R. C. M. Silva

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An implementable proximal point algorithm is established for the smooth nonconvex uncon-strained minimization problem. At each iteration, the algorithm minimizes approximately a general quadratic by a truncated strategy with step length control. The main contributions are: (i) a framework for updating the proximal parameter; (ii) inexact criteria for(More)
The convergence of primal and dual central paths associated to entropy and exponential functions, respectively, for semidefinite programming problem are studied in this paper. As an application, the proximal point method with the Kullback-Leibler distance applied to semidefi-nite programming problems is considered, and the convergence of primal and dual(More)
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