Meximum Subarray Sum using Divide-And-Conquer - c++

Working on an implementation of finding the Greatest Contiguous Sum of a sequence using the Divide and Conquer method as seen here.
My return value is often incorrect.
For example:
{5, 3} returns 5 instead of 8.
{-5, 3} returns 0 instead of 3.
{ 6, -5, 7 } returns 7 instead of 8.
Other notes:
decrementing or incrementing AT the first or last iterators throws an exception, saying that I either can't increment, decrement, or dereference at that point. There's a bug somewhere in GCSMid, I think, but I haven't been able to solve it.
this implementation uses random-access iterators, signified as RAIter
//function max- finds greatest number given 3 size_ts
size_t max(size_t a, size_t b, size_t c)
{
if (a >= b && a >= c)
{
return a;
}
else if (b >= a && b >= c)
{
return b;
}
else
{
return c;
}
}
//function gcsMid
//main algorithm to find subsequence if it spans across the center line
template<typename RAIter>
size_t gcsMid(RAIter first, RAIter center, RAIter last)
{
size_t sum = 0;
size_t leftSum = 0;
size_t rightSum = 0;
//to the left of center
for (RAIter i = center; i > first; i--)
{
sum += *i;
if(sum > leftSum)
{
leftSum = sum;
}
}
//to right of center
sum = 0;
for (RAIter j = (center + 1); j < last; j++)
{
sum += *j;
if (sum > rightSum)
{
rightSum = sum;
}
}
//return the sums from mid
return leftSum + rightSum;
}
//main function to call
template<typename RAIter>
int gcs(RAIter first, RAIter last)
{
size_t size = distance(first, last);
//base case is when the subarray only has 1 element. when first == last
if (first == last || size == 1)
{
if (size < 1)
{
return 0;
}
if (*first < 0)
{
return 0;
}
return *first;
}
//middle point
RAIter center = first + (size/2);
//return max of leftsum, rightsum, and midsum
return max(gcs(first, center),
gcs(center + 1, last),
gcsMid(first, center, last));
}

You have two problems with your code:
A. This loop:
for (RAIter i = center; i > first; i--)
does not include first in the loop. The reference algorithm does. You can't just use >= as the reference algorithm does as it doesn't work for iterators. Either add an extra bit of code to check first at the end, or change your loop so it somehow includes first (maybe a do while loop would suit better).
B. These definitions:
size_t sum = 0;
size_t leftSum = 0;
size_t rightSum = 0;
should not be size_t as size_t is unsigned. This means that when the sum goes negative, checks like if(sum > leftSum) no longer work, as the negative value (which underflows) is bigger than the positive value.
Change them to int.
The best way to find these kinds of errors is to run the code through a debugger. You can then step through each line of your code and see what the variable values are. This makes it easy to spot things like negative numbers becoming large positive numbers as above.

Related

Find the kth smallest element in an unsorted array of non-negative integers

Not allowed to modify the array ( The array is read only ).
Using constant extra space is allowed.
ex:
A : [2 1 4 3 2]
k : 3
answer : 2
I did it below way. The answer is correct but need to be more memory efficient.
void insert_sorted(vector<int> &B, int a,int k)
{
for(int i=0;i<k;i++)
{
if(B[i]>=a)
{
for(int j=k-1;j>i;j--)
B[j]=B[j-1];
B[i]=a;
return;
}
}
}
int Solution::kthsmallest(const vector<int> &A, int k) {
vector <int> B;
for(int i=0;i<k;i++)
{
B.push_back(INT_MAX);
}
int l=A.size();
for(int i=0;i<l;i++)
{
if(B[k-1]>=A[i])
insert_sorted(B,A[i],k);
}
return B[k-1];
}
One possible solution is binary search.
Let A be the input array; we want to find a number b such that exactly k items in A are smaller than b.
Obviously, b must be inside the range [0, max(A)].
And we do binary search starting with this range.
Suppose we are searching within range [lo, hi].
Let c = (lo + hi)/2 which is the middle pivot.
There are three cases:
number of items in A less than c are less than k.
In this case the number we search for should be larger than c, so it should be in range (c, hi]
number of items in A less than c are larger than k.
Similarly, the number we search for is in range [lo, c)
number of items in A less than c equals to k.
In this case, the answer is the minimum element in A that is greater than or equals to c. This can be find by doing a linear search in A again
The complexity is O(n log m), where m is the max element in A.
/* assume k is 0 based, i.e. 0 <= k < n */
int kth_element(const vector<int> &A, int k){
int lo = 0, hi = *max_element(A.begin(), A.end());
while (lo <= hi){
int mid = (lo + hi) / 2;
int rank_lo = count_if(A.begin(), A.end(), [=](int i){ return i < mid;});
int rank_hi = count_if(A.begin(), A.end(), [=](int i){ return i <= mid;});
if (rank_lo <= k && k < rank_hi)
return mid;
if (k >= rank_hi)
lo = mid + 1;
else
hi = mid - 1;
}
}
Although it's not the answer to this particular problem (as it requires a modifiable collection), there is a function called std::nth_element, which rearranges the elements so that the kth element is at position k, and all elements at positions less than k are smaller than or equal to the kth element, where k is a input parameter.
The question does not ask for any time constraints. An O(nk) solution is fairly simple, by iterating the array k times (at most), and discarding one element (and its duplicates) each time.
int FindKthSmallesr(const std::vector<int>& v, int k) {
// assuming INT_MIN cannot be a value. Could be relaxed by an extra iteration.
int last_min = INT_MIN;
while (k > 0) {
int current_min = INT_MAX;
for (int x : v) {
if (x <= last_min) continue;
current_min = std::min(current_min, x);
}
last_min = current_min;
for (int x : v) {
if (x == current_min) k--;
}
}
return last_min;
}
Code on ideone: http://ideone.com/RjRIkM
If only constant extra space is allowed, we can use a simple O(n*k) algorithm.
int kth_smallest(const vector<int>& v, int k) {
int curmin = -1;
int order = -1;
while (order < k) { // while kth element wasn't reached
curmin = *min_element(v.begin(), v.end(), [curmin](int a, int b) {
if (a <= curmin) return false;
if (b <= curmin) return true;
return a < b;
}); // find minimal number among not counted yet
order += count(v.begin(), v.end(), curmin); // count all 'minimal' numbers
}
return curmin;
}
online version to play with: http://ideone.com/KNMYxA

Improving the performance of this search?

Is there way to do the following search using a faster way? The items on A array are sorted in DESC order.
int find_pos(int A[], int value, int items_no, bool only_exact_match)
{
for(int i = 0; i < items_no; i++)
if(value == A[i] || (value > A[i] && !only_exact_match))
return i;
return -1;
}
You can use std::lower_bound algorithm in your case. It performs binary search with O(log N), as other people wrote. It will be something like this:
int find_pos(int A[], int value, int items_no, bool only_exact_match)
{
const int *pos_ptr = std::lower_bound(A, A + items_no, value, std::greater<int>());
const ptrdiff_t pos = pos_ptr - A;
if (pos >= items_no)
return -1;
if (*pos_ptr != value && only_exact_match)
return -1;
return pos;
}
A binary search
int left = 0;
int right = items_no; // Exclusive
while (left < right) {
int mid = (left + right) / 2;
if (value == A[mid])
return mid;
if (value < A[mid]) {
left = mid + 1;
} else {
right = mid;
}
}
return only_exact_match ? -1 : right - 1; // The greater
Because your array is sorted, you can search in steps, akin to a bisection. First, check the midpoint against your value. If it's equal, you have your answer. If it's greater, your value is in the lower half of the array. If not, your value is on the upper half. Repeat this process by bisecting the remaining elements of the array until you find your value, or run out of elements. As for your second if clause, if no matching value is found, the closest smaller element is element i+1, if that exists (i.e. you are not at the end of the array).

Binary search for finding the lowest and largest element in a sorted array than a given value?

So, I was trying to implement the binary search algorithm (as generic as possible which can adapt to different cases). I searched for this on the internet, and some use, while (low != high) and some use, while (low <= high) and some other different condition which is very confusing.
Hence, I started writing the code for finding the first element which is greater than a given element. I wish to know if there is a more elegant solution than this?
Main code:
#include <iostream>
#include <map>
#include <vector>
#include <string>
#include <utility>
#include <algorithm>
#include <stack>
#include <queue>
#include <climits>
#include <set>
#include <cstring>
using namespace std;
int arr1[2000];
int n;
int main (void)
{
int val1,val2;
cin>>n;
for (int i = 0; i < n; i++)
cin>>arr1[i];
sort(arr1,arr1+n);
cout<<"Enter the value for which next greater element than this value is to be found";
cin>>val1;
cout<<"Enter the value for which the first element smaller than this value is to be found";
cin>>val2;
int ans1 = binarysearch1(val1);
int ans2 = binarysearch2(val2);
cout<<ans1<<"\n"<<ans2<<"\n";
return 0;
}
int binarysearch1(int val)
{
while (start <= end)
{
int mid = start + (end-start)/2;
if (arr[mid] <= val && arr[mid+1] > val)
return mid+1;
else if (arr[mid] > val)
end = mid-1;
else
start = mid+1;
}
}
Similarly, for finding the first element which is smaller than the given element,
int binarysearch2(int val)
{
while (start <= end)
{
int mid = start + (end-start)/2;
if (arr[mid] >= val && arr[mid] < val)
return mid+1;
else if (arr[mid] > val)
end = mid-1;
else
start = mid+1;
}
}
I often get super confused when I have to modify binary search for such abstraction. Please let me know if there is simpler method for the same? Thanks!
As you say, there are different ways to express the end condition for binary search and it completely depends on what your two limits mean. Let me explain mine, which I think it's quite simple to understand and it lets you modify it for other cases without thinking too much.
Let me call the two limits first and last. We want to find the first element greater than a certain x. The following invariant will hold all the time:
Every element past last is greater than x and every element before
first is smaller or equal (the opposite case).
Notice that the invariant doesn't say anything about the interval [first, last]. The only valid initialization of the limits without further knowledge of the vector is first = 0 and last = last position of the vector. This satisfies the condition as there's nothing after last and nothing before first, so everything is right.
As the interval [first, last] is unknown, we will have to proceed until it's empty, updating the limits in consequence.
int get_first_greater(const std::vector<int>& v, int x)
{
int first = 0, last = int(v.size()) - 1;
while (first <= last)
{
int mid = (first + last) / 2;
if (v[mid] > x)
last = mid - 1;
else
first = mid + 1;
}
return last + 1 == v.size() ? -1 : last + 1;
}
As you can see, we only need two cases, so the code is very simple. At every check, we update the limits to always keep our invariant true.
When the loop ends, using the invariant we know that last + 1 is greater than x if it exists, so we only have to check if we're still inside our vector or not.
With this in mind, you can modify the binary search as you want. Let's change it to find the last smaller than x. We change the invariant:
Every element before first is smaller than x and every element
after last is greater or equal than x.
With that, modifying the code is really easy:
int get_last_smaller(const std::vector<int>& v, int x)
{
int first = 0, last = int(v.size()) - 1;
while (first <= last)
{
int mid = (first + last) / 2;
if (v[mid] >= x)
last = mid - 1;
else
first = mid + 1;
}
return first - 1 < 0 ? -1 : first - 1;
}
Check that we only changed the operator (>= instead of >) and the return, using the same argument than before.
It is hard to write correct programs. And once a program has been verified to be correct, it should have to be modified rarely and reused more. In that line, given that you are using C++ and not C I would advise you to use the std C++ libraries to the fullest extent possible. Both features that you are looking for is given to you within algorithm.
http://en.cppreference.com/w/cpp/algorithm/lower_bound
http://en.cppreference.com/w/cpp/algorithm/upper_bound
does the magic for you, and given the awesome power of templates you should be able to use these methods by just adding other methods that would implement the ordering.
HTH.
To answer the question in part, it would be possible to factor out the actual comparison (using a callback function or similar), depending on whether the first element which is larger than the element is to be searched or the first element which is smaller. However, in the first code block, you use
arr[mid] <= val && arr[mid+1] > val
while in the second block, the index shift in the second condition
if (arr[mid] >= val && arr[mid] < val)
is omitted, which seems to be inconsistent.
Your search routines had some bugs [one was outright broken]. I've cleaned them up a bit, but I started from your code. Note: no guarantees--it's late here, but this should give you a starting point. Note the "lo/hi" is standard nomenclature (e.g. lo is your start and hi is your end). Also, note that hi/lo get set to mid and not mid+1 or mid-1
There are edge cases to contend with. The while loop has to be "<" or "mid+1" will run past the end of the array.
int
binarysearch_larger(const int *arr,int cnt,int val)
// arr -- array to search
// cnt -- number of elements in array
// val -- desired value to be searched for
{
int mid;
int lo;
int hi;
int match;
lo = 0;
hi = cnt - 1;
match = -1;
while (lo < hi) {
mid = (hi + lo) / 2;
if (arr[mid] <= val) && (arr[mid+1] > val)) {
if ((mid + 1) < cnt)
match = mid + 1;
break;
}
if (arr[mid] > val)
hi = mid;
else
lo = mid;
}
return match;
}
int
binarysearch_smaller(const int *arr,int cnt,int val)
// arr -- array to search
// cnt -- number of elements in array
// val -- desired value to be searched for
{
int mid;
int lo;
int hi;
int match;
lo = 0;
hi = cnt - 1;
match = -1;
while (lo < hi) {
mid = (hi + lo) / 2;
if (arr[mid] <= val) && (arr[mid+1] > val)) {
match = mid;
break;
}
if (arr[mid] > val)
hi = mid;
else
lo = mid;
}
// the condition here could be "<=" or "<" as you prefer
if ((match < 0) && (arr[cnt - 1] <= val))
match = cnt - 1;
return match;
}
Below is a generic algorithm that given a sorted range of elements and a value, it returns a pair of iterators, where the value of the first iterator is the first element in the sorted range that compares smaller than the entered value, and the value of the second iterator is the first element in that range that compares greater than the entered value.
If the pair of the returned iterators points to the end of the range it means that entered range was empty.
I've made it as generic as I could and it also handles marginal cases and duplicates.
template<typename BidirectionalIterator>
std::pair<BidirectionalIterator, BidirectionalIterator>
lowhigh(BidirectionalIterator first, BidirectionalIterator last,
typename std::iterator_traits<BidirectionalIterator>::value_type const &val) {
if(first != last) {
auto low = std::lower_bound(first, last, val);
if(low == last) {
--last;
return std::make_pair(last, last);
} else if(low == first) {
if(first != last - 1) {
return std::make_pair(first, std::upper_bound(low, last - 1, val) + 1);
} else {
return std::make_pair(first, first);
}
} else {
auto up = std::upper_bound(low, last, val);
return (up == last)? std::make_pair(low - 1, up - 1) : std::make_pair(low - 1, up);
}
}
return std::make_pair(last, last);
}
LIVE DEMO

How to get the equilibrium index of an array in O(n)?

I have done a test in C++ asking for a function that returns one of the indices that splits the input vector in 2 parts having the same sum of the elements, for eg: for the vec = {1, 2, 3, 5, 4, -1, 1, 1, 2, -1}, it may return 3, because 1+2+3 = 6 = 4-1+1+1+2-1. So I have done the function that returns the correct answer:
int func(const std::vector< int >& vecIn)
{
for (std::size_t p = 0; p < vecin.size(); p++)
{
if (std::accumulator(vecIn.begin(), vecIn.begin() + p, 0) ==
std::accumulator(vecIn.begin() + p + 1, vecIn.end(), 0))
return p;
}
return -1;
}
My problem was when the input was a very long vector containing just 1 (or -1), the return of the function was slow. So I have thought of starting the search for the wanted index from middle, and then go left and right. But the best approach I suppose is the one where the index is in the merge-sort algorithm order, that means: n/2, n/4, 3n/4, n/8, 3n/8, 5n/8, 7n/8... where n is the size of the vector. Is there a way to write this order in a formula, so I can apply it in my function?
Thanks
EDIT
After some comments I have to mention that I had done the test a few days ago, so I have forgot to put and mention the part of no solution: it should return -1... I have updated also the question title.
Specifically for this problem, I would use the following algorithm:
Compute the total sum of the vector. This gives two sums (empty vector, and full vector)
for each element in order, move one element from full to empty, which means adding the value of next element from sum(full) to sum(empty). When the two sums are equal, you have found your index.
This give a o(n) algorithm instead of o(n2)
You can solve the problem much faster without calling std::accumulator at each step:
int func(const std::vector< int >& vecIn)
{
int s1 = 0;
int s2 = std::accumulator(vecIn.begin(), vecIn.end(), 0);
for (std::size_t p = 0; p < vecin.size(); p++)
{
if (s1 == s2)
return p;
s1 += vecIn[p];
s2 -= vecIn[p];
}
}
This is O(n). At each step, s1 will contain the sum of the first p elements, and s2 the sum of the rest. You can update both of them with an addition and a subtraction when moving to the next element.
Since std::accumulator needs to iterate over the range you give it, your algorithm was O(n^2), which is why it was so slow for many elements.
To answer the actual question: Your sequence n/2, n/4, 3n/5, n/8, 3n/8 can be rewritten as
1*n/2
1*n/4 3*n/4
1*n/8 3*n/8 5*n/8 7*n/8
...
that is to say, the denominator runs from i=2 up in powers of 2, and the nominator runs from j=1 to i-1 in steps of 2. However, this is not what you need for your actual problem, because the example you give has n=10. Clearly you don't want n/4 there - your indices have to be integer.
The best solution here is to recurse. Given a range [b,e], pick a value middle (b+e/2) and set the new ranges to [b, (b+e/2)-1] and [(b+e/2)=1, e]. Of course, specialize ranges with length 1 or 2.
Considering MSalters comments, I'm afraid another solution would be better. If you want to use less memory, maybe the selected answer is good enough, but to find the possibly multiple solutions you could use the following code:
static const int arr[] = {5,-10,10,-10,10,1,1,1,1,1};
std::vector<int> vec (arr, arr + sizeof(arr) / sizeof(arr[0]) );
// compute cumulative sum
std::vector<int> cumulative_sum( vec.size() );
cumulative_sum[0] = vec[0];
for ( size_t i = 1; i < vec.size(); i++ )
{ cumulative_sum[i] = cumulative_sum[i-1] + vec[i]; }
const int complete_sum = cumulative_sum.back();
// find multiple solutions, if there are any
const int complete_sum_half = complete_sum / 2; // suggesting this is valid...
std::vector<int>::iterator it = cumulative_sum.begin();
std::vector<int> mid_indices;
do {
it = std::find( it, cumulative_sum.end(), complete_sum_half );
if ( it != cumulative_sum.end() )
{ mid_indices.push_back( it - cumulative_sum.begin() ); ++it; }
} while( it != cumulative_sum.end() );
for ( size_t i = 0; i < mid_indices.size(); i++ )
{ std::cout << mid_indices[i] << std::endl; }
std::cout << "Split behind these indices to obtain two equal halfs." << std::endl;
This way, you get all the possible solutions. If there is no solution to split the vector in two equal halfs, mid_indices will be left empty.
Again, you have to sum up each value only once.
My proposal is this:
static const int arr[] = {1,2,3,5,4,-1,1,1,2,-1};
std::vector<int> vec (arr, arr + sizeof(arr) / sizeof(arr[0]) );
int idx1(0), idx2(vec.size()-1);
int sum1(0), sum2(0);
int idxMid = -1;
do {
// fast access without using the index each time.
const int& val1 = vec[idx1];
const int& val2 = vec[idx2];
// Precompute the next (possible) sum values.
const int nSum1 = sum1 + val1;
const int nSum2 = sum2 + val2;
// move the index considering the balanace between the
// left and right sum.
if ( sum1 - nSum2 < sum2 - nSum1 )
{ sum1 = nSum1; idx1++; }
else
{ sum2 = nSum2; idx2--; }
if ( idx1 >= idx2 ){ idxMid = idx2; }
} while( idxMid < 0 && idx2 >= 0 && idx1 < vec.size() );
std::cout << idxMid << std::endl;
It does add every value only once no matter how many values. Such that it's complexity is only O(n) and not O(n^2).
The code simply runs from left and right simultanuously and moves the indices further if it's side is lower than the other.
You want nth term of the series you mentioned. Then it would be:
numerator: (n - 2^((int)(log2 n)) ) *2 + 1
denominator: 2^((int)(log2 n) + 1)
I came across the same question in Codility tests. There is a similar looking answer above (didn't pass some of the unit tests), but below code segment was successful in tests.
#include <vector>
#include <numeric>
#include <iostream>
using namespace std;
// Returns -1 if equilibrium point is not found
// use long long to support bigger ranges
int FindEquilibriumPoint(vector<long> &values) {
long long lower = 0;
long long upper = std::accumulate(values.begin(), values.end(), 0);
for (std::size_t i = 0; i < values.size(); i++) {
upper -= values[i];
if (lower == upper) {
return i;
}
lower += values[i];
}
return -1;
}
int main() {
vector<long> v = {-1, 3, -4, 5, 1, -6, 2, 1};
cout << "Equilibrium Point:" << FindEquilibriumPoint(v) << endl;
return 0;
}
Output
Equilibrium Point:1
Here it is the algorithm in Javascript:
function equi(arr){
var N = arr.length;
if (N == 0){ return -1};
var suma = 0;
for (var i=0; i<N; i++){
suma += arr[i];
}
var suma_iz = 0;
for(i=0; i<N; i++){
var suma_de = suma - suma_iz - arr[i];
if (suma_iz == suma_de){
return i};
suma_iz += arr[i];
}
return -1;
}
As you see this code satisfy the condition of O(n)

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
}