An Adaptive Projected-Gradient Algorithm for Sample-Average Approximations of Stochastic Multi-Objective Optimization
A line-search-free and function-value-free adaptive projected-gradient algorithm for the sample-average approximation (SAA) problem that transfers vanishing SAA residuals to Pareto stationarity for the population problem, while an additional concentration argument gives a finite-sample residual bound on compact sets.