{"id":"https://openalex.org/W4414165906","doi":"https://doi.org/10.1109/tsp.2025.3607946","title":"Vector Approximate Survey Propagation","display_name":"Vector Approximate Survey Propagation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414165906","doi":"https://doi.org/10.1109/tsp.2025.3607946"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2025.3607946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2025.3607946","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","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":null,"display_name":"Qun Chen","orcid":"https://orcid.org/0009-0008-5041-6828"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]},{"id":"https://openalex.org/I4210142539","display_name":"Guangdong Institute of Intelligent Manufacturing","ror":"https://ror.org/049jpjz09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210142539"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qun Chen","raw_affiliation_strings":["Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration, School of Automation, Guangdong University of Technology, Guangzhou, China","School of Automation, Guangdong University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration, School of Automation, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I4210142539","https://openalex.org/I139024713"]},{"raw_affiliation_string":"School of Automation, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002651328","display_name":"Haochuan Zhang","orcid":"https://orcid.org/0000-0002-0922-0937"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]},{"id":"https://openalex.org/I4210142539","display_name":"Guangdong Institute of Intelligent Manufacturing","ror":"https://ror.org/049jpjz09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210142539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haochuan Zhang","raw_affiliation_strings":["Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration, School of Automation, Guangdong University of Technology, Guangzhou, China","School of Automation, Guangdong University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration, School of Automation, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I4210142539","https://openalex.org/I139024713"]},{"raw_affiliation_string":"School of Automation, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101435324","display_name":"Huimin Zhu","orcid":"https://orcid.org/0000-0002-2291-225X"},"institutions":[{"id":"https://openalex.org/I117532281","display_name":"Guangzhou University of Chinese Medicine","ror":"https://ror.org/03qb7bg95","country_code":"CN","type":"education","lineage":["https://openalex.org/I117532281"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huimin Zhu","raw_affiliation_strings":["Research Centre of Basic Integrative Medicine, Department of Physiology, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Research Centre of Basic Integrative Medicine, Department of Physiology, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China","institution_ids":["https://openalex.org/I117532281"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139024713","https://openalex.org/I4210142539"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23199194,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"73","issue":null,"first_page":"4257","last_page":"4271"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.942799985408783,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.942799985408783,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9387999773025513,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9175000190734863,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/expectation-propagation","display_name":"Expectation propagation","score":0.6193000078201294},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5728999972343445},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.49239999055862427},{"id":"https://openalex.org/keywords/ansatz","display_name":"Ansatz","score":0.46880000829696655},{"id":"https://openalex.org/keywords/message-passing","display_name":"Message passing","score":0.46299999952316284},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.43380001187324524},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4309000074863434},{"id":"https://openalex.org/keywords/saddle-point","display_name":"Saddle point","score":0.415800005197525},{"id":"https://openalex.org/keywords/belief-propagation","display_name":"Belief propagation","score":0.361299991607666}],"concepts":[{"id":"https://openalex.org/C2779363554","wikidata":"https://www.wikidata.org/wiki/Q5420835","display_name":"Expectation propagation","level":4,"score":0.6193000078201294},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5728999972343445},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47200000286102295},{"id":"https://openalex.org/C130979935","wikidata":"https://www.wikidata.org/wiki/Q568954","display_name":"Ansatz","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.46299999952316284},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44699999690055847},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.43380001187324524},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4309000074863434},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.42559999227523804},{"id":"https://openalex.org/C2681867","wikidata":"https://www.wikidata.org/wiki/Q690935","display_name":"Saddle point","level":2,"score":0.415800005197525},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38940000534057617},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.361299991607666},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C64812099","wikidata":"https://www.wikidata.org/wiki/Q176604","display_name":"Random matrix","level":3,"score":0.3458000123500824},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3391999900341034},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.3230000138282776},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C2777798563","wikidata":"https://www.wikidata.org/wiki/Q7603916","display_name":"State vector","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C189237950","wikidata":"https://www.wikidata.org/wiki/Q2500758","display_name":"Stationary point","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3025999963283539},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.28790000081062317},{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2025.3607946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2025.3607946","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2553657849","display_name":null,"funder_award_id":"2023A1515110853","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G4457728882","display_name":null,"funder_award_id":"82505267","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7548219036","display_name":null,"funder_award_id":"2022A1515010196","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1518885151","https://openalex.org/W1622584740","https://openalex.org/W1873595945","https://openalex.org/W1984830458","https://openalex.org/W2022083710","https://openalex.org/W2048269188","https://openalex.org/W2082029531","https://openalex.org/W2090842051","https://openalex.org/W2149117519","https://openalex.org/W2160051394","https://openalex.org/W2166670884","https://openalex.org/W2211290501","https://openalex.org/W2566505556","https://openalex.org/W2610971674","https://openalex.org/W2723734710","https://openalex.org/W2794190039","https://openalex.org/W2811244594","https://openalex.org/W2896991873","https://openalex.org/W2945463326","https://openalex.org/W2950654313","https://openalex.org/W2962760202","https://openalex.org/W2963072996","https://openalex.org/W2963521429","https://openalex.org/W3015536096","https://openalex.org/W3034368263","https://openalex.org/W3039347249","https://openalex.org/W3040412406","https://openalex.org/W3117873470","https://openalex.org/W3212098349","https://openalex.org/W4206502013","https://openalex.org/W4226391017","https://openalex.org/W4248709896","https://openalex.org/W4292482091","https://openalex.org/W4382365057","https://openalex.org/W4385516969"],"related_works":[],"abstract_inverted_index":{"Approximate":[0,27,33,95],"Message":[1,28],"Passing":[2,29],"(AMP),":[3],"originally":[4],"designed":[5],"to":[6,69,106,116],"solve":[7],"high-dimensional":[8],"linear":[9],"inverse":[10],"problems,":[11],"has":[12],"found":[13],"broad":[14],"applications":[15],"in":[16,41,134,213,230],"signal":[17],"processing":[18],"and":[19,31,62,72,110,112,132,144,176,183],"statistical":[20],"inference.":[21],"Among":[22],"its":[23],"key":[24],"variants,":[25],"Vector":[26,94],"(VAMP)":[30],"Generalized":[32],"Survey":[34,96],"Propagation":[35,97],"(GASP)":[36],"have":[37,64],"demonstrated":[38],"effectiveness":[39],"even":[40],"scenarios":[42],"where":[43],"the":[44,50,54,77,121,139,145,162,169,177,186,195,202,205,214,222,231,240,244],"assumed":[45,140],"generative":[46],"models":[47],"differ":[48],"from":[49],"true":[51],"models.":[52],"However,":[53],"maximum":[55],"a":[56,99,151],"posteriori":[57],"(MAP)":[58],"versions":[59],"of":[60,79,153,168,243],"VAMP":[61,66,131],"GASP":[63,75,133],"limitations:":[65],"is":[67,142,148,227,247],"restricted":[68],"differentiable":[70],"priors":[71,109],"likelihoods,":[73],"while":[74],"requires":[76],"elements":[78],"measurement":[80,122,146],"matrix":[81,123,147],"be":[82],"independent":[83],"identically":[84],"distributed":[85],"(i.i.d.).":[86],"To":[87],"overcome":[88],"these":[89],"limitations,":[90],"this":[91,174],"paper":[92],"introduces":[93],"(VASP),":[98],"new":[100],"algorithm":[101],"that":[102,127,194,239],"utilizes":[103],"survey":[104],"propagation":[105],"handle":[107],"non-differentiable":[108],"likelihoods":[111],"employs":[113],"vector-form":[114],"messages":[115],"account":[117],"for":[118],"correlations":[119],"among":[120],"elements.":[124],"Simulations":[125],"reveal":[126],"VASP":[128,218],"significantly":[129],"surpasses":[130],"estimation":[135],"accuracy,":[136],"particularly":[137],"when":[138],"prior":[141],"discrete-supported":[143],"non-i.i.d.":[149],"Additionally,":[150],"set":[152],"state":[154],"evolution":[155],"(SE)":[156],"recursion,":[157],"derived":[158],"heuristically,":[159],"accurately":[160],"reflects":[161],"per-iteration":[163],"mean":[164],"squared":[165],"error":[166],"(MSE)":[167],"VASP.":[170],"A":[171],"comparison":[172],"between":[173],"SE":[175],"free":[178,206,245],"energy":[179,246],"computed":[180],"by":[181],"Takahashi":[182],"Kabashima":[184],"under":[185],"one-step":[187],"replica":[188],"symmetry":[189],"breaking":[190],"(1RSB)":[191],"ansatz":[192,242],"shows":[193],"SE\u2019s":[196],"fixed":[197],"point":[198,209],"equations":[199],"are":[200],"exactly":[201],"same":[203],"as":[204],"energy\u2019s":[207],"saddle":[208],"equations,":[210],"thus":[211],"suggesting":[212],"large":[215],"system":[216],"limits,":[217],"can":[219],"efficiently":[220],"approximate":[221],"postulated":[223],"MAP":[224],"estimator":[225],"(which":[226],"computationally":[228],"NP-hard":[229],"worst":[232],"case)":[233],"with":[234],"only":[235],"cubic":[236],"complexity,":[237],"provided":[238],"1RSB":[241],"valid.":[248]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
