Abstract

Trust-region methods have proven highly effective for unconstrained nonconvex stochastic optimization problems where objective and gradient information are available only through noisy stochastic oracles. ASTRO is a class of adaptive sampling trust-region methods that dynamically determine sampling effort while constructing local quadratic models from noisy function and gradient observations. By exploiting dependence among samples and the stochastic structure of the problem, ASTRO achieves strong convergence and complexity guarantees. Its derivative-free variant, ASTRO-DF, relies solely on noisy function evaluations and also enjoys almost-sure convergence guarantees.

Short Bio

Sara Shashaani is an Associate Professor and Bowman Faculty Scholar in the Fitts Department of Industrial and Systems Engineering at North Carolina State University. Her research lies at the intersection of stochastic optimization and Monte Carlo simulation, with a focus on zeroth-order methods for nonconvex problems and digital twin methodologies. She is a 2024 Goodnight Innovator and a 2025 MGB-SIAM Early Career Fellow. She received the Best Contributed Paper Award at the 2025 Winter Simulation Conference, and her doctoral student was recognized as a second-place winner of the 2025 IISE Pritsker Doctoral Dissertation Award. Dr. Shashaani has served the INFORMS Simulation Society as an elected board member and is a co-creator of SimOpt, an open-source library for simulation optimization. Her research has had impact across a range of application domains, including renewable energy, climate adaptation, advanced manufacturing, and public health.