{"id":30706,"date":"2021-06-16T09:10:53","date_gmt":"2021-06-16T13:10:53","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/event\/special-seminar-faculty-candidate-ben-grimmer\/"},"modified":"2022-09-08T12:54:37","modified_gmt":"2022-09-08T16:54:37","slug":"special-seminar-faculty-candidate-ben-grimmer","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/special-seminar-faculty-candidate-ben-grimmer\/","title":{"rendered":"Special Seminar \u2013 Faculty Candidate Ben Grimmer"},"content":{"rendered":"<p>Title \u2013 The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization<br \/>\nAbstract<br \/>\nMinimax optimization has become a central tool for modern machine learning with applications in generative adversarial networks, robust training, reinforcement learning, etc. These applications are often nonconvex-nonconcave, but the existing theory is unable to identify and deal with the fundamental difficulties this poses. In this talk, we will overcome these limitations, describing the convergence landscape of the classic proximal point method on nonconvex-nonconcave minimax problems. Our key theoretical insight lies in identifying a modified objective, generalizing the Moreau envelope, that smoothes the original objective and convexifies and concavifies it based on the interaction between the minimizing and maximizing variables. When interaction is sufficiently strong, we derive global linear convergence guarantees. When interaction is weak, we derive local linear convergence guarantees under proper initialization. Between these two settings, we show undesirable behaviors like divergence and cycling can occur.<br \/>\nBio:\u00a0\u00a0Benjamin\u00a0Grimmer is a PhD student in Operations Research at Cornell University.\u00a0\u00a0He received his BS and MS degrees in Computer Science from Illinois Institute of Technology.\u00a0\u00a0His research\u00a0focuses\u00a0on\u00a0 theoretical\u00a0foundations\u00a0of\u00a0optimization.<br \/>\nPlease email Meg Tully \u2013 mtully4@jhu.edu for more information<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Title \u2013 The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization Abstract Minimax optimization has become a central tool for modern machine learning with applications in generative adversarial&hellip;<\/p>\n","protected":false},"author":3,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[],"class_list":["post-30706","tribe_events","type-tribe_events","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Special Seminar \u2013 Faculty Candidate Ben Grimmer | Department of Applied Mathematics and Statistics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/ams\/event\/special-seminar-faculty-candidate-ben-grimmer\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Special Seminar \u2013 Faculty Candidate Ben Grimmer | Department of Applied Mathematics and Statistics\" \/>\n<meta property=\"og:description\" content=\"Title \u2013 The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization Abstract Minimax optimization has become a central tool for modern machine learning with applications in generative adversarial&hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/engineering.jhu.edu\/ams\/event\/special-seminar-faculty-candidate-ben-grimmer\/\" \/>\n<meta property=\"og:site_name\" content=\"Department of Applied Mathematics and Statistics\" \/>\n<meta property=\"article:modified_time\" content=\"2022-09-08T16:54:37+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Special Seminar \u2013 Faculty Candidate Ben Grimmer | Department of Applied Mathematics and Statistics","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/engineering.jhu.edu\/ams\/event\/special-seminar-faculty-candidate-ben-grimmer\/","og_locale":"en_US","og_type":"article","og_title":"Special Seminar \u2013 Faculty Candidate Ben Grimmer | Department of Applied Mathematics and Statistics","og_description":"Title \u2013 The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization Abstract 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