Abstract The objective of this work was to propose a new stopping criterion shorten the computing time PampaPlus genetic improvement software, while maximizing qualification index (GQI) progeny, controlling inbreeding, and avoiding unintended culling. Data from two beef-cattle herds integrating were used. Five mating scenarios built using different numbers sires (9 37) dams (142 568). analyzed ...

Asynchronous iterative algorithms can reduce much of the data dependencies associated with synchronization barriers. The reported study investigates the potentials of asynchronous iterative algorithms by quantifying the critical parallel processing factors. Specifically, a time complexity-based analysis method is used to understand the inherent interdependencies between computing and communicat...

INTRODUCTION AND OBJECTIVES The aim of this study was to examine the efficiency of the averaging technique for estimating multiple auditory steady state responses in normal hearing subjects and to provide quantifiable stopping criteria at near-threshold intensities. METHODS Multiple amplitude-modulated (89-115 Hz) tones (500, 1,000, 2,000 and 4,000 Hz) were simultaneously presented to both ea...

We consider the setting of estimating the mean of a random variable by a sequential stopping rule Monte Carlo (MC) method. The performance of a typical second moment based sequential stopping rule MC method is shown to be unreliable in such settings both by numerical examples and through analysis. By analysis and approximations, we construct a higher moment based stopping rule which is shown in...

Deblurring from motion problem with or without noise is ill-posed inverse problem and almost all inverse problem require some sort of parameter selection. Quality of restored image in iterative motion deblurring is dependent on optimal stopping point or regularization parameter selection. At optimal point reconstructed image is best matched to original image and for other points either data mis...

We explain an interesting property of minimum residual iterative methods for the solution of the linear least squares (LS) problem. Our analysis demonstrates that the stopping criteria commonly used with these methods can in some situations be too conservative, causing any chosen method to perform too many iterations or even fail to detect that an acceptable iterate has been obtained. We propos...

Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence learning algorithm (CD), an approximation to the gradient of the data log-likelihood. A simple reconstruction error is often used to decide whether the approximation provided by the CD algorithm is good enoug...

This work extends the results of Arioli [1], [2] on stopping criteria for iterative solution methods for finite element problems to the case of nonsymmetric positive-definite problems. We show that the residual measured in the norm induced by the symmetric part of the inverse of the system matrix is relevant to convergence in a finite element context. We then provide alternative ways of calcula...

In this paper, we investigate the properties of commonly used prepruning heuristics for rule learning by visualizing them in PN-space. PN-space is a variant of ROC-space, which is particularly suited for visualizing the behavior of rule learning and its heuristics. On the one hand, we think that our results lead to a better understanding of the effects of stopping and filtering criteria, and he...