Berkeley Computational Optimization Lab

Welcome to Berkeley Computational Optimization Lab!

Optimization is now at the center of every engineering discipline and every sector of the economy. Airlines and logistics companies run optimization algorithms to schedule their daily operations most efficiently; 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 identify cancer-causing mutations and develop individualized treatments. UC Berkeley's IEOR Department is at the forefront of optimization research. Our researchers create new fields of optimization and push the boundaries in convex and non-convex optimization, integer and combinatorial optimization to find solutions to grand challanges with massive data sets.

Research activities at BCOL are concentrated predominantly on mathematical optimization with discrete decision variables, referred to as integer optimization. Integer optimization is a powerful and versatile modeling and algorithmic approach for optimization problems with discrete choices for the decision variables. These computationally challenging, non-convex optimization problems arise in diverse applications ranging from understanding cancerous genetic mutations to machine learning, from network security to generation and transmission of electricity.

We are in an exciting period in integer optimization research. Recent innovations in optimization theory and algorithms coupled with the advances in computer technology enable us to solve large-scale practical optimization problems that we could not have imagined attacking a decade ago. Researchers in our group develop cutting-edge theories and algorithms that push the limits of solving large-scale integer optimization problems.

Recent research projects of our group include sparse and smooth signal recovery, combinatorial optimization with risk, discrete utility maximization, conic integer programming, robust optimization, polyhedral cutting planes for mixed-integer programming, superadditive lifting techniques, optimization of logistics networks, polyhedral methods for lot sizing, network flow and design under uncertainty, and survivable network design.

We encourage you to visit BCOL Research Reports page for detailed information on our research projects.

A few researcher positions at doctoral and postdoc levels are available. We are especially interested in applicants with training and/or prior research experience in polyhedral theory, conic optimization, integer programming, and network design. We welcome visiting scholars with similar interests. Please contact Professor Atamturk for further information.


BCOL is equipped with state-of-the art Xeon Linux workstations having access to Berkeley DECF Linux computing grid with more than 500 CPUs. The complete suite of IBM CPLEX and Gurobi Optimization libraries, Mosek, SeDuMi, Matlab, and AMPL modeling system, and R statistics package are available for the researchers of the lab.


Research projects at BCOL are supported, in part, by grants from the National Science Foundation, the Office of the Secretary of Defense, the Advanced Research Projects Agency-Energy, the US Department of Energy, the IBM Corporation, the Federel Energy Regulatory Commission, and the University of California-Berkeley.

NSF     DoD     ARPA-E     DoE     IBM     FERC     UC Berkeley