- Time Complexity: Time complexity measures the amount of time an algorithm takes to complete as a function of the input size. It helps developers understand how the runtime of an algorithm grows with increasing input.
- Space Complexity: Space complexity evaluates the amount of memory an algorithm requires to execute as a function of the input size. It helps developers identify how much memory resources an algorithm consumes.
- Asymptotic Analysis: Big O notation focuses on the behavior of algorithms as the input size approaches infinity. It ignores constant factors and lower-order terms, providing a simplified approximation of algorithmic efficiency.
Common Notations:
Big O notation uses symbols to represent the worst-case time or space complexity of algorithms:
- O(1): Constant time complexity, indicating that the algorithm's runtime remains constant regardless of the input size.
- O(n): Linear time complexity, indicating that the algorithm's runtime grows linearly with the input size.
- O(log n): Logarithmic time complexity, indicating that the algorithm's runtime grows logarithmically with the input size.
- O(n^2): Quadratic time complexity, indicating that the algorithm's runtime grows quadratically with the input size.
- O(2^n): Exponential time complexity, indicating that the algorithm's runtime doubles with each additional input element.
By understanding and applying Big O notation, developers can make informed decisions when designing and optimizing algorithms, leading to more efficient and scalable software solutions.
