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In number theory, the general number field sieve ( GNFS) is the most efficient classical algorithm known for factoring integers larger than 10100. Heuristically, its complexity for factoring an integer n (consisting of ⌊log2 n⌋ + 1 bits) is of the form. in O and L-notations. [1] It is a generalization of the special number field sieve ...
In this case, the array from which samples are taken is [2, 3, -1, -20, 5, 10]. In computer science, the maximum sum subarray problem, also known as the maximum segment sum problem, is the task of finding a contiguous subarray with the largest sum, within a given one-dimensional array A [1...n] of numbers. It can be solved in time and space.
In computer science, a heap is a tree -based data structure that satisfies the heap property: In a max heap, for any given node C, if P is a parent node of C, then the key (the value) of P is greater than or equal to the key of C. In a min heap, the key of P is less than or equal to the key of C. [ 1] The node at the "top" of the heap (with no ...
The above is an approximation. The exact worst-case number of comparisons during the heap-construction phase of heapsort is known to be equal to 2n − 2s 2 (n) − e 2 (n), where s 2 (n) is the number of 1 bits in the binary representation of n and e 2 (n) is the number of trailing 0 bits. [6] [7]
A longest common subsequence ( LCS) is the longest subsequence common to all sequences in a set of sequences (often just two sequences). It differs from the longest common substring: unlike substrings, subsequences are not required to occupy consecutive positions within the original sequences. The problem of computing longest common ...
Definition. The most common problem being solved is the 0-1 knapsack problem, which restricts the number of copies of each kind of item to zero or one. Given a set of items numbered from 1 up to , each with a weight and a value , along with a maximum weight capacity , subject to and . Here represents the number of instances of item to include ...
The subset sum problem (SSP) is a decision problem in computer science. In its most general formulation, there is a multiset of integers and a target-sum , and the question is to decide whether any subset of the integers sum to precisely .[ 1] The problem is known to be NP-complete. Moreover, some restricted variants of it are NP-complete too ...
The d-ary heap or d-heap is a priority queue data structure, a generalization of the binary heap in which the nodes have d children instead of 2. [1] [2] [3] Thus, a binary heap is a 2-heap, and a ternary heap is a 3-heap. According to Tarjan [2] and Jensen et al., [4] d -ary heaps were invented by Donald B. Johnson in 1975.