Advanced, Adaptive and Flexible Algorithms for Decentralized Optimization
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Abstract: The problem of optimizing an objective function by employing a decentralized procedure using multiple agents in a connected network has gained significant attention over the last decades. This is due to the wide applicability of decentralized optimization to many important science and engineering applications such as, optimal control, machine learning, robotics, sensor networks, and smart grids. Decentralized optimization problems come in diverse shapes and forms, and could have very different characteristics. In this talk, we discuss novel flexible approaches for solving decentralized optimization problems that adapt to problem characteristics. We present two unifying algorithmic frameworks that recover popular algorithms as special cases. We discuss the rationale behind our proposed techniques, convergence in expectation and complexity guarantees for our algorithms, and present encouraging numerical results. This is joint work with Raghu Bollapragada, Shagun Gupta and Suhail Shah.
Bio: Albert S. Berahas is an Assistant Professor in the Industrial and Operations Engineering department at the University of Michigan. Before joining the University of Michigan, he was a Postdoctoral Research Fellow in the Industrial and Systems Engineering department at Lehigh University working with Professors Katya Scheinberg, Frank Curtis and Martin Takáč. Prior to that appointment, he was a Postdoctoral Research Fellow in the Industrial Engineering and Management Sciences department at Northwestern University working with Professor Jorge Nocedal. Berahas completed his PhD studies in the Engineering Sciences and Applied Mathematics (ESAM) department at Northwestern University in 2018, advised by Professor Jorge Nocedal. He received his undergraduate degree in Operations Research and Industrial Engineering (ORIE) from Cornell University in 2009, and in 2012 obtained an MS degree in Applied Mathematics from Northwestern University. Berahas’ research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization. Berahas is served as the vice-chair of the Nonlinear Optimization cluster for the INFORMS Optimization Society (2020-2022), the chair of the Nonlinear Optimization cluster for the INFORMS Optimization Society Conference (2021-2022), and the co-chair of the Nonlinear Optimization cluster for the ICCOPT 2022 conference (2021-2022). Berahas is the president of the INFORMS Junior Faculty Interest Group (JFIG).
***Event will take place in hybrid format. The location for in-person attendance will be room 1500 EECS. Attendance will also be possible via Zoom. The Zoom link and password will be distributed to the Controls Group e-mail list-serv.
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