Title: 
Multi-period mixed-integer quadratic programming

Abstract: 
In this talk, we consider multi-period convex quadratic optimization problems with indicator variables. This problem class has important applications in machine learning and model predictive control. We study a sub-class with a factorable or block-factorable cost matrix and show that it is solvable in polynomial time. We also give a compact convex hull description in an extended space with linear and conic quadratic inequalities. Our computational experiments with data from neuron activation inference and hybrid-electric vehicle power management indicate promises as well as challenges.  

Joint work with Andres Gomez and Jisun Lee.

Bio: 
Alper Atamturk is the Earl J. Isaac Chair in the Science and Analysis of Decision Making, Professor and Chair of the Department of Industrial Engineering and Operations at the University of California, Berkeley. He received his Ph.D. from the Georgia Institute of Technology in 1998. His research interests are in optimization, integer programming, optimization under uncertainty with applications to machine learning, energy systems, portfolio and network design.   

Alper serves as the UC Berkeley site director of the NSF AI Institute for Advances in Optimization. He serves as co-editor for Mathematical Programming, area editor for Mathematical Programming Computation, and associate editor for Operations Research, Discrete Optimization, and Journal of Risk. He is a Fellow of INFORMS and Vannevar Bush Fellow of the US Department of Defense. He received the Farkas Prize from INFORMS Optimization Society in 2023.