Applied Math Colloquium: Benjamin Grimmer (JHU)
Location
Mathematics/Psychology : 104
Date & Time
October 10, 2025, 12:00 pm – 1:00 pm
Description
Title: Beyond Minimax Optimality: A Subgame Perfect Gradient Method
Abstract: 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. We will present a 
    "subgame perfect" 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 at solving actual 
    problems. 
Bio: 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's work primarily focuses 
    on novel methods for the design and analysis of first-order methods, 
    recently receiving the INFORMS Optimization Societies
     Young Researcher Prize. Some of his recent computer-assisted works have
     received substantial interest, being featured in popular mathematics 
    venues like Quanta.
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