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pro vyhledávání: '"Juhl, Austin"'
Autor:
Juhl, Austin, Shirokoff, David
In this work, we present approaches to rigorously certify $A$- and $A(\alpha)$-stability in Runge-Kutta methods through the solution of convex feasibility problems defined by linear matrix inequalities. We adopt two approaches. The first is based on
Externí odkaz:
http://arxiv.org/abs/2405.13921
Machine-learning paradigms such as imitation learning and reinforcement learning can generate highly performant agents in a variety of complex environments. However, commonly used methods require large quantities of data and/or a known reward functio
Externí odkaz:
http://arxiv.org/abs/2403.01059
Autor:
Juhl, Austin, Shirokoff, David
Publikováno v:
In Applied Numerical Mathematics January 2025 207:136-155