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Autore principale: Roberts, Lindon
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2403.14960
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author Roberts, Lindon
author_facet Roberts, Lindon
contents We develop a new approximation theory for linear and quadratic interpolation models, suitable for use in convex-constrained derivative-free optimization (DFO). Most existing model-based DFO methods for constrained problems assume the ability to construct sufficiently accurate approximations via interpolation, but the standard notions of accuracy (designed for unconstrained problems) may not be achievable by only sampling feasible points, and so may not give practical algorithms. This work extends the theory of convex-constrained linear interpolation developed in [Hough & Roberts, SIAM J. Optim, 32:4 (2022), pp. 2552-2579] to the case of linear regression models and underdetermined quadratic interpolation models.
format Preprint
id arxiv_https___arxiv_org_abs_2403_14960
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Model Construction for Convex-Constrained Derivative-Free Optimization
Roberts, Lindon
Optimization and Control
We develop a new approximation theory for linear and quadratic interpolation models, suitable for use in convex-constrained derivative-free optimization (DFO). Most existing model-based DFO methods for constrained problems assume the ability to construct sufficiently accurate approximations via interpolation, but the standard notions of accuracy (designed for unconstrained problems) may not be achievable by only sampling feasible points, and so may not give practical algorithms. This work extends the theory of convex-constrained linear interpolation developed in [Hough & Roberts, SIAM J. Optim, 32:4 (2022), pp. 2552-2579] to the case of linear regression models and underdetermined quadratic interpolation models.
title Model Construction for Convex-Constrained Derivative-Free Optimization
topic Optimization and Control
url https://arxiv.org/abs/2403.14960