A Guide to Sample Average Approximation

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Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information (2018, background): “155) and Kim et al. (2015) in view of its position as a de facto standard in approximate stochastic programming: it is broadly applicable, commonly used, based on simple principles and supported by an abundant knowledge of its properties, both theoretical and empirical.”
Stochastic optimization models for location and inventory prepositioning of disaster relief supplies (2020, background): “…SP assumes that the decision-maker has enough data to evaluates the random second-stage costs (e.g., shortage in the aftermath) using the sample average, in which the approximation accuracy improves with the increment of sample size, but the computation often becomes challenging (Kim et al., 2015).”
A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing (2015, methodology): “[83] S.”
Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the O(1/T) Convergence Rate (2019, methodology): “The convergence rate in Corollary 2 matches the state-of-the-art convergence rate of SAA (Zhang et al., 2017a).”
Monte Carlo sampling-based methods for stochastic optimization (2014, background): “[131], who focus on a comparative study of rates of convergence.”