Name some types of Big O complexity and corresponding algorithms

Big O notation is a mathematical notation used to describe the limiting behavior of a function when the argument tends towards a particular value or infinity. It is commonly used in computer science to classify algorithms based on their time and space complexity. Here are some common types of Big O complexity and corresponding algorithms:

O(1) - Constant Time Complexity

• Description: The execution time of the algorithm is constant and does not change with the input size.
• Example Algorithms:
  • Accessing an element in an array by index.
  • Push and pop operations on a stack or queue.
  • Initializing a variable and printing it[1][4][6].

O(log n) - Logarithmic Time Complexity

• Description: The execution time grows logarithmically with the input size.
• Example Algorithms:
  • Binary search in a sorted array.
  • Operations on balanced trees like AVL trees and Red-Black trees[1][4][6].

O(n) - Linear Time Complexity

• Description: The execution time grows linearly with the input size.
• Example Algorithms:
  • Linear search through an unsorted array.
  • Iterating through elements of a list or array[1][4][6].

O(n log n) - Linearithmic Time Complexity

• Description: The execution time grows in proportion to the input size multiplied by the logarithm of the input size.
• Example Algorithms:
  • Efficient sorting algorithms like mergesort and heapsort.
  • Certain divide-and-conquer algorithms[1][4][6].

O(n^2) - Quadratic Time Complexity

• Description: The execut...