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In this paper we consider the problem of minimizing a nonlinear function using partial derivative knowledge. Namely, the objective function is such that its derivatives with respect to a pre-specified block of variables cannot be computed. To solve the problem we propose a block decomposition method that takes advantage of both derivative-free and(More)
— Optimization methods are a powerful tool in protein structure analysis. In this paper we show that they can be profitably used to solve relevant problems in drug design such as the comparison and recognition of protein binding sites and the protein-peptide docking. Binding sites recognition is generally based on geometry often combined with(More)
In this paper we propose convex and LP bounds for Standard Quadratic Programming (StQP) problems and employ them within a branch-and-bound approach. We first compare different bounding strategies for StQPs in terms both of the quality of the bound and of the computation times. It turns out that the polyhedral bounding strategy is the best one to be used(More)
Molecular docking programs play a crucial role in drug design and development. In recent years, much attention has been devoted to the protein-peptide docking problem in which docking of a flexible peptide with a given protein is sought. In this work we present a docking algorithm which is based on the use of a filling function method for continuous global(More)
We consider the problem of minimizing a continuously differentiable function of several variables subject to simple bound and general nonlinear inequality constraints, where some of the variables are restricted to take integer values. We assume that the first order derivatives of the objective and constraint functions can be neither calculated nor(More)
Recently a new derivative-free algorithm [6] has been proposed for the solution of linearly constrained finite minimax problems. Such an algorithm can also be used to solve systems of nonlinear inequalities in the case when the derivatives are not available and provided that a suitable reformu-lation of the system into a minimax problem is carried out. In(More)
In this paper we propose a new derivative-free algorithm for linearly constrained finite minimax problems. Due to the nonsmoothness of this class of problems, standard derivative-free algorithms can locate only points which satisfy weak necessary optimality conditions. In this work we define a new derivative-free algorithm which is globally convergent(More)
In this paper we are concerned with the design of a small low-cost, low-field multipolar magnet for Magnetic Resonance Imaging with a high field uniformity. By introducing appropriate variables, the considered design problem is converted into a global optimization one. This latter problem is solved by means of a new derivative free global optimization(More)
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