Cross-entropy (CE)-based stopping criteria for turbo iterative decoding are known to outperform fixed-iteration stopping criteria at high signal-to-noise ratios (SNRs). While CE-based stopping criteria have a range of thresholds, a highvalue threshold for small frame sizes, and vice versa, should be used. It is difficult to advocate the value that can be categorized as either a small or large f...

Abstract. We study stopping criteria that are suitable in the solution by Krylov space based methods of linear and non linear systems of equations arising from the mixed and the mixed-hybrid finite-element approximation of saddle point problems. Our approach is based on the equivalence between the Babuška and Brezzi conditions of stability which allows us to apply some of the results obtained i...

In most literature dealing with evolutionary algorithms the stopping criterion consists of reaching a certain number of objective function evaluations (or a number of generations, respectively). A disadvantage is that the number of function evaluations that is necessary for convergence is unknown a priori, so trialand-error methods have to be applied for finding a suitable number. By using othe...

We consider a family of practical stopping criteria for linear solvers for adaptive finite element methods for symmetric elliptic problems. A contraction property between two consecutive levels of refinement of the adaptive algorithm is shown when the a family of smallness criteria for the corresponding linear solver residuals are assumed on each level or refinement. More importantly, based on ...

In this paper, we address the issue of deciding when to stop active learning for building a labeled training corpus. Firstly, this paper presents a new stopping criterion, classification-change, which considers the potential ability of each unlabeled example on changing decision boundaries. Secondly, a multi-criteriabased combination strategy is proposed to solve the problem of predefining an a...