{"id":"https://openalex.org/W2051259245","doi":"https://doi.org/10.1137/070701704","title":"Playing Games with Approximation Algorithms","display_name":"Playing Games with Approximation Algorithms","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W2051259245","doi":"https://doi.org/10.1137/070701704","mag":"2051259245"},"language":"en","primary_location":{"id":"doi:10.1137/070701704","is_oa":false,"landing_page_url":"https://doi.org/10.1137/070701704","pdf_url":null,"source":{"id":"https://openalex.org/S153560523","display_name":"SIAM Journal on Computing","issn_l":"0097-5397","issn":["0097-5397","1095-7111"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://repository.upenn.edu/bitstreams/21d11aaa-6d16-4a38-82ac-34f77a3df5ae/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108381794","display_name":"Sham M. Kakade","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sham M. Kakade","raw_affiliation_strings":["sham@tti-c.org#TAB#"],"affiliations":[{"raw_affiliation_string":"sham@tti-c.org#TAB#","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086904495","display_name":"Adam Tauman Kalai","orcid":"https://orcid.org/0000-0002-4559-8574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adam Tauman Kalai","raw_affiliation_strings":["atk@cc.gatech.edu#TAB#"],"affiliations":[{"raw_affiliation_string":"atk@cc.gatech.edu#TAB#","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076701535","display_name":"Katrina Ligett","orcid":"https://orcid.org/0000-0003-2780-6656"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Katrina Ligett","raw_affiliation_strings":["katrina@cs.cmu.edu#TAB#"],"affiliations":[{"raw_affiliation_string":"katrina@cs.cmu.edu#TAB#","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5108381794"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4995,"has_fulltext":true,"cited_by_count":42,"citation_normalized_percentile":{"value":0.89331459,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"39","issue":"3","first_page":"1088","last_page":"1106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":1.0,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":1.0,"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/T12288","display_name":"Optimization and Search Problems","score":0.9988999962806702,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9973999857902527,"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/online-algorithm","display_name":"Online algorithm","score":0.7347504496574402},{"id":"https://openalex.org/keywords/approximation-algorithm","display_name":"Approximation algorithm","score":0.6152752041816711},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5711323022842407},{"id":"https://openalex.org/keywords/hindsight-bias","display_name":"Hindsight bias","score":0.5575644373893738},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5326069593429565},{"id":"https://openalex.org/keywords/travelling-salesman-problem","display_name":"Travelling salesman problem","score":0.4923781156539917},{"id":"https://openalex.org/keywords/competitive-analysis","display_name":"Competitive analysis","score":0.47268491983413696},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4196358919143677},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41721558570861816},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.40115687251091003},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35873931646347046},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.35017961263656616},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.2940266728401184}],"concepts":[{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.7347504496574402},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.6152752041816711},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5711323022842407},{"id":"https://openalex.org/C10347200","wikidata":"https://www.wikidata.org/wiki/Q1960297","display_name":"Hindsight bias","level":2,"score":0.5575644373893738},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5326069593429565},{"id":"https://openalex.org/C175859090","wikidata":"https://www.wikidata.org/wiki/Q322212","display_name":"Travelling salesman problem","level":2,"score":0.4923781156539917},{"id":"https://openalex.org/C102408133","wikidata":"https://www.wikidata.org/wiki/Q5156350","display_name":"Competitive analysis","level":3,"score":0.47268491983413696},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4196358919143677},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41721558570861816},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.40115687251091003},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35873931646347046},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35017961263656616},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.2940266728401184},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1137/070701704","is_oa":false,"landing_page_url":"https://doi.org/10.1137/070701704","pdf_url":null,"source":{"id":"https://openalex.org/S153560523","display_name":"SIAM Journal on Computing","issn_l":"0097-5397","issn":["0097-5397","1095-7111"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Computing","raw_type":"journal-article"},{"id":"pmh:oai:repository.upenn.edu:statistics_papers-1485","is_oa":true,"landing_page_url":"https://repository.upenn.edu/statistics_papers/117","pdf_url":"https://repository.upenn.edu/bitstreams/21d11aaa-6d16-4a38-82ac-34f77a3df5ae/download","source":{"id":"https://openalex.org/S4306402083","display_name":"ScholarlyCommons (University of Pennsylvania)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79576946","host_organization_name":"University of Pennsylvania","host_organization_lineage":["https://openalex.org/I79576946"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Statistics Papers","raw_type":"text"},{"id":"pmh:oai:authors.library.caltech.edu:92261","is_oa":false,"landing_page_url":"https://authors.library.caltech.edu/92261/","pdf_url":null,"source":{"id":"https://openalex.org/S4306402161","display_name":"CaltechAUTHORS (California Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I122411786","host_organization_name":"California Institute of Technology","host_organization_lineage":["https://openalex.org/I122411786"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Book Section"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.141.6014","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.6014","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~katrina/papers/gameapprox.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.187.9728","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.187.9728","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cornell.edu/%7Ekatrina/papers/gameapprox-sicomp.pdf","raw_type":"text"},{"id":"pmh:oai:repository.upenn.edu:20.500.14332/47468","is_oa":false,"landing_page_url":"https://repository.upenn.edu/handle/20.500.14332/47468","pdf_url":null,"source":{"id":"https://openalex.org/S4377196331","display_name":"Scholarly Commons (University of Pennsylvania)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79576946","host_organization_name":"University of Pennsylvania","host_organization_lineage":["https://openalex.org/I79576946"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"published","raw_type":"Presentation"}],"best_oa_location":{"id":"pmh:oai:repository.upenn.edu:statistics_papers-1485","is_oa":true,"landing_page_url":"https://repository.upenn.edu/statistics_papers/117","pdf_url":"https://repository.upenn.edu/bitstreams/21d11aaa-6d16-4a38-82ac-34f77a3df5ae/download","source":{"id":"https://openalex.org/S4306402083","display_name":"ScholarlyCommons (University of Pennsylvania)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79576946","host_organization_name":"University of Pennsylvania","host_organization_lineage":["https://openalex.org/I79576946"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Statistics Papers","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2051259245.pdf","grobid_xml":"https://content.openalex.org/works/W2051259245.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W1508384000","https://openalex.org/W1526218779","https://openalex.org/W1575658237","https://openalex.org/W1600545090","https://openalex.org/W1965270141","https://openalex.org/W1985123706","https://openalex.org/W1998498767","https://openalex.org/W2004001705","https://openalex.org/W2014482607","https://openalex.org/W2069730673","https://openalex.org/W2120745256","https://openalex.org/W2148825261","https://openalex.org/W2169401877","https://openalex.org/W2912794399"],"related_works":["https://openalex.org/W2109997134","https://openalex.org/W3121728867","https://openalex.org/W2046731583","https://openalex.org/W1806598812","https://openalex.org/W2999289543","https://openalex.org/W3194414553","https://openalex.org/W1930615501","https://openalex.org/W2793361976","https://openalex.org/W2004604480","https://openalex.org/W2051259245"],"abstract_inverted_index":{"In":[0,55],"an":[1,10,144,180,229],"online":[2,11,84,112,114,119,122,146,215,238],"linear":[3,50,209],"optimization":[4,137,210,274],"problem,":[5],"on":[6,240],"each":[7],"period":[8],"t,":[9],"algorithm":[12,37,85,138,147,178,182,206,227,239,271,312],"chooses":[13,30],"$s_t\\in\\mathcal{S}$":[14],"from":[15],"a":[16,31,44,141,208,213,219,276,299,318],"fixed":[17,45,95,167],"(possibly":[18],"infinite)":[19],"set":[20,124],"$\\mathcal{S}$":[21],"of":[22,82,164,195,236,251,263],"feasible":[23],"decisions.":[24],"Nature":[25],"(who":[26],"may":[27],"be":[28,281,315],"adversarial)":[29],"weight":[32,53],"vector":[33,60],"$w_t\\in\\mathbb{R}^n$,":[34],"and":[35,68,121,152],"the":[36,52,56,59,66,70,74,83,93,150,153,165,173,176,187,225,233,237,252,256,261,290,294,303],"incurs":[38],"cost":[39,46,75,158,235],"$c(s_t,w_t)$,":[40,77],"where":[41,175,255,325],"c":[42],"is":[43,49,62,78,86,179,243,258,268,305],"function":[47],"that":[48,163,250,279,298],"in":[51,69,97,148,169,172,222],"vector.":[54],"full-information":[57,151],"setting,":[58,72,296],"$w_t$":[61],"then":[63,232],"revealed":[64],"to":[65,87,131,201,283,293],"algorithm,":[67,217],"bandit":[71,154,295],"only":[73,191,329],"experienced,":[76],"revealed.":[79],"The":[80],"goal":[81],"perform":[88],"nearly":[89,159],"as":[90,92,111,160,162,309,317],"well":[91],"best":[94,166,253,257,331,335],"$s\\in\\mathcal{S}$":[96,168],"hindsight.":[98,170,264],"Many":[99],"repeated":[100,323],"decision-making":[101],"problems":[102],"with":[103,156,183,218,260,275],"weights":[104],"fit":[105],"naturally":[106],"into":[107,143,212],"this":[108],"framework,":[109],"such":[110,140],"shortest-path,":[113],"traveling":[115],"salesman":[116],"problem":[117,142,211,304],"(TSP),":[118],"clustering,":[120],"weighted":[123],"cover.":[125],"Previously,":[126],"it":[127],"was":[128],"shown":[129],"how":[130,200],"convert":[132,202],"any":[133,203,241,284],"efficient":[134,145],"exact":[135],"offline":[136,177,204,226],"for":[139,192,207,272,302,320],"both":[149],"settings,":[155],"average":[157],"good":[161],"However,":[171],"case":[174],"approximation":[181,196,205,216,285],"ratio":[184],"$\\alpha":[185],">1$,":[186],"previous":[188],"approach":[189],"worked":[190],"special":[193],"types":[194],"algorithms.":[197],"We":[198],"show":[199],"corresponding":[214],"polynomial":[220],"blowup":[221],"runtime.":[223],"If":[224],"has":[228],"$\\alpha$-approximation":[230],"guarantee,":[231],"expected":[234],"sequence":[242],"not":[244],"much":[245],"larger":[246],"than":[247,334],"$\\alpha$":[248],"times":[249],"$s\\in\\mathcal{S}$,":[254],"chosen":[259],"benefit":[262],"Our":[265,311],"main":[266],"innovation":[267],"combining":[269],"Zinkevich's":[270],"convex":[273],"geometric":[277],"transformation":[278],"can":[280,313,327],"applied":[282],"algorithm.":[286],"Standard":[287],"techniques":[288],"generalize":[289],"above":[291],"result":[292],"except":[297],"\u201cbarycentric":[300],"spanner\u201d":[301],"also":[306,314],"(provably)":[307],"necessary":[308],"input.":[310],"viewed":[316],"method":[319],"playing":[321],"large":[322],"games,":[324],"one":[326],"compute":[328],"approximate":[330],"responses,":[332],"rather":[333],"responses.":[336]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
