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Cited by 6
An Adaptive Univariate Global Optimization Algorithm and Its Convergence Rate under the Wiener Measure

Bi-objective Decisions and Partition-Based Methods in Bayesian Global Optimization
Anatoly Zhigljavsky, Antanas Žilinskas
https://doi.org/10.1007/978-3-030-64712-4_2
Book:  SpringerBriefs in Optimization (Bayesian and High-Dimensional Global Optimization) (2021), p. 41
AIP Conference Proceedings
James M. Calvin, Antanas Žilinskas
https://doi.org/10.1063/1.5090002
Conference:  AIP Conference Proceedings Volume 2070 (2019), p. 020035
Acceleration of Univariate Global Optimization Algorithms Working with Lipschitz Functions and Lipschitz First Derivatives
Daniela Lera, Yaroslav D. Sergeyev
https://doi.org/10.1137/110859129
Journal:  SIAM Journal on Optimization Volume 23, Issue 1 (2013), p. 508
Adaptive approximation of the minimum of Brownian motion
James M. Calvin, Mario Hefter, André Herzwurm
https://doi.org/10.1016/j.jco.2016.11.002
Journal:  Journal of Complexity Volume 39 (2017), p. 17
On convergence rate of a rectangular partition based global optimization algorithm
James Calvin, Gražina Gimbutienė, William O. Phillips, Antanas Žilinskas
https://doi.org/10.1007/s10898-018-0636-z
Journal:  Journal of Global Optimization Volume 71, Issue 1 (2018), p. 165
On efficiency of a single variable bi-objective optimization algorithm
James M. Calvin, Antanas Žilinskas
https://doi.org/10.1007/s11590-019-01471-4
Journal:  Optimization Letters Volume 14, Issue 1 (2020), p. 259


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