Question

I was reading sorting method which include bubble sort, selection sort, merge sort, heap sort, bucket sort etc.. They also contain time complexity which help us to know which sorting is efficient. So I had a basic question. If we contain data than how will we be choose sorting. Time complexity is one of parameter which help us to decide sorting method. But do we have another parameter to choose sorting method?.

Just trying to figure out sorting for better understanding.

Having some query about heap sort:

1. Where do we use heap sort?

2. What is bigger advantage of heap sort (except time complexity O(n log n))?

3. What is disadvantage of heap sort?

4. What is build time for heap? (I heard O(n) but I’m not sure.)

5. Any scenario where we have to use heap sort or heap sort is better option (except priority queue)?

6. Before applying the heap sort on data, what are the parameter will we look into data?

Answer 1

The two main theoretical features of sorting algorithms are time complexity and space complexity.

In general, time complexity lets us know how the performance of the algorithm changes as the size of the data set increases. Things to consider:

• How much data are you expecting to sort? This will help you know whether you need to look for an algorithm with a very low time complexity.
• How sorted will your data be already? Will it be partly sorted? Randomly sorted? This can affect the time complexity of the sorting algorithm. Most algorithms will have worst and best cases – you want to make sure you’re not using an algorithm on a worst-case data set.
• Time complexity is not the same as running time. Remember that time complexity only describes how the performance of an algorithm varies as the size of the data set increases. An algorithm that always does one pass over all the input would be O(n) – it’s performance is linearly correlated with the size of the input. But, an algorithm that always does two passes over the data set is also O(n) – the correlation is still linear, even if the constant (and actual running time) is different.

Similarly, space complexity describes how much space an algorithm needs to run. For example, a simple sort such as insertion sort needs an additional fixed amount of space to store the value of the element currently being inserted. This is an auxiliary space complexity of O(1) – it doesn’t change with the size of the input. However, merge sort creates extra arrays in memory while it runs, with an auxiliary space complexity of O(n). This means the amount of extra space it requires is linearly correlated with the size of the input.

Of course, algorithm design is often a trade-off between time and space – algorithms with a low space complexity may require more time, and algoithms with a low time complexity may require more space.

For more information, you may find this tutorial useful.

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To answer your updated question, you may find the wikipedia page on Heap Sort useful.