# ℋ2-Optimal Model Reduction Using Projected Nonlinear Least Squares

@article{Hokanson20202OptimalMR,
title={ℋ2-Optimal Model Reduction Using Projected Nonlinear Least Squares},
author={Jeffrey M. Hokanson and Caleb C. Magruder},
journal={SIAM J. Sci. Comput.},
year={2020},
volume={42},
pages={A4017-A4045}
}
• Published 2020
• Computer Science, Mathematics
• SIAM J. Sci. Comput.
• In many applications throughout science and engineering, model reduction plays an important role replacing expensive large-scale linear dynamical systems by inexpensive reduced order models that capture key features of the original, full order model. One approach to model reduction is to find reduced order models that are locally optimal approximations in the $\mathcal{H}_2$ norm, an approach taken by the Iterative Rational Krylov Algorithm (IRKA) and several others. Here we introduce a new… CONTINUE READING
1 Citations

#### References

SHOWING 1-10 OF 59 REFERENCES
H2 Model Reduction for Large-Scale Linear Dynamical Systems
• Mathematics, Computer Science
• SIAM J. Matrix Anal. Appl.
• 2008
• 497
• PDF
Fast H2-optimal model order reduction exploiting the local nature of Krylov-subspace methods
• Mathematics, Computer Science
• 2016 European Control Conference (ECC)
• 2016
• 6
Projected Nonlinear Least Squares for Exponential Fitting
• 9
• PDF
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
• Mathematics, Computer Science
• SIAM J. Sci. Comput.
• 2008
• 287
• PDF
Quadrature-Based Vector Fitting for Discretized H2 Approximation
• Computer Science, Mathematics
• SIAM J. Sci. Comput.
• 2015
• 40
• Highly Influential
Model Reduction by Rational Interpolation
• Computer Science, Mathematics
• ArXiv
• 2014
• 45
• PDF
H2-Optimal Model Reduction with Higher-Order Poles
• Mathematics, Computer Science
• SIAM J. Matrix Anal. Appl.
• 2010
• 37
• PDF