Time complexity of an algorithm refers to the amount of computational time that an algorithm takes to complete as a function of the size of the input. It helps in analyzing the efficiency of an algorithm by estimating how the runtime increases as the input size grows.

Time complexity is usually expressed using Big O notation (e.g., O(1), O(n), O(log n), O(n²), etc.), which describes the worst-case, best-case, or average-case performance. For example, if an algorithm has a time complexity of O(n), it means the time taken grows linearly with the input size.

Key Points:

- It does not measure actual execution time, but rather how the number of operations grows.

- Helps in comparing different algorithms for the same task.

- Essential for choosing the most optimal algorithm, especially for large inputs.

In summary, time complexity is a theoretical measure that helps us understand and predict the performance of algorithms based on input size.