Phương pháp số sử dụng hai cách tiếp cận khác nhau của các đường cong lấp đầy không gian cho tối ưu hóa toàn cầu hộp đen

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Paulavičius, R., Sergeyev, Y.D., Kvasov, D.E., Žilinskas, J.: Globally-biased BIRECT algorithm with local accelerators for expensive global optimization. Expert Syst. Appl. 144, 113052 (2020)

Rios, L.M., Sahinidis, N.V.: Derivative-free optimization: A review of algorithms and comparison of software implementations. J. Global Optim. 56, 1247–1293 (2013)

Sergeyev, Y.D.: Efficient strategy for adaptive partition of $$n$$-dimensional intervals in the framework of diagonal algorithms. J. Optim. Theory Appl. 107(1), 145–168 (2000)

Sergeyev, Y.D., Mukhametzhanov, M.S., Kvasov, D.E., Lera, D.: Derivative-free local tuning and local improvement techniques embedded in the univariate global optimization. J. Optim. Theory Appl. 171(1), 186–208 (2016)

Sergeyev, Y.D., Nasso, M.C., Mukhametzhanov, M.S., Kvasov, D.E.: Novel local tuning techniques for speeding up one-dimensional algorithms in expensive global optimization using Lipschitz derivatives. J. Comput. Appl. Math. 383, 113134 (2021)

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Zhigljavsky, A., Žilinskas, A.: Bayesian and High-Dimensional Global Optimization. Springer, New York (2021)

Archetti, F., Candelieri, A.: Bayesian Optimization and Data Science. Springer, New York (2019)

Candelieri, A., Giordani, I., Archetti, F., Barkalov, K., Meyerov, I., Polovinkin, A., Sysoyev, A., Zolotykh, N.: Tuning hyperparameters of a svm-based water demand forecasting system through parallel global optimization. Comput. Oper. Res. 106, 202–209 (2019)

Cavoretto, R., De Rossi, A., Mukhametzhanov, M.S., Sergeyev, Y.D.: On the search of the shape parameter in radial basis functions using univariate global optimization methods. J. Global Optim. 79, 305–327 (2021)

Daponte, P., Grimaldi, D., Molinaro, A., Sergeyev, Y.D.: An algorithm for finding the zero-crossing of time signals with Lipschitzean derivatives. Measurement 16(1), 37–49 (1995)

Famularo, D., Pugliese, P., Sergeyev, Y.D.: Global optimization technique for checking parametric robustness. Automatica 35(9), 1605–1611 (1999)

Floudas, C.A., Pardalos, P.M.: State of the Art in Global Optimization. Kluwer Academic Publishers, Dordrecht (1996)

Kvasov, D.E., Sergeyev, Y.D.: Lipschitz global optimization methods in control problems. Autom. Remote. Control. 74(9), 1435–1448 (2013)

Kvasov, D.E., Sergeyev, Y.D.: Deterministic approaches for solving practical black-box global optimization problems. Adv. Eng. Softw. 80, 58–66 (2015)

Lera, D., Posypkin, M., Sergeyev, Y.D.: Space-filling curves for numerical approximation and visualization of solutions to systems of nonlinear inequalities with applications in robotics. Appl. Math. Comput. 390, 125660 (2021)

Sergeyev, Y.D., Candelieri, A., Kvasov, D.E., Perego, R.: Safe global optimization of expensive noisy black-box functions in the $$\delta $$-Lipschitz framework. Soft. Comput. 24(23), 17715–17735 (2020)

Sergeyev, Y.D., Daponte, P., Grimaldi, D., Molinaro, A.: Two methods for solving optimization problems arising in electronic measurements and electrical engineering. SIAM J. Optim. 10(1), 1–21 (1999)

Sergeyev, Y.D., Kvasov, D.E., Mukhametzhanov, M. S.: A generator of multiextremal test classes with known solutions for black-box constrained global optimization. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2021.3139263, in press

Gergel, V.P., Grishagin, V.A., Israfilov, R.: Multiextremal optimization in feasible regions with computable boundaries on the base of the adaptive nested scheme. In: Numerical Computations: Theory and Algorithms – NUMTA 2019, volume 11974 of LNCS, pages 112–123. Springer, 2020

Grishagin, V., Israfilov, R., Sergeyev, Y.D.: Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes. Appl. Math. Comput. 318, 270–280 (2018)

Kvasov, D.E., Sergeyev, Y.D.: Multidimensional global optimization algorithm based on adaptive diagonal curves. Comput. Math. Math. Phys. 43(1), 40–56 (2003)

Paulavičius, R., Žilinskas, J.: Simplicial Global Optimization. Springer, New York (2014)

Sergeyev, Y.D.: On convergence of “Divide the Best’’ global optimization algorithms. Optimization 44, 303–325 (1998)

Sergeyev, Y.D., Grishagin, V.A.: A parallel method for finding the global minimum of univariate functions. J. Optim. Theory Appl. 80, 513–536 (1994)

Sergeyev, Y.D., Kvasov, D.E.: Diagonal Global Optimization Methods. FizMatLit, Moscow (2008). (In Russian)

Sergeyev, Y.D., Kvasov, D.E.: Deterministic Global Optimization: An Introduction to the Diagonal Approach. SpringerBriefs in Optimization. Springer, New York (2017)

Sergeyev, Y.D., Strongin, R.G., Lera, D.: Introduction to Global Optimization Exploiting Space-Filling Curves. Springer, New York (2013)

Strongin, R.G., Sergeyev, Y.D.: Global multidimensional optimization on parallel computer. Parallel Comput. 18(11), 1259–1273 (1992)

Strongin, R.G., Sergeyev, Y.D.: Global Optimization with Non-convex Constraints: Sequential and Parallel Algorithms. Kluwer Academic Publishers, Dordrecht (2000)

Peano, G.: Sur une courbe, qui remplit toute une aire plane. Math. Ann. 36, 157–160 (1890)

Hilbert, D.: Über die stetige abbildung einer linie auf ein flächenstück. Math. Ann. 38, 459–460 (1891)

Lera, D., Sergeyev, Y.D.: Lipschitz and Hölder global optimization using space-filling curves. Appl. Numer. Math. 60(1), 115–129 (2010)

Lera, D., Sergeyev, Y.D.: Deterministic global optimization using space-filling curves and multiple estimates of Lipschitz and Hölder constants. Commun. Nonlinear Sci. Numer. Simul. 23(1–3), 328–342 (2015)

Lera, D., Sergeyev, Y.D.: GOSH: derivative-free global optimization using multi-dimensional space-filling curves. J. Global Optim. 71(1), 193–211 (2018)

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Lera, D., Sergeyev, Y.D.: Global minimization algorithms for Hölder functions. BIT 42(1), 119–133 (2002)

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Sergeyev, Y.D., Kvasov, D.E.: Global search based on efficient diagonal partitions and a set of Lipschitz constants. SIAM J. Optimization 16(3), 910–937 (2006)

Gaviano, M., Lera, D., Kvasov, D.E., Sergeyev, Y.D.: Algorithm 829: Software for generation of classes of test functions with known local and global minima for global optimization. ACM Trans. Math. Software 29, 469–480 (2003)

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