Benders decomposition

Tuesday, August 9, 2016 - 11:00am - 12:30pm
Jeff Linderoth (University of Wisconsin, Madison)
Continuing the first lecture, we will introduce advanced features that
improve the performance of algorithms for solving the Benders-based
decomposition. Aggregating scenarios and regularization approaches
will be a primary focus. We will also introduce a different dual
decomposition technique that can be effective for solving two-stage
stochastic programs, and discuss algorithmic approaches for solving
the dual decomposition.
Tuesday, August 9, 2016 - 9:00am - 10:30am
Jim Luedtke (University of Wisconsin, Madison)
We present the Benders decomposition algorithm for solving two-stage stochastic optimization models. The main feature of this algorithm is that it alternates between solving a relatively compact master problem, and a set of subproblems, one per scenario, which can be solved independently (hence decomposing the large problem into many small problems). After presenting and demonstrating correctness of the basic algorithm, several computational enhancements will be discussed, including effective selection of cuts, multi-cut vs.
Subscribe to RSS - Benders decomposition