Deriving robust counterparts of nonlinear uncertain inequalities

310 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Data-driven robust optimization (2013, methodology): “, ([15]), only consider the special case when f(u,x) is bi-affine, but one can generalize them to (2) using techniques from [4] (see also Sec.”
Distributionally Robust Optimization: A Review (2019, unknown): “[29].”
Stochastic optimization models for location and inventory prepositioning of disaster relief supplies (2020, background): “Instead, RO assumes that uncertain parameters reside in a so-called “uncertainty set” of possible outcomes (Bertsimas and Sim, 2004; Ben-Tal et al., 2015; Soyster, 1973), and optimization is based on the worst-case scenario occurring within the uncertainty set.”
Adaptive Distributionally Robust Optimization (2019, methodology): “Dynamic optimization models, where decisions adapt to the uncertain outcomes as they unfold in stages, typically suffer from the “curse of dimensionality” and are computationally intractable (see, for instance, Shapiro and Nemirovski 2005, Dyer and Stougie 2006, Ben-Tal et al. 2004). To yield tracta”