Milan Drazic

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We report on the theory and implementation of a global optimization solver for general constrained nonlinear programming problems based on Variable Neighbourhood Search, and we give comparative computational results on several instances of continuous nonconvex problems. Compared to an efficient multi-start global optimization solver, the VNS solver proposed(More)
We suggest a new heuristic for solving unconstrained continuous optimization problems. It is based on a generalized version of the variable neighborhood search metaheuristic. Different neighborhoods and distributions, induced from different metrics are ranked and used to get random points in the shaking step. We also propose VNS for solving constrained(More)
Censored quantile regression models are very useful for the analysis of censored data when standard linear models are felt to be appropriate. However, fitting censored quantile regression is hard numerically due to the fact that the objective function that has to be minimized is not convex nor concave in regressors. The performance of standard methods is(More)
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