A unified approach to reduced-order modelling and control of large-scale systems with multiple decision makers

1985 ◽  
Vol 6 (4) ◽  
pp. 403-418 ◽  
Author(s):  
Vikram R. Saksena ◽  
J. B. Cruz
Keyword(s):  
Large Scale ◽  
Decision Makers ◽  
Unified Approach ◽  
Large Scale Systems ◽  
Reduced Order ◽  
Multiple Decision Makers ◽  
Reduced Order Modelling ◽  
And Control

Generalized Hessenberg transformations for reduced-order modelling of large-scale systems†

1978 ◽  
Vol 27 (4) ◽  
pp. 493-512 ◽  
Author(s):  
EDWIN C. Y. TSE ◽  
JURAJ V. MEDANIC ◽  
WILLIAM R. PERKINS
Keyword(s):  
Large Scale ◽  
Large Scale Systems ◽  
Reduced Order ◽  
Reduced Order Modelling

scholarly journals Reduced Order Modeling for Prediction and Control of Large-Scale Systems.

2014 ◽  
Author(s):  
Irina Kalashnikova ◽  
Srinivasan Arunajatesan ◽  
Matthew Franklin Barone ◽  
Bart Gustaaf van Bloemen Waanders ◽  
Jeffrey A. Fike
Keyword(s):  
Large Scale ◽  
Reduced Order Modeling ◽  
Large Scale Systems ◽  
Reduced Order ◽  
Prediction And Control ◽  
And Control

scholarly journals Model reduction methods based on Krylov subspaces

Acta Numerica ◽  
2003 ◽  
Vol 12 ◽  
pp. 267-319 ◽  
Author(s):  
Roland W. Freund
Keyword(s):  
Large Scale ◽  
Krylov Subspace ◽  
Circuit Simulation ◽  
Krylov Subspaces ◽  
Lanczos Algorithm ◽  
Linear Dynamical Systems ◽  
Reduced Order ◽  
Reduced Order Modelling ◽  
Reduction Methods ◽  
Modelling Techniques

In recent years, reduced-order modelling techniques based on Krylov-subspace iterations, especially the Lanczos algorithm and the Arnoldi process, have become popular tools for tackling the large-scale time-invariant linear dynamical systems that arise in the simulation of electronic circuits. This paper reviews the main ideas of reduced-order modelling techniques based on Krylov subspaces and describes some applications of reduced-order modelling in circuit simulation.

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