Multi-period mixed-integer quadratic programming

Abstract

In this talk, we consider multi-period convex quadratic optimization problems with indicator variables, with important applications in machine learning and model predictive control. We study a subclass with a (block-)factorizable 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 reveal both promising and challenging findings. This is a joint work with Jisun Lee and Andrés Gómez.

Bio

Alper Atamturk is the Earl J. Isaac Chair in the Science and Analysis of Decision Making and Professor of Industrial Engineering and Operations Research at the University of California, Berkeley. He received his Ph.D. from the Georgia Institute of Technology in 1998. His main research interests are integer programming and optimization under uncertainty, with applications to machine learning, energy systems, portfolio management, 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 a Vannevar Bush Fellow of the US Department of Defense. He received the Farkas Prize from the INFORMS Optimization Society in 2023 and the ICS Prize from the INFORMS Computing Society in 2025.