IEOR - Designing a More Efficient World

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

Professor
Head Graduate Advisor

Javad Lavaei

Associate Professor

Barna Saha

Assistant Professor

Rajan Udwani

Assistant Professor

Laurent El Ghaoui

Joint Faculty, EECS

Selected Publications

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.

A new approximation technique for resource‐allocation problems

Barna Saha, Aravind Srinivasan. “A new approximation technique for resource‐allocation problems“. Random Structure & Algorithms

Convexification of generalized network flow problem

Somayeh Sojoudi, Salar Fattahi and Javad Lavaei, Convexification of Generalized Network Flow Problem, to appear in Mathematical Programming, pp. 1-39, 2017.

Transformation of Optimal Centralized Controllers Into Near-Globally Optimal Static Distributed Controllers

Somayeh Sojoudi, Salar Fattahi and Javad Lavaei, Convexification of Generalized Network Flow Problem, to appear in Mathematical Programming, pp. 1-39, 2017.