New computer methods for global optimization
New computer methods for global optimization
Journal of Optimization Theory and Applications
Random tunneling by means of acceptance-rejection sampling for global optimization
Journal of Optimization Theory and Applications
Numerical methods for global optimization
Recent advances in global optimization
On using estimates of Lipschitz constants in global optimization
Journal of Optimization Theory and Applications
Convergence qualification of adaptive partition algorithms in global optimization
Mathematical Programming: Series A and B
Convergence rates of a global optimization algorithm
Mathematical Programming: Series A and B
A deterministic algorithm for global optimization
Mathematical Programming: Series A and B
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Global optimization requires global information
Journal of Optimization Theory and Applications
Parallel Characteristical Algorithms for Solving Problems of GlobalOptimization
Journal of Global Optimization
A partition-based global optimization algorithm
Journal of Global Optimization
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In this paper we face a classical global optimization problem—minimization of a multiextremal multidimensional Lipschitz function over a hyperinterval. We introduce two new diagonal global optimization algorithms unifying the power of the following three approaches: efficient univariate information global optimization methods, diagonal approach for generalizing univariate algorithms to the multidimensional case, and local tuning on the behaviour of the objective function (estimates of the local Lipschitz constants over different subregions) during the global search. Global convergence conditions of a new type are established for the diagonal information methods. The new algorithms demonstrate quite satisfactory performance in comparison with the diagonal methods using only global information about the Lipschitz constant.