Data Fitting and Optimization With Randomized Sampling
|Affiliation:||University of British Columbia|
|Room:||Mathematics and Computer Building (MC) 5168|
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.
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