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Talking to Gabriel: we should add an option to run some expected values cuts (off by default).
This might be very useful in cyclic problems to cheaply get the value function to a place where it is the right height before adding more cuts.
We could do this by averaging the noise terms. We probably need an API like:
SDDP.train( model; average_value_iterations = SDDP.AverageValueIterator(; num_iterations = 50) do P, Ω return sum(p * ω for (p, ω) in zip(P, Ω)) end, )
The text was updated successfully, but these errors were encountered:
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Talking to Gabriel: we should add an option to run some expected values cuts (off by default).
This might be very useful in cyclic problems to cheaply get the value function to a place where it is the right height before adding more cuts.
We could do this by averaging the noise terms. We probably need an API like:
The text was updated successfully, but these errors were encountered: