Scientific Program

Semi-plenary Lecture

Program -> Plenary and Semi-Plenary -> Thu.17:00.H 0104 title only | abstract | bio sketch

Thursday, 17:00 - 17:50 h, H 0104


Michael Friedlander
Data fitting and optimization with randomized sampling


Chair: Luís Nunes Vicente


Abstract:
For many structured data-fitting applications, incremental gradient methods (both deterministic and randomized) 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 an inexact gradient method and sampling scheme that exhibit the benefits of both incremental and full gradient methods.

 

 

Program -> Plenary and Semi-Plenary -> Thu.17:00.H 0104 title only | abstract | bio sketch

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