By Mifflin R., Sagastizabal C.
For convex minimization we introduce an set of rules in accordance with VU-space decomposition. the strategy makes use of a package subroutine to generate a chain of approximate proximal issues. whilst a primal-dual tune resulting in an answer and nil 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 tune issues and the objective's U-Hessian are approximated good adequate.
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A VU-algorithm for convex minimization by Mifflin R., Sagastizabal C.