Time and space complexity measure how efficiently an algorithm uses computational resources such as time and memory. Understanding complexity helps you compare and choose better algorithms.
Time complexity measures how the running time of an algorithm increases as the input size increases. It is expressed using Big O Notation.
If an algorithm loops through an array of size n once, its time complexity is O(n).
Space complexity measures how much extra memory an algorithm requires to execute. It includes:
Big O describes the upper bound of an algorithm’s growth rate. It helps estimate the worst-case performance.
| Algorithm | Best Case | Worst Case |
|---|---|---|
| Linear Search | O(1) | O(n) |
| Binary Search | O(1) | O(log n) |
| Bubble Sort | O(n) | O(n²) |
| Merge Sort | O(n log n) | O(n log n) |
Analyzing time and space complexity is critical for building scalable and efficient software. Always aim for the best possible performance within acceptable resource limits.
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