Integer Programming Research at Berkeley

Integer programming is a powerful and versatile modeling and optimization framework for solving problems involving decision variables that come from a discrete set. These hard-to-solve, non-convex optimization problems arise in diverse applications ranging from radiotherapy to electronic financial/commodity exchanges, from fiber-optic network design to power generation.

This is a very exciting period in integer programming research. Recent innovations in polyhedral 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. The researchers in our group develop cutting-edge theories and methodologies that push the limits of solving large-scale integer programs.

Recent research projects of our group include polyhedral cutting planes for general mixed-integer programming, lifting techniques, optimization of logistics networks, robust mixed 0-1 programming, polyhedral methods for production lot-sizing, network flow and design with demand uncertainty, and survivable telecom network design. Please visit the website of Berkeley Computational Optimization Lab for more information.