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This work concerns the global optimization of a continuous objective function f on a closed bounded domain S ⊂ Rn. Neither f nor S are assumed to be convex, but the point of global minimum x* is assumed to be unique on S. We establish a representation formula for the point of global optimum, analogous to the Feynman-Kac representations of the solutions...
This work concerns the global optimization of a non convex objective function under nonlinear differentiable restrictions defining a bounded domain. The projected gradient descent method combined to suitable random perturbation furnishes a stochastic method generating a feasible sequence. A mathematical result of convergence to a global minimum is derived from the stochastic framework. Numerical Examples...
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