Parametric error bounds for convex approximations of two-stage mixed-integer recourse models with a random second-stage cost vector

E. Ruben van Beesten, Ward Romeijnders

Research output: Working paperPreprintAcademic


We consider two-stage recourse models in which the second-stage problem has integer decision variables and uncertainty in the second-stage cost vector, technology matrix, and the right-hand side vector. Such mixed-integer recourse models are typically non-convex and thus hard to solve. There exist convex approximations of these models with accompanying error bounds. However, it is unclear how these error bounds depend on the distributions of the second-stage cost vector q. In fact, the only error bound that is known hinges on the assumption that q has a finite support. In this paper, we derive parametric error bounds whose dependence on the distribution of q is explicit and that hold for any distribution of q, provided it has a finite expected ℓ1-norm. We find that the error bounds scale linearly in the expected value of the ℓ1-norm of q.
Original languageEnglish
Publication statusSubmitted - 3-Jun-2022

Publication series

PublisherCornell University Press
ISSN (Print)2331-8422

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