The title of the paper was "ASTRO-DF: Adaptive Sampling Trust-Region Optimization Algorithms, Heuristics, and Numerical Experience", co-written with IE Assistant Professor Susan Hunter and Associate Professor of Statistics Raghu Pasupathy. Shashaani is now a post-doctoral researcher at the University of Michigan, Ann Arbor.

Abstract: 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.

The 2016 Winter Simulation Conference (WSC) was held Dec. 11-14 in Washington, DC, and the topic was “Simulating Complex Service Systems”. WSC is the premiere international forum for disseminating recent advances in the field of system simulation. It provides the central meeting place for simulation practitioners, researchers, and vendors working in all disciplines in industry, service, government, military and academic sectors.