Optimization and Algorithms Research
Optimization is in the center of every engineering discipline and every sector of the economy. Airlines and logistics companies run optimization algorithms to schedule their daily operations; power utilities rely on optimization to efficiently operate generators and renewable resources and distribute electricity; biotechnology firms search through massive genetic data using optimization to find new discoveries. UC Berkeley IEOR Department is at the forefront of optimization research. Our faculty and their students create new fields of optimization and push the boundaries in convex and nonconvex optimization, integer and combinatorial optimization to solve problems with massive data sets. Research activities are funded by NSF, DOE, DOD, ONR, and IBM Corporation.
Faculty
Selected Publications
Gradient-based Algorithms for Convex Discrete Optimization via Simulation
Haixiang Zhang, Zeyu Zheng and Javad Lavaei, Gradient-based Algorithms for Convex Discrete Optimization via Simulation, Operations Research, 2022.
Dynamic Regret Bounds for Online Nonconvex Optimization
Julie Mulvaney-Kemp, SangWoo Park, Ming Jin, and Javad Lavaei, Dynamic Regret Bounds for Online Nonconvex Optimization, to appear in IEEE Transactions on Control of Network Systems, 2022.
Statistical consistency of set-membership estimator for linear systems
A. Aswani and P. Hespanhol (2020), Statistical consistency of set-membership estimator for linear systems, IEEE Control Systems Letters, vol. 4, no. 3: 668-673.
Role of Sparsity and Structure in the Optimization Landscape of Non-convex Matrix Sensing
Igor Molybog, Somayeh Sojoudi, and Javad Lavaei, Role of Sparsity and Structure in the Optimization Landscape of Non-convex Matrix Sensing, to appear in Mathematical Programming, 2020.
New Methods for Regularization Path Optimization via Differential Equations
Heyuan Liu, Paul Grigas. “New Methods for Regularization Path Optimization via Differential Equations“. NeurIPS 2019 Workshop on Beyond First Order Methods in Machine Learning.
An Optimally-Competitive Algorithm for Maximum Online Perfect Bipartite Matching with i.i.d. Arrivals
Minjun Chang, Dorit S. Hochbaum, Quico Spaen and Mark Velednitsky. An Optimally-Competitive Algorithm for Maximum Online Perfect Bipartite Matching with iid Arrivals. Theory of Computing Systems, 64 (2020) pp.645-661.
Simplex QP-based Methods for Minimizing a Conic Quadratic Function over Polyhedra
A. Atamturk and A. Gomez. Simplex QP-based Methods for Minimizing a Conic Quadratic Function over Polyhedra. Mathematical Programming Computation 11, 311-340, 2019. https://doi.org/10.1007/s12532-018-0152-7
A Mixed-Integer Linear Programming Method for Optimal Orificing in Breed-and-Burn Cores
Chris Keckler, Alper Atamturk, Massimiliano Fratoni and Ehud Greenspan. A Mixed-Integer Linear Programming Method for Optimal Orificing in Breed-and-Burn Cores. Transactions of the American Nuclear Society, Vol. 118, 887-890, 2018. https://atamturk.ieor.berkeley.edu/pubs/orificing.pdf.
Strong Formulations for Quadratic Optimization with M-matrices and Indicator Variables
Alper Atamturk and Andres Gomez. Strong Formulations for Quadratic Optimization with M-matrices and Indicator Variables. Mathematical Programming 170, 141-176, 2018. https://link.springer.com/article/10.1007%2Fs10107-018-1301-5.