In this paper, an iterative optimization algorithm, called the Comb algorithm, is presented for approximating the global solution to MAP image restoration and segementation. The Comb derives new initial conngurations based on the best local minimum found so far and leads a local search towards the global minimum. Experimental comparisons show that the Comb produces solutions of quality comparab...

In this paper, we propose a stochastic dynamic local search (SDLS) method for Multiple-Valued Logic (MVL) learning by introducing stochastic dynamics into the traditional local search method. The proposed learning network maintains some trends of quick descent to either global minimum or a local minimum, and at the same time has some chance of escaping from local minima by permitting temporary ...

A novel approach based on genetic algorithms (GA) is developed to find a global minimum-jerk trajectory of a space robotic manipulator in joint space. The jerk, the third derivative of position of desired joint trajectory, adversely affects the efficiency of the control algorithms and stabilization of whole space robot system and therefore should be minimized. On the other hand, the importance ...

A new approach of edge integration for shape modeling is presented. It is used to nd the global minimum of an active contour model's energy between two points. Ini-tialization is made easier and the curve is not trapped at a local minimum by spurious edges. We modify the \snake" energy by including the internal regularization term in the external potential term. Our method is based on the inter...

An unusual atomic cluster structure corresponding to the global minimum of the 98-atom Lennard-Jones cluster has been found using a variant of the basin-hopping global optimization algorithm. The structure has tetrahedral symmetry and an energy of -543.665 361 epsilon, which is 0.022 404 epsilon lower than the previous lowest-energy minimum. The LJ(98) structure is of particular interest becaus...

We address the theoretical problems of optical ow estimation and image registration in a multi-scale framework in any dimension. We start by showing, in the translation case, that convergence to the global minimum is made easier by applying a low pass lter to the images hence making the energy \convex enough". In order to keep convergence to the global minimum in the general case, we introduce ...

We introduce a covariance cleaning method which works well in the very high-dimensional regime, i.e. when there are many more assets than data points per asset. This opens way to unconditional reactive portfolio optimization not enough calibrate dynamical conditional models, happens, for example, new appear market. The is k-fold boosted version of Bootstrapped Average Hierarchical Clustering pr...