{"id":53613,"date":"2025-09-02T11:17:00","date_gmt":"2025-09-02T15:17:00","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=53613"},"modified":"2025-09-08T15:42:56","modified_gmt":"2025-09-08T19:42:56","slug":"ams-weekly-seminar-ben-grimmer","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-ben-grimmer\/","title":{"rendered":"AMS Weekly Seminar | Ben Grimmer"},"content":{"rendered":"

Location:\u00a0<\/strong>Krieger 205<\/p>\n

When:<\/strong> September 11th at 1:30 p.m.<\/p>\n

Title:<\/strong>\u00a0Optimizing Optimization Methods, To and Beyond Minimax Optimality<\/p>\n

Abstract:<\/strong> This talk will take up the task of designing the provably best possible gradient method for smooth convex optimization. Methods with big-O optimal worst-case guarantees were (famously) discovered in the 80s by Nesterov. Methods with exactly minimax optimal worst-case guarantees were developed in the last decade. As a first result, we will present a \u201csubgame perfect\u201d method that is not only optimal against a worst-case problem instance but also optimally leverages all gradient information revealed at each step. This corresponds to being dynamically minimax optimal, or in game theory terms, provides us with a subgame perfect strategy for optimization. Besides attaining this high standard (beyond minimax optimality), our subgame perfect gradient method is also very fast. As a second result, time permitting, we will address the problem of optimally selecting stepsizes for gradient descent. A construction and analysis of the (conjectured) minimax optimal stepsizes will be given. These optimized stepsizes arise from a beautiful fractal\/dynamic programming construction.<\/p>\n

Bio:<\/strong> Ben Grimmer is an assistant professor of applied mathematics and statistics at Johns Hopkins University, supported by AFOSR, NSF, and as a Sloan Fellow. Prior to joining Hopkins, Ben did his PhD at Cornell, advised by James Renegar and Damek Davis, spending a couple of semesters with Google Research and Simons. Ben\u2019s work primarily focuses on novel methods for the design and analysis of first-order methods. Some of his recent computer-assisted works have received substantial interest, being featured in popular mathematics venues like Quanta.<\/p>\n

Zoom link: <\/strong>https:\/\/wse.zoom.us\/j\/93600407710?pwd=JBL8VsObRxX6MkhdjAUxCadqJDoZrZ.1<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Location:\u00a0Krieger 205 When: September 11th at 1:30 p.m. Title:\u00a0Optimizing Optimization Methods, To and Beyond Minimax Optimality Abstract: This talk will take up the task of designing the provably best 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