Model Reduction by Rational Interpolation

Abstract

The last two decades have seen major developments in interpolatory methods for model reduction of large-scale linear dynamical systems. Advances of note include the ability to produce (locally) optimal reduced models at modest cost; refined methods for deriving interpolatory reduced models directly from input/output measurements; and extensions for the reduction of parametrized systems. This chapter offers a survey of interpolatory model reduction methods starting from basic principles and ranging up through recent developments that include weighted model reduction and structure-preserving methods based on generalized coprime representations. Our discussion is supported by an assortment of numerical examples.

References in corpus (5)

• Model Reduction of Descriptor Systems by Interpolatory Projection Methods
• Interpolatory Weighted-H2 Model Reduction
• Near-optimal Frequency-weighted Interpolatory Model Reduction
• A Spectral Expression for the Frequency-Limited H2-norm
• Recycling BiCGSTAB with an Application to Parametric Model Order Reduction

Cited by in corpus (5)

• Interpolatory rational model order reduction of parametric problems lacking uniform inf-sup stability
• ${\mathcal{H}}_2$-Optimal Model Reduction Using Projected Nonlinear Least Squares
• Practicable Simulation-Free Model Order Reduction by Nonlinear Moment Matching
• Interpolatory methods for ${\mathcal{H}}_{\infty }$ model reduction of multi-input/multi-output systems
• On frequency- and time-limited H2-optimal model order reduction