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Binary search Visualization of the binary search algorithm where 7 is the target value Class Search algorithm Data structure Array Worst-case performance O (log n) Best-case performance O (1) Average performance O (log n) Worst-case space complexity O (1) Optimal Yes In computer science, binary search, also known as half-interval search, logarithmic search, or binary chop, is a search ...
The C++ template construct used above is widely cited [citation needed] as the genericity construct that popularized the notion among programmers and language designers and supports many generic programming idioms. The D language also offers fully generic-capable templates based on the C++ precedent but with a simplified syntax.
The Standard Template Library (STL) is a software library originally designed by Alexander Stepanov for the C++ programming language that influenced many parts of the C++ Standard Library. It provides four components called algorithms , containers , functions , and iterators .
For example, given a binary tree of infinite depth, a depth-first search will go down one side (by convention the left side) of the tree, never visiting the rest, and indeed an in-order or post-order traversal will never visit any nodes, as it has not reached a leaf (and in fact never will). By contrast, a breadth-first (level-order) traversal ...
Fig. 1: A binary search tree of size 9 and depth 3, with 8 at the root. In computer science, a binary search tree (BST), also called an ordered or sorted binary tree, is a rooted binary tree data structure with the key of each internal node being greater than all the keys in the respective node's left subtree and less than the ones in its right subtree.
C++ programmers expect the latter on every major implementation of C++; it includes aggregate types (vectors, lists, maps, sets, queues, stacks, arrays, tuples), algorithms (find, for_each, binary_search, random_shuffle, etc.), input/output facilities (iostream, for reading from and writing to the console and files), filesystem library ...
Searching is similar to searching a binary search tree. Starting at the root, the tree is recursively traversed from top to bottom. At each level, the search reduces its field of view to the child pointer (subtree) whose range includes the search value. A subtree's range is defined by the values, or keys, contained in its parent node.
Example comparing two search algorithms. To look for "Morin, Arthur" in some ficitious participant list, linear search needs 28 checks, while binary search needs 5. Svg version: File:Binary search vs Linear search example svg.svg.