[{"id":15927,"title":"The (Surprising) Rate Optimality of Greedy Procedures for Large-Scale Ranking and Selection","permalink":"https:\/\/bschool.nus.edu.sg\/biz-events\/event\/the-surprising-rate-optimality-of-greedy-procedures-for-large-scale-ranking-and-selection\/","category":"Seminars and talks","event_dept":{"value":"analytics-operations","label":"Analytics & Operations"},"event_sec_dept":false,"event_details":{"event_start_date":"14  December  2022","event_end_date":"14  December  2022","event_start_time":"10:30 am","event_end_time":"11:30 am","event_dress_code":"NA"},"event_loc":{"eve_address_selection":"1","eve_location_1":{"eve_org":"NUS Business School","eve_build":"Mochtar Riady Building","eve_room":"BIZ1 0509","eve_add":"15 Kent Ridge Drive","eve_count":"Singapore","eve_copos":"119245","eve_map_url":"https:\/\/goo.gl\/maps\/Q1kyjwxHNE22"},"eve_location_2":{"eve_org":"Shaw Foundation Alumni House","eve_build":"","eve_room":"Clove and Lemongrass Room Level 2","eve_add":"11 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119244,"eve_map_url":"https:\/\/goo.gl\/maps\/docgThkDWFxKdb9c7"},"eve_location_3":{"eve_org":"Hon Sui Sen Memorial Library Auditorium","eve_build":"","eve_room":"","eve_add":"1 Hon Sui Sen Drive","eve_count":"Singapore","eve_copos":117588,"eve_map_url":"https:\/\/goo.gl\/maps\/NJjWK4RMpC92"},"eve_location_4":{"eve_org":"NUSS Kent Ridge Guild House","eve_build":"","eve_room":"Dalvey Room","eve_add":"9 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119241,"eve_map_url":"https:\/\/goo.gl\/maps\/nXn2Luh96pH2"},"eve_location_5":{"eve_org":"Institute of Data Science","eve_build":"Innovation 4.0","eve_room":"1-3","eve_add":"3 Research Link","eve_count":"Singapore","eve_copos":117602,"eve_map_url":"https:\/\/goo.gl\/maps\/i1xocvvDh27QUXem7"},"eve_location_6":{"eve_org":"","eve_build":"","eve_room":"","eve_add":"","eve_count":"","eve_copos":"","eve_map_url":""},"eve_location_7":""},"event_introduction":"","event_short_intro":"","event_topic":null,"event_banner":false,"event_external_url":"","event_registration_details":{"event_registration_form":false,"event_registration_message":"","event_registration_deadline":null,"eve_registration_url":"","event_form":"","event_registration_ack":""},"event_speaker":[{"event_speaker_name":"L. Jeff Hong","event_speaker_designation":"Professor at Fudan University","event_speaker_affiliation":"","event_speaker_picture":false,"event_speaker_url":"","event_speaker_introduction":"<p>Jeff Hong received his bachelor\u2019s and Ph.D. degrees from Tsinghua University and Northwestern University, respectively. He is currently with Fudan University, holding the positions of Fudan Distinguished Professor, Hongyi Chair Professor, Chair of Department of Management Science in School of Management, and Associate Dean of School of Data Science. He was Chair Professor of Management Sciences at City University of Hong Kong, and Professor of Industrial Engineering and Logistics Management at the Hong Kong University of Science and Technology. Prof. Hong\u2019s research interests include stochastic simulation, stochastic optimization, risk management and supply chain management. He is currently the Simulation Area Editor of <em>Operations Research<\/em>, an Associate Editor of <em>Management Science<\/em> and <em>ACM Transactions on Modeling and Computer Simulation<\/em>, and he was the President of INFORMS Simulation Society from 2020 to 2022.<\/p>\n"}],"event_agenda":false,"event_photo_gallery":false,"event_presentations":false,"event_custom_heading":[{"event_custom_title":"Abstract","event_custom_details":"<p>Large-scale ranking-and-selection (R&amp;S), which aims to select the best alternative with the largest mean performance from a finite set of alternatives, has emerged as an important research topic in simulation optimization. An ideal large-scale R&amp;S procedure should be rate optimal, i.e., the total sample size required to deliver an asymptotically non-zero probability of correct selection (PCS) grows linearly in the number of alternatives. We discover that the na\u00efve greedy procedure that keeps sampling the alternative with the largest running sample mean performs surprisingly well and appears rate optimal in solving large-scale R&amp;S problems. We develop a boundary-crossing perspective and prove that the greedy procedure is indeed rate optimal, and we further show that the obtained PCS lower bound is tight asymptotically for the slippage configuration with a common variance. To develop a practically competitive procedure that harnesses the rate optimality of the greedy procedure, we propose the explore-first greedy (EFG) procedure that adds an exploration phase to the greedy procedure. We show that the new procedure is simple, rate optimal and consistent. The numerical study demonstrates that the EFG procedure performs well compared to the existing carefully designed rate-optimal R&amp;S procedures.\u00a0 This is a joint work with Zaile Li (Fudan University) and Weiwei Fan (Tongji University).<\/p>\n"}],"event_enquiry_details":{"event_enq_full_name":"WONG Cecilia\/TAN Dorothy ","event_enq_department":"","event_enq_email":"","event_enq_telephone":"6516 6225\/6516 3067","event_enq_website":""}}]