In a stack, the top element is the element that is inserted at the last or most recently inserted element.

Stacks are based on the LIFO principle, i.e., the element inserted at the last, is the first element to come out of the list. Queues are based on the FIFO principle, i.e., the element inserted at the first, is the first element to come out of the list.

The priority queue is a somewhat similar data structure to the queue. The difference lies in how the elements are being processed: A standard queue strictly follows the FIFO (First-In-Last-Out) principle. A priority queue does not follow the FIFO principle.

empty() method in Java is used to check whether a stack is empty or not. The method is of boolean type and returns true if the stack is empty else false. Parameters: The method does not take any parameters. Return Value: The method returns boolean true if the stack is empty else it returns false.

If it is EMPTY, then display “Stack is EMPTY!!!” and terminate the function. If it is NOT EMPTY, then define a variable i and initialize with top. Display stack[i] value and decrement i value by one ( i– ). Repeat above step until i value becomes 0 .

stack::empty() function is an inbuilt function in C++ STL, which is defined in <stack>header file. empty() is used to check whether the associated container is empty or not and return true or false accordingly. The function checks the container should be empty means the size of the container should be 0.

If the stack is full, then it is said to be an Overflow condition. If the stack is empty, then it is said to be an Underflow condition. Peek or Top: Returns top element of stack.

The pop() method removes and returns the top element of the stack. An EmptyStackException exception is thrown if we call the pop() method on an empty stack.

Stack has 3 primary methods: peek , push , and pop . There wasn’t much processing involved. It just returned a primitive value. This is O(1) for both time and space.

To calculate Big O, you can go through each line of code and establish whether it’s O(1), O(n) etc and then return your calculation at the end. For example it may be O(4 + 5n) where the 4 represents four instances of O(1) and 5n represents five instances of O(n).

Hence we can say Big-O run time of binary search is O(log n). So, binary search is far more faster-searching algorithm than linear searching if the array is sorted. And its Big-O run time is O(log n).

Big-O is a standard mathematical notation that shows how efficient an algorithm is in the worst-case scenario relative to its input size. To measure the efficiency of an algorithm, we need to consider two things: Time Complexity: How much time does it take to run completely?

Quicksort is a logarithmic-time algorithm, in other words, it has a Big O notation of O(log n)-(more about Big O Notation)- and depending on the way you implement it, it can be up to 2x or even 3x faster than Merge Sort or Heap Sort.

Typically, quicksort is significantly faster in practice than other O(nlogn) algorithms, because its inner loop can be efficiently implemented on most architectures, and in most real-world data, it is possible to make design choices that minimize the probability of requiring quadratic time.