Unraveling the Complexity of Algorithms 🐢

Understanding Complexity

Algorithm complexity is a measure used to determine the computational efficiency of an algorithm. The complexity is often expressed using Big O notation, which gives an upper bound on the time or space taken by an algorithm.

Time Complexity

Time complexity refers to the computational complexity that describes the amount of time it takes to run an algorithm. Common complexities include O(1), O(log n), O(n), O(n log n), and O(n^2).

Space Complexity

Space complexity refers to the amount of memory space required by an algorithm. This includes both the temporary space allocated by the algorithm and the space needed by the inputs.

Examples of Algorithm Complexity

• O(1) - Constant Time: Hash table operations
• O(n) - Linear Time: Simple for loop traversals
• O(n^2) - Quadratic Time: Nested loops, such as bubble sort