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• W2024366558 abstract "We propose a derivative-free algorithm for optimizing computationally expensive functions with computational error. The algorithm is based on the trust region regression method by Conn, Scheinberg, and Vicente [A. R. Conn, K. Scheinberg, and L. N. Vicente, IMA J. Numer. Anal., 28 (2008), pp. 721--748] but uses weighted regression to obtain more accurate model functions at each trust region iteration. A heuristic weighting scheme is proposed that simultaneously handles (i) differing levels of uncertainty in function evaluations and (ii) errors induced by poor model fidelity. We also extend the theory of $Lambda$-poisedness and strong $Lambda$-poisedness to weighted regression. We report computational results comparing interpolation, regression, and weighted regression methods on a collection of benchmark problems. Weighted regression appears to outperform interpolation and regression models on nondifferentiable functions and functions with deterministic noise." @default.
• W2024366558 created "2016-06-24" @default.
• W2024366558 creator A5008431584 @default.
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• W2024366558 date "2013-01-01" @default.
• W2024366558 modified "2023-09-23" @default.
• W2024366558 title "Derivative-Free Optimization of Expensive Functions with Computational Error Using Weighted Regression" @default.
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• W2024366558 doi "https://doi.org/10.1137/100814688" @default.
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