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Comment by bowaggoner | Hacker News Reader

Hmm, that example is really interesting. I think you're right once we formalize L_infinity, because the goal will be to minimize the maximum possible distance. But the other scores can be phrased as penalties and minimizing expected penalty or total penalty summed over the data points, and it's not clear how to phrase L_infinity this way because the "penalty" would be infinity for any outcome...

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I might be missing some context here, but usually the L_infinity norm means the max norm, or the maximum absolute difference over all components (data points). This gives (min + max)/2 as GP suggested.

Wikipedia entry: https://en.wikipedia.org/wiki/Norm_(mathematics)#Maximum_nor...

For what it's worth, I think the confusion of your comment and jules's below comes from the idea that the lp-norm is sum(x_i^p), when it's actually [sum(x_i^p)^1/p] (please forgive my notation). Since raising to the 1/p power is monotonic, it doesn't actually make a difference in minimizing or maximizing the norm, so people often use them interchangeably.

You're right and we're on the same page about the solution. The reasoning behind my comment of "interesting" is that formula you gave is not well-defined for p=infinity, although we can define it as the limit of that expression as p-->infinity. Furthermore, for p < infinity the lp-norm can assign a well-defined penalty to each x, and the goal is to minimize the sum of the penalties. But that's not really true for p=infinity.

In the limit it's indeed the max of the absolute differences, so that's how you conventionally define the infinity norm (even though the base formula itself is not well defined, just as with 0). And when you try to minimise the max of the distances, you get the midrange.

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