@article{ha_shashaani_2024, title={Iteration complexity and finite-time efficiency of adaptive sampling trust-region methods for stochastic derivative-free optimization}, volume={4}, ISSN={["2472-5862"]}, url={https://doi.org/10.1080/24725854.2024.2335513}, DOI={10.1080/24725854.2024.2335513}, abstractNote={Adaptive sampling with interpolation-based trust regions or ASTRO-DF is a successful algorithm for stochastic derivative-free optimization with an easy-to-understand-and-implement concept that guarantees almost sure convergence to a first-order critical point. To reduce its dependence on the problem dimension, we present local models with diagonal Hessians constructed on interpolation points based on a coordinate basis. We also leverage the interpolation points in a direct search manner whenever possible to boost ASTRO-DF's performance in a finite time. We prove that the algorithm has a canonical iteration complexity of $\mathcal{O}(\epsilon^{-2})$ almost surely, which is the first guarantee of its kind without placing assumptions on the quality of function estimates or model quality or independence between them. Numerical experimentation reveals the computational advantage of ASTRO-DF with coordinate direct search due to saving and better steps in the early iterations of the search.}, journal={IISE TRANSACTIONS}, author={Ha, Yunsoo and Shashaani, Sara}, year={2024}, month={Apr} }
 @article{menickelly_ha_otten_2023, title={Latency considerations for stochastic optimizers in variational quantum algorithms}, volume={7}, ISSN={["2521-327X"]}, DOI={10.22331/q-2023-03-16-949}, abstractNote={Variational quantum algorithms, which have risen to prominence in the noisy intermediate-scale quantum setting, require the implementation of a stochastic optimizer on classical hardware. To date, most research has employed algorithms based on the stochastic gradient iteration as the stochastic classical optimizer. In this work we propose instead using stochastic optimization algorithms that yield stochastic processes emulating the dynamics of classical deterministic algorithms. This approach results in methods with theoretically superior worst-case iteration complexities, at the expense of greater per-iteration sample (shot) complexities. We investigate this trade-off both theoretically and empirically and conclude that preferences for a choice of stochastic optimizer should explicitly depend on a function of both latency and shot execution times.}, journal={QUANTUM}, author={Menickelly, Matt and Ha, Yunsoo and Otten, Matthew}, year={2023}, month={Mar} }