Download A VU-algorithm for convex minimization by Mifflin R., Sagastizabal C. PDF

By Mifflin R., Sagastizabal C.

For convex minimization we introduce an set of rules according to VU-space decomposition. the tactic makes use of a package deal subroutine to generate a chain of approximate proximal issues. while a primal-dual song resulting in an answer and nil subgradient pair exists, those issues approximate the primal song issues and provides the algorithm's V, or corrector, steps. The subroutine additionally approximates twin tune issues which are U-gradients wanted for the method's U-Newton predictor steps. With the inclusion of an easy line seek the ensuing set of rules is proved to be globally convergent. The convergence is superlinear if the primal-dual song issues and the objective's U-Hessian are approximated good sufficient.

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