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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 ...
In the first stage, assuming that the list is sorted in ascending order, the algorithm looks for the first exponent, j, where the value 2 j is greater than the search key. This value, 2 j becomes the upper bound for the binary search with the previous power of 2, 2 j - 1, being the lower bound for the binary search. [3]
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.
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.
A bounded wildcard is one with either an upper or a lower inheritance constraint. The bound of a wildcard can be either a class type, interface type, array type, or type variable. Upper bounds are expressed using the extends keyword and lower bounds using the super keyword. Wildcards can state either an upper bound or a lower bound, but not both.
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.
A Fenwick tree or binary indexed tree (BIT) is a data structure that stores an array of values and can efficiently compute prefix sums of the values and update the values. It also supports an efficient rank-search operation for finding the longest prefix whose sum is no more than a specified value.
Master Theorem bound Notational examples 1 Work to split/recombine a problem is dominated by subproblems. i.e. the recursion tree is leaf-heavy. When () = where < (upper-bounded by a lesser exponent polynomial)