Scenario reduction in stochastic programming

934 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.

Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “In fact, data reduction is most similar to the scenario reduction methods in the stochastic programming literature (see e.g., Dupačová et al., 2000, 2003).”
Scenario Reduction Applied to Geostatistical Simulations (2013, methodology): “Typical examples of the function h for applications are h ≡ 1 and h(r) = rp−1 for r ∈ R+ with p ≥ 1 (Dupacova et al. 2003 and Heitsch and Romisch 2003).”
A unified framework for stochastic optimization (2019, background): “There is a growing literature focusing on strategies for creating effective sam ples so that the set ˆ does not have to be too large ( Bayraksan & Morton, 2011; Dupacova et al., 2003; Heitsch & Romisch, 2009 ).”
Distributionally Robust Optimization: A Review (2019, methodology): “[268] study continuous and discrete scenario reduction [97, 139, 140, 141, 6], where p-Wasserstein metric utilizing `p-norm is used as a measure of discrepancy between distributions.”
Local flexibility markets: Literature review on concepts, models and clearing methods (2020, positive): “Submitted to Elsevier Science 36 usually carried out to reduce the number of scenarios and the computational burden of the problem [176].”