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
Head Undergraduate Advisor

Alper Atamturk

Professor
Department Chair

Ying Cui

Assistant Professor

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

Stochastic Localization Methods for Convex Discrete Optimization via Simulation

Zhang, Haixiang & Zheng, Zeyu & Lavaei, Javad. (2023). Stochastic Localization Methods for Convex Discrete Optimization via Simulation. Operations Research. 10.1287/opre.2022.0030.

A Decomposition Algorithm for Two-Stage Stochastic Programs with Nonconvex Recourse

Li, H., & Cui, Y. (2022). A Decomposition Algorithm for Two-Stage Stochastic Programs with Nonconvex Recourse. ArXiv. /abs/2204.01269

Randomized FIFO Mechanisms

Castro, F., Ma, H., Nazerzadeh, H., & Yan, C. (2021). Randomized FIFO Mechanisms. ArXiv. /abs/2111.10706

A new complexity metric for nonconvex rank-one generalized matrix completion

Zhang, Haixiang & Yalcin, Baturalp & Lavaei, Javad & Sojoudi, Somayeh. (2023). A new complexity metric for nonconvex rank-one generalized matrix completion. Mathematical Programming. 10.1007/s10107-023-02008-5.

Joint Online Learning and Decision-making via Dual Mirror Descent

Lobos, Alfonso & Grigas, Paul & Wen, Zheng. (2021). Joint Online Learning and Decision-making via Dual Mirror Descent.

Generalization Bounds in the Predict-then-Optimize Framework

El Balghiti, Othman & Elmachtoub, Adam & Grigas, Paul & Tewari, Ambuj. (2019). Generalization Bounds in the Predict-then-Optimize Framework.

Asymptotically Optimal Competitive Ratio for Online Allocation of Reusable Resources

Goyal, V., Iyengar, G., & Udwani, R. (2020). Asymptotically Optimal Competitive Ratio for Online Allocation of Reusable Resources.

Periodic Reranking for Online Matching of Reusable Resources

Udwani, Rajan. (2022). Periodic Reranking for Online Matching of Reusable Resources. 966-966. 10.1145/3490486.3538344.

Online Bipartite Matching with Reusable Resources

Delong, Steven and Farhadi, Alireza and Niazadeh, Rad and Sivan, Balasubramanian and Udwani, Rajan, Online Bipartite Matching with Reusable Resources (October 23, 2022). Available at SSRN: https://ssrn.com/abstract=4256240 or http://dx.doi.org/10.2139/ssrn.4256240

Adwords with Unknown Budgets and Beyond

Udwani, Rajan. “Adwords with Unknown Budgets and Beyond.” (2021).