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Bayesilik segaintervall-optimeerimine — asendusmudeliga toetatud optimeerimine segaintervall-iga otsinguavaruutes

Bayesilik segaintervall-optimeerimine (BO-MIP) ühendab tõenäosusliku asendusmudeli — tavaliselt Gaussi protsessi — segaintervall-optimeerimislahendajaga, et tõhusalt optimeerida kalleid musta kasti funktsioone, mis on defineeritud ruumides, mis sisaldavad nii pidevaid kui ka diskreetseid või täisarvulisi otsustusmuutujaid. See on eriti väärtuslik, kui iga funktsiooni hindamine on kulukas ja ammendav otsing on ebateostatav.

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Allikad

  1. Baptista, R., Poloczek, M. (2018). Bayesian Optimization of Combinatorial Structures. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80:462–471. link
  2. Bonami, P., Biegler, L. T., Conn, A. R., Cornuejols, G., Grossmann, I. E., Laird, C. D., Lee, J., Lodi, A., Margot, F., Sawaya, N., Wächter, A. (2008). An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optimization, 5(2), 186–204. DOI: 10.1016/j.disopt.2006.10.011

Kuidas sellele lehele viidata

ScholarGate. (2026, June 3). Bayesian Mixed-Integer Programming — Surrogate-Assisted Optimization over Mixed-Integer Search Spaces. ScholarGate. https://scholargate.app/et/simulation/bayesian-mixed-integer-programming

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Sellele viitavad

ScholarGateBayesian Mixed-Integer Programming (Bayesian Mixed-Integer Programming — Surrogate-Assisted Optimization over Mixed-Integer Search Spaces). Loetud 2026-06-15 aadressilt https://scholargate.app/et/simulation/bayesian-mixed-integer-programming · Andmestik: https://doi.org/10.5281/zenodo.20539026