3 papers
cs.SE2025
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…
cs.DC2025
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…
math.OC2025
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…