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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 ...
push 1L (the number one with type long) onto the stack ldc 12 0001 0010 1: index → value push a constant #index from a constant pool (String, int, float, Class, java.lang.invoke.MethodType, java.lang.invoke.MethodHandle, or a dynamically-computed constant) onto the stack ldc_w 13 0001 0011 2: indexbyte1, indexbyte2 → value
Uniform binary search is an optimization of the classic binary search algorithm invented by Donald Knuth and given in Knuth's The Art of Computer Programming.It uses a lookup table to update a single array index, rather than taking the midpoint of an upper and a lower bound on each iteration; therefore, it is optimized for architectures (such as Knuth's MIX) on which
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 ...
In the one-dimensional case, we can use a search tree containing all the start and end points in the interval set, each with a pointer to its corresponding interval. A binary search in O ( log n ) {\displaystyle O(\log n)} time for the start and end of q {\displaystyle q} reveals the minimum and maximum points to consider.
Hence, one first finds the smallest representative r greater than k in the x-fast trie. Using this representative, one retrieves the predecessor of r. These two representatives point to two balanced binary search trees, both of which one searches for k. Finding the smallest representative r greater than k in the x-fast trie takes O(log log M).
To search for a given key value, apply a standard binary search algorithm in a binary search tree, ignoring the priorities. To insert a new key x into the treap, generate a random priority y for x. Binary search for x in the tree, and create a new node at the leaf position where the binary search determines a node for x should exist.
Thorough tutorial Julienne Walker with lots of code, including a practical implementation; Object Oriented implementation with tests; A Disquisition on The Performance Behavior of Binary Search Tree Data Structures (pages 67–75) – comparison of AA trees, red–black trees, treaps, skip lists, and radix trees; An Objective-C implementation