5 papers
Multilevel Stochastic Gradient Descent for Risk-Averse PDE-Constrained Optimization
Niklas Baumgarten, Philipp A. Guth, David Schneiderhan +1
We present recent advances in applying and analyzing multilevel stochastic gradient descent algorithms to risk-averse, three-dimensional PDE-constrained optimization problems. The…
LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization
Eliott Van Dieren, Tommaso Vanzan, Fabio Nobile
We introduce LAGO, a LocAl-Global Optimization framework coupling Bayesian Optimization (BO) and gradient-based trust region local refinement through an adaptive competition mechan…
Optimized multilevel Monte Carlo methods in Banach spaces
Kristin Kirchner, Fabio Nobile, Christoph Schwab +1
We present a theoretical and numerical analysis of Monte Carlo methods for the estimation of statistical moments of random variables taking values in a Banach s…
Low-rank solutions to a class of parametrized systems using Riemannian optimization
Marco Sutti, Tommaso Vanzan
We propose a computational framework for computing low-rank approximations to the ensemble of solutions of a parametrized system of the form for multipl…
An adaptive importance sampling algorithm for risk-averse optimization
Sandra Pieraccini, Tommaso Vanzan
Adaptive sampling algorithms are modern and efficient methods that dynamically adjust the sample size throughout the optimization process. However, they may encounter difficulties…