1 citations · 2 across the 6 of their papers we have counts for
7 papers
A Consistency-Centric Approach to Set-Based Optimization with Multiple Models of Unranked Fidelity
Danielle F. Morey, Giulia Pedrielli, Cherry Y. Wakayama +1
In complex real-world settings, optimization is challenged by the presence of diverse models of differing fidelity. In many optimization problems, a single model is treated as the…
Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
Hao Huang, Zelda B. Zabinsky
A multiple objective simulation optimization algorithm named Multiple Objective Probabilistic Branch and Bound with Single Observation (MOPBnB(so)) is presented for approximating t…
Branching Adaptive Surrogate Search Optimization (BASSO)
Pariyakorn Maneekul, Zelda B. Zabinsky, Giulia Pedrielli
Global optimization of black-box functions is challenging in high dimensions. We introduce a conceptual adaptive random search framework, Branching Adaptive Surrogate Search Optimi…
A Stochastic Record-Value Approach to Global Simulation Optimization
Rohan Rele, Zelda Zabinsky, Giulia Pedrielli +1
Black-box optimization is ubiquitous in machine learning, operations research and engineering simulation. Black-box optimization algorithms typically do not assume structural infor…
Hesitant Adaptive Search with Estimation and Quantile Adaptive Search for Global Optimization with Noise
David D. Linz, Zelda B. Zabinsky
Adaptive random search approaches have been shown to be effective for global optimization problems, where under certain conditions, the expected performance time increases only lin…
Optimal control of COVID-19 infection rate considering social costs
Aaron Z. Palmer, Zelda B. Zabinsky, Shan Liu
The COVID-19 pandemic has posed a policy making crisis where efforts to slow down or end the pandemic conflict with economic priorities. This paper provides mathematical analysis o…
Near Optimal Hamiltonian-Control and Learning via Chattering
Peeyush Kumar, Wolf Kohn, Zelda B. Zabinsky
Many applications require solving non-linear control problems that are classically not well behaved. This paper develops a simple and efficient chattering algorithm that learns nea…