Energy-aware frugal Bayesian optimization
cs.LG, cs.AI, math.OC, physics.class-ph
Submitted: 2026-09-11
Updated: 2026-09-11
Comments: Published in MATEC Web of Conferences 422, 02009. 7th International Conference on Engineering Optimization
Journal ref: G. Plat, P. Saves, N. Bartoli, T. Lefebvre, J. Morlier, Energy-aware frugal Bayesian optimization, EngOpt, 2026, Volume 422, 2026, pp 02009
DOI: 10.1051/matecconf/202642202009
License: http://creativecommons.org/licenses/by/4.0/
The gist: Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead.
Terminology
Abstract
Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled architecture and multidisciplinary design optimization problems are difficult to address, even with sample-efficient Bayesian optimizers. In this paper, a metric quantifying the computational energy footprint is introduced within a Bayesian optimization framework to guide the parameter setting of a model towards configurations that balance both performance and frugality. The computer experiments highlighted existing tradeoffs between optimum convergence and the underlying energy footprint, and sometimes resulted in both a better-found optimum and lower energy consumption.
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