Analysis. For a list with n items, the best case is when the value is equal to the first element of the list, in which case only one comparison is needed. The worst case is when the value is not in the list (or occurs only once at the end of the list), in which case n comparisons are needed.

A Linear Search is the most basic type of searching algorithm. A Linear Search sequentially moves through your collection (or data structure) looking for a matching value. In other words, it looks down a list, one item at a time, without jumping. Think of it as a way of finding your way in a phonebook.

What is the disadvantage of a linear search? Linear Search is very slow for large lists. As the number of elements in the array/list increases the time complexity also increases. As the number of elements in the array/list increases the time complexity also increases.

Binary Search Algorithm Disadvantages- It employs recursive approach which requires more stack space. Programming binary search algorithm is error prone and difficult. The interaction of binary search with memory hierarchy i.e. caching is poor.

(algorithm) Definition: A sort algorithm in which the sorted items occupy the same storage as the original ones. These algorithms may use o(n) additional memory for bookkeeping, but at most a constant number of items are kept in auxiliary memory at any time. Also known as sort in place.

Explanation: An additional space of O(n) is required in order to merge two sorted arrays. Thus merge sort is not an in place sorting algorithm.

Closed 7 years ago. So the space efficiency of Quicksort is O(log(n)). This is the space required to maintain the call stack. Now, according to the Wikipedia page on Quicksort, this qualifies as an in-place algorithm, as the algorithm is just swapping elements within the input data structure.

Which Sorting Algorithms are In-Place and which are not? In Place : Bubble sort, Selection Sort, Insertion Sort, Heapsort. Not In-Place : Merge Sort. Note that merge sort requires O(n) extra space.

Stable sorting algorithms maintain the relative order of records with equal keys (i.e. values). That is, a sorting algorithm is stable if whenever there are two records R and S with the same key and with R appearing before S in the original list, R will appear before S in the sorted list.

Several common sorting algorithms are stable by nature, such as Merge Sort, Timsort, Counting Sort, Insertion Sort, and Bubble Sort. Others such as Quicksort, Heapsort and Selection Sort are unstable. We can modify unstable sorting algorithms to be stable.

Quicksort

Different Sorting Algorithms

• Bubble Sort.
• Insertion Sort.
• Selection Sort.
• Quick Sort.
• Merge Sort.
• Heap Sort.

Bubble sort

Time Complexity: O(N2.709). Therefore, it is slower than even the Bubble Sort that has a time complexity of O(N2). Recursively call slowsort function for the maximum of the first N/2 elements. Recursively call slowsort function for the maximum of the remaining N/2 elements.

Merge sort is more efficient and works faster than quick sort in case of larger array size or datasets. Quick sort is more efficient and works faster than merge sort in case of smaller array size or datasets. Sorting method : The quick sort is internal sorting method where the data is sorted in main memory.

Heap sort is faster than Shell sort.

The same applies for data sets that have only a few items that need to be swapped a few times. The main disadvantage of the bubble sort method is the time it requires. With a running time of O(n^2), it is highly inefficient for large data sets. Additionally, the presence of turtles can severely slow the sort.

Number of swaps reduced than bubble sort. For smaller values of N, insertion sort performs efficiently like other quadratic sorting algorithms. Adaptive: total number of steps is reduced for partially sorted array. In-Place sort.

The inner loop does O(n) work on each iteration, and the outer loop runs for O(n) iterations, so the total work is O(n2).

Bubble sort is mainly used in educational purposes for helping students understand the foundations of sorting. This is used to identify whether the list is already sorted. When the list is already sorted (which is the best-case scenario), the complexity of bubble sort is only O(n) .

An example of merge sort. First divide the list into the smallest unit (1 element), then compare each element with the adjacent list to sort and merge the two adjacent lists. Finally all the elements are sorted and merged. Merge sort is a divide and conquer algorithm that was invented by John von Neumann in 1945.

Real-world examples of the bubble sort algorithm are hard to find. However with a little imagination, we can see how a bubble sort might happen in a real situation. Imagine there are five cars all travelling down a straight road.

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The best case for bubble sort occurs when the list is already sorted or nearly sorted. In the case where the list is already sorted, bubble sort will terminate after the first iteration, since no swaps were made.

What is the best case efficiency of bubble sort in the improvised version? Explanation: Some iterations can be skipped if the list is sorted, hence efficiency improves to O(n). 10. The given array is arr = {1,2,4,3}.

The algorithm runs as follows:

1. Look at the first number in the list.
2. Compare the current number with the next number.
3. Is the next number smaller than the current number?
4. Move to the next number along in the list and make this the current number.
5. Repeat from step 2 until the last number in the list has been reached.