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Minimax (sometimes Minmax, MM[ 1] or saddle point[ 2]) is a decision rule used in artificial intelligence, decision theory, game theory, statistics, and philosophy for minimizing the possible loss for a worst case ( max imum loss) scenario. When dealing with gains, it is referred to as "maximin" – to maximize the minimum gain.
Also known as min-max scaling or min-max normalization, rescaling is the simplest method and consists in rescaling the range of features to scale the range in [0, 1] or [−1, 1]. Selecting the target range depends on the nature of the data. The general formula for a min-max of [0, 1] is given as: [3]
Arg max. As an example, both unnormalised and normalised sinc functions above have of {0} because both attain their global maximum value of 1 at x = 0. The unnormalised sinc function (red) has arg min of {−4.49, 4.49}, approximately, because it has 2 global minimum values of approximately −0.217 at x = ±4.49.
The golden-section search is a technique for finding an extremum (minimum or maximum) of a function inside a specified interval. For a strictly unimodal function with an extremum inside the interval, it will find that extremum, while for an interval containing multiple extrema (possibly including the interval boundaries), it will converge to one of them.
A min-max heap is a complete binary tree containing alternating min (or even) and max (or odd) levels. Even levels are for example 0, 2, 4, etc, and odd levels are respectively 1, 3, 5, etc. We assume in the next points that the root element is at the first level, i.e., 0. Example of Min-max heap.
Priority queue: A priority queue is an abstract concept like "a list" or "a map"; just as a list can be implemented with a linked list or an array, a priority queue can be implemented with a heap or a variety of other methods. K-way merge: A heap data structure is useful to merge many already-sorted input streams into a single sorted output ...
In mathematical optimization, the proximal operator is an operator associated with a proper, [note 1] lower semi-continuous convex function from a Hilbert space to [, +], and is defined by: [1]
In mathematical analysis, the maximum and minimum[ a ] of a function are, respectively, the largest and smallest value taken by the function. Known generically as extremum, [ b ] they may be defined either within a given range (the local or relative extrema) or on the entire domain (the global or absolute extrema) of a function. [ 1 ][ 2 ][ 3 ...