BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250210T123531
DTSTART;TZID=America/Detroit:20250221T150000
DTEND;TZID=America/Detroit:20250221T160000
SUMMARY:Livestream / Virtual:AIM Seminar:  Cyclic Block Optimization: How they work\, why they work\, and where they work
DESCRIPTION:Abstract:  When facing challenging tasks in life\, one natural strategy is to solve simple sub-tasks one by one and hope that it eventually lead to somewhere better. Many challenging optimization problems in machine learning and scientific computing follow this approach—optimizing a small block of parameters cyclically\, one at a time. Notable examples include Sinkhorn’s algorithm for computing optimal transport maps and Schrödinger bridges\, as well as alternating least squares and multiplicative updates for matrix and tensor factorization. In this talk\, we will explore the principles behind these methods (block majorization-minimization)\, why they often require less parameter tuning than first-order gradient-based approaches (via second-order analysis)\, and their recent extensions to Riemannian manifolds\, with new results in Wasserstein variational inference. \nThis talk is based on recent works with Yuchen Li (UW)\, Joowon Lee (UW)\, Laura Balzano (Michigan)\, Deanna Needell (UCLA)\, and Sumit Mukerjee (Columbia). \n\nContact:  Laura Balzano
UID:130188-21865575@events.umich.edu
URL:https://events.umich.edu/event/130188
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:Off Campus Location - 1084
CONTACT:
END:VEVENT
END:VCALENDAR