Special Seminar – Faculty Candidate Diego Cifuentes

January 8, 2021 @ 11:00 am – 12:00 pm

Title – Advancing scalable, provable optimization methods in semidefinite & polynomial programs


Optimization is a broad area with ramifications in many disciplines, including machine learning, control theory, signal processing, robotics, computer vision, power systems, and quantum information. I will talk about some novel algorithmic and theoretical results in two broad classes of optimization problems. The first class of problems are semidefinite programs (SDP). I will present the first polynomial time guarantees for the Burer-Monteiro method, which is widely used for solving large scale SDPs. I will also discuss some general guarantees on the quality of SDP solutions for parameter estimation problems. The second class of problems I will consider are polynomial systems. I will introduce a novel technique for solving polynomial systems that, by taking advantage of graphical structure, is able to outperform existing techniques by orders of magnitude.


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