Continuing the first lecture, we will introduce advanced features thatimprove the performance of algorithms for solving the Benders-baseddecomposition. Aggregating scenarios and regularization approacheswill be a primary focus. We will also introduce a different 'dual'decomposition technique that can be effective for solving two-stagestochastic ...
Creator:
Linderoth, Jeff (University of Wisconsin, Madison)
Created:
2016-08-09
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
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 p...
Creator:
Luedtke, Jim (University of Wisconsin, Madison)
Created:
2016-08-09
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.