A computationally efficient numerical method for the solution of nonlinear sea ice dynamics models employing viscous-plastic rheologies is presented. The method is based on a semi-implicit decoupling of the x and y ice momentum equations into a form having better convergence properties than the coupled equations. While this decoupled form also speeds up solutions employing point relaxation meth...

INTRODUCTION Synthetic mesh has been used traditionally to repair abdominal wall defects, but its use is limited in the case of bacterial contamination. New biological materials are now being used successfully for delayed primary closure of contaminated abdominal wall defects. The costs of biological materials may prevent surgeons from using them. We compared the conventional staged repair of c...

A detailed calorimetric study on an epoxy-based nanocomposite system was performed employing bisphenol diglycidyl ether cured with diethylenetriamine as the polymer matrix and a taurine-modified MgAL layered double hydroxide nanofiller.

A random matrix is likely to be well conditioned, and motivated by this well known property we employ random matrix multipliers to advance some fundamental matrix computations. This includes numerical stabilization of Gaussian elimination with no pivoting as well as block Gaussian elimination, approximation of the leading and trailing singular spaces of an ill conditioned matrix, associated wit...

Symmetric positive semi-definite (SPSD) matrix approximation methods have been extensively used to speed up large-scale eigenvalue computation and kernel learning methods. The sketching based method, which we call the prototype model, produces relatively accurate approximations. The prototype model is computationally efficient on skinny matrices where one of the matrix dimensions is relatively ...

Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, research citation network analysis, social network analysis, and regulatory networks in genes. Low rank decompositions, such as SVD and CUR, are powerful techniques for revealing latent/hidden variables and associated pattern...

In this work, we consider linear and nonlinear fractional stochastic delay systems driven by the Rosenblatt process. With aid of delayed Mittag-Leffler matrix functions representation solutions these systems, derive controllability results as an application. By introducing a Gramian matrix, provide sufficient necessary criteria for systems. Furthermore, employing Krasnoselskii’s fixed point the...

This paper investigates a scalable optimization procedure to the low-rank matrix completion problem posed by Candes and Recht [2]. We identify the singular value decomposition as a computational bottleneck for large problem instances, and propose utilizing an approximately computed SVD borne out of recent advances in random linear algebra. We then use this approximately computed SVD to implemen...

A random matrix is likely to be well conditioned, and motivated by this well known property we employ random matrix multipliers to advance some fundamental matrix computations. This includes numerical stabilization of Gaussian elimination with no pivoting as well as block Gaussian elimination, approximation of the leading and trailing singular spaces of an ill conditioned matrix, associated wit...