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You are absolutely right about the remainder in O-maximization! Thanks for spotting that. About the ascending then descending question, I think the confusion is rather about the naming O-maximization. It means order maximization, thus when we minimize the expected value of V, we maximize the mass in ascending order of V (assign most probability to the states with the least value in V). Does that help your understanding? Please do feel free to add a comment in the docs to explain this peculiarity. |
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Okay it's clearer! In my case I wanted to maximize the sum, so I take the inverse order in the algorithm. I thought it was O-maximization, but it is in fact O-minimization. Thanks! |
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Many thanks @Zinoex for the great package and doc!
Only 80% sure, but the remainder of the O-maximization algorithm should be
1 - sum(p)right?Also, something that puzzled me is that the ordering is first in ascending order in the doc (for O-minimization), and then in descending order (for O-maximization). But I didn't change anything yet.