{"id":"https://openalex.org/W2058172940","doi":"https://doi.org/10.1145/1774088.1774291","title":"Reparameterization based consistent graph-structured linear programs","display_name":"Reparameterization based consistent graph-structured linear programs","publication_year":2010,"publication_date":"2010-03-22","ids":{"openalex":"https://openalex.org/W2058172940","doi":"https://doi.org/10.1145/1774088.1774291","mag":"2058172940"},"language":"en","primary_location":{"id":"doi:10.1145/1774088.1774291","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1774088.1774291","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2010 ACM Symposium on Applied Computing","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/A5102963273","display_name":"Hongbo Zhou","orcid":"https://orcid.org/0000-0002-7007-9477"},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hongbo Zhou","raw_affiliation_strings":["Southern Illinois University, Carbondale, IL"],"affiliations":[{"raw_affiliation_string":"Southern Illinois University, Carbondale, IL","institution_ids":["https://openalex.org/I110378019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100768223","display_name":"Qiang Cheng","orcid":"https://orcid.org/0000-0002-5329-8811"},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiang Cheng","raw_affiliation_strings":["Southern Illinois University, Carbondale, IL"],"affiliations":[{"raw_affiliation_string":"Southern Illinois University, Carbondale, IL","institution_ids":["https://openalex.org/I110378019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022816848","display_name":"Zhikun She","orcid":"https://orcid.org/0000-0003-2762-8730"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhikun She","raw_affiliation_strings":["Beihang University, Beijing, China","BeiHang University, BeiJing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"BeiHang University, BeiJing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102963273"],"corresponding_institution_ids":["https://openalex.org/I110378019"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1597711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"974","last_page":"978"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11321","display_name":"Error Correcting Code Techniques","score":0.9962000250816345,"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"}},"topics":[{"id":"https://openalex.org/T11321","display_name":"Error Correcting Code Techniques","score":0.9962000250816345,"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"}},{"id":"https://openalex.org/T11392","display_name":"Energy Harvesting in Wireless Networks","score":0.9839000105857849,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9800000190734863,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6912710666656494},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6532567143440247},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.6531597375869751},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6019269824028015},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5936771631240845},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5718716979026794},{"id":"https://openalex.org/keywords/random-field","display_name":"Random field","score":0.519080638885498},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5058628916740417},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5045017004013062},{"id":"https://openalex.org/keywords/linear-programming","display_name":"Linear programming","score":0.4700770676136017},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.4553932547569275},{"id":"https://openalex.org/keywords/relaxation","display_name":"Relaxation (psychology)","score":0.43779534101486206},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4290620684623718},{"id":"https://openalex.org/keywords/message-passing","display_name":"Message passing","score":0.4256192445755005},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.4128928482532501},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.40247124433517456},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3231840133666992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.15933501720428467},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.13711276650428772},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.08690494298934937},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.0837363600730896}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6912710666656494},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6532567143440247},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.6531597375869751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6019269824028015},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5936771631240845},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5718716979026794},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.519080638885498},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5058628916740417},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5045017004013062},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.4700770676136017},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.4553932547569275},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.43779534101486206},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4290620684623718},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.4256192445755005},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4128928482532501},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40247124433517456},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3231840133666992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.15933501720428467},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.13711276650428772},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.08690494298934937},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.0837363600730896},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1774088.1774291","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1774088.1774291","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2010 ACM Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W74727102","https://openalex.org/W98731650","https://openalex.org/W1762430620","https://openalex.org/W1936569394","https://openalex.org/W1964938049","https://openalex.org/W2098387242","https://openalex.org/W2108619558","https://openalex.org/W2113997865","https://openalex.org/W2120340025","https://openalex.org/W2133038339","https://openalex.org/W2150675045","https://openalex.org/W2160988325","https://openalex.org/W2161246326","https://openalex.org/W2296319761","https://openalex.org/W2356183642","https://openalex.org/W2402272678","https://openalex.org/W2952129587","https://openalex.org/W2963217435","https://openalex.org/W4285719527","https://openalex.org/W4300973511"],"related_works":["https://openalex.org/W2097090565","https://openalex.org/W2008444830","https://openalex.org/W2804375118","https://openalex.org/W2112506753","https://openalex.org/W2029809897","https://openalex.org/W2132110568","https://openalex.org/W2160831725","https://openalex.org/W1495937069","https://openalex.org/W1522441193","https://openalex.org/W2180905035"],"abstract_inverted_index":{"A":[0,4],"class":[1],"of":[2,13,143],"Maximum":[3],"Posteriori(MAP)":[5],"formulations":[6],"built":[7],"on":[8],"various":[9,38],"graph":[10,116],"models":[11],"are":[12,52,72,123,134],"great":[14],"interests":[15],"for":[16,75,184],"both":[17,44],"theoretical":[18],"and":[19,37,48,63,107,130,160,171],"practical":[20],"applications.":[21],"Recent":[22],"advances":[23],"in":[24,96,125,168],"this":[25,148],"field":[26],"have":[27,173],"extended":[28],"the":[29,32,59,64,92,131,144,161,165],"connections":[30],"between":[31,58],"linear":[33,127],"program":[34,128],"(LP)":[35],"relaxation":[36],"tree-reweighted":[39],"message":[40],"passing":[41],"algorithms.":[42],"At":[43],"sides,":[45],"many":[46],"algorithms":[47,163],"their":[49,114],"optimality":[50,156,181],"certificates":[51],"proved,":[53],"provided":[54],"no":[55],"conflict":[56],"exists":[57],"node":[60,102,132],"marginal":[61,67],"maximum":[62],"corresponding":[65],"edge":[66,121],"maximum.":[68],"However,":[69],"these":[70],"conflicts":[71,89],"usually":[73],"inevitable":[74],"general":[76],"non-trivial":[77],"Markov":[78,98],"random":[79,99],"fields":[80],"(MRFs).":[81],"Our":[82],"work":[83],"is":[84],"aimed":[85],"at":[86],"reducing":[87],"such":[88],"by":[90],"reparameterizing":[91],"original":[93,166],"energy":[94],"distributions":[95],"pairwise":[97],"field.":[100],"All":[101],"potentials":[103],"will":[104],"be":[105],"decomposed":[106],"attached":[108],"to":[109,113,137,177,179],"local":[110,115],"edges":[111],"according":[112],"structures.":[117],"And":[118],"thus,":[119],"only":[120,135],"marginals":[122,133],"needed":[124],"our":[126],"relaxation,":[129],"used":[136],"exchange":[138],"information":[139],"among":[140],"different":[141],"parts":[142],"graph.":[145],"We":[146],"incorporated":[147],"consistent":[149],"graph-structured":[150],"reparameterization":[151],"into":[152],"some":[153,185],"latest":[154],"LP":[155],"guaranteed":[157],"proximal":[158],"solvers,":[159],"resulted":[162],"outperform":[164],"ones":[167],"convergence":[169],"rate":[170],"also":[172],"a":[174],"better":[175],"behavior":[176],"converge":[178],"MAP":[180],"monotonously":[182],"even":[183],"highly":[186],"noisy":[187],"MRFs.":[188]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}