Titlebook: Supercomputational Science; R. G. Evans,S. Wilson Book 1990 Plenum Press, New York 1990 Approximation.algorithms.dynamics.supercomputer

BAIT

J. J. Du Croz,P. J. D. Mayesles in the computation of the infima and the solutions to both continuous-time and discrete-time .. optimization problems. Thus a good non-ambiguous understanding of linear system structures is essential for our study. In our opinion, the best way to display all the structural properties of linear s

先行

Peristalsis

K. A. Berringtonive is to present a solution to the discrete-time .. control problem. One way to approach this problem is to transform the discrete-time .. optimal control problem into an equivalent continuous-time .. control problem via bilinear transformation (see Chapter 3). Then the continuous-time controllers

ADORN

阴郁

Harry Quineyimodel-family is designed. The objective of this procedure is to adapt the closed-loop behaviour of the different models of the multimodel-family to an required behaviour. Here the required behaviour is described by a pole-configuration of the closed-loop system. One can get this desired pole-config

陈旧

ticlopidine

D. J. Baker,S. Wilson,D. Moncrieffg eigenvector . of the corresponding shape matrix. We consider this problem under asymptotic scenarios that allow the difference . := . − . between both largest eigenvalues of the underlying shape matrix to converge to zero as the sample size . diverges to infinity. Such scenarios make the problem o

Pedagogy

Peter J. Knowlesnts. Peña et al. (J Am Stat Assoc 114(528):1683–1694, 2019) defined one-sided dynamic principal components as linear combinations of the present and past values of the series with optimal reconstruction properties. In order to make the estimation of these components robust to outliers, we propose he

Insensate

I. Haneef Recently, estimates of a sphericity measure are needed in high-dimensional shrinkage covariance matrix estimation problems, wherein the (oracle) shrinkage parameter minimizing the mean squared error (MSE) depends on the unknown sphericity parameter. The purpose of this chapter is to investigate the

Diaphragm

I. Haneef Recently, estimates of a sphericity measure are needed in high-dimensional shrinkage covariance matrix estimation problems, wherein the (oracle) shrinkage parameter minimizing the mean squared error (MSE) depends on the unknown sphericity parameter. The purpose of this chapter is to investigate the