TL;DR
DEFT-FUNNEL is an open-source global optimization solver designed for constrained grey-box and black-box problems, utilizing trust-region methods, polynomial surrogates, and multistart strategies, showing competitive performance on benchmark problems.
Contribution
It extends trust-region sequential quadratic optimization algorithms to a global solver that exploits both closed-form and black-box function information.
Findings
DEFT-FUNNEL performs favorably against state-of-the-art methods.
The solver effectively handles problems with mixed known and unknown functions.
Numerical experiments validate the efficiency and robustness of the approach.
Abstract
The fast-growing need for grey-box and black-box optimization methods for constrained global optimization problems in fields such as medicine, chemistry, engineering and artificial intelligence, has contributed for the design of new efficient algorithms for finding the best possible solution. In this work, we present DEFT-FUNNEL, an open-source global optimization algorithm for general constrained grey-box and black-box problems that belongs to the class of trust-region sequential quadratic optimization algorithms. It extends the previous works by Sampaio and Toint (2015, 2016) to a global optimization solver that is able to exploit information from closed-form functions. Polynomial interpolation models are used as surrogates for the black-box functions and a clustering-based multistart strategy is applied for searching for the global minima. Numerical experiments show that DEFT-FUNNEL…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsTest
