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The median trick is a generic approach that increases the chances of a probabilistic algorithm to succeed. [1] Apparently first used in 1986 [ 2 ] by Jerrum et al. [ 3 ] for approximate counting algorithms , the technique was later applied to a broad selection of classification and regression problems.
If, say, 22% of the observations are of value 2 or below and 55.0% are of 3 or below (so 33% have the value 3), then the median is 3 since the median is the smallest value of for which () is greater than a half. But the interpolated median is somewhere between 2.50 and 3.50.
Thus, this formula is non-constructive. [3] Approaches exist for an explicit formula for majority of polynomial size: Take the median from a sorting network, where each compare-and-swap "wire" is simply an OR gate and an AND gate. The Ajtai–Komlós–Szemerédi (AKS) construction is an example. Combine the outputs of smaller majority circuits ...
Using this approximate median as an improved pivot, the worst-case complexity of quickselect reduces from quadratic to linear, which is also the asymptotically optimal worst-case complexity of any selection algorithm. In other words, the median of medians is an approximate median-selection algorithm that helps building an asymptotically optimal ...
In the empirical sciences, the so-called three-sigma rule of thumb (or 3 σ rule) expresses a conventional heuristic that nearly all values are taken to lie within three standard deviations of the mean, and thus it is empirically useful to treat 99.7% probability as near certainty.
As a baseline algorithm, selection of the th smallest value in a collection of values can be performed by the following two steps: . Sort the collection; If the output of the sorting algorithm is an array, retrieve its th element; otherwise, scan the sorted sequence to find the th element.
Grundy number of a directed graph. [3]: GT56 Hamiltonian completion [3]: GT34 Hamiltonian path problem, directed and undirected. [2] [3]: GT37, GT38, GT39 Induced subgraph isomorphism problem; Graph intersection number [3]: GT59 Longest path problem [3]: ND29 Maximum bipartite subgraph or (especially with weighted edges) maximum cut.
If there are n-1 phantoms at 0, then the median rule returns the minimum of all real votes. If there are n-1 phantoms at 100, then the median rule returns the maximum of all real votes. If there are n-1 phantoms at 50, then the median rule returns 50 if some ideal points are above and some are below 50; otherwise, it returns the vote closest to 50.