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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 ...
Specific applications of search algorithms include: Problems in combinatorial optimization, such as: . The vehicle routing problem, a form of shortest path problem; The knapsack problem: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as ...
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.
Binary-code compatibility (binary compatible or object-code compatible) is a property of a computer system, meaning that it can run the same executable code, typically machine code for a general-purpose computer central processing unit (CPU), that another computer system can run.
Binary search is usually one of the first algorithms taught to computer science students. The premise is quite simple: given a sorted list of numbers, binary search eliminates halves of the list in which the number you are looking for cannot lie until it finds the number. However, binary search has lots of subtleties.
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.
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). This creates a theoretical tradeoff between the number of possible states and the running time: given k comparisons, the algorithm can only reach O(2 k ) possible states and/or possible goals.
The static optimality problem is the optimization problem of finding the binary search tree that minimizes the expected search time, given the + probabilities. As the number of possible trees on a set of n elements is ( 2 n n ) 1 n + 1 {\displaystyle {2n \choose n}{\frac {1}{n+1}}} , [ 2 ] which is exponential in n , brute-force search is not ...