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A trust-region funnel algorithm for gray-box optimization
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DOI：10.1002/aic.70258.png)
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摘要
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Gray-box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black-box models lacking analytic derivatives, remains a challenge. Trust-region (TR) methods provide a robust framework for gray-box problems through local reduced models (RMs) for black-box components, but they are complex and require extensive parameter tuning. Motivated by recent advances in funnel-based convergence theory for nonlinear optimization, we propose a novel TR funnel algorithm for gray-box optimization, replacing the filter acceptance criterion with a uni-dimensional funnel, maintaining a monotonically decreasing upper bound on approximation error of local black-box RMs. A global convergence proof to a first-order critical point is established. The algorithm, implemented open-source in Pyomo, supports multiple RM forms and globalization strategies (filter or funnel). Benchmark tests show the TR funnel algorithm achieves comparable and often improved performance relative to the classical TR filter method, thus providing a simpler, effective alternative for gray-box optimization.
Keyword:
derivative-free optimization
funnel mechanism
gray-box optimization
NLP
trust-region method
期刊
IF:
4
论文数:
1.1W
被引数:
2.9W
机构
引用论文
The trust region filter strategy: Survey of a rigorous approach for optimization with surrogate models信任域过滤器策略：基于代理模型的优化中一种严谨方法的综述
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