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UID:20261010T124118EDT-2265OlgPcG@132.216.98.100
DTSTAMP:20261010T164118Z
DESCRIPTION:Applied Mathematics Seminar\n	\n	Speaker: Laurent Lessard (Northe
 astern University)\n\nHost: J. Forbes (McGill\, CIM)\n\nAbstract: Most com
 plicated optimization problems\, in particular those involving a large num
 ber of variables\, are solved in practice using iterative algorithms. The 
 problem of selecting a suitable algorithm is currently more of an art than
  a science\; a great deal of expertise is required to know which algorithm
 s to try and how to properly tune them. Moreover\, there are seldom perfor
 mance guarantees. In this talk\, I will show how the problem of algorithm 
 selection can be approached using tools from robust control theory. By sol
 ving simple semidefinite programs (that do not scale with problem size)\, 
 we can derive robust bounds on convergence rates for popular algorithms su
 ch as the gradient method\, proximal methods\, fast/accelerated methods\, 
 and operator-splitting methods such as ADMM. The bounds derived in this ma
 nner either match or improve upon the best known bounds from the literatur
 e. The bounds also lead to a natural energy dissipation interpretation and
  an associated Lyapunov function. Finally\, our framework can be used to e
 fficiently search for algorithms that meet desired performance specificati
 ons\, thus establishing a principled methodology for designing new algorit
 hms. We give examples of novel algorithm designs to address distributed op
 timization and stochastic optimization problems.\n
DTSTART:20230306T213000Z
DTEND:20230306T223000Z
LOCATION:BURN 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Robust control perspectives on algorithm analysis and design
URL:https://www.mcgill.ca/cim/channels/event/robust-control-perspectives-al
 gorithm-analysis-and-design-351765
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