Global Optimization with Non-Convex Constraints - Sequential and Parallel Algorithms (Nonconvex Optimization and its Applications Volume 45) (Nonconvex Optimization and Its Applications)

By R. Strongin, Yaroslav D. Sergeyev. 2000

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Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes (2018, methodology): “The second algorithm was proposed by Strongin [5, 48, 51].”
A review of recent advances in global optimization (2009, background): “…technique (RLT), Horst et al. (2000), Floudas (2000a) and Horst and Tuy (2003) on deterministic approaches, Rubinov (2000) on abstract convexity, Strongin and Sergeyev (2000) on sequential and parallel algorithms, Tawarmalani and Sahinidis (2002a) on modeling and implementation issues,…”
From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (2014, methodology): “A large body of algorithmic work has been developed using branch-and-bound techniques [94, 67, 79, 71, 98, 57, 110] such as Lipschitz optimization where the function is assumed to be globally Lipschitz.”