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# Merge sort worst case

In order to generate the worst case of merge sort, the merge operation that resulted in above sorted array should result in maximum comparisons. In order to do so, the left and right sub-array involved in merge operation should store alternate elements of sorted array. i.e. left sub-array should be {1,3,5,7} and right sub-array should be {2,4,6,8}. Now every element of array will be compared at-least once and that will result in maximum comparisons. We apply the same logic for. 2 Answers2. The worst case of merge sort will be the one where merge sort will have to do maximum number of comparisons. For worst case the array before this step must be {0,2,4,6,1,3,5,7} because here left subarray= {0,2,4,6} and right subarray= {1,3,5,7} will result in maximum comparisons In order to generate the worst case of merge sort, the merge operation that resulted in above sorted array should result in maximum comparisons. In order to do so, the left and right sub-array involved in merge operation should store alternate elements of sorted array. i.e. left sub-array should be {1,3,5,7} and right sub-array should be {2,4,6,8}. Now every element of array will be compared. Mergesort ist ein stabiles Sortierverfahren, vorausgesetzt der Merge-Schritt ist entsprechend implementiert. Seine Komplexität beträgt im Worst-, Best- und Average-Case in Landau-Notation ausgedrückt stets (⁡ ()) In the worst case, merge sort uses approximately 39% fewer comparisons than quicksort does in its average case, and in terms of moves, merge sort's worst case complexity is O (n log n) - the same complexity as quicksort's best case. [citation needed] Merge sort is more efficient than quicksort for some types of lists if the data to be sorted can only be efficiently accessed sequentially, and.

### Find a permutation that causes worst case of Merge Sort

• Dies ist, weil ob worst-case oder average-case der merge-sort nur teilen das array in zwei Hälften, auf jeder Stufe, die es gibt lg (n) - Komponente und die andere N-Komponente kommt von seiner Vergleiche, die sind auf jeder Bühne. Also die Kombination, wird fast O (nlg n)
• Merge Sort is an efficient, stable sorting algorithm with an average, best-case, and worst-case time complexity of O(n log n). Merge Sort has an additional space complexity of O(n) in its standard implementation. This can be circumvented by in-place merging, which is either very complicated or severely degrades the algorithm's time complexity
• Mergesort hat gegenüber Quicksort den Vorteil, dass auch im worst case die Zeitkomplexität O(n log n) nicht überschritten wird und dass es stabil ist. Diese Vorteile erkauft man sich durch schlechtere Performance und einen zusätzlichen Platzbedarf in der Größenordnung O(n). Zusamenfassun

Time complexity of Merge Sort is θ(nLogn) in all 3 cases (worst, average and best) as merge sort always divides the array into two halves and takes linear time to merge two halves. Auxiliary Space: O(n QUICK SORT: MERGE SORT: Splitting of the array depends on the value of pivot and other array elements: Splitting of array generally done on half: Worst-case time complexity is O(n2) Worst-case time complexity is O(n log n) It takes less n space than merge sort: It takes more n space than quicksor

### arrays - When will the worst case of Merge Sort occur

We know, time complexity of merge sort algorithm is Θ(nlogn). Step-01: It is given that a merge sort algorithm in the worst case takes 30 seconds for an input of size 64. So, we have- k x nlogn = 30 (for n = 64) k x 64 log64 = 30. k x 64 x 6 = 30. From here, k = 5 / 64. Step-02 Merge Sort is a stable comparison sort algorithm with exceptional performance. Merge Sort uses the merging method and performs at O (n log (n)) in the best, average, and worst case Er leitet sich im Allgemeinen vom englischen merge, also verschmelzen und sort, dem sortieren ab. Der Sinn dahinter ist einfach nur, dass der Algorithmus die vorhandenen Daten als eine gesamte Liste betrachtet, die er dann in kleinere Listen unterteilt. Man kann also sagen, er zerlegt ein Gesamtproblem in mehrere Teilprobleme und löst diese dann Stück für Stück. Im Endeffekt setzt er sie dann zum Schluss zu einer Gesamtlösung zusammen. Heißt also verallgemeinert, dass der. Case 1: L [i] ≤ . R [j] •By LI, A Recursion Tree for Merge Sort . Continue expanding until the problem size reduces to 1. cn . cn /2 . cn /2 . cn /4 . cn /4 . cn /4 . cn /4 . c c . c . c . c . c • Each level has total cost cn. • Each time we go down one level, the number of subproblems doubles, but the cost per subproblem halves ⇒ cost per level remains the same. • There are lg.

Worst Case O(nlogn): The worst-case scenario for merge sort is when the merge sort must do the maximum amount of comparison for the array's value. It means that each value on the given array, fulfilling the condition for continuing the process until every value inside the array is fully compared an Now in order to build the worst case of merge sort, the merge operation that resulted in above sorted array should result in largest comparisons. As a result of this, the left and right sub-array involved in merge operation should store alternate elements of sortedarray such that, left subarray should be {11, 13, 15, 17} and right sub-array should be {12, 14, 16, 18}. So every element of array. Time complexity of Merge Sort is O (n*Log n) in all the 3 cases (worst, average and best) as merge sort always divides the array in two halves and takes linear time to merge two halves. It requires equal amount of additional space as the unsorted array. Hence its not at all recommended for searching large unsorted arrays UNBALANCED MERGE SORT is a sorting algorithm, which is a modified version of the standard MERGE SORT algorithm. The only difference is that instead of dividing the input into 2 equal parts in each stage, we divide it into two unequal parts - the first 2/5 of the input, and the other 3/5. a. Write the recurrence relation for the worst case. ### Mergesort - Wikipedi

• In the worst case, merge sort does about 39% fewer comparisons than quicksort does in the average case. In terms of moves, merge sort's worst case complexity is O(n log n)—the same complexity as quicksort's best case, and merge sort's best case takes about half as many iterations as the worst case. Merge sort is more efficient than quicksort for some types of lists if the data to be sorted.
• The Best and Worst-case time complexity of MergeSort is O(NlogN), where N is the number of elements in the input array. Recurrence relation for MergeSort is T(n) = T(n/2) + O(n) here n is the number of elements to be sorted. MergeSort properties at a glance: 1. Best case time complexity = O(NlogN) 2. Worst-case time complexity = O(NlogN) 3
• Compared to insertion sort [Θ(n 2) worst-case time], merge sort is faster. Trading a factor of n for a factor of lg n is a good deal. On small inputs, insertion sort may be faster. But for large enough inputs, merge sort will always be faster, because its running time grows more slowly than insertion sorts. Recursion Tree . We can understand how to solve the merge-sort recurrence without the.
• Time complexity of Merge Sort is O(n*logn) in all 3 cases (worst, average and best) as in merge sort , array is recursively divided into two halves and take linear time to merge two halves. It is not an in-place sorting algorithm as it requires additional scratch space proportional to the size of the input array. It requires an equal amount of additional space as the unsorted list. Hence, it.
• The worst case scenario for Merge Sort is when, during every merge step, exactly one value remains in the opposing list; in other words, no comparisons were skipped. This situation occurs when the two largest value in a merge step are contained in opposing lists. When this situation occurs, Merge Sort must continue comparing list elements in each of the opposing lists until the two largest.
• For Best case Insertion Sort and Heap Sort are the Best one as their best case run time complexity is O(n). For average case best asymptotic run time complexity is O(nlogn) which is given by Merge Sort, Heap Sort, Quick Sort. For Worst Case best run time complexity is O(nlogn) which is given by Merge Sort, Heap Sort

### Merge sort - Wikipedi The worst case scenario for Merge Sort is when, during every merge step, exactly one value remains in the opposing list; in other words, no comparisons were skipped. This situation occurs when the two largest value in a merge step are contained in opposing lists. When this situation occurs, Merge Sort mus > 1.Merge sort(Average, Best, Worst) = O(n * logn) Also, Merge sort is not inplace(uses more space than the size of the given array cause it uses an extra array to. Merge Sort is a type of recursive algorithm. We can express time complexity of merge sort by this recurrence relation: T (n) = 2T (n/2) + O (n) Using Masters Theorem, we get -> T (n)=O (n*logn). Time complexity of Merge Sort is O (n*logn) in all 3 cases (worst, average and best) as in merge sort , array is recursively divided into two halves and. QuickSort worst case time is quadratic, i.e., Ω(n2), which occurs when the splitting consistently leaves up to, say, up to K elements in one portion or the other. In that case the timing would satisfy something like this: T (n) ≥ n // compare every element to pivot + T (n-K) // sort the remaining part with at least n-K element

### algorithm - Warum ist Merge Sort Worst Case Laufzeit O (n

Insertion Sort on Small Arrays in Merge Sort: Although merge sort runs in Θ(nlg⁡n) worst-case time and insertion sort runs in Θ(n^2) worst-case time, the constant factors in insertion sort can make it faster in practice for small problem sizes on many machines. Thus, it makes sense to coarsen the leaves of the recursion by using insertion sort within merge sort when subproblems become sufficiently small Clarification: The time complexity of merge sort is not affected by worst case as its algorithm has to implement the same number of steps in any case. So its time complexity remains to be O(n log n). 6. Which of the following method is used for sorting in merge sort? a) merging b) partitioning c) selection d) exchanging. Answer: a Clarification: Merge sort algorithm divides the array into two.

Merge-Sort (A, p, r) INPUT: a sequence of n numbers stored in array A . OUTPUT: an ordered sequence of n numbers. MergeSort (A, p, r) // sort A[p..r] by divide & conquer 1 if p < r 2 then q ← (p+r)/2 3 MergeSort (A, p, q) 4 MergeSort (A, q+1, r) 5 Merge (A, p, q, r) // merges A[p..q] with A[q+1..r Mergesort 1. Das zu sortierende Array wird fortlaufend rekursiv in zwei Teile aufgeteilt. Die Teilstücke werden über ein Hilfsarray sortiert und im Ausgangsarray zusammengeführt. public class Mergesort {. public static int [] intArr = { 16, 23, 14, 7, 21, 20, 6, 1, 17, 13, 12, 9, 3, 19 }; public int [] sort (int l, int r) {. if (l < r) { - to merge them you had, at worst, to perform one comparison (to find the smallest element between the 2 subarrays) per element - 1 * n = n comparisons But with 3 subarrays of size n/3 : - now (supposing we do it in a straight forward manner), we have at worst, 2 comparisons (to find the smallest element between the 3 subarrays) per elemen

However, its worst-case scenario was O(n²), which is really slow isn't it? and I promised you to more sophisticated and faster algorithms to sort an array that is Merge Sort. So how can we save time Sign in. Archive; Write for us; Learning Algorithms — Merge Sort. How to write a Merge Sort algorithm in Python. Jenny Yang. Follow. Oct 7, 2020 · 5 min read. Previously, we had a look of. Merge Sort is a stable sort which means that the same element in an array maintain their original positions with respect to each other. Overall time complexity of Merge sort is O (nLogn). It is more efficient as it is in worst case also the runtime is O (nlogn) The space complexity of Merge sort is O (n) Overall time complexity of Merge sort is O(nLogn). It is more efficient as it is in worst case also the runtime is O(nlogn) The space complexity of Merge sort is O(n). This means that this. I know that merging two sorted arrays takes worst case n comparisons. However, there will often be cases where one array is depleted before the other, letting us just append the remainder of one array to the combined sorted array. I've also read that merging takes the same amount of comparisons (wrt to n) to any input of size n In python, merge sort is defined as one of the sorting algorithms which is general-purpose, uses comparison based sorting by divide and conquer algorithm where the idea is to break down the list into sub-lists until each sub-list has max one element and merge all the sub-lists in reverse order to get the sorted sub-lists and finally a single list which is sorted is called merge sort. It is an efficient, general-purpose, and best sorting algorithm with the overall, average, and worst-case. The question asks to show the lower bound on the number of comparisons in merging two sorted arrays of length \$N\$. Therefore, you need to argue that no matter what comparison-based algorithm you use, it has to make \$2N-1\$ comparisons, otherwise it would make an error on some input, i.e., it's a worst-case

Before the stats, You must already know what is Merge sort, Selection Sort, Insertion Sort, Bubble Sort, Quick Sort, Arrays, how to get current time. What is Stable Sorting ? A sorting algorithm is said to be stable if and only if two records R and S with the same key and with R appearing before S in the original list, R must appear before S in the sorted list MergeSort (A, p, r): if p > r return q = (p+r)/2 mergeSort (A, p, q) mergeSort (A, q+1, r) merge (A, p, q, r) To sort an entire array, we need to call MergeSort (A, 0, length (A)-1). As shown in the image below, the merge sort algorithm recursively divides the array into halves until we reach the base case of array with 1 element

\Theta (n^2) Θ(n2) worst-case time, the constant factors in insertion sort can make it faster in practice for small problem sizes on many machines. Thus, it makes sense to coarsen the leaves of the recursion by using insertion sort within merge sort when subproblems become sufficiently small. Consider a modification to merge sort in whic small, average, and large data sets, with all three constraints (best case, average case, and worst case). Key words: Bubble Sort, Selectio n Sort, Quick Sort, Merg e Sort, Optimized Bubble Sor t. Unter einem Sortierverfahren versteht man in der Informatik einen Algorithmus, der dazu dient, ein Tupel (i. Allg. ein Array) zu sortieren.Voraussetzung ist, dass auf der Menge der Elemente eine strenge schwache Ordnung definiert ist (kleiner-gleich), z. B. die lexikographische Ordnung von Zeichenketten oder die numerische Ordnung von Zahlen Worst Case. The Worst Case represents the slowest speed that the algorithm will opperate in in the worst conditions

Which of the following sorting algorithms does not have a worst case running time of O(n2)? a. Quick sort: b. Bubble sort: c. Merge sort: d. Insertion sort: Answer: Merge sort Worst case vs. best case: Your algorithm has di erent runtimes on di erent inputs, and a lot of times we want to know how fast the algorithm runs on an input of a certain size. Worst case runtime means that you are feeding the worst possible input (of that size) into your algorithm. Best case runtime means that you are feeding the best possible input into your algorithm. For an input of size n. O(N2 ) average, worst case: - Selection Sort, Bubblesort, Insertion Sort O(N log N) average case: - Heapsort: In-place, not stable. - BST Sort: O(N) extra space (including tree pointers, possibly poor memory locality), stable. - Mergesort: O(N) extra space, stable. - Quicksort: claimed fastest in practice, but O(N2 ) worst case. Recursion/stack requirement. No The worst case of Merge Sort, on the other hand, occurs when it has to do maximum number of comparisons. Considering the linear performance for merging, the worst case performance of the Merge Sort is O (n log2 n)

### Merge Sort - Algorithm, Source Code, Time Complexit

The worst case complexity of quick sort is O(n 2) as it takes a lot more comparisons in the worst condition. In contrast, merge sort have the same worst case and average case complexities, that is O(n log n). Merge sort can operate well on any type of data sets whether it is large or small. On the contrary, the quick sort cannot work well with large datasets. Quick sort is faster than merge. Best-, Average-, Worst-Case Speicherbedarf in-place (zusätzlicher Speicher von der Eingabegröße unabhängig) out-of-place (Speichermehrbedarf von Eingabegröße abhängig) rekursiv oder iterativ Stabilität stabile Verfahren verändern die Reihenfolge von äquivalenten Elementen nicht (wichtig bei mehrfacher Sortierung nach verschiedenen Schlüsseln) verwendete Operationen Vertauschen.

Although merge sort runs in (lg n) worst-case time and insertion sort runs in ( n2) worst-case time, the constant factors in insertion sort can make it faster in practice for small problem sizes on many machines. Thus, it makes sense to coarsen the leaves of the recursion by using insertion sort within merge sort when subproblems become su ciently small. Consider a modi cation to merge sort in. c) Bubble Sort has worst-case run-time complexity O(n2). d) Merge Sort has worst-case run-time complexity O(n). e) Merge Sort has worst-case run-time complexity O(nlogN). f) Merge Sort has worst-case run-time complexity O(n2). g) Quick Sort has worst-case run-time complexity O(n). h) Quick Sort has worst-case run-time complexity O(nlogN). i. Insertion Sort: The best case is the already sorted input and the worst case is the already reverse sorted input. In this experiment, the task is to sort the numbers in descending so data3.txt is the best case and data1.txt is the worst case. Radix Sort:: The run-times are almost the same for all inputs because radix sort's performance is independent from input order. Thus, linearly sorted.

Recent Posts. Identify the use of the inset keyword used in the box-shadow property. How to insert node at first in circular linked list in C? How to create a circular linkedlist in C Merge sort is a fast comparison based sorting algorithm which can be utilized in sorting a Linked List as well. Merge Sort is preferred for sorting a linked list. The slow random-access performance of a linked list makes some other algorithms such as Quick Sort perform poorly, and others such as Heap Sort completely impossible   ### Mergesort - Algorithmus, Quellcode, Zeitkomplexitä

Merge Sort does Θ(nLogn) operations in all cases. Most of the other sorting algorithms have worst and best cases. For example, in the typical implementation of Quick Sort (where pivot is chosen as a corner element), the worst occurs when the input array is already sorted and the best occurs when the pivot elements always divide array in two halves. For insertion sort, the worst case occurs. Merge sort. Merge sort is the algorithm which follows divide and conquer approach. Consider an array A of n number of elements. The algorithm processes the elements in 3 steps. If A Contains 0 or 1 elements then it is already sorted, otherwise, Divide A into two sub-array of equal number of elements

### Merge Sort - GeeksforGeek

1. Worst case space Complexity: O(1) Merge Sort . Merge sort is an example of a divide and conquer algorithm. In Merge sort, the input list is divided into two parts recursively to convert the input list into sub list of single element and then these sub-lists are merged into a sorted list. This sorting technique is very stable and provides a worst-case complexity of nlogn. Java internally uses.
2. Although merge-sort runs in ?(n log n) worst-case time andinsertion sort runs in ?(n 2 ) worst-case time, the constantfactors in insertion sort can make it faster in practice for smallproblem sizes on many machines. Thus, it makes sense to coarsen theleaves of the recursion by using insertion sort within merge sortwhen subproblems become sufficiently small. Consider a modificationto merge sort.
3. Time complexity of Merge Sort is ɵ(nLogn) in all 3 cases (worst, average and best) as merge sort always divides the array in two halves and take linear time to merge two halves. It divides input array in two halves, calls itself for the two halves and then merges the two sorted halves. The merg() function is used for merging two halves. The merge(arr, l, m, r) is key process that assumes that.
4. In terms of moves, merge sort's worst case complexity is O ( n log n )—the same complexity as quicksort's best case, and merge sort's best case takes about half as many iterations as the worst case. [ citation needed ] Merge sort is more efficient than quicksort for some types of lists if the data to be sorted can only be efficiently accessed sequentially, and is thus popular in.
5. Merge Sort does Θ(nLogn) operations in all cases. Most of the other sorting algorithms have worst and best cases. For example, in the typical implementation of Quick Sort (where pivot is chosen as a corner element), the worst occurs when the input array is already sorted and the best occur when the pivot elements always divide array in two halves. For insertion sort, the worst case occurs.
6. Although merge sort runs in \$\Theta(\lg{n})\$ worst-case time and insertion sort runs in \$\Theta(n^2)\$ worst-case time, the constant factors in insertion sort can make it faster in practice for small problem sizes on many machines. Thus, it makes sense to coarsen the leaves of the recursion by using insertion sort within merge sort when subproblems become sufficiently small. Consider a.
7. Quick sort's worst case running time is O(n 2) and merge/heap sort runs on O(n log n) on their worst case then why is quick sort superior then merge/heap sort? We will give this answer later in this article, first we analyze the algorithm. Implementation. We will use C# to implement the quick sort and structure of class would be: public class Quick { public int[] A; //Array which has to be.

### Merge Sort Algorithms and Examples Merge Sort using Java, C+

1. Solutions to Introduction to Algorithms Third Edition. CLRS Solutions. The textbook that a Computer Science (CS) student must read
2. 由於 Merge Sort 過程相當於一棵 Binary Tree，故 Merge Sort 的回合數相當於「Binary Tree 的高度 - 1」。 n 個 Data 的 Binary Tree 高度為 log2 n + 1，故回合數為 log2 n。 Merge Sort 的總時間 = 回合數 * 每回合所花的時間 = log n * O(n) = O(n logn) Average/Best/Worst Case 的時間皆為 O(n logn)。 改用 Recursive Time Function 運算仍可得.
3. Problem: Any ideas on what I need to do to fix this? Thanks! worst case time complexity of merge sort ### Merge Sort Algorithm Example Time Complexity Gate

1. imum element.
2. 2-1 Insertion sort on small arrays in merge sort Although merge sort runs in (n lg n) worst-case time and insertion sort runs in (n?) worst-case time, the constant factors in insertion sort can make it faster in practice for small problem sizes on many machines. Thus, it makes sense to coarsen the leaves of the recursion by using insertion sort within merge sort when Chapter 2 Getting Started.
3. The running time of merge sort in the average case and the worst case is O(n log n). 2. The worst case running time complexity of quick sort is O(n log n). 3. Heap sort is an efficient and a stable sorting algorithm. Calculate your paper price. Type of paper. Academic level. Deadline. Pages (550 words) − + Approximate price: -Why Work with Us. Top Quality and Well-Researched Papers. We.
4. In each step problem will be divided into 2 sub problems of half size and we are merging them. For merge operation it will take O(n) time in worst case. Recursive formula T(n) = 2T(n/2) + O(n) If we solve this equation we will get O(n logn) where n is input size. In best case and worst case merge sort will exhibit same complexity. Space Complexit

The average and worst-case time complexity of Merge Sort is O (nlogn), which is fair for a sorting algorithm. Here's how it performed after sorting an array containing 10,000 integers in random order Descending order is considered the worst unsorted case. Merge sort is very different than the other sorting techniques we have seen so far. Merge Sort can be used to sort an unsorted list or to merge two sorted lists. Sort an unsorted list . The idea is to split the unsorted list into smaller groups until there is only one element in a group. Then, group two elements in the sorted order and.

The merge sort is a recursive sort of order n*log(n). It is notable for having a worst case and average complexity of O(n*log(n)), and a best case complexity of O(n) (for pre-sorted input) The worst-case time complexity of iterative merge sort remains the same as the recursive implementation, i.e., O(n.log(n)) for an input containing n items. However, it saves the auxiliary space required by the call stack. Also See: External Merge Sort Algorithm References: http://csg.sph.umich.edu/abecasis/class/2006/615.09.pd Although merge sort runs in ( nlogn) worst-case time and insertion sort runs in ( n2) worst-case time, the constant factors in insertion sort can make it faster in practice for small problem sizes on many machines. Thus, it makes sense to coarsen the leaves of the recursion by using insertion sort within merge sort when subproblems become su ciently small. Consider a modi cation to merge sort. Although merge sort runs in Θ(nlgn) worst-case time and insertion sort runs in Θ(n2) worst-case time, the constant factors in insertion sort make it faster for small n. Thus, it makes sense to use insertion sort within merge sort when subproblems become sufficiently small. Consider a modification to merge sort in which n/k sublists of length k are sorted using insertion sort and then merged.   ### Merge Sort Sorting Algorithm - Big-

1. Obwohl der Algorithmus einen schlechten Worst-Case hat, dessen Wahrscheinlichkeit verringert werden kann, nutzt man ihn aufgrund des relativ guten Durchschnitts häufiger in der Praxis als andere Algorithmen. Mergesort - ein schnelles (und rekursives) Sortierverfahren. Ein Feld wird in zwei Teilfelder aufgeteilt, die dann rekursiv sortiert werden. Anschließend werden diese sortierten Teilfelder wieder zu einem Feld zusammengefügt. Dabei macht man sich zu Nutze, dass die beiden Teilfelder.
2. e the sorted order
3. imum number of compares that any compare-based sorting algorithm can guarantee are ~N lg N
4. The problem with this method is you iterate over the array one time for every element, no matter what, so you're stuck with O(n^2) as your best and worst-case scenario. Merge Sort. I went ahead and did the hacker version of the pset which called for me to figure out how to implement merge sort, so here goes: /** * Sorts array of n values. */ void sort(int values[], int n) { // If 1 or less elements stop if (n < 2) { return; } // Set middle value, and left and right arrays int middle = n / 2.

### Mergesort: Erklärung mit Beispiel, Pseudocode, Jav

1. From the following sorting algorithms which has the lowest worst case complexity? - (a) Bubble sort - (b) Quick sort - (c) Merge sort - (d) Selection sort 1 Answer Correct Answer: (c) Merge sort 0 Answered on 29/10/2020 at 02:36PM by Thamizh.
2. The time complexity for merge sort is the same in all three cases (worst, best and average) as it always divides the array into sub-arrays and then merges the sub-arrays taking linear time. Merge sort always takes an equal amount of space as unsorted arrays. Hence when the list to be sorted is an array, merge sort should not be used for very.
3. If the array has two or more elements in it, we will break it in half, sort the two halves, and then go through and merge the elements. The Java method to do it: public void sort(int[] array) { // create tempArray for use in merging int[] tempArray = new int[array.length]; mergeSort(array, 0, array.length-1, tempArray); } /* * PRE: left and right are valid indexes of array

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