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Quicksort is an efficient, general-purpose sorting algorithm. Quicksort was developed by British computer scientist Tony Hoare in 1959 [1] and published in 1961. [2] It is still a commonly used algorithm for sorting. Overall, it is slightly faster than merge sort and heapsort for randomized data, particularly on larger distributions. [3]
Implementation of Ramer–Douglas–Peucker and many other simplification algorithms with open source licence in C++; XSLT implementation of the algorithm for use with KML data. You can see the algorithm applied to a GPS log from a bike ride at the bottom of this page; Interactive visualization of the algorithm; Implementation in F#; Ruby gem ...
Examples of algorithms for this task include New Edge-Directed Interpolation (NEDI), [1] [2] Edge-Guided Image Interpolation (EGGI), [3] Iterative Curvature-Based Interpolation (ICBI), [citation needed] and Directional Cubic Convolution Interpolation (DCCI). [4] A study found that DCCI had the best scores in PSNR and SSIM on a series of test ...
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The value of the line function at this midpoint is the sole determinant of which point should be chosen. The adjacent image shows the blue point (2,2) chosen to be on the line with two candidate points in green (3,2) and (3,3). The black point (3, 2.5) is the midpoint between the two candidate points.
The divide-and-conquer technique is the basis of efficient algorithms for many problems, such as sorting (e.g., quicksort, merge sort), multiplying large numbers (e.g., the Karatsuba algorithm), finding the closest pair of points, syntactic analysis (e.g., top-down parsers), and computing the discrete Fourier transform . [1]
More efficient algorithms such as quicksort, timsort, or merge sort are used by the sorting libraries built into popular programming languages such as Python and Java. [ 2 ] [ 3 ] However, if parallel processing is allowed, bubble sort sorts in O(n) time, making it considerably faster than parallel implementations of insertion sort or selection ...
In its simplest implementation for linear cases such as lines, the DDA algorithm interpolates values in interval by computing for each x i the equations x i = x i−1 + 1, y i = y i−1 + m, where m is the slope of the line. This slope can be expressed in DDA as follows: