Learning-Based Surrogate Method for Stochastic Optimization under Decision-Dependent Uncertainty with Adaptive Random Designs

We study stochastic programs in which the latent decision-dependent uncertainty is described via a nonparametric regression model. The major challenge is that, without convexity assumptions on either the cost function or the regression model, the resulting objective is both nonconvex and nonsmooth, and its first-order information is unavailable due to the unknown decision-dependent distribution. To address this issue, we construct a learning-based surrogate model that integrates simulation and statistical learning by embedding Jacobian estimates of the regression function, which are updated iteratively and interactively during the optimization procedure. We develop an adaptive random design that concentrates design points around the current iterate for Jacobian estimation and we show that the mean squared error of Jacobian estimates achieves a dimension-independent convergence rate. Building on this, we propose the learning-based stochastic prox-linear (L-SPL) algorithm with adaptive random design and establish its nonasymptotic convergence rates under various parameter settings. Numerical results demonstrate that L-SPL algorithm significantly improves sample efficiency and achieves substantially lower objective values compared to the state-of-the-art methods. More broadly, our method implies that the statistical design in an iterative learning-based optimization algorithm can be novelly tailored to the local information of the optimization procedure to sharpen estimates and enhance the convergence performance and sample efficiency of the resulting algorithm.