{"id":"https://openalex.org/W4250246931","doi":"https://doi.org/10.1109/wsc.2017.8247941","title":"Computational methods for optimization via simulation using Gaussian Markov Random Fields","display_name":"Computational methods for optimization via simulation using Gaussian Markov Random Fields","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W4250246931","doi":"https://doi.org/10.1109/wsc.2017.8247941"},"language":"en","primary_location":{"id":"doi:10.1109/wsc.2017.8247941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2017.8247941","pdf_url":null,"source":{"id":"https://openalex.org/S4363607803","display_name":"2017 Winter Simulation Conference (WSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Winter Simulation Conference (WSC)","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/A5011525209","display_name":"Mark Semelhago","orcid":null},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mark Semelhago","raw_affiliation_strings":["Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026731126","display_name":"Barry L. Nelson","orcid":"https://orcid.org/0000-0002-1325-2624"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Barry L. Nelson","raw_affiliation_strings":["Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112274231","display_name":"Andreas W\u00e4chter","orcid":null},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andreas Wachter","raw_affiliation_strings":["Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"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/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eunhye Song","raw_affiliation_strings":["Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, PA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011525209"],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":0.4153,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.71700163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2080","last_page":"2091"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9997000098228455,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.996999979019165,"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/T11195","display_name":"Simulation Techniques and Applications","score":0.9962000250816345,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5139071941375732},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5078855156898499},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5014348030090332},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.49729230999946594},{"id":"https://openalex.org/keywords/covariance-function","display_name":"Covariance function","score":0.48584362864494324},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.47952011227607727},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.461078405380249},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.45924878120422363},{"id":"https://openalex.org/keywords/random-field","display_name":"Random field","score":0.4343183636665344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3920701742172241},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3863774240016937},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38288411498069763},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11714246869087219}],"concepts":[{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5139071941375732},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5078855156898499},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5014348030090332},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.49729230999946594},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.48584362864494324},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.47952011227607727},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.461078405380249},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.45924878120422363},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.4343183636665344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3920701742172241},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3863774240016937},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38288411498069763},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11714246869087219},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc.2017.8247941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2017.8247941","pdf_url":null,"source":{"id":"https://openalex.org/S4363607803","display_name":"2017 Winter Simulation Conference (WSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6800000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320320466","display_name":"Gallipoli Medical Research Foundation","ror":"https://ror.org/02p32hv70"},{"id":"https://openalex.org/F4320338335","display_name":"H2020 European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W143236119","https://openalex.org/W1510052597","https://openalex.org/W1992516166","https://openalex.org/W2103438028","https://openalex.org/W2155927283","https://openalex.org/W2909310974","https://openalex.org/W2950922226","https://openalex.org/W3124902085","https://openalex.org/W3142374920","https://openalex.org/W3149369476","https://openalex.org/W4234925047","https://openalex.org/W4244046736","https://openalex.org/W4245839224","https://openalex.org/W4249753629","https://openalex.org/W4312258136","https://openalex.org/W6758535689"],"related_works":["https://openalex.org/W1971337326","https://openalex.org/W782485990","https://openalex.org/W1983957588","https://openalex.org/W2354365489","https://openalex.org/W2039299898","https://openalex.org/W3096432517","https://openalex.org/W2258123186","https://openalex.org/W4285599564","https://openalex.org/W4390904026","https://openalex.org/W4316843564"],"abstract_inverted_index":{"There":[0],"has":[1],"been":[2],"recent":[3],"interest,":[4],"and":[5,10,67],"significant":[6],"success,":[7],"in":[8],"adapting":[9],"extending":[11],"ideas":[12],"from":[13],"statistical":[14],"learning":[15,76,126],"via":[16,23],"Gaussian":[17,86],"process":[18],"(GP)":[19],"regression":[20],"to":[21,77,103,112,117],"optimization":[22],"simulation":[24],"(OvS)":[25],"problems.":[26,80,129],"At":[27],"the":[28,40,51,57,70,82,92,97,119],"heart":[29],"of":[30,50,96,121],"all":[31],"such":[32],"methods":[33],"is":[34,46,54,69,84],"a":[35,62,85],"GP":[36,75,83],"representing":[37],"knowledge":[38],"about":[39],"objective":[41],"function":[42],"whose":[43],"conditional":[44,58],"distribution":[45,59],"updated":[47],"as":[48],"more":[49],"feasible":[52],"region":[53],"explored.":[55],"Calculating":[56],"requires":[60],"inverting":[61],"large,":[63],"dense":[64],"covariance":[65,98],"matrix,":[66],"this":[68,107,114],"primary":[71],"bottleneck":[72],"for":[73,127],"applying":[74],"large-scale":[78],"OvS":[79,122],"If":[81],"Markov":[87],"Random":[88],"Field":[89],"(GMRF),":[90],"then":[91],"precision":[93],"matrix":[94],"(inverse":[95],"matrix)":[99],"can":[100],"be":[101,104],"constructed":[102],"sparse.":[105],"In":[106],"paper":[108],"we":[109],"show":[110],"how":[111],"exploit":[113],"sparse-matrix":[115],"structure":[116],"extend":[118],"reach":[120],"based":[123],"on":[124],"GMRF":[125],"discrete-decision-variable":[128]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}