José Eduardo Souza de Cursi

Learn More
We consider the global optimization of a nonsmooth (nondifferentiable) nonconvex real function. We introduce a variable metric descent method adapted to nonsmooth situations, which is modified by the incorporation of suitable random perturbations. Convergence to a global minimum is established and a simple method for the generation of suitable perturbations(More)
Facility location problems are frequent in OR literature. In districting problems, on the other hand, the aim is to partition a territory into smaller units, called districts or zones, while an objective function is optimized and some constraints are satisfied, such as balance, contiguity, and compactness. Although many location and districting problems(More)
Reliability analysis is often based on stochastic d iscrete event models like stochastic Petri Nets. For complex dynamical systems with numerous compone nts, analytical expressions of the steady state are tedious to work out because of the combinatory explosion with discrete models. For this reason, fluidification is an interesting alternative to est imate(More)
This work aims at developing a genetic algorithm (GA) to pursue the optimization of hybrid laminated composite structures. Fiber orientation (predefined ply angles), material (glass-epoxy or carbon-epoxy layer) and total number of plies are considered as design variables. The GA is chosen as an optimization tool because of its ability to deal with(More)
We present a random perturbation of the projected variable metric method for solving linearly constrained nonsmooth (i.e., nondifferentiable) nonconvex optimization problems, and we establish the convergence to a global minimum for a locally Lipschitz continuous objective function which may be nondifferentiable on a countable set of points. Numerical(More)
  • 1