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
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).