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Diego Cifuentes, MIT, Advancing scalable, provable optimization methods in semidefinite & polynomial programs

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January 15 | 12:00 pm - 1:00 pm EST

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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.

Zoom Link:  https://ncsu.zoom.us/j/92555512643?pwd=c3pJeCtOSEg3LzEwZU9WK0kwWVdjZz09

Website: http://www.mit.edu/~diegcif/



January 15

12:00 pm - 1:00 pm
Event Category: