Compute the sum recursively by dividing array - c++

I'm trying to create a function to compute the sum of elements in the array recursively. I wanted to try the approach of halving the array every iteration.
Here's what I have so far.
int sumRec(int *A, int n, int start, int end)
{
if (start == end){
return A[end];
}
mid = n/2;
return sumRec(A, n, start, mid) + sumRec(A, n, start, mid + 1);
}
Am I on the right track?
Thanks.

You don't need to pass n to the function.That's not needed.
Currently your program will run into an infinite recursion.
You can use
mid = (start+end)/2;
There are many more errors in your code.
Here's a similar code that could do the job
int sumRec(int *A, int start, int end)
{
if (start <= end)
{
int mid = (start+end)/2;
return A[mid] + sumRec(A,start, mid-1) + sumRec(A,mid+1,end);
}
return 0;
}

Related

How to count the amount of comparisons in a quick sort algorithm?

I have the following quick sort algorithm:
int quicksort(int data[], size_t n, int &counter)
// Library facilities used: cstdlib
{
size_t pivot_index; // Array index for the pivot element
size_t n1; // Number of elements before the pivot element
size_t n2; // Number of elements after the pivot element
if (n > 1)
{
// Partition the array, and set the pivot index.
partition(data, n, pivot_index, counter);
// Compute the sizes of the subarrays.
n1 = pivot_index;
n2 = n - n1 - 1;
// Recursive calls will now sort the subarrays.
quicksort(data, n1, counter);
quicksort((data + pivot_index + 1), n2, counter);
}
return counter;
}
void partition(int data[], size_t n, size_t& pivot_index, int &counter){
int pivot = data[0];
size_t too_big_index = 1;
size_t too_small_index = n - 1;
while (too_big_index <= too_small_index)
{
while (++counter && (too_big_index < n) && (data[too_big_index] <= pivot)) too_big_index++;
while (++counter && data[too_small_index] > pivot ) too_small_index--;
counter++;
if (too_big_index < too_small_index) swap(data[too_big_index], data[too_small_index]);
};
pivot_index = too_small_index;
data[0] = data[pivot_index];
data[pivot_index] = pivot;
}
I have added the three counter increments in the partition function, however the the counter comes out with a value of 32019997 when using a sorted array of 8000 elements, I am using a pivot of the left-most element (I know that gives me a terrible worst-case in terms of sorted array), unless I am incorrect, shouldn't the worst case be n^2 i.e 64000000? So I assume the way I am counting comparisons is wrong, but I am not sure how.

counting inversions with merge sort gives a negative number if the array length is 100000

I am still a beginner at programming and i am taking an online course (algorithms)
one of the practice questions was to count the number of inversions in a file containing 100000 numbers randomly ordered. I have tried this code on small data sets and it worked fine but when passing the actual data set it gives inversion count in negative number. Tried various solutions from different platforms but still couldn't resolve it yet.
so this is my code
#include "stdafx.h"
#include <iostream>;
#include <conio.h>:
#include <fstream>
using namespace std;
long merge(int a[], int start, int mid, int end)
int i = start;
int j = mid + 1;
int k = start;
int inversion=0;
int temp[100000];
while (i <= mid && j <= end)
{
if (a[i] < a[j])
{
temp[k++] = a[i++];
}
else
{
temp[k++] = a[j++];
inversion =inversion + (mid - i);
}
}
while (i <= mid)
{
temp[k++] = a[i++];
}
while (j <= end)
{
temp[k++] = a[j++];
}
for (int i = start; i <= end; i++)
{
a[i] = temp[i];
}
return inversion;
long Msort(int a[], int start,int end)
{
if (start >= end)
{
return 0;
}
int inversion = 0;
int mid = (start + end) / 2;
inversion += Msort(a, start, mid);
inversion += Msort(a, mid + 1, end);
inversion += merge(a, start, mid, end)
return inversion;
}
long ReadFromFile(char FileName[], int storage[],int n)
{
int b;
int count=0;
ifstream get(FileName);
if (!get)
{
cout << "no file found";
}
while (!get.eof())
{
get >> storage[count];
count++;
}
b = count;
return b;
}
int main()
{
int valuescount = 0;
int arr[100000];
char filename[] = { "file.txt" };
long n = sizeof(arr) / sizeof(arr[0]);
valuescount=ReadFromFile(filename, arr,n);
int no_Of_Inversions = Msort(arr, 0, valuescount -1);
cout << endl << "No of inversions are" << '\t' << no_Of_Inversions <<'\t';
cout <<endl<< "Total no of array values sorted"<< valuescount<<endl;
system("pause");
}
`
The issue with your code is not directly related to the input size. Rather, in an indirect way, the negative number of inversions you find is the result of an overflow in the variable inversion of the function merge.
Consider the case for your input size N = 100000. If this array of numbers is sorted in decreasing order, then all the ordered pairs in that array will be an inversion. In other words, there will be N * (N-1) / 2 inversions to be counted. As you may have noticed, that value is slightly higher than the bounds of unsigned int type. Consequently, when you try and count this value in a variable of type int, overflow occurs, leading to a negative result.
To remedy this issue, you should change the type of the variable inversion from int to long long, in functions merge and Msort. (You should also update the return type of the functions merge and Msort) Naturally, you should assign the return value of the Msort call in the main function to a variable of type long long as well. In other words, change the type of variable no_Of_Inversions into a long long as well.

Counting basic operations in Quicksort algorithm

I am trying to count the amount of basic operations done by Hoare's quicksort algorithm. I am wondering if I placed the counter in the correct position. My assignment is to count the number of basic operations on 100 randomly generated arrays of sizes 10,100,1k, and 10k.
Here is the algorithm with the counter placed (line 6):
void QuickSort(int* array, int startIndex, int endIndex, int &counter) {
int pivot = array[startIndex]; //pivot element is the leftmost element
int splitPoint;
if (endIndex > startIndex)
{
counter++; //counting
splitPoint = SplitArray(array, pivot, startIndex, endIndex);
array[splitPoint] = pivot;
QuickSort(array, startIndex, splitPoint - 1, counter); //Quick sort first half
QuickSort(array, splitPoint + 1, endIndex, counter); //Quick sort second half
}
}
void swap(int &a, int &b) {
int temp;
temp = a;
a = b;
b = temp;
}
int SplitArray(int* array, int pivot, int startIndex, int endIndex) {
int leftBoundary = startIndex;
int rightBoundary = endIndex;
while (leftBoundary < rightBoundary)
{
while (pivot < array[rightBoundary]
&& rightBoundary > leftBoundary)
{
rightBoundary--;
}
swap(array[leftBoundary], array[rightBoundary]);
while (pivot >= array[leftBoundary]
&& leftBoundary < rightBoundary)
{
leftBoundary++;
}
swap(array[rightBoundary], array[leftBoundary]);
}
return leftBoundary;
}
Do these results make sense?
Array[Amount] Array[10] Array[100] Array[1k] Array[10k]
MAX: 8 72 682 7122
MIN: 5 63 653 7015
AVERAGE: 6.36 66.54 667.87 7059.41
Or did I put the counter in the wrong place.
The counter is in the wrong place. Your assignment is to count basic operations. What is a basic operation in sorting? Typically we count the number of compare operations to measure complexity of sort.
We know that QuickSort is O(N Log N) on average where N is the number of items being sorted, while worst case it is O(N^2).
Your numbers are smaller than N, which is not possible since each of the N items must be compared to some other elemnt at least once, the cost of sorting cannot be less than N (otherwise at least one element was not compared to anything, so you could not guaranteed that it is sorted).
In your algorithm, the compare operation occurs when you compare the elements of the array to the pivot value. So increment your counter every time you compare an array element to Pivot. Your measured numbers should be at least N, and will typically be about N*log N, and rarely be be close to N^2.
See the suggested points below in SplitArray where to increment counter:
void QuickSort(int* array, int startIndex, int endIndex, int &counter) {
int pivot = array[startIndex]; //pivot element is the leftmost element
int splitPoint;
if (endIndex > startIndex)
{
// counter++; // Don't count here
splitPoint=SplitArray(array, pivot, startIndex, endIndex, counter);
array[splitPoint] = pivot;
QuickSort(array, startIndex, splitPoint - 1, counter); //Quick sort first half
QuickSort(array, splitPoint + 1, endIndex, counter); //Quick sort second half
}
}
No change to swap:
void swap(int &a, int &b) {
int temp;
temp = a;
a = b;
b = temp;
}
SplitArray does the comparisons, so the counter should be incremented here:
int SplitArray(int* array,int pivot,int startIndex,int endIndex,int &counter) {
int leftBoundary = startIndex;
int rightBoundary = endIndex;
while ((++counter) && (leftBoundary < rightBoundary))
{
while (pivot < array[rightBoundary]
&& rightBoundary > leftBoundary)
{
rightBoundary--;
}
swap(array[leftBoundary], array[rightBoundary]);
while ((++counter) && (pivot >= array[leftBoundary])
&& leftBoundary < rightBoundary)
{
leftBoundary++;
}
swap(array[rightBoundary], array[leftBoundary]);
}
return leftBoundary;
}

QUICK SORT stack overflow c++ big numbers

good day
I am trying to use quick sort with 10000 numbers but it is giving me stack overflow error. it works with random numbers but it does not with descending and ascending numbers.
'
thank you
void quickSort(long* array, long start, long last)
{
if (start < last)
{
int p = partition(array, start, last);
quickSort(array, start, p-1);
quickSort(array, p + 1, last);
}
}
int partition(long* array, long start, long last)//first partition
{
int j = start + 1;
for (long i = start + 1;i <= last;i++)
{
if (array[i] < array[start])
{
swap(array[i], array[j]);
j++;
}
}
swap(array[start], array[j - 1]);
return j - 1;
}
'
For sorted elements, you can avoid this problem by choosing the median of the three elements array[start], array[last] and array[(start + last + 1)/2] as your pivot value.
int median_of_3(long* array, long start, long last)
{
long a = (start + last + 1)/2, b = start, c = last;
if (array[c] < array[a]) swap(array[c], array[a]);
if (array[b] < array[a]) swap(array[b], array[a]);
if (array[c] < array[b]) swap(array[c], array[b]);
return partition(array, start, last);
}
An additional strategy to avoid a large stack depth is to calculate which partition is smaller, and recursively call the smaller one. The other partition can then be optimized into a loop (tail recursion optimization).
void quickSort(long* array, long start, long last)
{
if (start >= last) return;
int p = median_of_3(array, start, last);
int next_start[2] = { start, p + 1 };
int next_last[2] = { p - 1, last };
bool i = p > (start + last)/2;
quickSort(array, next_start[i], next_last[i]);
/*
* If the compiler does not optimize the tail call below into
* a loop, it is easy to do the optimization manually.
*/
quickSort(array, next_start[!i], next_last[!i]);
}
Introspection can also be used to avoid a large stack depth. You track your recursive call depth, and if it is "too deep", you fail safe into a different sorting strategy, like merge sort or heap sort. This is the behavior currently used by std::sort.
void introSortImpl(long* array, long start, long last, int depth)
{
if (--depth == 0) {
heapSort(array, start, last);
return;
}
if (start >= last) return;
int p = median_of_3(array, start, last);
int next_start[2] = { start, p + 1 };
int next_last[2] = { p - 1, last };
bool i = p > (start + last)/2;
introSortImpl(array, next_start[i], next_last[i], depth);
introSortImpl(array, next_start[!i], next_last[!i], depth);
}
void introspectionSort(long* array, long start, long last)
{
introSortImpl(array, start, last, log2(start - last) * 3);
}
the code is okay but your compiler uses stack very ineffectively. you just need to raise reserved stack amount. it happens much more often in debug profiles rather than release ones just because compiler preserves large stack chunks to check if stack was broken during execution of your procedure.
Example of Lomuto partition scheme like quicksort that uses recursion on the smaller partition, updates l or r, then loops back to split the larger partition into two partitions, repeating the process. Worst case stack space is O(log2(n)) which should avoid stack overflow. Worst case time complexity is still O(n^2) (depending on how partition is implemented).
Some call this example a half recursion. It's not an example of tail recursion, since tail recursion means that the recursive function just returns after calling itself. The second call in the original question example is a tail call.
void quicksort(int * tab, int l, int r)
{
int q;
while(l < r)
{
q = partition(tab, l, r);
if(q - l < r - q) { // recurse into the smaller partition
quicksort(tab, l, q - 1);
l = q + 1;
} else {
quicksort(tab, q + 1, r);
r = q - 1;
}
} // loop on the larger partition
}

Searching in a sorted and rotated array

While preparing for an interview I stumbled upon this interesting question:
You've been given an array that is sorted and then rotated.
For example:
Let arr = [1,2,3,4,5], which is sorted
Rotate it twice to the right to give [4,5,1,2,3].
Now how best can one search in this sorted + rotated array?
One can unrotate the array and then do a binary search. But that is no better than doing a linear search in the input array, as both are worst-case O(N).
Please provide some pointers. I've googled a lot on special algorithms for this but couldn't find any.
I understand C and C++.
This can be done in O(logN) using a slightly modified binary search.
The interesting property of a sorted + rotated array is that when you divide it into two halves, atleast one of the two halves will always be sorted.
Let input array arr = [4,5,6,7,8,9,1,2,3]
number of elements = 9
mid index = (0+8)/2 = 4
[4,5,6,7,8,9,1,2,3]
^
left mid right
as seem right sub-array is not sorted while left sub-array is sorted.
If mid happens to be the point of rotation them both left and right sub-arrays will be sorted.
[6,7,8,9,1,2,3,4,5]
^
But in any case one half(sub-array) must be sorted.
We can easily know which half is sorted by comparing start and end element of each half.
Once we find which half is sorted we can see if the key is present in that half - simple comparison with the extremes.
If the key is present in that half we recursively call the function on that half
else we recursively call our search on the other half.
We are discarding one half of the array in each call which makes this algorithm O(logN).
Pseudo code:
function search( arr[], key, low, high)
mid = (low + high) / 2
// key not present
if(low > high)
return -1
// key found
if(arr[mid] == key)
return mid
// if left half is sorted.
if(arr[low] <= arr[mid])
// if key is present in left half.
if (arr[low] <= key && arr[mid] >= key)
return search(arr,key,low,mid-1)
// if key is not present in left half..search right half.
else
return search(arr,key,mid+1,high)
end-if
// if right half is sorted.
else
// if key is present in right half.
if(arr[mid] <= key && arr[high] >= key)
return search(arr,key,mid+1,high)
// if key is not present in right half..search in left half.
else
return search(arr,key,low,mid-1)
end-if
end-if
end-function
The key here is that one sub-array will always be sorted, using which we can discard one half of the array.
The accepted answer has a bug when there are duplicate elements in the array. For example, arr = {2,3,2,2,2} and 3 is what we are looking for. Then the program in the accepted answer will return -1 instead of 1.
This interview question is discussed in detail in the book 'Cracking the Coding Interview'. The condition of duplicate elements is specially discussed in that book. Since the op said in a comment that array elements can be anything, I am giving my solution as pseudo code in below:
function search( arr[], key, low, high)
if(low > high)
return -1
mid = (low + high) / 2
if(arr[mid] == key)
return mid
// if the left half is sorted.
if(arr[low] < arr[mid]) {
// if key is in the left half
if (arr[low] <= key && key <= arr[mid])
// search the left half
return search(arr,key,low,mid-1)
else
// search the right half
return search(arr,key,mid+1,high)
end-if
// if the right half is sorted.
else if(arr[mid] < arr[high])
// if the key is in the right half.
if(arr[mid] <= key && arr[high] >= key)
return search(arr,key,mid+1,high)
else
return search(arr,key,low,mid-1)
end-if
else if(arr[mid] == arr[low])
if(arr[mid] != arr[high])
// Then elements in left half must be identical.
// Because if not, then it's impossible to have either arr[mid] < arr[high] or arr[mid] > arr[high]
// Then we only need to search the right half.
return search(arr, mid+1, high, key)
else
// arr[low] = arr[mid] = arr[high], we have to search both halves.
result = search(arr, low, mid-1, key)
if(result == -1)
return search(arr, mid+1, high, key)
else
return result
end-if
end-function
You can do 2 binary searches: first to find the index i such that arr[i] > arr[i+1].
Apparently, (arr\[1], arr[2], ..., arr[i]) and (arr[i+1], arr[i+2], ..., arr[n]) are both sorted arrays.
Then if arr[1] <= x <= arr[i], you do binary search at the first array, else at the second.
The complexity O(logN)
EDIT:
the code.
My first attempt would be to find using binary search the number of rotations applied - this can be done by finding the index n where a[n] > a[n + 1] using the usual binary search mechanism.
Then do a regular binary search while rotating all indexes per shift found.
int rotated_binary_search(int A[], int N, int key) {
int L = 0;
int R = N - 1;
while (L <= R) {
// Avoid overflow, same as M=(L+R)/2
int M = L + ((R - L) / 2);
if (A[M] == key) return M;
// the bottom half is sorted
if (A[L] <= A[M]) {
if (A[L] <= key && key < A[M])
R = M - 1;
else
L = M + 1;
}
// the upper half is sorted
else {
if (A[M] < key && key <= A[R])
L = M + 1;
else
R = M - 1;
}
}
return -1;
}
If you know that the array has been rotated s to the right, you can simply do a binary search shifted s to the right. This is O(lg N)
By this, I mean, initialize the left limit to s and the right to (s-1) mod N, and do a binary search between these, taking a bit of care to work in the correct area.
If you don't know how much the array has been rotated by, you can determine how big the rotation is using a binary search, which is O(lg N), then do a shifted binary search, O(lg N), a grand total of O(lg N) still.
Reply for the above mentioned post "This interview question is discussed in detail in the book 'Cracking the Coding Interview'. The condition of duplicate elements is specially discussed in that book. Since the op said in comment that array elements can be anything, I am giving my solution as pseudo code in below:"
Your solution is O(n) !! (The last if condition where you check both halves of the array for a single condition makes it a sol of linear time complexity )
I am better off doing a linear search than getting stuck in a maze of bugs and segmentation faults during a coding round.
I dont think there is a better solution than O(n) for a search in a rotated sorted array (with duplicates)
If you know how (far) it was rotated you can still do a binary search.
The trick is that you get two levels of indices: you do the b.s. in a virtual 0..n-1 range and then un-rotate them when actually looking up a value.
You don't need to rotate the array first. You can use binary search on the rotated array (with some modifications).
Let N be the number you are searching for:
Read the first number (arr[start]) and the number in the middle of the array (arr[end]):
if arr[start] > arr[end] --> the first half is not sorted but the second half is sorted:
if arr[end] > N --> the number is in index: (middle + N - arr[end])
if N repeat the search on the first part of the array (see end to be the middle of the first half of the array etc.)
(the same if the first part is sorted but the second one isn't)
public class PivotedArray {
//56784321 first increasing than decreasing
public static void main(String[] args) {
// TODO Auto-generated method stub
int [] data ={5,6,7,8,4,3,2,1,0,-1,-2};
System.out.println(findNumber(data, 0, data.length-1,-2));
}
static int findNumber(int data[], int start, int end,int numberToFind){
if(data[start] == numberToFind){
return start;
}
if(data[end] == numberToFind){
return end;
}
int mid = (start+end)/2;
if(data[mid] == numberToFind){
return mid;
}
int idx = -1;
int midData = data[mid];
if(numberToFind < midData){
if(midData > data[mid+1]){
idx=findNumber(data, mid+1, end, numberToFind);
}else{
idx = findNumber(data, start, mid-1, numberToFind);
}
}
if(numberToFind > midData){
if(midData > data[mid+1]){
idx = findNumber(data, start, mid-1, numberToFind);
}else{
idx=findNumber(data, mid+1, end, numberToFind);
}
}
return idx;
}
}
short mod_binary_search( int m, int *arr, short start, short end)
{
if(start <= end)
{
short mid = (start+end)/2;
if( m == arr[mid])
return mid;
else
{
//First half is sorted
if(arr[start] <= arr[mid])
{
if(m < arr[mid] && m >= arr[start])
return mod_binary_search( m, arr, start, mid-1);
return mod_binary_search( m, arr, mid+1, end);
}
//Second half is sorted
else
{
if(m > arr[mid] && m < arr[start])
return mod_binary_search( m, arr, mid+1, end);
return mod_binary_search( m, arr, start, mid-1);
}
}
}
return -1;
}
First, you need to find the shift constant, k.
This can be done in O(lgN) time.
From the constant shift k, you can easily find the element you're looking for using
a binary search with the constant k. The augmented binary search also takes O(lgN) time
The total run time is O(lgN + lgN) = O(lgN)
To find the constant shift, k. You just have to look for the minimum value in the array. The index of the minimum value of the array tells you the constant shift.
Consider the sorted array
[1,2,3,4,5].
The possible shifts are:
[1,2,3,4,5] // k = 0
[5,1,2,3,4] // k = 1
[4,5,1,2,3] // k = 2
[3,4,5,1,2] // k = 3
[2,3,4,5,1] // k = 4
[1,2,3,4,5] // k = 5%5 = 0
To do any algorithm in O(lgN) time, the key is to always find ways to divide the problem by half.
Once doing so, the rest of the implementation details is easy
Below is the code in C++ for the algorithm
// This implementation takes O(logN) time
// This function returns the amount of shift of the sorted array, which is
// equivalent to the index of the minimum element of the shifted sorted array.
#include <vector>
#include <iostream>
using namespace std;
int binarySearchFindK(vector<int>& nums, int begin, int end)
{
int mid = ((end + begin)/2);
// Base cases
if((mid > begin && nums[mid] < nums[mid-1]) || (mid == begin && nums[mid] <= nums[end]))
return mid;
// General case
if (nums[mid] > nums[end])
{
begin = mid+1;
return binarySearchFindK(nums, begin, end);
}
else
{
end = mid -1;
return binarySearchFindK(nums, begin, end);
}
}
int getPivot(vector<int>& nums)
{
if( nums.size() == 0) return -1;
int result = binarySearchFindK(nums, 0, nums.size()-1);
return result;
}
// Once you execute the above, you will know the shift k,
// you can easily search for the element you need implementing the bottom
int binarySearchSearch(vector<int>& nums, int begin, int end, int target, int pivot)
{
if (begin > end) return -1;
int mid = (begin+end)/2;
int n = nums.size();
if (n <= 0) return -1;
while(begin <= end)
{
mid = (begin+end)/2;
int midFix = (mid+pivot) % n;
if(nums[midFix] == target)
{
return midFix;
}
else if (nums[midFix] < target)
{
begin = mid+1;
}
else
{
end = mid - 1;
}
}
return -1;
}
int search(vector<int>& nums, int target) {
int pivot = getPivot(nums);
int begin = 0;
int end = nums.size() - 1;
int result = binarySearchSearch(nums, begin, end, target, pivot);
return result;
}
Hope this helps!=)
Soon Chee Loong,
University of Toronto
For a rotated array with duplicates, if one needs to find the first occurrence of an element, one can use the procedure below (Java code):
public int mBinarySearch(int[] array, int low, int high, int key)
{
if (low > high)
return -1; //key not present
int mid = (low + high)/2;
if (array[mid] == key)
if (mid > 0 && array[mid-1] != key)
return mid;
if (array[low] <= array[mid]) //left half is sorted
{
if (array[low] <= key && array[mid] >= key)
return mBinarySearch(array, low, mid-1, key);
else //search right half
return mBinarySearch(array, mid+1, high, key);
}
else //right half is sorted
{
if (array[mid] <= key && array[high] >= key)
return mBinarySearch(array, mid+1, high, key);
else
return mBinarySearch(array, low, mid-1, key);
}
}
This is an improvement to codaddict's procedure above. Notice the additional if condition as below:
if (mid > 0 && array[mid-1] != key)
There is a simple idea to solve this problem in O(logN) complexity with binary search.
The idea is,
If the middle element is greater than the left element, then the left part is sorted. Otherwise, the right part is sorted.
Once the sorted part is determined, all you need is to check if the value falls under that sorted part or not. If not, you can divide the unsorted part and find the sorted part from that (the unsorted part) and continue binary search.
For example, consider the image below. An array can be left rotated or right rotated.
Below image shows the relation of the mid element compared with the left most one and how this relates to which part of the array is purely sorted.
If you see the image, you find that the mid element is >= the left element and in that case, the left part is purely sorted.
An array can be left rotated by number of times, like once, twice, thrice and so on. Below image shows that for each rotation, the property of if mid >= left, left part is sorted still prevails.
More explanation with images can be found in below link. (Disclaimer: I am associated with this blog)
https://foolishhungry.com/search-in-rotated-sorted-array/.
Hope this will be helpful.
Happy coding! :)
Here is a simple (time,space)efficient non-recursive O(log n) python solution that doesn't modify the original array. Chops down the rotated array in half until I only have two indices to check and returns the correct answer if one index matches.
def findInRotatedArray(array, num):
lo,hi = 0, len(array)-1
ix = None
while True:
if hi - lo <= 1:#Im down to two indices to check by now
if (array[hi] == num): ix = hi
elif (array[lo] == num): ix = lo
else: ix = None
break
mid = lo + (hi - lo)/2
print lo, mid, hi
#If top half is sorted and number is in between
if array[hi] >= array[mid] and num >= array[mid] and num <= array[hi]:
lo = mid
#If bottom half is sorted and number is in between
elif array[mid] >= array[lo] and num >= array[lo] and num <= array[mid]:
hi = mid
#If top half is rotated I know I need to keep cutting the array down
elif array[hi] <= array[mid]:
lo = mid
#If bottom half is rotated I know I need to keep cutting down
elif array[mid] <= array[lo]:
hi = mid
print "Index", ix
Try this solution
bool search(int *a, int length, int key)
{
int pivot( length / 2 ), lewy(0), prawy(length);
if (key > a[length - 1] || key < a[0]) return false;
while (lewy <= prawy){
if (key == a[pivot]) return true;
if (key > a[pivot]){
lewy = pivot;
pivot += (prawy - lewy) / 2 ? (prawy - lewy) / 2:1;}
else{
prawy = pivot;
pivot -= (prawy - lewy) / 2 ? (prawy - lewy) / 2:1;}}
return false;
}
This code in C++ should work for all cases, Although It works with duplicates, please let me know if there's bug in this code.
#include "bits/stdc++.h"
using namespace std;
int searchOnRotated(vector<int> &arr, int low, int high, int k) {
if(low > high)
return -1;
if(arr[low] <= arr[high]) {
int p = lower_bound(arr.begin()+low, arr.begin()+high, k) - arr.begin();
if(p == (low-high)+1)
return -1;
else
return p;
}
int mid = (low+high)/2;
if(arr[low] <= arr[mid]) {
if(k <= arr[mid] && k >= arr[low])
return searchOnRotated(arr, low, mid, k);
else
return searchOnRotated(arr, mid+1, high, k);
}
else {
if(k <= arr[high] && k >= arr[mid+1])
return searchOnRotated(arr, mid+1, high, k);
else
return searchOnRotated(arr, low, mid, k);
}
}
int main() {
int n, k; cin >> n >> k;
vector<int> arr(n);
for(int i=0; i<n; i++) cin >> arr[i];
int p = searchOnRotated(arr, 0, n-1, k);
cout<<p<<"\n";
return 0;
}
In Javascript
var search = function(nums, target,low,high) {
low= (low || low === 0) ? low : 0;
high= (high || high == 0) ? high : nums.length -1;
if(low > high)
return -1;
let mid = Math.ceil((low + high) / 2);
if(nums[mid] == target)
return mid;
if(nums[low] < nums[mid]) {
// if key is in the left half
if (nums[low] <= target && target <= nums[mid])
// search the left half
return search(nums,target,low,mid-1);
else
// search the right half
return search(nums,target,mid+1,high);
} else {
// if the key is in the right half.
if(nums[mid] <= target && nums[high] >= target)
return search(nums,target,mid+1,high)
else
return search(nums,target,low,mid-1)
}
};
Input: nums = [4,5,6,7,0,1,2], target = 0
Output: 4
import java.util.*;
class Main{
public static void main(String args[]){
Scanner sc = new Scanner(System.in);
int n=sc.nextInt();
int arr[]=new int[n];
int max=Integer.MIN_VALUE;
int min=Integer.MAX_VALUE;
int min_index=0,max_index=n;
for(int i=0;i<n;i++){
arr[i]=sc.nextInt();
if(arr[i]>max){
max=arr[i];
max_index=i;
}
if(arr[i]<min){
min=arr[i];
min_index=i;
}
}
int element=sc.nextInt();
int index;
if(element>arr[n-1]){
index=Arrays.binarySearch(arr,0,max_index+1,element);
}
else {
index=Arrays.binarySearch(arr,min_index,n,element);
}
if(index>=0){
System.out.println(index);
}
else{
System.out.println(-1);
}
}
}
Here are my two cents:
If the array does not contain duplicates, one can find the solution in O(log(n)). As many people have shown it the case, a tweaked version of binary search can be used to find the target element.
However, if the array contains duplicates, I think there is no way to find the target element in O(log(n)). Here is an example shows why I think O(log(n)) is not possible. Consider the two arrays below:
a = [2,.....................2...........3,6,2......2]
b = [2.........3,6,2........2......................2]
All the dots are filled with the number 2. You can see that both arrays are sorted and rotated. If one wants to consider binary search, then they have to cut the search domain by half every iteration -- this is how we get O(log(n)). Let us assume we are searching for the number 3. In the frist case, we can see it hiding in the right side of the array, and on the second case it is hiding in the second side of the array. Here is what we know about the array at this stage:
left = 0
right = length - 1;
mid = left + (right - left) / 2;
arr[mid] = 2;
arr[left] = 2;
arr[right] = 2;
target = 3;
This is all the information we have. We can clearly see it is not enough to make a decision to exclude one half of the array. As a result of that, the only way is to do linear search. I am not saying we can't optimize that O(n) time, all I am saying is that we can't do O(log(n)).
There is something i don't like about binary search because of mid, mid-1 etc that's why i always use binary stride/jump search
How to use it on a rotated array?
use twice(once find shift and then use a .at() to find the shifted index -> original index)
Or compare the first element, if it is less than first element, it has to be near the end
do a backwards jump search from end, stop if any pivot tyoe leement is found
if it is > start element just do a normal jump search :)
Implemented using C#
public class Solution {
public int Search(int[] nums, int target) {
if (nums.Length == 0) return -1;
int low = 0;
int high = nums.Length - 1;
while (low <= high)
{
int mid = (low + high) / 2;
if (nums[mid] == target) return mid;
if (nums[low] <= nums[mid]) // 3 4 5 6 0 1 2
{
if (target >= nums[low] && target <= nums[mid])
high = mid;
else
low = mid + 1;
}
else // 5 6 0 1 2 3 4
{
if (target >= nums[mid] && target <= nums[high])
low= mid;
else
high = mid - 1;
}
}
return -1;
}
}
Search An Element In A Sorted And Rotated Array In Java
package yourPackageNames;
public class YourClassName {
public static void main(String[] args) {
int[] arr = {3, 4, 5, 1, 2};
// int arr[]={16,19,21,25,3,5,8,10};
int key = 1;
searchElementAnElementInRotatedAndSortedArray(arr, key);
}
public static void searchElementAnElementInRotatedAndSortedArray(int[] arr, int key) {
int mid = arr.length / 2;
int pivotIndex = 0;
int keyIndex = -1;
boolean keyIndexFound = false;
boolean pivotFound = false;
for (int rightSide = mid; rightSide < arr.length - 1; rightSide++) {
if (arr[rightSide] > arr[rightSide + 1]) {
pivotIndex = rightSide;
pivotFound = true;
System.out.println("1st For Loop - PivotFound: " + pivotFound + ". Pivot is: " + arr[pivotIndex] + ". Pivot Index is: " + pivotIndex);
break;
}
}
if (!pivotFound) {
for (int leftSide = 0; leftSide < arr.length - mid; leftSide++) {
if (arr[leftSide] > arr[leftSide + 1]) {
pivotIndex = leftSide;
pivotFound = true;
System.out.println("2nd For Loop - PivotFound: " + pivotFound + ". Pivot is: " + arr[pivotIndex] + ". Pivot Index is: " + pivotIndex);
break;
}
}
}
for (int i = 0; i <= pivotIndex; i++) {
if (arr[i] == key) {
keyIndex = i;
keyIndexFound = true;
break;
}
}
if (!keyIndexFound) {
for (int i = pivotIndex; i < arr.length; i++) {
if (arr[i] == key) {
keyIndex = i;
break;
}
}
}
System.out.println(keyIndex >= 0 ? key + " found at index: " + keyIndex : key + " was not found in the array.");
}
}
Another approach that would work with repeated values is to find the rotation and then do a regular binary search applying the rotation whenever we access the array.
test = [3, 4, 5, 1, 2]
test1 = [2, 3, 2, 2, 2]
def find_rotated(col, num):
pivot = find_pivot(col)
return bin_search(col, 0, len(col), pivot, num)
def find_pivot(col):
prev = col[-1]
for n, curr in enumerate(col):
if prev > curr:
return n
prev = curr
raise Exception("Col does not seem like rotated array")
def rotate_index(col, pivot, position):
return (pivot + position) % len(col)
def bin_search(col, low, high, pivot, num):
if low > high:
return None
mid = (low + high) / 2
rotated_mid = rotate_index(col, pivot, mid)
val = col[rotated_mid]
if (val == num):
return rotated_mid
elif (num > val):
return bin_search(col, mid + 1, high, pivot, num)
else:
return bin_search(col, low, mid - 1, pivot, num)
print(find_rotated(test, 2))
print(find_rotated(test, 4))
print(find_rotated(test1, 3))
My simple code :-
public int search(int[] nums, int target) {
int l = 0;
int r = nums.length-1;
while(l<=r){
int mid = (l+r)>>1;
if(nums[mid]==target){
return mid;
}
if(nums[mid]> nums[r]){
if(target > nums[mid] || nums[r]>= target)l = mid+1;
else r = mid-1;
}
else{
if(target <= nums[r] && target > nums[mid]) l = mid+1;
else r = mid -1;
}
}
return -1;
}
Time Complexity O(log(N)).
Question: Search in Rotated Sorted Array
public class SearchingInARotatedSortedARRAY {
public static void main(String[] args) {
int[] a = { 4, 5, 6, 0, 1, 2, 3 };
System.out.println(search1(a, 6));
}
private static int search1(int[] a, int target) {
int start = 0;
int last = a.length - 1;
while (start + 1 < last) {
int mid = start + (last - start) / 2;
if (a[mid] == target)
return mid;
// if(a[start] < a[mid]) => Then this part of the array is not rotated
if (a[start] < a[mid]) {
if (a[start] <= target && target <= a[mid]) {
last = mid;
} else {
start = mid;
}
}
// this part of the array is rotated
else {
if (a[mid] <= target && target <= a[last]) {
start = mid;
} else {
last = mid;
}
}
} // while
if (a[start] == target) {
return start;
}
if (a[last] == target) {
return last;
}
return -1;
}
}
Swift Solution 100% working tested
func searchInArray(A:[Int],key:Int)->Int{
for i in 0..<A.count{
if key == A[i] {
print(i)
return i
}
}
print(-1)
return -1
}