[228,242],"will":[229,243],"still":[230],"interpolate":[231],"but":[234],"approximate":[236],"Furthermore,":[240],"depend":[244],"analytically":[245],"provides":[253],"robustness,":[254],"barrier":[257],"increases":[259],"solution.":[264]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
