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We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and ...
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We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and-bound ...
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Jun 25, 2021 · We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the ...
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A reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded, which reduces computational time by orders of ...
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Jan 18, 2022 · Schweidtmann, Artur M.; Bongartz, Dominik; Grothe, Daniel; Kerkenhoff, Tim; Lin, Xiaopeng; Najman, Jaromił; Mitsos, Alexander.
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This essay introduces the bayesian framework for regression using GP's. Based on that, global optimiza- tion approaches are introduced. Including (briefly) ...
Sep 15, 2016 · Principles of the Gaussian Process approach (GP): suppose that, a priori, f is a realization of a GP (Zx)x∈D and approximate f and/or the ...
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Jul 2, 2021 · A multicriteria generalization of Bayesian global optimization. In Advances in. Stochastic and Deterministic Global Optimization, pages 229–242.
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Duration: 1:26:12
Posted: Sep 16, 2015
Posted: Sep 16, 2015
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Title. Deterministic global optimization with Gaussian processes embedded ; Is Part Of. MATHEMATICAL PROGRAMMING COMPUTATION vol:13 issue:3 pages:553-581.
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