I want to find The number of ways to divide an array into 3 contiguous parts such that the sum of the three parts is equal
-10^9 <= A[i] <= 10^9
My approach:
Taking Input and Checking for Base Case:
for(int i=0;i<n;i++){
a[i]= in.nextLong();
sum+=a[i];
}
if(sum%3!=0)System.out.println("0");
If The answer is not above Then Forming the Prefix and Suffix Sum.
for(int i=1;i<=n-2;i++){
xx+=a[i-1];
if(xx==sum/3){
dp[i]=1;
}
}
Suffix Sum and Updating the Binary Index Tree:
for(int i=n ;i>=3;i--){
xx+=a[i-1];
if(xx==sum/3){
update(i, 1, suffix);
}
}
And Now simple Looping the array to find the Total Ways:
int ans=0;
for(int i=1;i<=n-2;i++){
if(dp[i]==1)
{
ans+= (query(n, suffix) - query(i+1, suffix));
// Checking For the Sum/3 in array where index>i+1
}
}
I Getting the wrong answer for the above approachI don't Know where I have made mistake Please Help to Correct my mistake.
Update and Query Function:
public static void update(int i , int value , int[] arr){
while(i<arr.length){
arr[i]+=value;
i+=i&-i;
}
}
public static int query(int i ,int[] arr){
int ans=0;
while(i>0){
ans+=arr[i];
i-=i&-i;
}
return ans;
}
As far as your approach is concerned its correct. But there are some points because of which it might give WA
Its very likely that sum overflows int as each element can magnitude of 10^9, so use long long .
Make sure that suffix and dp array are initialized to 0.
Having said that using a BIT tree here is an overkill , because it can be done in O(n) compared to your O(nlogn) solution ( but does not matter if incase you are submitting on a online judge ).
For the O(n) approach just take your suffix[] array.And as you have done mark suffix[i]=1 if sum from i to n is sum/3, traversing the array backwards this can be done in O(n).
Then just traverse again from backwards doing suffix[i]+=suffix[i-1]( apart from base case i=n).So now suffix[i] stores number of indexs i<=j<=n such that sum from index j to n is sum/3, which is what you are trying to achieve using BIT.
So what I suggest either write a bruteforce or this simple O(n) and check your code against it,
because as far as your approach is concerned it is correct, and debugging is something not suited for
stackoverflow.
First, we calculate an array dp, with dp[i] = sum from 0 to i, this can be done in O(n)
long[]dp = new long[n];
for(int i = 0; i < n; i++)
dp[i] = a[i];
if(i > 0)
dp[i] += dp[i - 1];
Second, let say the total sum of array is x, so we need to find at which position, we have dp[i] == x/3;
For each i position which have dp[i] == 2*x/3, we need to add to final result, the number of index j < i, which dp[j] == x/3.
int count = 0;
int result = 0;
for(int i = 0; i < n - 1; i++){
if(dp[i] == x/3)
count++;
else if(dp[i] == x*2/3)
result += count;
}
The answer is in result.
What wrong with your approach is,
if(dp[i]==1)
{
ans+= (query(n, suffix) - query(i+1, suffix));
// Checking For the Sum/3 in array where index>i+1
}
This is wrong, it should be
(query(n, suffix) - query(i, suffix));
Because, we only need to remove those from 1 to i, not 1 to i + 1.
Not only that, this part:
for(int i=1;i<=n-2;i++){
//....
}
Should be i <= n - 1;
Similarly, this part, for(int i=n ;i>=3;i--), should be i >= 1
And first part:
for(int i=0;i<n;i++){
a[i]= in.nextLong();
sum+=a[i];
}
Should be
for(int i=1;i<=n;i++){
a[i]= in.nextLong();
sum+=a[i];
}
A lot of small errors in your code, which you need to put in a lot of effort to debugging first, jumping to ask here is not a good idea.
In the question asked we need to find three contiguous parts in an array whose sum is the same.
I will mention the steps along with the code snippet that will solve the problem for you.
Get the sum of the array by doing a linear scan O(n) and compute sum/3.
Start scanning the given array from the end. At each index we need to store the number of ways we can get a sum equal to (sum/3) i.e. if end[i] is 3, then there are 3 subsets in the array starting from index i till n(array range) where sum is sum/3.
Third and final step is to start scanning from the start and find the index where sum is sum/3. On finding the index add to the solution variable(initiated to zero), end[i+2].
The thing here we are doing is, start traversing the array from start till len(array)-3. On finding the sum, sum/3, on let say index i, we have the first half that we require.
Now, dont care about the second half and add to the solution variable(initiated to zero) a value equal to end[i+2]. end[i+2] tells us the total number of ways starting from i+2 till the end, to get a sum equal to sum/3 for the third part.
Here, what we have done is taken care of the first and the third part, doing which we have also taken care of the second part which will be by default equal to sum/3. Our solution variable will be the final answer to the problem.
Given below are the code snippets for better understanding of the above mentioned algorithm::-
Here we are doing the backward scanning to store the number of ways to get sum/3 from the end for each index.
long long int *end = (long long int *)calloc(numbers, sizeof(long long int);
long long int temp = array[numbers-1];
if(temp==sum/3){
end[numbers-1] = 1;
}
for(i=numbers-2;i>=0;i--){
end[i] = end[i+1];
temp += array[i];
if(temp==sum/3){
end[i]++;
}
}
Once we have the end array we do the forward loop and get our final solution
long long int solution = 0;
temp = 0;
for(i=0;i<numbers-2;i++){
temp+= array[i];
if(temp==sum/3){
solution+=end[i+2];
}
}
solution stores the final answer i.e. the number of ways to split the array into three contiguous parts having equal sum.
Related
Is it possible to get the sum of an array using divide and conquer? I've tried it, but I always miss some numbers, or I calculate a number twice.
int[] arr = new int[]{1,2,3,4,5};
public int sum(int[] arr) {
int begin = 0;
int end = array.length - 1;
int counter = 0;
while (begin <= end) {
int mid = (begin + end) / 2;
counter += arr[end] + arr[mid];
end = mid - 1;
}
return counter;
}
Of course, Diveide-and-conquer computation of the array's sum is possible. But I cannot see a UseCase for it? You're still computing at least the same amount of sums, you're still running into issues if the sum of arrayis greater than Integer.MAX_VALUE, ...
There is also no performance benefit like Codor showed in his answer to a related question.
Starting with Java 8 you can compute the sum in 1 line using streams.
int sum = Arrays.stream(array).sum();
The main flaw with your above code is the fact that you're only summing up index mid(2) and end(4). After that you skip to the lower bracket (index mid = 0 and end = 2). So you're missing index 3. This problem will become even more prevelant with larger arrays because you're skipping even more indices after the while's first iteration.
A quick Google search brought up this nice-looking talk about the general Divide-and-Conquer principle using a mechanism called Recursion. Maybe it can guide you to a working solution.
I'm studying sorting algorithms, including selection sort, so i decided to write a method and it works fine, but when i checked the book it had 2 variables so i checked it and found that it's using a variable to store the current index and the other as temporary to swap
while mine had only the temporary variable that also stored the initial value in the index as the lowest, then compared it to the other values in the array and swapped if a larger value was found.
Here's my code:
public static void selectionSort(int[] arr){
int lowest;
for(int i = 0; i < arr.length - 1; i++){
lowest = arr[i];
for(int j = i+1; j<arr.length; j++){
if(arr[j]<lowest){
lowest = arr[j];
arr[j] = arr[i];
arr[i] = lowest;
}
}
}
}
and Here's the book's
public static void selectionSort(int[] list){
int min;
int temp;
for(int i = 0; i < list.length - 1; i++) {
min = i;
for(int j = i + 1; j < list.length; j++)
if( list[j] < list[min] )
min = j;
temp = list[i];
list[i] = list[min];
list[min] = temp;
}
}
so I looked on the web and all of them follow the same way as the book, so is my code bad or slower, or it is not considered Selection sort ?
sorry for any english mistakes :P
So, the original code works like this:
Start with the first element of the array
Find the smallest number in the array
Swap the two numbers
Repeat the process Until You reach the end of the list
While Yours does this:
Start with the first element of the array
For each element smaller than the current Swap the two numbers
Replace the lowest number with the swapped
Repeat the process
The result should be the same, but probably You are swapping more numbers than the first one. This probably will make it a little bit slower than the original.
Actually it kinda looks like the insertion sort now, so basically You are swapping the elements with all that are bigger than the one You have.
It looks like you're doing a lot more swaps in the nested for loop.
What happens if you do a sort of [4,3,2,1]? I think you'll have more swap operations than the actual selectionSort.
I'm not sure if your code is bad or slower.
SelectionSort is known to be correct, but it isn't fast either.
I got this interview question and I am still very confused about it.
The question was as the title suggest, i'll explain.
You are given a random creation function to use.
the function input is an integer n. let's say I call it with 3.
it should give me a permutation of the numbers from 1 - 3. so for example it will give me 2, 3 , 1.
after i call the function again, it won't give me the same permutation, now it will give me 1, 2, 3 for example.
Now if i will call it with n = 4. I may get 1,4,3,2.
Calling it with 3 again will not output 2,3,1 nor 1,2,3 as was outputed before, it will give me a different permutation out of the 3! possible permutations.
I was confused about this question there and I still am now. How is this possible within normal running time ? As I see it, there has to be some static variable that remembers what was called before or after the function finishes executing.
So my thought is creating a static hashtable (key,value) that gets the input as key and the value is an array of the length of the n!.
Then we use the random method to output a random instance out of these and move this instance to the back, so it will not be called again, thus keeping the output unique.
The space time complexity seems huge to me.
Am I missing something in this question ?
Jonathan Rosenne's answer was downvoted because it was link-only, but it is still the right answer in my opinion, being that this is such a well-known problem. You can also see a minimal explanation in wikipedia: https://en.wikipedia.org/wiki/Permutation#Generation_in_lexicographic_order.
To address your space-complexity concern, generating permutations in lexicographical ordering has O(1) space complexity, you don't need to store nothing other than the current permutation. The algorithm is quite simple, but most of all, its correctness is quite intuitive. Imagine you had the set of all permutations and you order them lexicographically. Advancing to the next in order and then cycling back will give you the maximum cycle without repetitions. The problem with that is again the space-complexity, since you would need to store all possible permutations; the algorithm gives you a way to get the next permutation without storing anything. It may take a while to understand, but once I got it it was quite enlightening.
You can store a static variable as a seed for the next permutation
In this case, we can change which slot each number will be put in with an int (for example this is hard coded to sets of 4 numbers)
private static int seed = 0;
public static int[] generate()
{
//s is a copy of seed, and increment seed for the next generation
int s = seed++ & 0x7FFFFFFF; //ensure s is positive
int[] out = new int[4];
//place 4-2
for(int i = out.length; i > 1; i--)
{
int pos = s % i;
s /= i;
for(int j = 0; j < out.length; j++)
if(out[j] == 0)
if(pos-- == 0)
{
out[j] = i;
break;
}
}
//place 1 in the last spot open
for(int i = 0; i < out.length; i++)
if(out[i] == 0)
{
out[i] = 1;
break;
}
return out;
}
Here's a version that takes the size as an input, and uses a HashMap to store the seeds
private static Map<Integer, Integer> seeds = new HashMap<Integer, Integer>();
public static int[] generate(int size)
{
//s is a copy of seed, and increment seed for the next generation
int s = seeds.containsKey(size) ? seeds.get(size) : 0; //can replace 0 with a Math.random() call to seed randomly
seeds.put(size, s + 1);
s &= 0x7FFFFFFF; //ensure s is positive
int[] out = new int[size];
//place numbers 2+
for(int i = out.length; i > 1; i--)
{
int pos = s % i;
s /= i;
for(int j = 0; j < out.length; j++)
if(out[j] == 0)
if(pos-- == 0)
{
out[j] = i;
break;
}
}
//place 1 in the last spot open
for(int i = 0; i < out.length; i++)
if(out[i] == 0)
{
out[i] = 1;
break;
}
return out;
}
This method works because the seed stores the locations of each element to be placed
For size 4:
Get the lowest digit in base 4, since there are 4 slots remaining
Place a 4 in that slot
Shift the number to remove the data used (divide by 4)
Get the lowest digit in base 3, since there are 3 slots remaining
Place a 3 in that slot
Shift the number to remove the data used (divide by 3)
Get the lowest digit in base 2, since there are 2 slots remaining
Place a 2 in that slot
Shift the number to remove the data used (divide by 2)
There is only one slot remaining
Place a 1 in that slot
This method is expandable up to 12! for ints, 13! overflows, or 20! for longs (21! overflows)
If you need to use bigger numbers, you may be able to replace the seeds with BigIntegers
pretty simple question:
Given an array, find all subsets which sum to value k
I am trying to do this in Java and seem to have found a solution which solves it in O(n^2) time. Is this solution a correct O(n^2) implementation?
#Test
public void testFindAllSubsets() {
int[] array = {4,6,1,6,2,1,7};
int k=7;
// here the algorithm starts
for(int i = 0; i < array.length;i++){
// now work backwords
int sum = array[i];
List<Integer> subset = new ArrayList<Integer>();
subset.add(array[i]);
for(int j = array.length -1; j > i && sum < k; j--){
int newSum = sum + array[j];
// if the sum is greater, than ditch this subset
if(newSum <= k){
subset.add(array[j]);
sum = newSum;
}
}
// we won't always find a subset, but if we do print it out
if(sum == k){
System.out.print("{");
System.out.print(subset.get(0));
for(int l = 1; l < subset.size(); l++){
System.out.print(","+subset.get(l));
}
System.out.print("}");
System.out.println();
}
}
}
I have tried it with various examples and have not found any that seem to break it. However, when I have searched online, this does not appear to be the common solution to the problem, and many solution claim this problem is O(2^n).
P.S.
This is not a homework question, I'm a brogrammer with a job trying to work on my Comp Sci fundamentals in Java. Thanks!
No this is not correct.Take this simple example
Your array is 4,6,1,2,3,1 and target sum is 7 then in your logic it
would only find (4,3) (6,1) (1,2,3,1) your code would miss (4,2,1), (4,3).
I would refer to go through wiki
An elegant solution is to simply think of a subset as each member answering the question "Am I in or not?" So essentially each can answer yes/no, so you have 2N subsets(including the empty subset). The most natural way to code this up is to recurse through each element and do one of the following:
Pick it
Skip it
Thus the time complexity is O(2N) simply because you have so many answers possible in the worst case.
The problem in question can be found at http://projecteuler.net/problem=14
I'm trying what I think is a novel solution. At least it is not brute-force. My solution works on two assumptions:
1) The less times you have iterate through the sequence, the quicker you'll get the answer. 2) A sequence will necessarily be longer than the sequences of each of its elements
So I implemented an array of all possible numbers that could appear in the sequence. The highest number starting a sequence is 999999 (as the problem only asks you to test numbers less than 1,000,000); therefore the highest possible number in any sequence is 3 * 999999 + 1 = 2999998 (which is even, so would then be divided by 2 for the next number in the sequence). So the array need only be of this size. (In my code the array is actually 2999999 elements, as I have included 0 so that each number matches its array index. However, this isn't necessary, it is for comprehension).
So once a number comes in a sequence, its value in the array becomes 0. If subsequent sequences reach this value, they will know not to proceed any further, as it is assumed they will be longer.
However, when i run the code I get the following error, at the line introducing the "wh:
Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: 3188644
For some reason it is trying to access an index of the above value, which shouldn't be reachable as it is over the possible max of 29999999. Can anyone understand why this is happening?
Please note that I have no idea if my assumptions are actually sound. I'm an amateur programmer and not a mathematician. I'm experimenting. Hopefully I'll find out whether it works as soon as I get the indexing correct.
Code is as follows:
private static final int MAX_START = 999999;
private static final int MAX_POSSIBLE = 3 * MAX_START + 1;
public long calculate()
{
int[] numbers = new int[MAX_POSSIBLE + 1];
for(int index = 0; index <= MAX_POSSIBLE; index++)
{
numbers[index] = index;
}
int longestChainStart = 0;
for(int index = 1; index <= numbers.length; index++)
{
int currentValue = index;
if(numbers[currentValue] != 0)
{
longestChainStart = currentValue;
while(numbers[currentValue] != 0 && currentValue != 1)
{
numbers[currentValue] = 0;
if(currentValue % 2 == 0)
{
currentValue /= 2;
}
else
{
currentValue = 3 * currentValue + 1;
}
}
}
}
return longestChainStart;
}
Given that you can't (easily) put a limit on the possible maximum number of a sequence, you might want to try a different approach. I might suggest something based on memoization.
Suppose you've got an array of size 1,000,000. Each entry i will represent the length of the sequence from i to 1. Remember, you don't need the sequences themselves, but rather, only the length of the sequences. You can start filling in your table at 1---the length is 0. Starting at 2, you've got length 1, and so on. Now, say we're looking at entry n, which is even. You can look at the length of the sequence at entry n/2 and just add 1 to that for the value at n. If you haven't calculated n/2 yet, just do the normal calculations until you get to a value you have calculated. A similar process holds if n is odd.
This should bring your algorithm's running time down significantly, and prevent any problems with out-of-bounds errors.
You can solve this by this way
import java.util.LinkedList;
public class Problem14 {
public static void main(String[] args) {
LinkedList<Long> list = new LinkedList<Long>();
long length =0;
int res =0;
for(int j=10; j<1000000; j++)
{
long i=j;
while(i!=1)
{
if(i%2==0)
{
i =i/2;
list.add(i);
}
else
{
i =3*i+1;
list.add(i);
}
}
if(list.size()>length)
{
length =list.size();
res=j;
}
list.clear();
}
System.out.println(res+ " highest nuber and its length " + length);
}}