Accelerated Optimization in the PDE Framework

Yezzi, Anthony

Title:

Accelerated Optimization in the PDE Framework

dc.contributor.author	Yezzi, Anthony
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Robotics and Intelligent Machines	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Electrical and Computer Engineering	en_US
dc.date.accessioned	2018-11-09T21:34:58Z
dc.date.available	2018-11-09T21:34:58Z
dc.date.issued	2018-10-24
dc.description	Presented on October 24, 2018 from 12:15 p.m.-1:15 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118, Georgia Tech.	en_US
dc.description	Anthony J. Yezzi was born in Gainesville, Florida and grew up in Minneapolis, Minnesota. He obtained both his Bachelor's degree and his Ph.D. in the Department of Electrical Engineering at the University of Minnesota with minors in mathematics and music. After completing his Ph.D., he continued his research as a post-doctoral research associate in the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology in Boston. His research interests fall broadly within the fields of image processing and computer vision. In particular, he is interested in curve and surface evolution theory and partial differential equation techniques as they apply to topics within these fields, such as segmentation, image smoothing, and enhancement, optical flow, stereo disparity, shape from shading, object recognition, and visual tracking. Much of Yezzi's work is tailored to problems in medical imaging, including cardiac ultrasound, MRI, and CT. Yezzi joined the Georgia Tech faculty in the fall of 1999 where he has taught courses in digital signal processing and is working to develop advanced courses in computer vision and medical image processing. He consults with industry in the areas of visual inspection and medical imaging. His hobbies include classical guitar, opera, and martial arts.	en_US
dc.description	Runtime: 64:25 minutes	en_US
dc.description.abstract	Following the seminal work of Nesterov, accelerated optimization methods (sometimes referred to as momentum methods) have been used to powerfully boost the performance of first-order, gradient-based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical. Not only does accelerated gradient descent converge considerably faster than traditional gradient descent, but it performs a more robust local search of the parameter space by initially overshooting and then oscillating back as it settles into a final configuration, thereby selecting only local minimizers with an attraction basin large enough to accommodate the initial overshoot. This behavior has made accelerated search methods particularly popular within the machine learning community where stochastic variants have been proposed as well. So far, however, accelerated optimization methods have been applied to searches over finite parameter spaces. We show how a variational setting for these finite dimensional methods (recently formulated by Wibisono, Wilson, and Jordan) can be extended to the infinite dimensional setting, both in linear functional spaces as well as to the more complicated manifold of 2D curves and 3D surfaces. Moreover, we also show how extremely simple explicit discretization schemes can be used to efficiently solve the resulting class of high-dimensional optimization problems.	en_US
dc.format.extent	64:25 minutes
dc.identifier.uri	http://hdl.handle.net/1853/60525
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	IRIM Seminar Series
dc.subject	Accelerated optimization	en_US
dc.title	Accelerated Optimization in the PDE Framework	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.author	Yezzi, Anthony
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries	IRIM Seminar Series
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