4 papers
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…
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 KopaniÄáková +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…