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Pages in category "Articles with example Python (programming language) code" The following 200 pages are in this category, out of approximately 201 total. This list may not reflect recent changes. (previous page)
Hi/Lo is an algorithm and a key generation strategy used for generating unique keys for use in a database as a primary key. It uses a sequence-based hi-lo pattern to generate values. Hi/Lo is used in scenarios where an application needs its entities to have an identity prior to persistence. It is a value generation strategy.
Widely used in many programs, e.g. it is used in Excel 2003 and later versions for the Excel function RAND [8] and it was the default generator in the language Python up to version 2.2. [9] Rule 30: 1983 S. Wolfram [10] Based on cellular automata. Inversive congruential generator (ICG) 1986 J. Eichenauer and J. Lehn [11] Blum Blum Shub: 1986
special designs based on mathematical hardness assumptions: examples include the Micali–Schnorr generator, [18] Naor-Reingold pseudorandom function and the Blum Blum Shub algorithm, which provide a strong security proof (such algorithms are rather slow compared to traditional constructions, and impractical for many applications)
In computer science and information theory, a Huffman code is a particular type of optimal prefix code that is commonly used for lossless data compression.The process of finding or using such a code is Huffman coding, an algorithm developed by David A. Huffman while he was a Sc.D. student at MIT, and published in the 1952 paper "A Method for the Construction of Minimum-Redundancy Codes".
This algorithm, also known as the "recursive backtracker" algorithm, is a randomized version of the depth-first search algorithm. Frequently implemented with a stack, this approach is one of the simplest ways to generate a maze using a computer. Consider the space for a maze being a large grid of cells (like a large chess board), each cell ...
The algorithm generates a random permutations uniformly so long as the hardware operates in a fair manner. In 2015, Bacher et al. produced MERGESHUFFLE, an algorithm that divides the array into blocks of roughly equal size, uses Fisher—Yates to shuffle each block, and then uses a random merge recursively to give the shuffled array. [12]
The algorithm considers two problems: the master problem and the subproblem. The master problem is the original problem with only a subset of variables being considered. The subproblem is a new problem created to identify an improving variable (i.e. which can improve the objective function of the master problem). The algorithm then proceeds as ...