Why does this Quicksort work? - c

I find this Quicksort partitioning approach confusing and wrong, yet it seems to work. I am referring to this pseudocode. Note: they also have a C implementation at the end of the article, but it's very different from their pseudocode, so I don't care about that.
I have also written it in C like this, trying to stay true to the pseudocode as much as possible, even if that means doing some weird C stuff:
#include <stdio.h>
int partition(int a[], int p, int r)
{
int x = a[p];
int i = p - 1;
int j = r + 1;
while (1)
{
do
j = j - 1;
while (!(a[j] <= x));
do
i = i + 1;
while (!(a[i] >= x));
if (i < j)
{
int t = a[i];
a[i] = a[j];
a[j] = t;
}
else
{
for (i = 1; i <= a[0]; ++i)
printf("%d ", a[i]);
printf("- %d\n", j);
return j;
}
}
}
int main()
{
int a[100] = //{8, 6,10,13,15,8,3,2,12};
{7, 7, 6, 2, 3, 8, 4, 1};
partition(a, 1, a[0]);
return 0;
}
If you run this, you'll get the following output:
1 6 2 3 4 8 7 - 5
However, this is wrong, isn't it? Clearly a[5] does not have all the values before it lower than it, since a[2] = 6 > a[5] = 4. Not to mention that 7 is supposed to be the pivot (the initial a[p]) and yet its position is both incorrect and lost.
The following partition algorithm is taken from wikipedia:
int partition2(int a[], int p, int r)
{
int x = a[r];
int store = p;
for (int i = p; i < r; ++i)
{
if (a[i] <= x)
{
int t = a[i];
a[i] = a[store];
a[store] = t;
++store;
}
}
int t = a[r];
a[r] = a[store];
a[store] = t;
for (int i = 1; i <= a[0]; ++i)
printf("%d ", a[i]);
printf("- %d\n", store);
return store;
}
And produces this output:
1 6 2 3 8 4 7 - 1
Which is a correct result in my opinion: the pivot (a[r] = a[7]) has reached its final position.
However, if I use the initial partitioning function in the following algorithm:
void Quicksort(int a[], int p, int r)
{
if (p < r)
{
int q = partition(a, p, r); // initial partitioning function
Quicksort(a, p, q);
Quicksort(a, q + 1, r); // I'm pretty sure q + r was a typo, it doesn't work with q + r.
}
}
... it seems to be a correct sorting algorithm. I tested it out on a lot of random inputs, including all 0-1 arrays of length 20.
I have also tried using this partition function for a selection algorithm, in which it failed to produce correct results. It seems to work and it's even very fast as part of the quicksort algorithm however.
So my questions are:
Can anyone post an example on which the algorithm DOESN'T work?
If not, why does it work, since the partitioning part seems to be wrong? Is this another partitioning approach that I don't know about?

I think the partitioning is correct. 7 is the pivot. The original array is partitioned into a sub array of length 5 containing elements less than or equal to 7 and a sub array of length 2, containing elements greater or equal to 7.

Extending on from above here is what it should look like
void swap(int *a, int *b)
{
int x;
x = *a;
*a = *b;
*b = x;
}
int partition(int s[], int l, int h)
{
int i;
int p;/* pivot element index */
int firsthigh;/* divider position for pivot element */
p = h;
firsthigh = l;
for (i = l; i < h; i++)
if(s[i] < s[p]) {
swap(&s[i], &s[firsthigh]);
firsthigh++;
}
swap(&s[p], &s[firsthigh]);
return(firsthigh);
}
void quicksort(int s[], int l, int h)
{
int p;/* index of partition */
if ((h - l) > 0) {
p = partition(s, l, h);
quicksort(s, l, p - 1);
quicksort(s, p + 1, h);
}
}
int main()
{
int a[100] = //{8, 6,10,13,15,8,3,2,12};
{7, 7, 6, 2, 3, 8, 4, 1};
quicksort(a, 0, 7);
return 0;
}

From Wikipedia (I've emphasized the part that I think addresses your question directly):
This is the in-place partition
algorithm. It partitions the portion
of the array between indexes left and
right, inclusively, by moving all
elements less than or equal to
array[pivotIndex] to the beginning of
the subarray, leaving all the greater
elements following them. In the
process it also finds the final
position for the pivot element, which
it returns. It temporarily moves the
pivot element to the end of the
subarray, so that it doesn't get in
the way. Because it only uses
exchanges, the final list has the same
elements as the original list. Notice
that an element may be exchanged
multiple times before reaching its
final place. It should also be noted
that in case of pivot duplicates in
the input array, they can be spread
across left subarray, possibly in
random order. This doesn't represent a
partitioning failure, as further
sorting will reposition and finally
"glue" them together.
Could that be what you were missing?

You are getting confused between the index of the item and the iten value
Look at your header
int partition(int a[], int p, int r) ;
Now if we changed the data type on the array a to some weird data type you will see the problem
int partition( Otherdatatype a[], int p, int r) ;
You call the function from within your main with
partition(a, 1, a[0]);
See the problem a[0] is the value of the entry in a[0] not an index value.
Imagine a[0] had the value 200 in your code simply change the first item value to 200 and you will get a runtime error "attempt to access memory out of range" because if you follow
thru a[0] = 200 that is passed into partition as value r then follow what happens inside partition.
The thing to remember is this is a sort routine in your partition header the list in array a may not be of the same type as the indexes .. p and r of your header are clearly indexes referring to an index position and a is the list to be sorted.
Thus your main start to a sort is
partition(a, 0, items_in_array-1);
Do you see why? Array a runs from a[0] ... a[items_in_array-1]
So in your sample above you have preloaded 8 values into your array so your partition call from main should be
partition(a, 0, 7);

Related

MInimum element in array ( C )

I'm a newbie both here in stackoverflow and both in the world of programming.
Today i was solving some exercise about recursion, and one of these asked to write a recursive function for finding minimum element of an array.
After many tries, I have finally wrote this working code, but i want to ask you if this is a "good" code. I mean, the fact it's working aside, is it written well? There's something that should be changed? And, above all, there's a way to make this functions working well without declaring that global int "min"? :)
Here's the code:
#include <stdio.h>
int recursiveMinimum(int array[], size_t size);
int min = 1000;
int main(void) {
int array[] = {55, 5, 1, 27, 95, 2};
printf("\nMinimum element of this array is: %d\n\n",
recursiveMinimum(array, 6));
}
int recursiveMinimum(int array[], size_t size) {
if (size == 1) {
return min;
} else {
if (array[size] <= min) min = array[size];
return min = recursiveMinimum(array, size - 1);
}
}
It is a bad idea when a function depends on a global variable.
But in any case your function is incorrect and invokes undefined behavior.
In the first call of the function this if statement
if (array[size] <= min) min = array[size];
trying to access memory outside the passed array because the valid range of indices is [0, size).
Also the array can contain all elements greater than the initial value of the global variable
int min = 1000;
And the function may not be called a second time because the value of the variable min is unspecified.
The function should return the index of the minimal element in the array. In general the user can pass the second argument equal to 0. In this case again the function will invoke undefined behavior if you will try to return a non-existent element of an empty array.
The function can be declared and defined the following way
size_t recursiveMinimum( const int a[], size_t n )
{
if ( n < 2 )
{
return 0;
}
else
{
size_t min1 = recursiveMinimum( a, n / 2 );
size_t min2 = recursiveMinimum( a + n / 2, n - n / 2 ) + n / 2;
return a[min2] < a[min1] ? min2 : min1;
}
}
Here is a demonstration program
#include <stdio.h>
size_t recursiveMinimum( const int a[], size_t n )
{
if (n < 2)
{
return 0;
}
else
{
size_t min1 = recursiveMinimum( a, n / 2 );
size_t min2 = recursiveMinimum( a + n / 2, n - n / 2 ) + n / 2;
return a[min2] < a[min1] ? min2 : min1;
}
}
int main( void )
{
int a[] = { 55, 5, 1, 27, 95, 2 };
const size_t N = sizeof( a ) / sizeof( *a );
size_t min = recursiveMinimum( a, N );
printf( "\nMinimum element of this array is: %d at the position %zu\n",
a[min], min );
}
The program output is
Minimum element of this array is: 1 at the position 2
Pay attention to that the first parameter has the qualifier const because the passed array is not being changed within the function. And to decrease the number of recursive calls the function calls itself for two halves of the array.
Recursion works by reducing the size at the call to the next iteration and comparing the result of the call with the current value and return the lower of the 2.
As recursion stop you can simply return the first element
int recursiveMinimum(int array[], size_t size) {
if (size == 1) return array[0];
int min_of_rest = recursiveMinimum(array, size - 1);
if (array[size - 1] <= min_of_rest) return array[size - 1];
return min_of_rest;
}
Full example: https://godbolt.org/z/sjnh8sYz3
In the past, we used to implement it with pointers, KR-C style.
Using pointers in a harsh way was a mean to deal with inefficiency of compilers at that time.
Not sure it is considered good practice now. An example is provided hereafter.
Anyway, it would be better (easier and more efficient) to implement it in a non recursive manner.
#include <stdio.h>
void recursiveMinimum(int *array, size_t size, int *min) {
if (size == 0) return;
if (*array < *min) *min = *array;
recursiveMinimum (array+1, size-1, min);
return;
}
int main(void) {
int array[] = {55, 5, 1, 27, 95, 2};
size_t size = sizeof(array)/sizeof(*array);
int min = array[0];
recursiveMinimum (array, size, &min);
printf("\nMinimum element of this array is: %d\n", min);
return 0;
}

Given a number N and a sorted array A, check if there are two numbers in A whose product is N

Given a number N and a sorted array A, design an algorithm - using the Divide and Conquer approach - to check if there exist index i and index j such that A[i]*A[j] == N (return 1 if present, 0 if not).
I'm having a hard time proceeding in the required (recursive) way. I think I figured out only a part of one possible solution, but even there I'm not a 100% sure if it's correct: I thought that if the product between the first element and the central element of the array is greater than N, then the numbers I'm looking for (if present) are certainly in the first half of the array, so I can recursively call the function on that part, like so (I'm using C):
int productN(int A[], int i, int j, int N){
// missing base case
int m = (i+j)/2;
if(A[i]*A[m] > N){
return productN(A, i, m, N);
} else{
// do something else
}
}
int main(){
int A[]={1, 2, 3, 4, 5};
printf("%d\n", productN(A, 0, 4, 15)); // initial value for i and j are the first and last index of the array
return 0;
}
Apart from that, I'm stuck, I can't even think of a base case, so any help will be greatly appreciated, thanks.
Edit:
Based on your very helpful answers, using binary search, I think I got it:
int productN(int A[], int i, int j, int N){
int m = (i+j)/2; // central element of the current array
int x;
for(x=i; x<=m; x++){
if(N%A[x]==0 && binarySearch(A, m, j, N/A[x]))
return 1;
}
if(i!=j){
if(productN(A, i, m, N) || productN(A, m+1, j, N))
return 1;
}
return 0;
}
Is it good? Can it be better?
Edit: it's been a while now since I asked this question, but I wrote another solution, simplier to read. I'll leave it here in case anyone is interested.
int productN(int A[], int i, int j, int N, int size){
if(i==j){ // base case
if(N%A[i]==0)
return binarySearch(A, 0, size-1, N/A[i]);
else
return 0;
}
int m = (i+j)/2;
if((N%A[m])==0){
if(binarySearch(A, 0, size-1, N/A[i]))
return 1;
}
return productN(A, i, m, N, size) || productN(A, m+1, j, N, size);
}
Using Divide and Conquer, you can use an approach similar to merge sort algorithm. As the comments suggest, there are easier approaches. But if Divide and Conquer is a must, this should suffice.
(I'm not proficient in C, so I'll just write the algorithm)
def productN(arr):
x = len(arr)
left_half = arr[0:x/2]
right_half = arr[x/2:]
if productN(left_half) or productN(right_half):
return True
for i in left_half:
if N%i==0 and binary_search(right_half, N/i):
return True
return False

swap pair to make sum equal

Question: Given two arrays of integers A[] and B[] of size N and M, the task is to check if a pair of values (one value from each array) exists such that swapping the elements of the pair will make the sum of two arrays equal.
My approach:
find sum of both arrays.
Identify array with larger sum(denote with A[]).
Sort A.
For all values in B binary search (sum(A)-sum(B)/2 + B[i]) in A, if found return true.
Return false.
code:
int sum(int a[], int n){
int s=0;
for(int i=0; i<n; i++){
s+= a[i];
}
return s;
}
int findSwapValues(int A[], int n, int B[], int m)
{
// Your code goes here
int a = sum(A, n);
int b = sum(B,m);
int t;
int *temp;
if(a<b){
temp = A;
A = B;
B = temp;
t = n;
n = m;
m = t;
t = a;
a = b;
b = t;
}
sort(A, A+n);
for(int i=0; i<m; i++){
if(binary_search(A,A+n,(a-b)/2+B[i])){
return 1;
}
}
return -1;
}
Doubt: My algorithm is failing for some test cases(not TLE). As the test cases are very large, it's difficult to reason out the problem in the algorithm. I searched online and understood other approaches. My only curiosity is why its incorrect?
I think the error in your code is that you find B[i] + (a-b)/2.
The problem with this is that if (a-b) is an odd value, division by 2 will round it down to the nearest integer and you end up finding the wrong value.
What you can instead do is check if the difference is odd before even swapping the arrays, and if it is true, straight-away return -1 because if the difference is odd, no such pair can ever exist.
I hope I cleared your doubt :).

Print the values of knapsack solution

I have the following solution for the knapsack problem:(wt[] is the weights array, val[] is the values array, n is the arrays size, index is the current item that we are trying (for the recursion) and arr is an array that represents weather or not item i was included in the solution.
int knapSack(int W, int wt[], int val[], int n, int index, int arr[])
{
if (n == index || W == 0)
return 0;
if (wt[index] > W)
return knapSack(W, wt, val, n, index+1 );
int with=val[index]+knapSack(W-wt[index], wt, val, n, index+1);
int without=knapSack(W, wt, val, n, index+1);
if(with>without){
arr[index]=1;
return with;
}
else{
arr[index]=0;
return without;
}
}
I am trying to print, in this recursive solution the items that are chosen, by setting the indexes of the taken ones in an array (res) to 1.
As I understand, if with>without, it means that I am choosing the current item, or item #index. So why is this not returning a right value?
I am using the recursive algorithm for a reason, I know using the memoization version can be easier here.
Example:
Weights: 5 6 7 10 11
Values: 2 4 5 6 9
W=25
Will return 5 ones in array res. When solution is 18 with items 2,3,5 (starting from index 1).
Premise 1: in your code, the recursion calls to knapSack are not passing arr, which should cause a compilation error, I assume it's just a copy/paste error.
Premise 2: with the data you proposed, the resulting arr value is not all 1 as you indicated, but 01011, which is still incorrect.
Consider the hypothetical situation in which, during the execution of your function, with is greater than without: during the with calculation arr is filled with the correct values; but then start the without calculation which is going to overwrite the arr values.
Since with is greater than without, the returned arr will be the wrong one, and this is the cause of the problem.
A simple fix would be to make a copy of the arr returned by the with calculation so it is not going to be overwritten by the without calculation, for example:
int with=val[index]+knapSack(W-wt[index], wt, val, n, index+1, arr);
// copy the "with" arr
int arrWith[n];
copyArr(arr, arrWith, n);
int without=knapSack(W, wt, val, n, index+1, arr);
if(with>without){
// restore the "with" arr
copyArr(arrWith, arr, n);
arr[index]=1;
return with;
}
else{
arr[index]=0;
return without;
}
copyArr is simply:
void copyArr(int arr[], int arrDest[], int n) {
int i;
for(i = 0; i < n; i++) {
arrDest[i] = arr[i];
}
}
With this fix the resulting value of arr is correctly 01101.

How to sort in-place using the merge sort algorithm?

I know the question is not too specific.
All I want is someone to tell me how to convert a normal merge sort into an in-place merge sort (or a merge sort with constant extra space overhead).
All I can find (on the net) is pages saying "it is too complex" or "out of scope of this text".
The only known ways to merge in-place (without any extra space) are too complex to be reduced to practical program. (taken from here)
Even if it is too complex, what is the basic concept of how to make the merge sort in-place?
Knuth left this as an exercise (Vol 3, 5.2.5). There do exist in-place merge sorts. They must be implemented carefully.
First, naive in-place merge such as described here isn't the right solution. It downgrades the performance to O(N2).
The idea is to sort part of the array while using the rest as working area for merging.
For example like the following merge function.
void wmerge(Key* xs, int i, int m, int j, int n, int w) {
while (i < m && j < n)
swap(xs, w++, xs[i] < xs[j] ? i++ : j++);
while (i < m)
swap(xs, w++, i++);
while (j < n)
swap(xs, w++, j++);
}
It takes the array xs, the two sorted sub-arrays are represented as ranges [i, m) and [j, n) respectively. The working area starts from w. Compare with the standard merge algorithm given in most textbooks, this one exchanges the contents between the sorted sub-array and the working area. As the result, the previous working area contains the merged sorted elements, while the previous elements stored in the working area are moved to the two sub-arrays.
However, there are two constraints that must be satisfied:
The work area should be within the bounds of the array. In other words, it should be big enough to hold elements exchanged in without causing any out-of-bound error.
The work area can be overlapped with either of the two sorted arrays; however, it must ensure that none of the unmerged elements are overwritten.
With this merging algorithm defined, it's easy to imagine a solution, which can sort half of the array; The next question is, how to deal with the rest of the unsorted part stored in work area as shown below:
... unsorted 1/2 array ... | ... sorted 1/2 array ...
One intuitive idea is to recursive sort another half of the working area, thus there are only 1/4 elements haven't been sorted yet.
... unsorted 1/4 array ... | sorted 1/4 array B | sorted 1/2 array A ...
The key point at this stage is that we must merge the sorted 1/4 elements B
with the sorted 1/2 elements A sooner or later.
Is the working area left, which only holds 1/4 elements, big enough to merge
A and B? Unfortunately, it isn't.
However, the second constraint mentioned above gives us a hint, that we can exploit it by arranging the working area to overlap with either sub-array if we can ensure the merging sequence that the unmerged elements won't be overwritten.
Actually, instead of sorting the second half of the working area, we can sort the first half, and put the working area between the two sorted arrays like this:
... sorted 1/4 array B | unsorted work area | ... sorted 1/2 array A ...
This setup effectively arranges the work area overlap with the sub-array A. This idea
is proposed in [Jyrki Katajainen, Tomi Pasanen, Jukka Teuhola. ``Practical in-place mergesort''. Nordic Journal of Computing, 1996].
So the only thing left is to repeat the above step, which reduces the working area from 1/2, 1/4, 1/8, … When the working area becomes small enough (for example, only two elements left), we can switch to a trivial insertion sort to end this algorithm.
Here is the implementation in ANSI C based on this paper.
void imsort(Key* xs, int l, int u);
void swap(Key* xs, int i, int j) {
Key tmp = xs[i]; xs[i] = xs[j]; xs[j] = tmp;
}
/*
* sort xs[l, u), and put result to working area w.
* constraint, len(w) == u - l
*/
void wsort(Key* xs, int l, int u, int w) {
int m;
if (u - l > 1) {
m = l + (u - l) / 2;
imsort(xs, l, m);
imsort(xs, m, u);
wmerge(xs, l, m, m, u, w);
}
else
while (l < u)
swap(xs, l++, w++);
}
void imsort(Key* xs, int l, int u) {
int m, n, w;
if (u - l > 1) {
m = l + (u - l) / 2;
w = l + u - m;
wsort(xs, l, m, w); /* the last half contains sorted elements */
while (w - l > 2) {
n = w;
w = l + (n - l + 1) / 2;
wsort(xs, w, n, l); /* the first half of the previous working area contains sorted elements */
wmerge(xs, l, l + n - w, n, u, w);
}
for (n = w; n > l; --n) /*switch to insertion sort*/
for (m = n; m < u && xs[m] < xs[m-1]; ++m)
swap(xs, m, m - 1);
}
}
Where wmerge is defined previously.
The full source code can be found here and the detailed explanation can be found here
By the way, this version isn't the fastest merge sort because it needs more swap operations. According to my test, it's faster than the standard version, which allocates extra spaces in every recursion. But it's slower than the optimized version, which doubles the original array in advance and uses it for further merging.
Including its "big result", this paper describes a couple of variants of in-place merge sort (PDF):
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.22.5514&rep=rep1&type=pdf
In-place sorting with fewer moves
Jyrki Katajainen, Tomi A. Pasanen
It is shown that an array of n
elements can be sorted using O(1)
extra space, O(n log n / log log n)
element moves, and n log2n + O(n log
log n) comparisons. This is the first
in-place sorting algorithm requiring
o(n log n) moves in the worst case
while guaranteeing O(n log n)
comparisons, but due to the constant
factors involved the algorithm is
predominantly of theoretical interest.
I think this is relevant too. I have a printout of it lying around, passed on to me by a colleague, but I haven't read it. It seems to cover basic theory, but I'm not familiar enough with the topic to judge how comprehensively:
http://comjnl.oxfordjournals.org/cgi/content/abstract/38/8/681
Optimal Stable Merging
Antonios Symvonis
This paper shows how to stably merge
two sequences A and B of sizes m and
n, m ≤ n, respectively, with O(m+n)
assignments, O(mlog(n/m+1))
comparisons and using only a constant
amount of additional space. This
result matches all known lower bounds...
It really isn't easy or efficient, and I suggest you don't do it unless you really have to (and you probably don't have to unless this is homework since the applications of inplace merging are mostly theoretical). Can't you use quicksort instead? Quicksort will be faster anyway with a few simpler optimizations and its extra memory is O(log N).
Anyway, if you must do it then you must. Here's what I found: one and two. I'm not familiar with the inplace merge sort, but it seems like the basic idea is to use rotations to facilitate merging two arrays without using extra memory.
Note that this is slower even than the classic merge sort that's not inplace.
The critical step is getting the merge itself to be in-place. It's not as difficult as those sources make out, but you lose something when you try.
Looking at one step of the merge:
[...list-sorted...|x...list-A...|y...list-B...]
We know that the sorted sequence is less than everything else, that x is less than everything else in A, and that y is less than everything else in B. In the case where x is less than or equal to y, you just move your pointer to the start of A on one. In the case where y is less than x, you've got to shuffle y past the whole of A to sorted. That last step is what makes this expensive (except in degenerate cases).
It's generally cheaper (especially when the arrays only actually contain single words per element, e.g., a pointer to a string or structure) to trade off some space for time and have a separate temporary array that you sort back and forth between.
An example of bufferless mergesort in C.
#define SWAP(type, a, b) \
do { type t=(a);(a)=(b);(b)=t; } while (0)
static void reverse_(int* a, int* b)
{
for ( --b; a < b; a++, b-- )
SWAP(int, *a, *b);
}
static int* rotate_(int* a, int* b, int* c)
/* swap the sequence [a,b) with [b,c). */
{
if (a != b && b != c)
{
reverse_(a, b);
reverse_(b, c);
reverse_(a, c);
}
return a + (c - b);
}
static int* lower_bound_(int* a, int* b, const int key)
/* find first element not less than #p key in sorted sequence or end of
* sequence (#p b) if not found. */
{
int i;
for ( i = b-a; i != 0; i /= 2 )
{
int* mid = a + i/2;
if (*mid < key)
a = mid + 1, i--;
}
return a;
}
static int* upper_bound_(int* a, int* b, const int key)
/* find first element greater than #p key in sorted sequence or end of
* sequence (#p b) if not found. */
{
int i;
for ( i = b-a; i != 0; i /= 2 )
{
int* mid = a + i/2;
if (*mid <= key)
a = mid + 1, i--;
}
return a;
}
static void ip_merge_(int* a, int* b, int* c)
/* inplace merge. */
{
int n1 = b - a;
int n2 = c - b;
if (n1 == 0 || n2 == 0)
return;
if (n1 == 1 && n2 == 1)
{
if (*b < *a)
SWAP(int, *a, *b);
}
else
{
int* p, * q;
if (n1 <= n2)
p = upper_bound_(a, b, *(q = b+n2/2));
else
q = lower_bound_(b, c, *(p = a+n1/2));
b = rotate_(p, b, q);
ip_merge_(a, p, b);
ip_merge_(b, q, c);
}
}
void mergesort(int* v, int n)
{
if (n > 1)
{
int h = n/2;
mergesort(v, h); mergesort(v+h, n-h);
ip_merge_(v, v+h, v+n);
}
}
An example of adaptive mergesort (optimized).
Adds support code and modifications to accelerate the merge when an auxiliary buffer of any size is available (still works without additional memory). Uses forward and backward merging, ring rotation, small sequence merging and sorting, and iterative mergesort.
#include <stdlib.h>
#include <string.h>
static int* copy_(const int* a, const int* b, int* out)
{
int count = b - a;
if (a != out)
memcpy(out, a, count*sizeof(int));
return out + count;
}
static int* copy_backward_(const int* a, const int* b, int* out)
{
int count = b - a;
if (b != out)
memmove(out - count, a, count*sizeof(int));
return out - count;
}
static int* merge_(const int* a1, const int* b1, const int* a2,
const int* b2, int* out)
{
while ( a1 != b1 && a2 != b2 )
*out++ = (*a1 <= *a2) ? *a1++ : *a2++;
return copy_(a2, b2, copy_(a1, b1, out));
}
static int* merge_backward_(const int* a1, const int* b1,
const int* a2, const int* b2, int* out)
{
while ( a1 != b1 && a2 != b2 )
*--out = (*(b1-1) > *(b2-1)) ? *--b1 : *--b2;
return copy_backward_(a1, b1, copy_backward_(a2, b2, out));
}
static unsigned int gcd_(unsigned int m, unsigned int n)
{
while ( n != 0 )
{
unsigned int t = m % n;
m = n;
n = t;
}
return m;
}
static void rotate_inner_(const int length, const int stride,
int* first, int* last)
{
int* p, * next = first, x = *first;
while ( 1 )
{
p = next;
if ((next += stride) >= last)
next -= length;
if (next == first)
break;
*p = *next;
}
*p = x;
}
static int* rotate_(int* a, int* b, int* c)
/* swap the sequence [a,b) with [b,c). */
{
if (a != b && b != c)
{
int n1 = c - a;
int n2 = b - a;
int* i = a;
int* j = a + gcd_(n1, n2);
for ( ; i != j; i++ )
rotate_inner_(n1, n2, i, c);
}
return a + (c - b);
}
static void ip_merge_small_(int* a, int* b, int* c)
/* inplace merge.
* #note faster for small sequences. */
{
while ( a != b && b != c )
if (*a <= *b)
a++;
else
{
int* p = b+1;
while ( p != c && *p < *a )
p++;
rotate_(a, b, p);
b = p;
}
}
static void ip_merge_(int* a, int* b, int* c, int* t, const int ts)
/* inplace merge.
* #note works with or without additional memory. */
{
int n1 = b - a;
int n2 = c - b;
if (n1 <= n2 && n1 <= ts)
{
merge_(t, copy_(a, b, t), b, c, a);
}
else if (n2 <= ts)
{
merge_backward_(a, b, t, copy_(b, c, t), c);
}
/* merge without buffer. */
else if (n1 + n2 < 48)
{
ip_merge_small_(a, b, c);
}
else
{
int* p, * q;
if (n1 <= n2)
p = upper_bound_(a, b, *(q = b+n2/2));
else
q = lower_bound_(b, c, *(p = a+n1/2));
b = rotate_(p, b, q);
ip_merge_(a, p, b, t, ts);
ip_merge_(b, q, c, t, ts);
}
}
static void ip_merge_chunk_(const int cs, int* a, int* b, int* t,
const int ts)
{
int* p = a + cs*2;
for ( ; p <= b; a = p, p += cs*2 )
ip_merge_(a, a+cs, p, t, ts);
if (a+cs < b)
ip_merge_(a, a+cs, b, t, ts);
}
static void smallsort_(int* a, int* b)
/* insertion sort.
* #note any stable sort with low setup cost will do. */
{
int* p, * q;
for ( p = a+1; p < b; p++ )
{
int x = *p;
for ( q = p; a < q && x < *(q-1); q-- )
*q = *(q-1);
*q = x;
}
}
static void smallsort_chunk_(const int cs, int* a, int* b)
{
int* p = a + cs;
for ( ; p <= b; a = p, p += cs )
smallsort_(a, p);
smallsort_(a, b);
}
static void mergesort_lower_(int* v, int n, int* t, const int ts)
{
int cs = 16;
smallsort_chunk_(cs, v, v+n);
for ( ; cs < n; cs *= 2 )
ip_merge_chunk_(cs, v, v+n, t, ts);
}
static void* get_buffer_(int size, int* final)
{
void* p = NULL;
while ( size != 0 && (p = malloc(size)) == NULL )
size /= 2;
*final = size;
return p;
}
void mergesort(int* v, int n)
{
/* #note buffer size may be in the range [0,(n+1)/2]. */
int request = (n+1)/2 * sizeof(int);
int actual;
int* t = (int*) get_buffer_(request, &actual);
/* #note allocation failure okay. */
int tsize = actual / sizeof(int);
mergesort_lower_(v, n, t, tsize);
free(t);
}
This answer has a code example, which implements the algorithm described in the paper Practical In-Place Merging by Bing-Chao Huang and Michael A. Langston. I have to admit that I do not understand the details, but the given complexity of the merge step is O(n).
From a practical perspective, there is evidence that pure in-place implementations are not performing better in real world scenarios. For example, the C++ standard defines std::inplace_merge, which is as the name implies an in-place merge operation.
Assuming that C++ libraries are typically very well optimized, it is interesting to see how it is implemented:
1) libstdc++ (part of the GCC code base): std::inplace_merge
The implementation delegates to __inplace_merge, which dodges the problem by trying to allocate a temporary buffer:
typedef _Temporary_buffer<_BidirectionalIterator, _ValueType> _TmpBuf;
_TmpBuf __buf(__first, __len1 + __len2);
if (__buf.begin() == 0)
std::__merge_without_buffer
(__first, __middle, __last, __len1, __len2, __comp);
else
std::__merge_adaptive
(__first, __middle, __last, __len1, __len2, __buf.begin(),
_DistanceType(__buf.size()), __comp);
Otherwise, it falls back to an implementation (__merge_without_buffer), which requires no extra memory, but no longer runs in O(n) time.
2) libc++ (part of the Clang code base): std::inplace_merge
Looks similar. It delegates to a function, which also tries to allocate a buffer. Depending on whether it got enough elements, it will choose the implementation. The constant-memory fallback function is called __buffered_inplace_merge.
Maybe even the fallback is still O(n) time, but the point is that they do not use the implementation if temporary memory is available.
Note that the C++ standard explicitly gives implementations the freedom to choose this approach by lowering the required complexity from O(n) to O(N log N):
Complexity:
Exactly N-1 comparisons if enough additional memory is available. If the memory is insufficient, O(N log N) comparisons.
Of course, this cannot be taken as a proof that constant space in-place merges in O(n) time should never be used. On the other hand, if it would be faster, the optimized C++ libraries would probably switch to that type of implementation.
This is my C version:
void mergesort(int *a, int len) {
int temp, listsize, xsize;
for (listsize = 1; listsize <= len; listsize*=2) {
for (int i = 0, j = listsize; (j+listsize) <= len; i += (listsize*2), j += (listsize*2)) {
merge(& a[i], listsize, listsize);
}
}
listsize /= 2;
xsize = len % listsize;
if (xsize > 1)
mergesort(& a[len-xsize], xsize);
merge(a, listsize, xsize);
}
void merge(int *a, int sizei, int sizej) {
int temp;
int ii = 0;
int ji = sizei;
int flength = sizei+sizej;
for (int f = 0; f < (flength-1); f++) {
if (sizei == 0 || sizej == 0)
break;
if (a[ii] < a[ji]) {
ii++;
sizei--;
}
else {
temp = a[ji];
for (int z = (ji-1); z >= ii; z--)
a[z+1] = a[z];
ii++;
a[f] = temp;
ji++;
sizej--;
}
}
}
I know I'm late to the game, but here's a solution I wrote yesterday. I also posted this elsewhere, but this appears to be the most popular merge-in-place thread on SO. I've also not seen this algorithm posted anywhere else, so hopefully this helps some people.
This algorithm is in its most simple form so that it can be understood. It can be significantly tweaked for extra speed. Average time complexity is: O(n.log₂n) for the stable in-place array merge, and O(n.(log₂n)²) for the overall sort.
// Stable Merge In Place Sort
//
//
// The following code is written to illustrate the base algorithm. A good
// number of optimizations can be applied to boost its overall speed
// For all its simplicity, it does still perform somewhat decently.
// Average case time complexity appears to be: O(n.(log₂n)²)
#include <stddef.h>
#include <stdio.h>
#define swap(x, y) (t=(x), (x)=(y), (y)=t)
// Both sorted sub-arrays must be adjacent in 'a'
// Assumes that both 'an' and 'bn' are always non-zero
// 'an' is the length of the first sorted section in 'a', referred to as A
// 'bn' is the length of the second sorted section in 'a', referred to as B
static void
merge_inplace(int A[], size_t an, size_t bn)
{
int t, *B = &A[an];
size_t pa, pb; // Swap partition pointers within A and B
// Find the portion to swap. We're looking for how much from the
// start of B can swap with the end of A, such that every element
// in A is less than or equal to any element in B. This is quite
// simple when both sub-arrays come at us pre-sorted
for(pa = an, pb = 0; pa>0 && pb<bn && B[pb] < A[pa-1]; pa--, pb++);
// Now swap last part of A with first part of B according to the
// indicies we found
for (size_t index=pa; index < an; index++)
swap(A[index], B[index-pa]);
// Now merge the two sub-array pairings. We need to check that either array
// didn't wholly swap out the other and cause the remaining portion to be zero
if (pa>0 && (an-pa)>0)
merge_inplace(A, pa, an-pa);
if (pb>0 && (bn-pb)>0)
merge_inplace(B, pb, bn-pb);
} // merge_inplace
// Implements a recursive merge-sort algorithm with an optional
// insertion sort for when the splits get too small. 'n' must
// ALWAYS be 2 or more. It enforces this when calling itself
static void
merge_sort(int a[], size_t n)
{
size_t m = n/2;
// Sort first and second halves only if the target 'n' will be > 1
if (m > 1)
merge_sort(a, m);
if ((n-m)>1)
merge_sort(a+m, n-m);
// Now merge the two sorted sub-arrays together. We know that since
// n > 1, then both m and n-m MUST be non-zero, and so we will never
// violate the condition of not passing in zero length sub-arrays
merge_inplace(a, m, n-m);
} // merge_sort
// Print an array */
static void
print_array(int a[], size_t size)
{
if (size > 0) {
printf("%d", a[0]);
for (size_t i = 1; i < size; i++)
printf(" %d", a[i]);
}
printf("\n");
} // print_array
// Test driver
int
main()
{
int a[] = { 17, 3, 16, 5, 14, 8, 10, 7, 15, 1, 13, 4, 9, 12, 11, 6, 2 };
size_t n = sizeof(a) / sizeof(a[0]);
merge_sort(a, n);
print_array(a, n);
return 0;
} // main
Leveraging C++ std::inplace_merge, in-place merge sort can be implemented as follows:
template< class _Type >
inline void merge_sort_inplace(_Type* src, size_t l, size_t r)
{
if (r <= l) return;
size_t m = l + ( r - l ) / 2; // computes the average without overflow
merge_sort_inplace(src, l, m);
merge_sort_inplace(src, m + 1, r);
std::inplace_merge(src + l, src + m + 1, src + r + 1);
}
More sorting algorithms, including parallel implementations, are available in https://github.com/DragonSpit/ParallelAlgorithms repo, which is open source and free.
I just tried in place merge algorithm for merge sort in JAVA by using the insertion sort algorithm, using following steps.
1) Two sorted arrays are available.
2) Compare the first values of each array; and place the smallest value into the first array.
3) Place the larger value into the second array by using insertion sort (traverse from left to right).
4) Then again compare the second value of first array and first value of second array, and do the same. But when swapping happens there is some clue on skip comparing the further items, but just swapping required.
I have made some optimization here; to keep lesser comparisons in insertion sort. The only drawback i found with this solutions is it needs larger swapping of array elements in the second array.
e.g)
First___Array : 3, 7, 8, 9
Second Array : 1, 2, 4, 5
Then 7, 8, 9 makes the second array to swap(move left by one) all its elements by one each time to place himself in the last.
So the assumption here is swapping items is negligible compare to comparing of two items.
https://github.com/skanagavelu/algorithams/blob/master/src/sorting/MergeSort.java
package sorting;
import java.util.Arrays;
public class MergeSort {
public static void main(String[] args) {
int[] array = { 5, 6, 10, 3, 9, 2, 12, 1, 8, 7 };
mergeSort(array, 0, array.length -1);
System.out.println(Arrays.toString(array));
int[] array1 = {4, 7, 2};
System.out.println(Arrays.toString(array1));
mergeSort(array1, 0, array1.length -1);
System.out.println(Arrays.toString(array1));
System.out.println("\n\n");
int[] array2 = {4, 7, 9};
System.out.println(Arrays.toString(array2));
mergeSort(array2, 0, array2.length -1);
System.out.println(Arrays.toString(array2));
System.out.println("\n\n");
int[] array3 = {4, 7, 5};
System.out.println(Arrays.toString(array3));
mergeSort(array3, 0, array3.length -1);
System.out.println(Arrays.toString(array3));
System.out.println("\n\n");
int[] array4 = {7, 4, 2};
System.out.println(Arrays.toString(array4));
mergeSort(array4, 0, array4.length -1);
System.out.println(Arrays.toString(array4));
System.out.println("\n\n");
int[] array5 = {7, 4, 9};
System.out.println(Arrays.toString(array5));
mergeSort(array5, 0, array5.length -1);
System.out.println(Arrays.toString(array5));
System.out.println("\n\n");
int[] array6 = {7, 4, 5};
System.out.println(Arrays.toString(array6));
mergeSort(array6, 0, array6.length -1);
System.out.println(Arrays.toString(array6));
System.out.println("\n\n");
//Handling array of size two
int[] array7 = {7, 4};
System.out.println(Arrays.toString(array7));
mergeSort(array7, 0, array7.length -1);
System.out.println(Arrays.toString(array7));
System.out.println("\n\n");
int input1[] = {1};
int input2[] = {4,2};
int input3[] = {6,2,9};
int input4[] = {6,-1,10,4,11,14,19,12,18};
System.out.println(Arrays.toString(input1));
mergeSort(input1, 0, input1.length-1);
System.out.println(Arrays.toString(input1));
System.out.println("\n\n");
System.out.println(Arrays.toString(input2));
mergeSort(input2, 0, input2.length-1);
System.out.println(Arrays.toString(input2));
System.out.println("\n\n");
System.out.println(Arrays.toString(input3));
mergeSort(input3, 0, input3.length-1);
System.out.println(Arrays.toString(input3));
System.out.println("\n\n");
System.out.println(Arrays.toString(input4));
mergeSort(input4, 0, input4.length-1);
System.out.println(Arrays.toString(input4));
System.out.println("\n\n");
}
private static void mergeSort(int[] array, int p, int r) {
//Both below mid finding is fine.
int mid = (r - p)/2 + p;
int mid1 = (r + p)/2;
if(mid != mid1) {
System.out.println(" Mid is mismatching:" + mid + "/" + mid1+ " for p:"+p+" r:"+r);
}
if(p < r) {
mergeSort(array, p, mid);
mergeSort(array, mid+1, r);
// merge(array, p, mid, r);
inPlaceMerge(array, p, mid, r);
}
}
//Regular merge
private static void merge(int[] array, int p, int mid, int r) {
int lengthOfLeftArray = mid - p + 1; // This is important to add +1.
int lengthOfRightArray = r - mid;
int[] left = new int[lengthOfLeftArray];
int[] right = new int[lengthOfRightArray];
for(int i = p, j = 0; i <= mid; ){
left[j++] = array[i++];
}
for(int i = mid + 1, j = 0; i <= r; ){
right[j++] = array[i++];
}
int i = 0, j = 0;
for(; i < left.length && j < right.length; ) {
if(left[i] < right[j]){
array[p++] = left[i++];
} else {
array[p++] = right[j++];
}
}
while(j < right.length){
array[p++] = right[j++];
}
while(i < left.length){
array[p++] = left[i++];
}
}
//InPlaceMerge no extra array
private static void inPlaceMerge(int[] array, int p, int mid, int r) {
int secondArrayStart = mid+1;
int prevPlaced = mid+1;
int q = mid+1;
while(p < mid+1 && q <= r){
boolean swapped = false;
if(array[p] > array[q]) {
swap(array, p, q);
swapped = true;
}
if(q != secondArrayStart && array[p] > array[secondArrayStart]) {
swap(array, p, secondArrayStart);
swapped = true;
}
//Check swapped value is in right place of second sorted array
if(swapped && secondArrayStart+1 <= r && array[secondArrayStart+1] < array[secondArrayStart]) {
prevPlaced = placeInOrder(array, secondArrayStart, prevPlaced);
}
p++;
if(q < r) { //q+1 <= r) {
q++;
}
}
}
private static int placeInOrder(int[] array, int secondArrayStart, int prevPlaced) {
int i = secondArrayStart;
for(; i < array.length; i++) {
//Simply swap till the prevPlaced position
if(secondArrayStart < prevPlaced) {
swap(array, secondArrayStart, secondArrayStart+1);
secondArrayStart++;
continue;
}
if(array[i] < array[secondArrayStart]) {
swap(array, i, secondArrayStart);
secondArrayStart++;
} else if(i != secondArrayStart && array[i] > array[secondArrayStart]){
break;
}
}
return secondArrayStart;
}
private static void swap(int[] array, int m, int n){
int temp = array[m];
array[m] = array[n];
array[n] = temp;
}
}

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