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math.OC2025
Non-Asymptotic Analysis of Projected Gradient Descent for Physics-Informed Neural Networks
Jonas Nießen, Johannes Müller
In this work, we provide a non-asymptotic convergence analysis of projected gradient descent for physics-informed neural networks for the Poisson equation. Under suitable assumptio…
math.OC2024
Optimal Rates of Convergence for Entropy Regularization in Discounted Markov Decision Processes
Johannes Müller, Semih Cayci
We study the error introduced by entropy regularization in infinite-horizon discrete discounted Markov decision processes. We show that this error decreases exponentially in the in…
math.OC2022
Algebraic optimization of sequential decision problems
Mareike Dressler, Marina Garrote-López, Guido Montúfar +2
We study the optimization of the expected long-term reward in finite partially observable Markov decision processes over the set of stationary stochastic policies. In the case of d…