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Seminar - Michael FriedlanderExport this event to calendar

Wednesday, January 15, 2014 — 9:30 AM EST

Data Fitting and Optimization With Randomized Sampling

Speaker: Michael Friedlander
Affiliation: University of British Columbia
Room: Mathematics and Computer Building (MC) 5168

Abstract: 

For many structured data-fitting applications, stochastic gradient methods offer inexpensive iterations by sampling only subsets of the data. They make great progress initially, but eventually stall. Full-gradient methods, in contrast, often achieve steady convergence, but may be prohibitively expensive for large problems. Applications in machine learning and robust seismic inversion motivate us to develop a sampling method that exhibits the benefits of both stochastic and full-gradient methods.

Location 
MC - Mathematics & Computer Building
5158
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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