Condition number for reliable microwave transistor modeling

2004 ◽  
Vol 14 (2) ◽  
pp. 174-181
Author(s):  
Fethi Choubani ◽  
Wahid Béjaoui ◽  
Ammar Bouallegue
Keyword(s):  
Condition Number ◽  
Microwave Transistor

Consensual noise modeling of a microwave transistor

2010 ◽  
Vol 7 (7) ◽  
pp. 1-8
Author(s):  
N. Tokan ◽  
F. Güneş ◽  
F. Gürgen
Keyword(s):  
Microwave Transistor ◽  
Noise Modeling

Improved microwave-transistor structure

1972 ◽  
Vol 8 (20) ◽  
pp. 499 ◽  
Author(s):  
J.A. Archer
Keyword(s):  
Transistor Structure ◽  
Microwave Transistor

scholarly journals Some Characterizations of the Distribution of the Condition Number of a Complex Gaussian Matrix

2020 ◽  
Vol 8 (1) ◽  
pp. 22-35
Author(s):  
M. Shakil ◽  
M. Ahsanullah
Keyword(s):  
Order Statistics ◽  
Condition Number ◽  
Record Values ◽  
Distributional Properties ◽  
Upper Record Values ◽  
Upper Record

AbstractThe objective of this paper is to characterize the distribution of the condition number of a complex Gaussian matrix. Several new distributional properties of the distribution of the condition number of a complex Gaussian matrix are given. Based on such distributional properties, some characterizations of the distribution are given by truncated moment, order statistics and upper record values.

Schur complement spectral bounds for large hybrid FETI-DP clusters and huge three-dimensional scalar problems

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Zdeněk Dostál ◽  
Tomáš Brzobohatý ◽  
Oldřich Vlach
Keyword(s):  
Condition Number ◽  
Three Dimensional ◽  
Schur Complement ◽  
Decomposition Methods ◽  
Stiffness Matrices ◽  
Spectral Bounds ◽  
Schur Complements ◽  
Linear Problems ◽  
Primal Method ◽  
Large Clusters

Abstract Bounds on the spectrum of Schur complements of subdomain stiffness matrices with respect to the interior variables are key ingredients of the convergence analysis of FETI (finite element tearing and interconnecting) based domain decomposition methods. Here we give bounds on the regular condition number of Schur complements of “floating” clusters arising from the discretization of 3D Laplacian on a cube decomposed into cube subdomains. The results show that the condition number of the cluster defined on a fixed domain decomposed into m × m × m cube subdomains connected by face and optionally edge averages increases proportionally to m. The estimates support scalability of unpreconditioned H-FETI-DP (hybrid FETI dual-primal) method. Though the research is most important for the solution of variational inequalities, the results of numerical experiments indicate that unpreconditioned H-FETI-DP with large clusters can be useful also for the solution of huge linear problems.

scholarly journals The spectral condition number plot for regularization parameter evaluation

2019 ◽  
Vol 35 (2) ◽  
pp. 629-646 ◽  
Author(s):  
Carel F. W. Peeters ◽  
Mark A. van de Wiel ◽  
Wessel N. van Wieringen
Keyword(s):  
Condition Number ◽  
Regularization Parameter ◽  
Spectral Condition ◽  
Parameter Evaluation

scholarly journals Improving the condition number of a simple eigenvalue by a rank one matrix

2016 ◽  
Vol 58 ◽  
pp. 7-12
Author(s):  
Rafael Bru ◽  
Rafael Cantó ◽  
Ana M. Urbano
Keyword(s):  
Condition Number ◽  
Simple Eigenvalue ◽  
Rank One

Condition number related to the outer inverse of a complex matrix

2009 ◽  
Vol 215 (8) ◽  
pp. 2826-2834 ◽  
Author(s):  
Dijana Mosić ◽  
Dragan S. Djordjević
Keyword(s):  
Condition Number ◽  
Complex Matrix ◽  
Outer Inverse

A Non-Normality Measure of the Condition Number for Monitoring and Control

1997 ◽  
Vol 119 (2) ◽  
pp. 217-222 ◽  
Author(s):  
Kunsoo Huh ◽  
Jeffrey L. Stein
Keyword(s):  
Optimal Control ◽  
Condition Number ◽  
Minimization Problem ◽  
Monitoring And Control ◽  
Proper Selection ◽  
Modal Structure ◽  
L2 Norm ◽  
And Control ◽  
Normality Measure ◽  
Selection Of

Because the behavior of the condition number can have highly steep and multi-modal structure, optimal control and monitoring problems based on the condition number cannot be easily solved. In this paper, a minimization problem is formulated for κ2(P), the condition number of an eigensystem (P) of a matrix in terms of the L2 norm. A new non-normality measure is shown to exist that guarantees small values for the condition number. In addition, this measure can be minimized by proper selection of controller and observer gains. Application to the design of well-conditioned controller and observer-based monitors is illustrated.

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