{"596262":{"#nid":"596262","#data":{"type":"event","title":"DOS Seminar- William B. Haskell","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE: Markov chain methods for analyzing algorithms\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cbr \/\u003E\r\n\u003Cstrong\u003EABSTRACT\u003C\/strong\u003E:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWe are interested in using Markov chain methods to establish convergence in probability for various algorithms in dynamic programming and optimization.\u0026nbsp; We start by investigating simple \u0026quot;empirical\u0026quot; variants of classical value and policy iteration for dynamic programming.\u0026nbsp; In this case, we show that the progress of these algorithms is stochastically dominated by an easy to analyze Markov chain, from which we can extract a convergence rate for the original algorithms.\u0026nbsp; We continue by showing that this same line of reasoning covers several empirical algorithms in optimization as well.\u0026nbsp; We argue that the advantage of this approach lies in its simplicity and intuitive appeal.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"DOS Seminar- William B. Haskell"}],"uid":"34547","created_gmt":"2017-09-20 18:48:16","changed_gmt":"2017-09-20 18:48:16","author":"nhendricks6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2017-09-22T13:10:00-04:00","event_time_end":"2017-09-22T14:10:00-04:00","event_time_end_last":"2017-09-22T14:10:00-04:00","gmt_time_start":"2017-09-22 17:10:00","gmt_time_end":"2017-09-22 18:10:00","gmt_time_end_last":"2017-09-22 18:10:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}