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

Ilan Adler

Professor
Head MEng Advisor

Anil Aswani

Associate Professor

Alper Atamturk

Professor
Department Chair

Paul Grigas

Assistant Professor

Dorit Hochbaum

Distinguished Professor
ORMS Advisor

Javad Lavaei

Associate Professor

Rajan Udwani

Assistant Professor

Laurent El Ghaoui

Joint Faculty, EECS

Selected Publications

Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods

Guo, X., Hu, A., & Zhang, J. (2022). Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods. Proceedings of the AAAI Conference on Artificial Intelligence36(6), 6774-6782. https://doi.org/10.1609/aaai.v36i6.20633

Entropy regularization for mean field games with learning

X. Guo, R. Xu, T. Zariphopoulou, “Entropy regularization for mean field games with learning”. Mathematics of Operations Research 47 (4), 3239-3260

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.