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math.OC2026
Bayesian Optimization with Lower Confidence Bounds for Minimization Problems with Known Outer Structure
Katrin Baumgärtner, Moritz Diehl
This paper considers Bayesian optimization (BO) for problems with known outer problem structure. In contrast to the classic BO setting, where the objective function itself is unkno…
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…