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Model–view–viewmodel (MVVM) is an architectural pattern in computer software that facilitates the separation of the development of a graphical user interface (GUI; the view)—be it via a markup language or GUI code—from the development of the business logic or back-end logic (the model) such that the view is not dependent upon any ...
Thus, if the number is prime then the answer is always correct, and if the number is composite then the answer is correct with probability at least 1−(1− 1 ⁄ 2) k = 1−2 −k. For Monte Carlo decision algorithms with two-sided error, the failure probability may again be reduced by running the algorithm k times and returning the majority ...
The NIST Dictionary of Algorithms and Data Structures [1] is a reference work maintained by the U.S. National Institute of Standards and Technology. It defines a large number of terms relating to algorithms and data structures. For algorithms and data structures not necessarily mentioned here, see list of algorithms and list of data structures.
LZ77 and LZ78 are the two lossless data compression algorithms published in papers by Abraham Lempel and Jacob Ziv in 1977 [1] and 1978. [2] They are also known as Lempel-Ziv 1 (LZ1) and Lempel-Ziv 2 (LZ2) respectively. [3] These two algorithms form the basis for many variations including LZW, LZSS, LZMA and others.
For example, for the array of values [−2, 1, −3, 4, −1, 2, 1, −5, 4], the contiguous subarray with the largest sum is [4, −1, 2, 1], with sum 6. Some properties of this problem are: If the array contains all non-negative numbers, then the problem is trivial; a maximum subarray is the entire array.
The Guarded Command Language (GCL) is a programming language defined by Edsger Dijkstra for predicate transformer semantics in EWD472. [1] It combines programming concepts in a compact way. It makes it easier to develop a program and its proof hand-in-hand, with the proof ideas leading the way; moreover, parts of a program can actually be ...
Converting the numerical values into fuzzy numbers is done with the membership function which consists of semantic descriptions like near, middle and far. [10] Each possible linguistic value is given by an individual neuron. The neuron “near” fires with a value from 0 until 1, if the distance is located within the category "near".
Simulation experiments show that, when the numbers are uniformly random in [0,1], LDM always performs better (i.e., produces a partition with a smaller largest sum) than greedy number partitioning. It performs better than the multifit algorithm when the number of items n is sufficiently large.