{"id":"https://openalex.org/W4392357317","doi":"https://doi.org/10.1145/3649463","title":"Projected Gaussian Markov Improvement Algorithm for High-Dimensional Discrete Optimization via Simulation","display_name":"Projected Gaussian Markov Improvement Algorithm for High-Dimensional Discrete Optimization via Simulation","publication_year":2024,"publication_date":"2024-03-01","ids":{"openalex":"https://openalex.org/W4392357317","doi":"https://doi.org/10.1145/3649463"},"language":"en","primary_location":{"id":"doi:10.1145/3649463","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3649463","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3649463","source":{"id":"https://openalex.org/S38703467","display_name":"ACM Transactions on Modeling and Computer Simulation","issn_l":"1049-3301","issn":["1049-3301","1558-1195"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Modeling and Computer Simulation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3649463","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100671315","display_name":"Xinru Li","orcid":"https://orcid.org/0009-0001-6647-2073"},"institutions":[{"id":"https://openalex.org/I118136607","display_name":"General Motors (United States)","ror":"https://ror.org/05addee68","country_code":"US","type":"company","lineage":["https://openalex.org/I118136607"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xinru Li","raw_affiliation_strings":["General Motors Corp, Warren, USA"],"raw_orcid":"https://orcid.org/0009-0001-6647-2073","affiliations":[{"raw_affiliation_string":"General Motors Corp, Warren, USA","institution_ids":["https://openalex.org/I118136607"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103141604","display_name":"Eunhye Song","orcid":"https://orcid.org/0000-0002-5171-0614"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eunhye Song","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, USA"],"raw_orcid":"https://orcid.org/0000-0002-5171-0614","affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100671315"],"corresponding_institution_ids":["https://openalex.org/I118136607"],"apc_list":null,"apc_paid":null,"fwci":1.34,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.8067105,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"34","issue":"3","first_page":"1","last_page":"29"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9886000156402588,"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/T11159","display_name":"Manufacturing Process and Optimization","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/markov-chain","display_name":"Markov chain","score":0.6212478876113892},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5555303692817688},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5420788526535034},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.501173734664917},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4583929777145386},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4250012934207916},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.4235537350177765},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.293975830078125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11598935723304749},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11116662621498108},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10621938109397888}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.6212478876113892},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5555303692817688},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5420788526535034},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.501173734664917},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4583929777145386},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4250012934207916},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4235537350177765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.293975830078125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11598935723304749},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11116662621498108},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10621938109397888},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3649463","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3649463","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3649463","source":{"id":"https://openalex.org/S38703467","display_name":"ACM Transactions on Modeling and Computer Simulation","issn_l":"1049-3301","issn":["1049-3301","1558-1195"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Modeling and Computer Simulation","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3649463","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3649463","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3649463","source":{"id":"https://openalex.org/S38703467","display_name":"ACM Transactions on Modeling and Computer Simulation","issn_l":"1049-3301","issn":["1049-3301","1558-1195"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Modeling and Computer Simulation","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3740708234","display_name":null,"funder_award_id":"DMS-1854659","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8201887589","display_name":"Collaborative Research: Adaptive Gaussian Markov Random Fields for Large-scale Discrete Optimization via Simulation","funder_award_id":"1854659","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392357317.pdf","grobid_xml":"https://content.openalex.org/works/W4392357317.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1510052597","https://openalex.org/W1583837637","https://openalex.org/W1973412675","https://openalex.org/W2008870058","https://openalex.org/W2050272999","https://openalex.org/W2088361060","https://openalex.org/W2134869213","https://openalex.org/W2141771453","https://openalex.org/W2145979309","https://openalex.org/W2311178916","https://openalex.org/W2408410815","https://openalex.org/W2604272474","https://openalex.org/W2604738573","https://openalex.org/W2787306014","https://openalex.org/W2802235618","https://openalex.org/W2909310974","https://openalex.org/W2914849365","https://openalex.org/W2947035977","https://openalex.org/W2980498459","https://openalex.org/W3006988978","https://openalex.org/W3035520687","https://openalex.org/W3087999580","https://openalex.org/W3092934784","https://openalex.org/W3106063097","https://openalex.org/W3214421366","https://openalex.org/W4212774754","https://openalex.org/W4231046447","https://openalex.org/W4250246931","https://openalex.org/W4289258965"],"related_works":["https://openalex.org/W2379651310","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2084326697","https://openalex.org/W2027903142","https://openalex.org/W2354322608","https://openalex.org/W2804608325","https://openalex.org/W2077211377","https://openalex.org/W2413828414","https://openalex.org/W2186675474"],"abstract_inverted_index":{"This":[0],"article":[1],"considers":[2],"a":[3,12,31,34,75,98,109,155,164,179,214],"discrete":[4],"optimization":[5,243],"via":[6],"simulation":[7],"(DOvS)":[8],"problem":[9],"defined":[10],"on":[11,90,168,193],"graph":[13,81,93,188],"embedded":[14],"in":[15,58],"the":[16,25,28,51,62,71,79,84,91,104,115,123,126,129,138,143,150,169,174,183,186,190,194,198,212,218,229],"high-dimensional":[17,241],"integer":[18],"grid.":[19],"Several":[20],"DOvS":[21],"algorithms":[22],"that":[23,222],"model":[24,142],"responses":[26,127],"at":[27,117,158],"solutions":[29,101,107,184],"as":[30],"realization":[32],"of":[33,54,86,100,125,189,217],"Gaussian":[35,64],"Markov":[36,65],"random":[37],"field":[38],"(GMRF)":[39],"have":[40],"been":[41],"proposed":[42],"exploiting":[43],"its":[44],"inferential":[45],"power":[46],"and":[47,225,234],"computational":[48,52],"benefits.":[49],"However,":[50],"cost":[53,85],"inference":[55,171,196],"increases":[56],"exponentially":[57],"dimension.":[59],"We":[60,113,220],"propose":[61,211],"projected":[63,102],"improvement":[66],"algorithm":[67],"(pGMIA),":[68],"which":[69],"projects":[70],"solution":[72],"space":[73,77],"onto":[74],"lower-dimensional":[76,110],"creating":[78],"region-layer":[80,92,119,139,144,165,175],"to":[82,97,103,121,141,153,228],"reduce":[83],"inference.":[87],"Each":[88],"node":[89,120,166,191],"can":[94],"be":[95,122],"mapped":[96],"set":[99],"node;":[105],"these":[106],"form":[108],"solution-layer":[111,131,187,199],"graph.":[112,132],"define":[114],"response":[116],"each":[118,159],"average":[124],"within":[128,185],"corresponding":[130],"From":[133],"this":[134],"relation,":[135],"we":[136,209],"derive":[137],"GMRF":[140],"responses.":[145],"The":[146],"pGMIA":[147,224],"alternates":[148],"between":[149],"two":[151],"layers":[152],"make":[154],"sampling":[156,180],"decision":[157,181],"iteration.":[160],"It":[161],"first":[162],"selects":[163],"based":[167,192],"lower-resolution":[170],"provided":[172],"by":[173],"GMRF,":[176],"then":[177],"makes":[178],"among":[182],"higher-resolution":[195],"from":[197],"GMRF.":[200],"To":[201],"solve":[202],"even":[203],"higher-dimensional":[204],"problems":[205],"(e.g.,":[206],"100":[207],"dimensions),":[208],"also":[210],"pGMIA+:":[213],"multi-layer":[215],"extension":[216],"pGMIA.":[219],"show":[221],"both":[223],"pGMIA+":[226],"converge":[227],"optimum":[230],"almost":[231],"surely":[232],"asymptotically":[233],"empirically":[235],"demonstrate":[236],"their":[237],"competitiveness":[238],"against":[239],"state-of-the-art":[240],"Bayesian":[242],"algorithms.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
