1 citations · 1 across the 5 of their papers we have counts for
6 papers
-optimal model order reduction using hyperbolic geometry
Andrea Angino, Tamás Dózsa, Matthias Voigt
We revisit the problem of approximating a linear time-invariant LTI model by a reduced-order model (ROM) in an -optimal sense. A widely used method for computing such ROMs is…
Adaptive Kernel Methods
Tamás Dózsa, Andrea Angino, Zoltán Szabó +2
Kernel methods approximate nonlinear maps in a data-driven manner by projecting the target map onto a finite-dimensional Hilbert space called the solution space. Traditionally, thi…
Trust-Region Methods with Low-Fidelity Objective Models
Andrea Angino, Matteo Aurina, Alena Kopaničáková +3
We introduce two multifidelity trust-region methods based on the Magical Trust Region (MTR) framework. MTR augments the classical trust-region step with a secondary, informative di…
Generalized rational Prony and Bernoulli methods
Tamás Dózsa, Matthias Voigt, Zoltán Szabó +2
The generalized operator-based Prony method is an important tool for describing signals which can be written as finite linear combinations of eigenfunctions of certain linear opera…
Adaptive Multilevel Newton: A Quadratically Convergent Optimization Method
Nick Tsipinakis, Panos Parpas, Matthias Voigt
Newton's method may exhibit slower convergence than vanilla Gradient Descent in its initial phase on strongly convex problems. Classical Newton-type multilevel methods mitigate thi…
Fast and memory-efficient optimization for large-scale data-driven predictive control
Philipp Schmitz, Manuel Schaller, Matthias Voigt +1
Recently, data-enabled predictive control (DeePC) schemes based on Willems' fundamental lemma have attracted considerable attention. At the core are computations using Hankel-like…