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SUMMARY:Stochastic Algorithms for Nonconvex Optimization - Mathukumalli Vi
 dyasagar\, IIT Hyderabad
DTSTART:20250320T140000Z
DTEND:20250320T150000Z
UID:TALK229534@talks.cam.ac.uk
CONTACT:Fulvio Forni
DESCRIPTION:Nowadays it is quite common to solve optimization problems in 
 10^9 or more variables. At these levels\, it is not practical to use the "
 true" gradient of the objective function. Instead\, a variety of methods a
 re based on using approximate gradients\, which are also random\; in other
  words\, they are stochastic gradients.  Often\, only some components of t
 he argument are updated at each iteration\, to reduce storage calls. As a 
 result\, nowadays optimization algorithms produce stochastic processes\, a
 s opposed to sequences of vectors in some Euclidean space. Another issue i
 s that the objective function is not convex.\n\nThe seminar will be held i
 n LR3A \, Department of Engineering\, and online (zoom): https://newnham.z
 oom.us/j/92544958528?pwd=YS9PcGRnbXBOcStBdStNb3E0SHN1UT09\n
LOCATION:LR3A\, Department of Engineering and online (Zoom)
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