4 citations · 5 across the 4 of their papers we have counts for
11 papers
Inexact GMRES Policy Iteration for Large-Scale Markov Decision Processes
Matilde Gargiani, Dominic Liao-McPherson, Andrea Zanelli +1
Policy iteration enjoys a local quadratic rate of contraction, but its iterations are computationally expensive for Markov decision processes (MDPs) with a large number of states.…
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli +2
Despite their success, policy gradient methods suffer from high variance of the gradient estimate, which can result in unsatisfactory sample complexity. Recently, numerous variance…
System Level Disturbance Reachable Sets and their Application to Tube-based MPC
Jerome Sieber, Andrea Zanelli, Samir Bennani +1
Tube-based model predictive control (MPC) methods leverage tubes to bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. This…
Convergence Analysis of Homotopy-SGD for non-convex optimization
Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh +2
First-order stochastic methods for solving large-scale non-convex optimization problems are widely used in many big-data applications, e.g. training deep neural networks as well as…
An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay
Barbara Barros Carlos, Tommaso Sartor, Andrea Zanelli +4
The advances in computer processor technology have enabled the application of nonlinear model predictive control (NMPC) to agile systems, such as quadrotors. These systems are char…
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs
Matilde Gargiani, Andrea Zanelli, Moritz Diehl +1
Following early work on Hessian-free methods for deep learning, we study a stochastic generalized Gauss-Newton method (SGN) for training DNNs. SGN is a second-order optimization me…