{"id":"https://openalex.org/W7133201009","doi":"https://doi.org/10.52202/075280-2047","title":"Unsupervised Learning for Solving the Travelling Salesman Problem","display_name":"Unsupervised Learning for Solving the Travelling Salesman Problem","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W7133201009","doi":"https://doi.org/10.52202/075280-2047"},"language":"en","primary_location":{"id":"doi:10.52202/075280-2047","doi":"https://doi.org/10.52202/075280-2047","is_oa":false,"landing_page_url":"https://doi.org/10.52202/075280-2047","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"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":"Advances in Neural Information Processing Systems 36","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2303.10538","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074546064","display_name":"Yimeng Min","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yimeng Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yiwei Bai","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiwei Bai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123490080","display_name":"Carla P. Gomes","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carla Gomes","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.373,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.94371738,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"47264","last_page":"47278"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.7954000234603882,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.7954000234603882,"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/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.156700000166893,"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"}},{"id":"https://openalex.org/T10720","display_name":"Complexity and Algorithms in Graphs","score":0.0066999997943639755,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/traveling-salesman-problem","display_name":"traveling salesman problem","score":0.8479999899864197},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.996999979019165},{"id":"https://openalex.org/keywords/combinatorial-optimization","display_name":"combinatorial optimization","score":0.9110000133514404},{"id":"https://openalex.org/keywords/metaheuristic-optimization","display_name":"metaheuristic optimization","score":0.718999981880188}],"concepts":[{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.640999972820282},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6261000037193298},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6071000099182129},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.384799987077713},{"id":"https://openalex.org/C175859090","wikidata":"https://www.wikidata.org/wiki/Q322212","display_name":"Travelling salesman problem","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2980000078678131},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26030001044273376},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2578999996185303}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.52202/075280-2047","doi":"https://doi.org/10.52202/075280-2047","is_oa":false,"landing_page_url":"https://doi.org/10.52202/075280-2047","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"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":"Advances in Neural Information Processing Systems 36","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2303.10538","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.10538","pdf_url":"https://arxiv.org/pdf/2303.10538","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2303.10538","doi":"https://doi.org/10.48550/arxiv.2303.10538","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2303.10538","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2303.10538","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.10538","pdf_url":"https://arxiv.org/pdf/2303.10538","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320315934","display_name":"Toyota Research Institute","ror":"https://ror.org/04fpkc108"},{"id":"https://openalex.org/F4320332299","display_name":"National Institute of Food and Agriculture","ror":"https://ror.org/05qx3fv49"},{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7133201009.pdf","grobid_xml":"https://content.openalex.org/works/W7133201009.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4319239077","https://openalex.org/W4393399754","https://openalex.org/W4399665688","https://openalex.org/W2012515029","https://openalex.org/W3207032584","https://openalex.org/W2948433391","https://openalex.org/W4415346062","https://openalex.org/W4407759537","https://openalex.org/W4210271172","https://openalex.org/W3041070278"],"abstract_inverted_index":{"We":[0,15,45],"propose":[1],"UTSP,":[2],"an":[3],"unsupervised":[4],"learning":[5,134,137],"(UL)":[6],"framework":[7],"for":[8,35,83],"solving":[9],"the":[10,33,42,57,69,73,77,84,87,100,115,121],"Travelling":[11],"Salesman":[12],"Problem":[13],"(TSP).":[14],"train":[16],"a":[17,23,29,81,91],"Graph":[18],"Neural":[19],"Network":[20],"(GNN)":[21],"using":[22],"surrogate":[24,82],"loss.":[25],"The":[26],"GNN":[27],"outputs":[28],"heat":[30,58],"map":[31],"representing":[32],"probability":[34],"each":[36],"edge":[37],"to":[38,50,71],"be":[39],"part":[40],"of":[41,64,120,123,128],"optimal":[43],"path.":[44],"then":[46],"apply":[47],"local":[48],"search":[49],"generate":[51],"our":[52],"final":[53],"prediction":[54],"based":[55],"on":[56],"map.":[59],"Our":[60,105],"loss":[61],"function":[62],"consists":[63],"two":[65],"parts:":[66],"one":[67],"pushes":[68],"model":[70,116],"find":[72],"shortest":[74],"path":[75],"and":[76,125],"other":[78],"serves":[79],"as":[80,110,112],"constraint":[85],"that":[86,97],"route":[88],"should":[89],"form":[90],"Hamiltonian":[92],"Cycle.":[93],"Experimental":[94],"results":[95],"show":[96],"UTSP":[98],"outperforms":[99],"existing":[101],"data-driven":[102],"TSP":[103],"heuristics.":[104],"approach":[106],"is":[107],"parameter":[108],"efficient":[109],"well":[111],"data":[113],"efficient:":[114],"takes":[117],"$\\sim$":[118,126],"10\\%":[119],"number":[122],"parameters":[124],"0.2\\%":[127],"training":[129],"samples":[130],"compared":[131],"with":[132],"reinforcement":[133],"or":[135],"supervised":[136],"methods.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":1}],"updated_date":"2026-10-21T09:56:26.102985","created_date":"2026-03-03T00:00:00"}
