ASTRO-DF: Adaptive sampling trust-region optimization algorithms, heuristics, and numerical experience
Sara Shashaani, Susan R. Hunter, Raghu Pasupathy · 2016
ASTRO-DF is a class of adaptive sampling algorithms for solving simulation optimization problems in which only estimates of the objective function are available by executing a Monte Carlo simulation. ASTRO-DF algorithms are iterative trust-region algorithms, where a local model is repeatedly constructed and optimized as iterates evolve through the search space. The ASTRO-DF class of algorithms is derivative-free in the sense that it does not rely on direct observations of the function derivatives. A salient feature of ASTRO-DF is the incorporation of adaptive sampling and replication to keep the model error and the trust-region radius in lock-step, to ensure efficiency. ASTRO-DF has been demonstrated to generate iterates that globally converge to a first-order critical point with probability one. In this paper, we describe and list ASTRO-DF, and discuss key heuristics that ensure good finite-time performance. We report our numerical experience with ASTRO-DF on test problems in low to moderate dimensions.