ℋ2-Optimal Model Reduction Using Projected Nonlinear Least Squares

  title={ℋ2-Optimal Model Reduction Using Projected Nonlinear Least Squares},
  author={Jeffrey M. Hokanson and Caleb C. Magruder},
  journal={SIAM J. Sci. Comput.},
  • Jeffrey M. Hokanson, Caleb C. Magruder
  • 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
    Revisiting IRKA: Connections with Pole Placement and Backward Stability
    • PDF


    H2 Model Reduction for Large-Scale Linear Dynamical Systems
    • 497
    • PDF
    Krylov Projection Methods for Model Reduction
    • 800
    Least Squares Rational Approximation
    • 4
    • PDF
    Fast H2-optimal model order reduction exploiting the local nature of Krylov-subspace methods
    • 6
    Projected Nonlinear Least Squares for Exponential Fitting
    • 9
    • PDF
    Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
    • 287
    • PDF
    Quadrature-Based Vector Fitting for Discretized H2 Approximation
    • 40
    • Highly Influential
    Computing Reduced Order Models via Inner-Outer Krylov Recycling in Diffuse Optical Tomography
    • 12
    • PDF
    Model Reduction by Rational Interpolation
    • 45
    • PDF
    H2-Optimal Model Reduction with Higher-Order Poles
    • 37
    • PDF