Myopic Policies for Budgeted Optimization with Constrained Experiments

Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f(x) given a budget. In our setting, it is not practical to request samples of f(x) at precise input values due to the formidable cost of precise experimental setup. Rather, we may request a constrained experiment, which is a subset r of the input space for which the experimenter returns x ∈ r and f(x). Importantly, as the constraints become looser, the experimental cost decreases, but the uncertainty about the location x of the next observation increases. Our goal is to manage this trade-off by selecting a sequence of constrained experiments to best optimize f within the budget. We introduce cost-sensitive policies for selecting constrained experiments using both model-free and model-based approaches, inspired by policies for unconstrained settings. Experiments on synthetic functions and functions derived from real-world experimental data indicate that our policies outperform random selection, that the model-based policies are superior to model-free ones, and give insights into which policies are preferable overall.

Authors

• Javad Azimi Oregon State University
• Xiaoli Fern
• Alan Fern Oregon State University
• Elizabeth Burrows Oregon State University
• Frank Chaplen Oregon State University
• Yanzhen Fan Oregon State University
• Hong Liu
• Jun Jaio Portland State University
• Rebecca Schaller Portland State University

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