{"id":800338,"date":"2021-11-29T18:01:50","date_gmt":"2021-11-30T02:01:50","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=800338"},"modified":"2021-11-29T18:02:20","modified_gmt":"2021-11-30T02:02:20","slug":"optimization-with-uncertainty","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/optimization-with-uncertainty\/","title":{"rendered":"Optimization with Uncertainty"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background  has-background- card-background--full-bleed\">\n\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1921\" height=\"601\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/11\/NeurIPS2021_header_11-2021_1920x720.png\" class=\"attachment-full size-full\" alt=\"dark purple background with fractal design pattern\" style=\"\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/11\/NeurIPS2021_header_11-2021_1920x720.png 1921w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/11\/NeurIPS2021_header_11-2021_1920x720-300x94.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/11\/NeurIPS2021_header_11-2021_1920x720-1024x320.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/11\/NeurIPS2021_header_11-2021_1920x720-768x240.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/11\/NeurIPS2021_header_11-2021_1920x720-1536x481.png 1536w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/11\/NeurIPS2021_header_11-2021_1920x720-240x75.png 240w\" sizes=\"(max-width: 1921px) 100vw, 1921px\" \/>\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 align-self-center\">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 id=\"optimization-with-uncertainty\">Optimization with Uncertainty<\/h1>\n\n\n\n<p><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p>Classical algorithms (exact\/ approximation) work with an input which is entirely specified up front. While this offline model is useful for static optimization problems, there are several domains which need algorithms to make decisions with partial\/uncertain information which evolves over time. We seek to design algorithms in such uncertain environments and also design frameworks to evaluate their quality using different metrics. Models such as online algorithms, stochastic optimization, and algorithms with recourse fall into this area of study.<\/p>\n\n\n","protected":false},"excerpt":{"rendered":"<p>Classical algorithms (exact\/ approximation) work with an input which is entirely specified up front. While this offline model is useful for static optimization problems, there are several domains which need algorithms to make decisions with partial\/uncertain information which evolves over time. We seek to design algorithms in such uncertain environments and also design frameworks to [&hellip;]<\/p>\n","protected":false},"featured_media":798118,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13561],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-800338","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-algorithms","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[479862,800590,800596],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Ravishankar Krishnaswamy","user_id":33330,"people_section":"Section name 0","alias":"rakri"}],"msr_research_lab":[199562],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/800338"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/800338\/revisions"}],"predecessor-version":[{"id":807949,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/800338\/revisions\/807949"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/798118"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=800338"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=800338"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=800338"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=800338"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=800338"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}