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DTSTART;TZID=America/Los_Angeles:20251103T161500
DTEND;TZID=America/Los_Angeles:20251103T171500
DTSTAMP:20260427T034025
CREATED:20251021T180716Z
LAST-MODIFIED:20251021T180716Z
UID:3909-1762186500-1762190100@colleges.claremont.edu
SUMMARY:Convergence analysis of the Alternating Anderson-Picard method for nonlinear fixed-point problems (Xue Feng\, UCLA)
DESCRIPTION:Abstract: Anderson Acceleration (AA) has been widely used to solve nonlinear fixed-point problems due to its rapid convergence. This talk focuses on a variant of AA in which multiple Picard iterations are performed between each AA step\, referred to as the Alternating Anderson-Picard (AAP) method. Despite introducing more `slow’ Picard iterations\, this method has been demonstrated to be efficient and even more robust in both linear and nonlinear cases. However\, there is a lack of theoretical analysis for AAP in the nonlinear context. In this work\, we address this gap by establishing the equivalence between AAP and a multisecant-GMRES method that employs GMRES to solve a multisecant linear system at each iteration. From this perspective\, we show that AAP actually “converges” the well-known Newton-GMRES method. These connections also help us understand the convergence behavior of AAP\, especially the asymptotic convergence rate.
URL:https://colleges.claremont.edu/ccms/event/convergence-analysis-of-the-alternating-anderson-picard-method-for-nonlinear-fixed-point-problems-xue-feng-ucla/
LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu
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DTSTART;TZID=America/Los_Angeles:20251110T161500
DTEND;TZID=America/Los_Angeles:20251110T171500
DTSTAMP:20260427T034025
CREATED:20251006T190434Z
LAST-MODIFIED:20251006T223009Z
UID:3883-1762791300-1762794900@colleges.claremont.edu
SUMMARY:To Wait or Not to Wait? A Trade-off Between Population Externality and Signal Quality (Lan-Yi Liu\, National Taiwan University)
DESCRIPTION:Abstract: Transparency is vital for efficiency in social systems\, yet individuals with critical information often strategically postpone disclosure\, even when required\, to benefit themselves.\nTo study this behavior\, we introduce a multi-stage Chinese restaurant game with incomplete information that features system-recommended action rules and varying levels of player foresight. In our model\, players initially receive a suggestion to join a queueing group based on their private signal\, but can choose to switch groups. Following this\, players sequentially select a final resource\, balancing the desire to avoid congested externalities with the need to acquire more information.\nWe prove a closed-form solution for the players’ pure-strategy Nash equilibrium. Our key finding is that players with high-quality signals have no incentive to reveal their information to those with low-quality signals. This suggests that allowing players to strategically determine their decision timing\, without further system design\, leads to an inefficient equilibrium allocation.\nOur results on congested externalities and system suggestions help explain the inherent trade-off between information quality and decision timing in various real-world scenarios\, such as the challenges of vaccine distribution during a pandemic\, the strategic crowding of factory location selection\, and the decision-making faced by political candidates positioning themselves on the spectrum.
URL:https://colleges.claremont.edu/ccms/event/to-wait-or-not-to-wait-a-trade-off-between-population-externality-and-signal-quality-lan-yi-liu-harvey-mudd-college/
LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251117T161500
DTEND;TZID=America/Los_Angeles:20251117T171500
DTSTAMP:20260427T034025
CREATED:20251111T194006Z
LAST-MODIFIED:20251111T194015Z
UID:3924-1763396100-1763399700@colleges.claremont.edu
SUMMARY:A Signal Separation View of Classification (Ryan O'Dowd\, CGU)
DESCRIPTION:Abstract: The problem of classification in machine learning has often been approached in terms of function approximation. In this talk\, we propose an alternative approach for classification in arbitrary compact metric spaces which\, in theory\, yields both the number of classes\, and a perfect classification using a minimal number of queried labels. Our approach uses localized trigonometric polynomial kernels initially developed for the point source signal separation problem in signal processing. Rather than point sources\, we examine a convex combination of probability distributions representing the various classes from the machine learning classification problem. The localized kernel technique developed for separating point sources is then shown to separate the supports of these distributions. This is done in a hierarchical manner in our MASC algorithm to accommodate touching/overlapping class boundaries. The algorithm works in an active learning paradigm\, deciding on points to query for their true class label and extending those labels to nearby points. We illustrate our theory on several simulated and real life data sets\, including the Salinas and Indian Pines hyperspectral data sets and a document data set.
URL:https://colleges.claremont.edu/ccms/event/a-signal-separation-view-of-classification-ryan-odowd-cgu/
LOCATION:Estella 1021 (Emmy Noether Room)\, Pomona College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu
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