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In computer science, divide and conquer is an algorithm design paradigm. A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem.
Pages in category "Divide-and-conquer algorithms" The following 9 pages are in this category, out of 9 total. This list may not reflect recent changes. ...
Divide and rule (Latin: divide et impera), or more commonly known as divide and conquer, in politics refers to an entity gaining and maintaining political power by using divisive measures. This includes the exploitation of existing divisions within a political group by its political opponents, and also the deliberate creation or strengthening ...
The closed form follows from the master theorem for divide-and-conquer recurrences. The number of comparisons made by merge sort in the worst case is given by the sorting numbers. These numbers are equal to or slightly smaller than (n ⌈lg n⌉ − 2 ⌈lg n⌉ + 1), which is between (n lg n − n + 1) and (n lg n + n + O(lg n)). [6]
It is a specific type of divide and conquer algorithm. A well-known example is binary search. [3] Abstractly, a dichotomic search can be viewed as following edges of an implicit binary tree structure until it reaches a leaf (a goal or final state).
Figure 1. Finding the shortest path in a graph using optimal substructure; a straight line indicates a single edge; a wavy line indicates a shortest path between the two vertices it connects (among other paths, not shown, sharing the same two vertices); the bold line is the overall shortest path from start to goal.
This technique can be used to improve the efficiency of many eigenvalue algorithms, but it has special significance to divide-and-conquer. For the rest of this article, we will assume the input to the divide-and-conquer algorithm is an real symmetric tridiagonal matrix . The algorithm can be modified for Hermitian matrices.
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