## Thursday, November 19, 2009

### C/C++ teaser

This is not very difficult. But just have a look.

1. Predict the outcome
#include < stdio.h >
main()
{
int a=0;
printf("%s",a);
}

2. Predict the outcome
#include < stdio.h >
main()
{
int arr[]={2,3,4,5,6};
int *iptr=arr;
iptr++;
printf("%d",*(iptr+i));
arr++;
printf("%d",*(arr+i));
}

3. Predict the outcome
#include < stdio.h >
main()
{
int arr[]={2,3,4,5,6};
int *iptr=arr;
int i=0;
printf("%d,%d%d,%d",*(iptr+i),i[arr],arr[i],*(arr+i));
}

Thanks
Layman

## Wednesday, November 18, 2009

### Art of computer programming -Sorting #3

Shell sort
For any sorting algorithm that moves only one position at a time , then the running time will always be proportional to n^2. Hence the idea was struck to make larger leaps rather than small ones. The small leaps is one of the prime reasons for insertion sort's low efficiency. And Donald Shell proposed shell sort.
Now you will be wondering how to choose he leap/step size. In shell sort we can start with an arbitrary step size in the first pass, after every pass the step size decreases. Hence shell sort is also known as decreasing increment sort. We'll take an example. In the first pass, let the step size be 8.
So during first pass element 0 is compared with element 8, element 16 , element 24.....element n.....element( n+8) etc. During second pass the increment is reduced to 4, so element 0 is compared with element 4, element 8........element n, element(n+4) etc. In the third pass increment size be 2, comparison will be between element 0, element 2, element 4....element n,element( n+2) etc. And finally increment becomes 1 in the last pass. In the last pass element 0, element 1, element 2 ....element n, element (n+1) etc is compared.

This is how the shell sort operates. During each pass the elements being compared are re-arranged in sorted order,ie, if element i and j are compared the position of i and j are rearranged so that the series i,j is in sorted order.

Points of interest
Like the insertion sort it has best case of O(n) hence its suitable to check whether an array is sorted or not.
Choosing the increment sequence is a tricky question. If you are concerned about the efficiency I have few recommendations for you:
The Sedgewick increment sequence..
$9\times4^i - 9\times2^i + 1$, or $4^{i} - 3\times2^i + 1$ ......
The best known sequence according to research by Marcin Ciura is 1, 4, 10, 23, 57, 132, 301, 701, 1750

Algorithm:
input: an array a of length n with array elements numbered 0 to n − 1inc ← round(n/2)while inc > 0 do:  for i = inc .. n − 1 do:      temp ← a[i]      j ← i      while j ≥ inc and a[j − inc] > temp do:          a[j] ← a[j − inc]          j ← j − inc      a[j] ← temp  inc ← round(inc / 2.2)

Worst case performance : Depends on the leap/step size sequence
Best case performance : O(n)
Average case performance : Depends on the leap/step size sequence
Worst case space complexity: O(n)

## Tuesday, November 17, 2009

### Art of computer programming -Sorting #2

Insertion sort
Insertion sort is one of those very traditional sorting methods. In this method, we scan through the list element by element and we assume that all elements preceding the present element is sorted. We start with the assumption that first element is sorted and we need to place the 2nd element to the left or the right of the first element based on whether its greater than or less than the 1st element. So we can generalize that for the i'th element we assume that the list from 0...i-1 is sorted and we can insert the ith element at the the correct position in this sorted sublist. So the problem reduces to inserting an element in a sorted list. But its not as simple as it sounds. Inserting in a sorted list may need shifting the elements of the sublist. Its a reasonably expensive operation. Hence using arrays will be quite expensive for insertion sort. Whereas if you are using linked lists, then shifting operations become much more easier(as it involves only 2-3 pointer operations).
On an average we will need a total of 0.5(1+2+...+n) shiftings ~ 0.25n^2

A method to improve this will be to use binary search to find the position of the element in the sorted list. This will reduce the position search time to log n. Hence the algorithm improves.

Points of interest
To check whether a list is already sorted insertion sort is highly recommended.
It can receive data as it is working. This is particularly useful when you have only an incomplete piece of data.
While insertion sort typically makes fewer comparisons than selection sort, it requires more writes because the inner loop can require shifting large sections of the sorted portion of the array.

Algorithm
insertionSort(array A)begin   for i := 1 to length[A]-1 do   begin       value := A[i];       j := i - 1;       while j >= 0 and A[j] > value do       begin           A[j + 1] := A[j];           j := j - 1;       end;       A[j + 1] := value;   end;end;

Worst case performance О(n^2)
Best case performance O(n)
Average case performance О(n^2)
Worst case space complexity O(1)