Big O notation helps us understand how well different sorting methods work. It shows us some problems with common sorting techniques:
Time Complexity: Some algorithms, like Bubble Sort, get very slow when dealing with a lot of data. They have a time complexity of . This means they are not a good choice for big datasets.
Space Complexity: Others, like Merge Sort, need extra space to work. They require additional space, which can be too much in some cases.
We can avoid these problems by using faster algorithms. Quick Sort and Heap Sort are two examples that usually perform better.
Big O notation helps us understand how well different sorting methods work. It shows us some problems with common sorting techniques:
Time Complexity: Some algorithms, like Bubble Sort, get very slow when dealing with a lot of data. They have a time complexity of . This means they are not a good choice for big datasets.
Space Complexity: Others, like Merge Sort, need extra space to work. They require additional space, which can be too much in some cases.
We can avoid these problems by using faster algorithms. Quick Sort and Heap Sort are two examples that usually perform better.