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Using little omega notation, it is ω(n c) time for all constants c, where n is the input parameter, typically the number of bits in the input. For example, an algorithm that runs for 2 n steps on an input of size n requires superpolynomial time (more specifically, exponential time).
For example, in the search path for a string of length k, there will be k traversals down middle children in the tree, as well as a logarithmic number of traversals down left and right children in the tree. Thus, in a ternary search tree on a small number of very large strings the lengths of the strings can dominate the runtime.
To show that NL is contained in C, we simply take an NL algorithm and choose a random computation path of length n, and execute this 2 n times. Because no computation path exceeds length n, and because there are 2 n computation paths in all, we have a good chance of hitting the accepting one (bounded below by a constant).
This popular sorting algorithm has an average-case performance of O(n log(n)), which contributes to making it a very fast algorithm in practice. But given a worst-case input, its performance degrades to O(n 2). Also, when implemented with the "shortest first" policy, the worst-case space complexity is instead bounded by O(log(n)).
The analysis of the former and the latter algorithm shows that it takes at most log 2 n and n check steps, respectively, for a list of size n. In the depicted example list of size 33, searching for "Morin, Arthur" takes 5 and 28 steps with binary (shown in cyan) and linear (magenta) search, respectively. Graphs of functions commonly used in the ...
In mathematics, a polylogarithmic function in n is a polynomial in the logarithm of n, [1] () + () + + () +.The notation log k n is often used as a shorthand for (log n) k, analogous to sin 2 θ for (sin θ) 2.
Created independently in 1977 by W. Eddy and in 1978 by A. Bykat. Just like the quicksort algorithm, it has the expected time complexity of O(n log n), but may degenerate to O(n 2) in the worst case. Divide and conquer, a.k.a. merge hull — O(n log n) Another O(n log n) algorithm, published in 1977 by Preparata and Hong. This algorithm is also ...
The iterated logarithm is useful in analysis of algorithms and computational complexity, appearing in the time and space complexity bounds of some algorithms such as: Finding the Delaunay triangulation of a set of points knowing the Euclidean minimum spanning tree: randomized O(n log * n) time. [3]