Wednesday, February 12, 2020 - 4:00pm to 4:30pm
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LIDS & Stats Tea
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Semidefinite programming is a powerful framework from convex optimization that has striking potential for data science applications. Even so, practitioners often critique this approach by asserting that it is not possible to solve semidefinite programs at the scale demanded by real-world applications. As a result, there is a recent trend where heuristics with unverifiable assumptions are overtaking more rigorous, conventional optimization techniques. We argue that convex optimization did not reach yet its limits of scalability. In particular, we present a new optimization algorithm that can solve large semidefinite programming instances with low-rank solutions to moderate accuracy using limited arithmetic and minimal storage.
Joint work with Joel Tropp, Olivier Fercoq, Madeleine Udell, and Volkan Cevher.
Alp Yurtsever joined the MIT Laboratory for Information and Decision Systems as a postdoctoral fellow in January 2020. He received his PhD in Computer and Communication Sciences from Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland in 2019, and his double major BSc degrees in Electrical & Electronics Engineering and Physics from the Middle East Technical University in Turkey in 2013. His main research focus is on designing algorithms for solving large-scale optimization problems, and his doctoral dissertation entitled "Scalable Convex Optimization Methods for Semidefinite Programming" is honored with a Thesis Distinction by the EPFL Program Committee.