23 citations · 45 across the 13 of their papers we have counts for
15 papers
Inside madupite: Technical Design and Performance
Matilde Gargiani, Robin Sieber, Philip Pawlowsky +1
In this work, we introduce and benchmark madupite, a newly proposed high-performance solver designed for large-scale discounted infinite-horizon Markov decision processes with fini…
madupite: A High-Performance Distributed Solver for Large-Scale Markov Decision Processes
Matilde Gargiani, Robin Sieber, Philip Pawlowsky +2
This paper introduces madupite, a high-performance distributed solver for large-scale Markov Decision Processes (MDPs). MDPs are widely used to model complex dynamical systems in v…
Semismooth Newton Methods for Risk-Averse Markov Decision Processes
Matilde Gargiani, Francesco Micheli, Anastasios Tsiamis +1
Inspired by semismooth Newton methods, we propose a general framework for designing solution methods with convergence guarantees for risk-averse Markov decision processes. Our appr…
Inexact Policy Iteration Methods for Large-Scale Markov Decision Processes
Matilde Gargiani, Robin Sieber, Efe Balta +2
We consider inexact policy iteration methods for large-scale infinite-horizon discounted MDPs with finite spaces, a variant of policy iteration where the policy evaluation step is…
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.…
Policy Iteration for Multiplicative Noise Output Feedback Control
Benjamin Gravell, Matilde Gargiani, John Lygeros +1
We propose a policy iteration algorithm for solving the multiplicative noise linear quadratic output feedback design problem. The algorithm solves a set of coupled Riccati equation…