Computational Techniques for Nonlinear Optimization and Learning Problems

Abstract: The design of efficient computational techniques for nonlinear optimization
has been an active area of research in the field of optimization theory for many years,
but has recently been shaping the mathematical foundation of data science.
In this walk, we investigate different strategies for finding a high-quality solution
of a nonlinear optimization or learning problem. To this end, we develop key notions
such as penalized semi-definite programs (SDPs), spurious local trajectory and graphical
mutual incoherence and also design algorithms with near-linear time/memory complexity
for sparse SDPs with localized constraints to break down the complexity of SDPs
and make them as usable as linear programs. Finally, we offer several case studies
on real-world systems such as power grids and transportations.

Biography: Javad Lavaei is an Associate Professor in the Department of Industrial
Engineering and Operations Research at UC Berkeley. He was an Assistant Professor
in Electrical Engineering at Columbia University in 2012-2015. He obtained his Ph.D. degree
in Control & Dynamical Systems from California Institute of Technology,
and was a postdoctoral scholar at Precourt Institute for Energy and Electrical Engineering
of Stanford University in 2011-2012. He has won several awards, including DARPA
Young Faculty Award, DARPA Director's Fellowship, ONRYoung Investigator Award,
ONR Director of Research Early Career Grant, AFOSR Young Investigator Award,
NSF CAREER Award, Google Faculty Award, and Canadian Governor General's Gold Medal.
Javad Lavaei is an associate editor for the IEEE Transactions on Smart Grid,
IEEE Transactions on Automatic Control, and IEEE Control Systems Letters,
and serves on the conference editorial boards of the IEEE Control Systems Society
and the European Control Association. He is a recipient of the 2015 INFORMS Optimization
Society Prize for Young Researchers, the 2015 Best Journal Paper Award given
by IEEE Power & Energy Society, the 2016 Donald P. Eckman Award given
by the American Automatic Control Council, the 2016 INFORMS ENRE Energy Best Publication Award,
the 2017 SIAM Control and Systems Theory Prize, and two best conference paper
finalist awards from the Control Systems Society in 2014 and 2019.  
He has also received the Presidential Early Career Award for Scientists and Engineers
given by the White House.