Stochastic Global Optimization

By Anatoly A. Zhigljavsky, A. Žilinskas. Optimization and its applications, 2007

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Optimization of Convex Functions with Random Pursuit (2011, background): “m control [5] and parameter estimation in systems biology networks [39]. While probabilistic convergence guarantees even for non-convex objectives are readily available for many randomized algorithms [41], provable convergence rates are often not known or unrealistically slow. Notable exceptions can”
Bayesian Optimization with Inequality Constraints (2014, background): “For many optimization problems, traditional global optimizers will effectively find minima (Liberti & Maculan, 2006).”
Minimax and maximin space-filling designs: some properties and methods for construction (2017, methodology): “We can then compute the Q distances d j = d ( x ( j ) , X n ) , with associated order statistics d Using results in (Zhigljavsky and Žilinskas, 2007, Chap.”
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010, neutral): “A number of approaches exist for this kind of global optimization and have been well-studied in the literature (e.g., [Törn and Žilinskas, 1989, Mongeau et al., 1998, Liberti and Maculan, 2006, Zhigljavsky and Žilinskas, 2008]).”