By Mifflin R., Sagastizabal C.
For convex minimization we introduce an set of rules in keeping with VU-space decomposition. the strategy makes use of a package deal subroutine to generate a chain of approximate proximal issues. whilst a primal-dual tune resulting in an answer and 0 subgradient pair exists, those issues approximate the primal music 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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A VU-algorithm for convex minimization by Mifflin R., Sagastizabal C.