Multidimensional Global Search Using Numerical Estimations of Minimized Function Derivatives and Adaptive Nested Optimization Scheme

DOI10.1007/978-3-030-40616-5_33OpenAlexW3005602510MaRDI QIDQ5122315

Alexey Goryachikh, Victor P. Gergel

Publication date: 22 September 2020

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-40616-5_33

zbMATH Keywords

numerical experimentsLipschitz conditiondimensionality reductionglobal search algorithmsmultiextremal optimizationnumerical estimations of derivative values

Mathematics Subject Classification ID

Numerical analysis (65-XX)

Cites Work

• Unnamed Item
• Advances in stochastic and deterministic global optimization
• Parallel global optimization on GPU
• Adaptive nested optimization scheme for multidimensional global search
• Deterministic global optimization using space-filling curves and multiple estimates of Lipschitz and Hölder constants
• A deterministic global optimization using smooth diagonal auxiliary functions
• Global one-dimensional optimization using smooth auxiliary functions
• A global optimization technique for checking parametric robustness
• On a method for computing the values of derivatives in the minimization of multi-extremal functions.
• Global optimization with non-convex constraints. Sequential and parallel algorithms
• Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms
• Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes
• Introduction to Global Optimization Exploiting Space-Filling Curves
• An algorithm for finding the absolute extremum of a function
• Deterministic Global Optimization
• A Sequential Method Seeking the Global Maximum of a Function
• Multidimensional global optimization using numerical estimates of objective function derivatives

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