Argmin and argmax formalize optimization

Argmin and argmax are functions which iterate over a set of candidate solutions to find the best ones as scored by a given attached function. In this, the two operators formalize the training objective in supervised learning, among countless other optimization tasks. Varying the size of the hypothesis space for optimization can be seen as a way of manipulating flexibility.

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