SUMMARY The Expectation-Maximization (EM) algorithm is a powerful computational technique for locating maxima of functions. It is widely used in statistics for maximum likelihood or maximum a posteriori estimation in incomplete data models. In certain situations however, this method is not applicable because the expectation step cannot be performed in closed{form. To deal with these problems, a...

Preconditioned gradient iterations for very large eigenvalue problems are efficient solvers with growing popularity. However, only for the simplest preconditioned eigensolver, namely the preconditioned gradient iteration (or preconditioned inverse iteration) with fixed step size, sharp non-asymptotic convergence estimates are known. These estimates require a properly scaled preconditioner. In t...

*Correspondence: [email protected] 1Department of Mathematics, Faculty of Science For Girls, King Abdulaziz University, Jeddah, 21593, Saudi Arabia Full list of author information is available at the end of the article Abstract Let (X ,‖ · ‖) be a Banach space. Let C be a nonempty, bounded, closed, and convex subset of X and T : C→ C be a monotone nonexpansive mapping. In this paper, it is ...

in this paper, several $delta$ and strong convergence theorems are established for the ishikawa iterations for nonexpansive mappings in the framework of cat(0) spaces. our results extend and improve the corresponding results

It is known that predictor-corrector methods in a large neighborhood of the central path are among the most efficient interior point methods (IPMs) for linear optimization (LO) problems. The best iteration bound based on the classical logarithmic barrier function is O ( n log n ǫ ) . In this paper we propose a family of self-regular proximity based predictorcorrector (SR-PC) IPM for LO in a lar...