Global Optimization

By Aimo A. Törn, A. Žilinskas. 1999

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Citations in the corpus

Reachability Analysis of Deep Neural Networks with Provable Guarantees (2018, methodology): “w(x) is a highly non-convex function which cannot be guaranteed to reach the global minimum by regular optimization schemes based on gradient descent. Inspired by an idea from optimisation, see e.g., [24, 25], we design another continuous function h(x;y), which serves as a lower bound of the origina”
DE/EDA: A new evolutionary algorithm for global optimization (2005, background): “DE/EDA combines global information extracted by EDA with differential information obtained by DE to create promising solutions.”
A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems (2005, background): “Törn and Žilinskas (1989) suggested a comparison by using success ratio which is the number of times that the algorithm successfully finds the optimum over the total number of times that the algorithm is applied, starting at random points, to the problem.”
Recent approaches to global optimization problems through Particle Swarm Optimization (2002, unknown): “…by many GO methods, either deterministic or evolutionary, when coping with the GO problem as defined in Equation (1), is the problem of local minima (Forgó, 1988; Hansen, 1992; Horst and Pardalos, 1995; Horst and Tuy, 1996; Pintér, 1996; Rao, 1996; Schwefel, 1995; Törn and Žilinskas, 1989).”
Bayesian Probabilistic Numerical Methods (2017, methodology): “The 1980s culminated in development of Bayesian optimisation methods (Mockus, 1989; Törn and Žilinskas, 1989), as well as the relation of smoothing splines to Bayesian estimation (Kimeldorf and Wahba, 1970b; Diaconis and Freedman, 1983).”
Oppositional biogeography-based optimization (2009, background): “10 [223] and is plotted as a mesh contour plot in Fig.”