1,721,431 research outputs found
I procedimenti di composizione della crisi da sovraindebitamento e la liquidazione del patrimonio del debitore civile
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A decomposition algorithm for unconstrained optimization problems with partial derivative information
No full textIn 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 derivative-based iterations to account for the features of the objective function. Under standard assumptions, we manage to prove global convergence of the method to stationary points of the problem. © 2010 Springer-Verlag
Global Optimization of Protein–peptide Docking by a Filling Function Method
No full textMolecular 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 optimization. In particular, the protein–peptide docking position is found by minimizing the conformational potential free energy function based on a new approximate mathematical model. The resulting global optimization problem presents some difficulties, since it is a large-scale one and the objective function is non-convex, so that it has many local minima. To solve the problem, we adopt a global optimization method based on the use of a filling function to escape from local solutions. Moreover, in order to obtain more accurate results, we search the correct docking position by performing a two-phase optimization process. In particular, in a first step, only the carbon Cα atoms of the protein and peptide are considered, thus obtaining an approximate docking solution. Then, the energy function is completed by considering all the peptide and protein atoms so that, starting from the solution of the first phase, the new minimization process gives a more accurate result. We present numerical results on a set of benchmark docking pairs and their comparison with those obtained by the known software package PacthDock for molecular docking
Solving l0-penalized problems with simple constraints via the Frank–Wolfe reduced dimension method
No full textl0-penalized problems arise in a number of applications in engineering, machine learning and statistics, and, in the last decades, the design of algorithms for these problems has attracted the interest of many researchers. In this paper, we are concerned with the definition of a first-order method for the solution of l0-penalized problems with simple constraints. We use a reduced dimension Frank–Wolfe algorithm Rinaldi (Optim Methods Softw, 26, 2011) and show that the subproblem related to the computation of the Frank–Wolfe direction can be solved analytically at least for some sets of simple constraints. This gives us a very easy to implement and quite general tool for dealing with l0-penalized problems. The proposed method is then applied to the numerical solution of two practical optimization problems, namely, the Sparse Principal Component Analysis and the Sparse Reconstruction of Noisy Signals. In both cases, the reported numerical performances and comparisons with state-of-the-art solvers show the efficiency of the proposed method
Effetti della sistemazione congiunta idraulica e idraulico-forestale del torrente Picone (Puglia)
No full textMetalloproteinases and their inhibitors as therapeutic targets for multiple sclerosis: current evidence and future perspectives
Full text linkThe treatment of multiple sclerosis (MS) has seen important changes in the last two decades with the introduction of several drugs able to modify the evolution of this disease. Current MS therapies primarily target the peripheral immune response, although it has been suggested that their ef cacy could be in part the result of the bene cial effect on other non- speci c targets, such as matrix metalloproteinases (MMPs). Numerous experimental studies have suggested that MMPs may be involved in MS pathogenesis by contributing to blood–brain barrier disruption, migration of leukocytes into the central nervous system, demyelination and axonal damage. Therefore, MMPs have been considered important therapeutic targets in the course of MS. In this respect, different attempts have been made to develop synthetic, low-molecular-weight inhibitors of MMPs for the potential treatment of diseases in which MMPs play a major role. However, technical dif culties, side effects and reduced patient compliance because of parenteral administration have greatly limited the development in the clinical practice of speci c anti-MMP drugs. By contrast, interesting results have been obtained with compounds that are already used in the clinical practice, such as MS drugs and natural compounds with anti-in ammatory and antioxidant activity. Here, we discuss the evidence and potential mechanisms for altered MMP function in MS. Furthermore, we outline the possible medical implications for the use of compounds that target MMP activity and we propose that together with anti-MS drugs, other compounds with anti-in ammatory and antioxidant properties, such as natural ω3 fatty acids, polyphenols and tetracyclines, which inhibit MMP functions, might represent potential therapeutic approaches to mitigate MMP-related damage during MS
A new branch-and-bound algorithm for standard quadratic programming problems
Full text linkIn 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 within a branch-and-bound scheme. Indeed, it guarantees a good quality of the bound at the expense of a very limited computation time. The proposed branch-and-bound algorithm performs an implicit enumeration of all the KKT (stationary) points of the problem. We compare different branching strategies exploiting the structure of the problem. Numerical results on randomly generated problems (with varying density of the underlying convexity graph) are reported which show the effectiveness of the proposed approach, in particular in limiting the growth of the number of nodes in the branch-and-bound tree as the density of the underlying graph increases
An algorithmic framework based on primitive directions and nonmonotone line searches for black-box optimization problems with integer variables
Full text linkIn this paper, we develop a new algorithmic framework to solve black-box problems with integer variables. The strategy included in the framework makes use of specific search directions (so called primitive directions) and a suitably developed nonmonotone line search, thus guaranteeing a high level of freedom when exploring the integer lattice. First, we describe and analyze a version of the algorithm that tackles problems with only bound constraints on the variables. Then, we combine it with a penalty approach in order to solve problems with simulation constraints. In both cases we prove finite convergence to a suitably defined local minimum of the problem. We report extensive numerical experiments based on a test bed of both bound-constrained and generally-constrained problems. We show the effectiveness of the method when compared to other state-of-the-art solvers for black-box integer optimization
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