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Extensions of Dynamic Programming, Machine Learning, Discrete Optimization
TREES
Extensions of Dynamic Programming, Machine Learning, Discrete Optimization

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computational efficiency

Splitting methods: merits and pitfalls

Prof. Alexander Ostermann

Dec 3, 16:00 - 17:00

B2 L5 R5220

splitting methods numerical integration computational efficiency pitfalls

Coffee Time: 15:30 - 16:00. Splitting methods are a well-established tool for numerically integrating time-dependent partial differential equations. These methods split the vector field into disjoint components, which are integrated separately using an appropriate time step. The individual flows are then combined to obtain the desired numerical approximation.

Extensions of Dynamic Programming, Machine Learning, Discrete Optimization (TREES)

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