Merge sort of data structure and algorithm 14

This article for Bo Master original article, reprint please indicate source : http://blog.csdn.net/eson_15/article/details/51193139

The center of the merge algorithm is the merging of two already ordered arrays. Merging two ordered arrays A and b produces a third array, C, which contains all the data items for arrays A and b, and arranges them in an orderly manner in array C. First, let's look at the merging process and see how it's used in the sort.

Suppose there are two ordered arrays, which do not require the same size. Array A has 4 data items, array B has 6 data items, they are to be merged into the array C, at the beginning of the array C has 10 storage space, the merge process as shown:

The idea of a merge sort is to divide an array into two halves and sort each half. The merge method is then used to merge the two halves of the array into an ordered array. Each half of the split is recursively divided and sorted again until the resulting sub-array contains only one data item. As mentioned above, the merge sort requires an extra space equal to the sum of the AB two arrays, and if the initial array is almost covered with the entire memory, then the merge sort will not work.

The idea of merging sort is simple, let's take a look at the specific implementation:

public void MergeSort (int[] source) {    int[] WorkSpace = new int[source.length];     Recmergesort (source,workspace, 0, source.length-1);} private void Recmergesort (int[] source, int[] workSpace, int lowerbound, int upperbound) {    if (Lowerboun D = = upperbound) {        return;    }    else {         int mid = (lowerbound + upperbound)/2;        recmergesor T (source, WorkSpace, lowerbound, mid); Left row         recmergesort (source, WorkSpace, mid+1, upperbound); Right row         merge (source, WorkSpace, lowerbound, mid+1, upperbound);//merge    & nbsp;}} private void Merge (int[] A, int[] workSpace, int lowptr, int highptr, int upperbound) {    int j = 0;     int lowerbound = lowptr;    int mid = highptr-1;     int n = upperbound-lowerbound + 1;    while (lowptr <= mid && highptr <= upperbound) {&nbsp ;       if (A[lowptr] < a[highptr]) {            workspace[j++] = a[lowptr++];        }        else {            workspace[j++] = a[highptr++];         }    }    while (lowptr <= mid) {         Workspace[j++] = a[lowptr++];    }        while (highptr <= upperbound ) {        workspace[j++] = a[highptr++];    }     & nbsp  for (j = 0; J < N; j + +) {        a[lowerbound + j] = workspace[j];   &nbsp ;}}

Algorithmic analysis: The worst, best, and average run time of a merge sort is O (Nlogn), but it requires additional storage space, which can be limited on some memory-intensive machines. The merging algorithm consists of two parts: Split and merge, for each part, if we use binary search, Time is O (Nlogn), Time is O (N) at the last merge, so the total time is O (Nlogn). The spatial complexity is O (N).

Merge sort to write so much, if there is a mistake, welcome message ~

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Merge sort of data structure and algorithm 14