For this algorithm to work properly, the data collection should be in the sorted form. Next, we will see the java implementation of iterative binary search, followed by its explanation. That means that you’ll get a runtime complexity of [math]O(n)[/math] - where n is the number of nodes in the binary tree. In binary search, we directly hit the middle element and then compare it with the element to be found. Height of the binary search tree becomes n. So, Time complexity of BST Operations = O(n). In this tutorial, you will understand the working of binary search with working code in C, C++, Java, and Python. One place where you might have heard about O(log n) time complexity the first time is Binary search algorithm. This search algorithm works on the principle of divide and conquer. It's an asymptotic notation to represent the time complexity. If the element to be found is equal to the middle element, then we have already found the element, otherwise, if it is smaller, then we know it is going to lie on the left side of it, else, on the right. Worst Case- In worst case, The binary search tree is a skewed binary search tree. The time complexity of algorithms is most commonly expressed using the big O notation. Here, h = Height of binary search tree . Binary search is a fast search algorithm with run-time complexity of Ο(log n). The time complexity of binary search is O(log n), where n is the number of elements in an array. So there must be some type of behavior that algorithm is showing to be given a complexity of log n. Let us see how it works. Binary Search is a searching algorithm for finding an element's position in a sorted array. Time Complexity is most commonly estimated by counting the number of elementary steps performed by any algorithm to finish execution. 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