The scenario approach for systems and control design

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Monte Carlo sampling-based methods for stochastic optimization (2014, result): “[38] improve upon an original result by Calafiore and Campi [36] and show that, given 0 < δ ≤ 1, by choosing N ≥ 2 α ( log 1 δ + dx )”
Distributionally Robust Chance-Constrained Optimal Power Flow With Uncertain Renewables and Uncertain Reserves Provided by Loads (2017, methodology): “[18] proposes a scenario approximation method for CC optimization problems by enforcing Ai x ≥ bi , ∀i = 1, .”
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research (2016, unknown): “A significant development in the area of stochastic optimization is the scenario approach [96]–[98]; see also [99] for a tutorial overview of this approach.”
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “While the scenario optimization problems with continuous decision variables are extensively studied [166], the mixed-integer scenario optimization was less developed.”