Kuidas määratakse algoritmi tõhusus ja keerukus?

The efficiency and complexity of an algorithm are usually evaluated through the analysis of time and space complexity.

• Time complexity shows how the execution time of the algorithm depends on the size of the input data. It is usually expressed in "Big O" notation, for example, O(n), O(n²), O(log n).
• Space complexity reflects how much memory the algorithm requires depending on the size of the input data.

To determine the complexity, the number of main operations (e.g., comparisons, assignments) is analyzed in the worst, average, and best cases.

Example: bubble sort has a time complexity of O(n²), as in the worst case, approximately n² comparisons are needed.

Efficiency can also be measured empirically by running the algorithm with different data volumes and measuring the time and memory consumption.