In the paper we propose an algorithm for nding approximate sparse solutions of convex optimization problem with conic constraints and examine convergence properties of the algorithm with application to the index tracking problem and unconstrained l1-penalized regression.

Consider the single-facility Euclidean j-centrum location problem in R. This problem is a generalization of the classical Euclidean 1-median problem and 1-center problem. In this paper, we develop two efficient algorithms that are particularly suitable for problems where n is large by using unconstrained optimization techniques. The first algorithm is based on the neural networks smooth approxi...

The self-scaling quasi-Newton method solves an unconstrained optimization problem by scaling the Hessian approximation matrix before it is updated at each iteration to avoid the possible large eigenvalues in the Hessian approximation matrices of the objective function. It has been proved in the literature that this method has the global and superlinear convergence when the objective function is...

In this paper, a Þlled function for escaping the current local minimizer of a constrained global optimization problem is proposed. Then a Þlled function method for obtaining a global minimizer of the constrained optimization problem is presented. By this method, a global minimizer of a constrained global optimization problem can be obtained just by searching local minimizers of the original con...

The self-scaling quasi-Newton method solves an unconstrained optimization problem by scaling the Hessian approximation matrix before it is updated at each iteration to avoid the possible large eigenvalues in the Hessian approximation matrices of the objective function. It has been proved in the literature that this method has the global and superlinear convergence when the objective function is...

The analysis of flow in water-distribution networks with several pumps by the Content Model may be turned into a non-convex optimization uncertain problem with multiple solutions. Newton-based methods such as GGA are not able to capture a global optimum in these situations. On the other hand, evolutionary methods designed to use the population of individuals may find a global solution even for ...

the analysis of flow in water-distribution networks with several pumps by the content model may be turned into a non-convex optimization uncertain problem with multiple solutions. newton-based methods such as gga are not able to capture a global optimum in these situations. on the other hand, evolutionary methods designed to use the population of individuals may find a global solution even for ...

Motivation: Computer modeling of protein-ligand interactions is one of the most important phases in a drug design process. The core part of this modeling is a resolution of a global unconstrained optimization problem. This paper presents a comparative computational experiments aimed at studying the efficiency of the different optimization methods applied to the docking problem. We present exper...