3 papers
math.OC2026
Gray-Box Optimization using Optimism in the Face of Uncertainty
Katrin Baumgärtner, Léo Simpson, Moritz Diehl
This paper considers sequential gray-box optimization where the objective function is given as the composition of a loss function and a parametric model. Crucially, the parameters…
math.OC2026
Anderson Acceleration for Linearly Converging SQP-Type Methods
Jonathan Frey, David Kiessling, Katrin Baumgärtner +1
Although Anderson acceleration (AA) is known to speed up fixed-point iterations, it is rarely applied in constrained optimization, in particular sequential quadratic programming (S…
math.ST2026
Finite-sample bounds for multi-output system identification
Léo Simpson, Katrin Baumgärtner, Johannes Köhler +1
This paper presents uniform-in-time finite-sample bounds for regularized linear regression with vector-valued outputs and conditionally zero-mean subgaussian noise. By revisiting c…