Develop Reference

Find consecutive range in a given list with limit

Find the maximum consecutive elements matching the given condition.
I have a list of numbers called A, another list called B and a limit called Limit.
The task is find the maximum k consecutive elements in A such that they satisfy below condition.
Max(B[i],B[i+1],...B[i+k]) + Sum(A[i], A[i+1], ..., A[i+k]) * k ≤ Limit
Example:
A = [2,1,3,4,5]
B = [3,6,1,3,4]
Limit = 25
Take 2 consecutive elements:
Highest sum occurs with elements in A = 4,5. The corresponding max in B is Max(3,4) = 4.
So value = 4 + (4+5) * 2 = 22. Here 22 ≤ 25, so 2 consecutive is possible
Take 3 consecutive elements:
Taking sum for 1st 3 elements of A = 2,1,3. The corresponding max in B is Max(3,6,1) = 6.
So value = 6 + (2+1+3) * 3 = 24. Here 24 ≤ 25, so 3 consecutive is possible
Take 4 consecutive elements:
Taking sum for 1st 4 elements of A = 2,1,3,4. The corresponding max in B is Max(3,6,1,3) = 6.
So value = 6 + (2+1+3+4) * 4 = 46. Here 46 > 25, so 4 consecutive is not possible
So correct answer to this input is 3.
Constraints:
n (Size of A) is up to 10⁵, A elements up to 10¹⁴, B elements up to 10⁹, Limit up to 10¹⁴.
Here is my code:
public int getMax(List<Integer> A, List<Integer> B, long limit) {
int result = 0;
int n = A.size();
for(int len=1; len<=n; len++) {
for(int i=0; i<=n-len; i++) {
int j=i+len-1;
int max = B.get(i);
long total = 0;
for(int k=i; k<=j; k++) {
total += A.get(k);
max = Math.max(max, B.get(k));
}
total = max + total * len;
if(total < limit) {
result = len;
break;
}
}
}
return result;
}
This code works for smaller range of inputs.
But fails with a time out for larger inputs. How can I reduce time complexity of this code?
Updated:
Updated code based on dratenik answer, but the sample test case mentioned in my post itself is failing. The program is returning 4 instead of 3.
public int getMax(List<Integer> A, List<Integer> B, long limit) {
int from = 0, to = 0, max = -1;
int n = A.size();
for (; from < n;) {
int total = 0;
int m = B.get(from); // updated here
for (int i = from; i < to; i++) {
total += A.get(i); // updated here
m = Math.max(m, B.get(i)); // updated here
}
total = m + total * (to - from); // updated here
if (total <= limit && to - from + 1 > max) {
max = to - from + 1;
}
if (total < limit && to < n) { // below target, extend window
to++;
} else { // otherwise contract window
from++;
}
if (from > to) {
to = from;
}
}
return max;
}

Since all the elements of A and B are positive, you can solve this with the usual two-pointer approach to finding a maximum length subarray:
Initialize two pointers s and e to the start of the arrays, and then advance e as far as possible without violating the limit. This finds the longest valid subarray that starts at s.
While e isn't at the end of the arrays, advance s by one position, and then again advance e as far as possible without violating the limit. This finds the longest valid subarray that starts at every position. This leads to an O(n) algorithm, because e can advance monotonically.
Your answer is the longest valid sequence you see.
In order to determine in O(1) whether or not a particular range from s to e is valid, you need to track the cumulative sum of A elements and the current maximum of B elements.
The sum is easy -- just add elements that e passes and subtract elements that s passes.
To track the current maximum of elements in B, you can use the standard sliding-window-maximum algorithm described here: Sliding window maximum in O(n) time. It works just fine with expanding and contracting windows, maintaining O(1) amortized cost per operation.
Here's an O(n) solution in Java. Note that I multiplied the sum of A elements by the length of the sequence, because it's what you seem to intend, even though the formula you wrote multiplies by length-1:
public static int getMax(List<Integer> A, List<Integer> B, long limit) {
final int size = A.size();
// a Queue containing indexes of elements that may become max in the window
// they must be monotonically decreasing
final int maxQ[] = new int[size];
int maxQstart = 0, maxQend = 0;
// current valid window start and end
int s=0, e = 0;
int bestLen = 0;
long windowSum = 0;
while (s < size && e < size) {
// calculate longer window max
long nextMax = maxQstart < maxQend ? B.get(maxQ[maxQstart]) : 0;
nextMax = Math.max(nextMax, B.get(e));
long sumPart = (windowSum + A.get(e)) * (e+1-s);
if (nextMax + sumPart <= limit) {
// extending the window is valid
int lastB = B.get(e);
while (maxQstart < maxQend && B.get(maxQ[maxQend-1]) <= lastB) {
--maxQend;
}
maxQ[maxQend++] = e;
windowSum += A.get(e);
++e;
if (e-s > bestLen) {
bestLen = e-s;
}
} else if (e > s) {
// extending the window is invalid.
// move up the start instead
windowSum -= A.get(s);
++s;
while(maxQstart < maxQend && maxQ[maxQstart] < s) {
++maxQstart;
}
} else {
// we need to move the start up, but the window is empty, so move them both
++s;
++e;
}
}
return bestLen;
}

Sliding window approach? Slightly pseudocodey version:
int from=0, to=0, max = -1;
for(;from<n;) {
total = (target expression on elements between from-to inclusive)
if (total<=target && to-from+1 > max) {max = to-from+1;}
if (total<target && to<n) { // below target, extend window
to++;
} else { // otherwise contract window
from++;
}
if (from>to) {to=from;}
}
return max;
The sum could be updated incrementally, but I don't know how to sensibly update the max(B[i],B[i+1],...B[i+k]) part when contracting the window, so let's recompute the whole thing at each step.

I tried to use meanigful names to make the code readable. Don't hesitate to ask where it is not clear:
public int getMax(List<Integer> a, List<Integer> b, long limit) {
int max = -1;
int numberOfElements = 2;
boolean found;
do{
found = false;
for ( int index = 0; index <= a.size() - numberOfElements; index++) {
int totalA = 0;
int maxB = b.get(index);
for (int i = index; i < index + numberOfElements; i++) {
totalA += a.get(i);
maxB = Math.max(maxB,b.get(i)); // updated here
}
int total = maxB + totalA * numberOfElements;
if (total <= limit && numberOfElements >= max) {
max = numberOfElements;
found = true;
break;
}
}
numberOfElements++;
} while(found && numberOfElements <= a.size());
return max;
}
(more test cases can be helpful for further debugging)

I think the main obstacle there is how to efficiently track maximum over sliding window.
Easy optimization in this respect without diving into dynamic programming is to make use of MaxHeap.
In java it is implemented as PriorityQueue.
Please consider following code.
private int findMaxRange(List<Long> listA, List<Long> listB, long limit) {
int maxRange = 0;
while (maxRange < listA.size() && isRangePossible(listA, listB, limit, maxRange+1)) {
maxRange++;
}
return maxRange;
}
private boolean isRangePossible(List<Long> listA, List<Long> listB, long limit, int rangeSize) {
//calculate initial values of max and sum
PriorityQueue<Long> maxHeap = new PriorityQueue<>(rangeSize, Comparator.reverseOrder());
listB.stream().limit(rangeSize).forEach(maxHeap::add);
Long max = maxHeap.peek();
Long sum = listA.stream().limit(rangeSize).mapToLong(i->i).sum();
//iterate with sliding window
for (int i = 0; i < listA.size() - rangeSize; i++) {
if (isConditionMet(max, sum, rangeSize, limit)) {
return true;
}
sum = sum + listA.get(i+rangeSize) - listA.get(i);
maxHeap.remove(listB.get(i));
maxHeap.add(listB.get(i+rangeSize));
max = maxHeap.peek();
}
return isConditionMet(max, sum, rangeSize, limit);
}
private boolean isConditionMet(Long max, Long sum, int rangeSize, long limit) {
return max + sum * rangeSize < limit;
}
Also please pay attention to value ranges. Such big values can easily overflow long and may require specialized types like BigInteger. You should also consider how much memory is used by auxiliary datatypes.

The problem here seems to be use of three nested for loops for calculating max and sum for every window.
We can avoid this unnecessary iterations by using calculations of previous iteration in the new iteration with the help of Dynamic programming.
In my solution, I made 2 2d Arrays, one to store max values for each windows and other to store sums for each windows, storing values of previous iterations will greatly reduce the time complexity.
here is the Java code:
import java.util.*;
public class MyClass {
public static void main(String[] args) {
System.out.println("Hello, World!");
List A = Arrays.asList(2,1,3,4,5);
List B = Arrays.asList(3,6,1,3,4);
System.out.println(MyClass.getMax(A, B, 25L));
}
public static int getMax(List<Integer> A, List<Integer> B, long limit) {
int n = A.size();
int[][] dp1 = new int[n + 1][n + 1];
int[][] dp2 = new int[n + 1][n + 1];
for(int i = 1; i <= n; i++) {
for(int j = i; j <= n; j++) {
dp1[i][j] = Math.max(dp1[i - 1][j- 1], B.get(j - 1));
dp2[i][j] = dp2[i - 1][j- 1] + A.get(j - 1);
}
}
for(int i = 0; i <= n; i++) {
for(int j = 0; j <= n; j++) {
System.out.print("{" + dp1[i][j] + ", " + dp2[i][j] + "}, ");
}
System.out.println();
}
int kMax = 0;
for(int i = 0; i <= n; i++) {
for(int j = i; j <= n; j++) {
if(dp1[i][j] + dp2[i][j] * i <= limit) {
kMax = i;
}
}
}
System.out.println("Max K: " + kMax);
return 0;
}
}

if you are dependent only on algorithms and not making any app or game, it's not necessary that you have to use java, try using python or c++ (or even c, c#), python is used mostly for algorithms,
or if you need java only then add breakpoints or make the program to print all work it does (ask it to print j, i, k, result variables in console) then you can easily debug.

Find a pair of natural numbers who have the least energy among all pairs having sum of n

There is a natural number n. You have to find a pair of natural numbers x, y whose sum is n and also have the least energy among other pair having the sum n.
Energy(x) = sum of all digits of x
Total Energy = Energy(x) + Energy(y)
1 <= n <= 10^9
For eg,
n = 10000
A few pairs:
5000 + 5000 -> Energy = 10
1000 + 9000 -> Energy = 10
9999 + 1 -> Energy = 37
2999 + 7001 -> Energy = 37
So possible answers are:
(5000, 5000), (1000, 9000) etc
I have tried the solution noted above so far but it is not an optimized approach
I will loop from 1 to n-1 and and try all pairs and check their sum of digits but it will take too much time for big numbers.
e.g.
n= 50
1,49--> energy 14
2,48--> energy 14
3,47--> energy 14
4,46--> energy 14
5,45--> energy 14
.
.
.
.
10,40-->energy 5
(Edited) After some thought, I arrived at the following solution. Would appreciate if somebody can come up with a better solution
public int sum(int n) {
String s = String.valueOf(n);
if (isNonZeroOnlyOne(n)) {
int num = getNonZeroNo(n);
if (num == 1)
return 10;
return num;
}
return calculateEnergy(s);
}
private int calculateEnergy(String s) {
int sum = 0;
for(int i=0; i<s.length(); i++)
sum += s.charAt(i) - '0';
return sum;
}
private int getNonZeroNo(int n) {
String s = String.valueOf(n);
for(int i=0; i<s.length(); i++) {
char c = s.charAt(i);
if (c != '0')
return c-'0';
}
return '0';
}
private boolean isNonZeroOnlyOne(int n) {
String s = String.valueOf(n);
int count = 0;
for(int i=0; i<s.length(); i++) {
char c = s.charAt(i);
if (c != '0')
count++;
if (count > 1)
return false;
}
return true;
}

It's simple.
if n is of type 10^x then the answer is 10. otherwise answer is the sum of digits of n.
The idea here is to break down the number into a pair containing digits less than that are present in n. if you break down into smaller digits then sum remains the same as the original number.
example for 7= 1-6,2-5,3-4.
for a number like 100, 1000....
digit 1 can't be broken down into further pairs, so we try to make 10 as the sum of digit so that the sum becomes n.
like for 10=5-5,2-8,3-7
100=20-80,40-60
for other numbers, like 123
it can be broken into 100-23, 120-3, 111-12... all will give you sum 6. which is the sum of digits of the original number.
if you try to break down into further pairs like 80-43, 52-71, you will see that the digit sum increases as you broken down to a number containing digits which is higher than those are present in n. like 8 4,5,7 are greater than 3.

The least energy can be derived by a simple formula.
1) Given N > 100, the pair can be N-100 and 100 , and the energy will be same as the energy of N.
eg : N = 500 ; Pair = 400 and 100 ; Energy = 5
2) N >=10 and N <=100 , pair = N-10 and 10
eg : N = 50 ; Pair = 40 and 10 ; Energy = 5
3) N >=2 and N <=10 , pair = N-1 and 1
eg : N = 5 ; Pair = 4 and 1 ; Energy = 5

I spent more than 1 hour on this problem. What should be answer for n = 1? So I think n should be greater than 1. I am assuming n > 1.
So brute-force solution won't work here because n is huge enough. So you need more optimized solution. You need to think think about how many times you have to carry 1 in the sum to make n. It is at most 9 times!
If you have some basic idea with digit-dp(Dynamic Programming) then this problem is easy. Try to place all possible digit on a place of n and take minimum energy among them. This problem is easy when you fully understand digit-dp technique. You can learn it from here and here.
For practice, you can find a lot of problems here (Dynamic programming section).
For your references, I wrote this code just now and it is working properly. Hope you can use this as a reference.
#include <bits/stdc++.h>
using namespace std;
const string INF_STRING = "9999999";
const int INF_INT = 9999999;
pair<string, int> INF = make_pair(INF_STRING, INF_INT);
int nod;
int digits[10];
int num_of_digits(int a) {
int cnt = 0;
while(a) {
digits[cnt] = a % 10;
a = a / 10;
cnt++;
}
return cnt;
}
pair<string, int> dp[10][2][2][2];
pair<string, int> solve(int ind, int carry, bool is1, bool is2) {
if(ind >= nod) {
if(carry != 0 || !is1 || !is2) return INF;
return make_pair("", 0);
}
pair<string, int> &ret = dp[ind][carry][is1][is2];
if(ret.second != -1) return ret;
ret = INF;
for(int i = 0; i < 10; i++) {
for(int j = 0; j < 10; j++) {
int s = (i + j + carry);
pair<string, int> cur = INF;
if(s % 10 == digits[ind]) {
cur = solve(ind + 1, s / 10, is1 || (i > 0? 1:0), is2 || (j > 0? 1:0));
}
if((cur.second + i + j) < ret.second) {
ret.second = cur.second + i + j;
ret.first = cur.first + (char)(i + '0');
}
}
}
return ret;
}
int stringToInt(string num) {
stringstream ss;
ss<<num;
int ret;
ss >> ret;
return ret;
}
int main() {
int i, t, cases = 1, j, k, pos;
int n;
scanf("%d", &n);
nod = num_of_digits(n);
for(int i = 0; i < 10; i++) {
for(int j = 0; j < 2; j++) {
dp[i][j][0][0] = make_pair(INF_STRING, -1);
dp[i][j][0][1] = make_pair(INF_STRING, -1);
dp[i][j][1][0] = make_pair(INF_STRING, -1);
dp[i][j][1][1] = make_pair(INF_STRING, -1);
}
}
pair<string, int> res = solve(0, 0, 0, 0);
string num1_str = res.first;
int num1 = stringToInt(num1_str);
int num2 = n - num1;
printf("Minimum Energy: %d\n", res.second);
printf("Num1 = %d, Num2 = %d\n", num1, num2);
return 0;
}
/*
Input:
10000
Output:
Minimum energy: 10
Num1 = 1000, Num2 = 9000
*/

Here is the answer in javascript in simple way.
function calculateEnergy(n) {
let e = 0
while(n > 0) {
e += n % 10
n = Math.floor(n / 10)
}
return e
}
function countMinEnergy(n) {
let minE = n
let i = 1
while(i <= n/2) {
let e = calculateEnergy(i) + calculateEnergy(n - i)
minE = e < minE ? e : minE
i++
}
return minE
}
countMinEnergy(4325)

Here is scala solution
object LeastEnergyPair extends App {
private def getCountOfPair(array: Array[Int],sum: Int): mutable.Set[(Int, Int)] = {
val seen = mutable.Set[Int]()
val out = mutable.Set[(Int,Int)]()
array map { x =>
val target = sum - x
if (seen.contains(target) || target*2 == sum)
out += ((Math.min(x,target),Math.max(x,target)))
else
seen += x
}
println(out)
out
}
private def sum(i:Int): Int = i.toString.toCharArray.map(_.asDigit).sum
def findLeastEnergyPair(a: mutable.Set[(Int,Int)]): (Int,Int) = {
var min = Int.MaxValue
var minPair = (0,0)
a.foreach {
case (i,j) =>
if (sum(i) + sum(j) < min) {
min = sum(i) + sum(j)
minPair = (i,j)
println(s"$min ----- $minPair")
}
}
minPair
}
println(findLeastEnergyPair(getCountOfPair((1 to 10000).toArray, 10000)))
}

The below logic will cover all scenarios
if (N%10 == 0) {
x1= (N/10);
x2 = N-x1
}else{
x1 = N-10;
x2 = 10;
}

how to determine if a number is a smart number in java?

I have this question I am trying to solve. I have tried coding for the past 4 hours.
An integer is defined to be a Smart number if it is an element in the infinite sequence
1, 2, 4, 7, 11, 16 …
Note that 2-1=1, 4-2=2, 7-4=3, 11-7=4, 16-11=5 so for k>1, the kth element of the sequence is equal to the k-1th element + k-1. For example, for k=6, 16 is the kth element and is equal to 11 (the k-1th element) + 5 ( k-1).
Write function named isSmart that returns 1 if its argument is a Smart number, otherwise it returns 0. So isSmart(11) returns 1, isSmart(22) returns 1 and isSmart(8) returns 0
I have tried the following code to
import java.util.Arrays;
public class IsSmart {
public static void main(String[] args) {
// TODO Auto-generated method stub
int x = isSmart(11);
System.out.println(x);
}
public static int isSmart(int n) {
int[] y = new int[n];
int j = 0;
for (int i = 1; i <= n; i++) {
y[j] = i;
j++;
}
System.out.println(Arrays.toString(y));
for (int i = 0; i <= y.length; i++) {
int diff = 0;
y[j] = y[i+1] - y[i] ;
y[i] = diff;
}
System.out.println(Arrays.toString(y));
for (int i = 0; i < y.length; i++) {
if(n == y[i])
return 1;
}
return 0;
}
}
When I test it with 11 it is giving me 0 but it shouldn't. Any idea how to correct my mistakes?

It can be done in a simpler way as follows
import java.util.Arrays;
public class IsSmart {
public static void main(String[] args) {
int x = isSmart(11);
System.out.println("Ans: "+x);
}
public static int isSmart(int n) {
//------------ CHECK THIS LOGIC ------------//
int[] y = new int[n];
int diff = 1;
for (int i = 1; i < n; i++) {
y[0] =1;
y[i] = diff + y[i-1];
diff++;
}
//------------ CHECK THIS LOGIC ------------//
System.out.println(Arrays.toString(y));
for (int i = 0; i < y.length; i++) {
if(n == y[i])
return 1;
}
return 0;
}
}

One of the problems is the way that your populating your array.
The array can be populated as such
for(int i = 0; i < n; i++) {
y[i] = (i == 0) ? 1 : y[i - 1] + i;
}
The overall application of the function isSmart can be simplified to:
public static int isSmart(int n) {
int[] array = new int[n];
for(int i = 0; i < n; i++) {
array[i] = (i == 0) ? 1 : array[i - 1] + i;
}
for (int i = 0; i < array.length; i++) {
if (array[i] == n) return 1;
}
return 0;
}

Note that you don't need to build an array:
public static int isSmart(int n) {
int smart = 1;
for (int i = 1; smart < n; i++) {
smart = smart + i;
}
return smart == n ? 1 : 0;
}

Here is a naive way to think of it to get you started - you need to fill out the while() loop. The important thing to notice is that:
The next value of the sequence will be the number of items in the sequence + the last item in the sequence.
import java.util.ArrayList;
public class Test {
public static void main(String[] args) {
System.out.println(isSmart(11));
}
public static int isSmart(int n) {
ArrayList<Integer> sequence = new ArrayList<Integer>();
// Start with 1 in the ArrayList
sequence.add(1);
// You need to keep track of the index, as well as
// the next value you're going to add to your list
int index = 1; // or number of elements in the sequence
int nextVal = 1;
while (nextVal < n) {
// Three things need to happen in here:
// 1) set nextVal equal to the sum of the current index + the value at the *previous* index
// 2) add nextVal to the ArrayList
// 3) incriment index by 1
}
// Now you can check to see if your ArrayList contains n (is Smart)
if (sequence.contains(n)) { return 1; }
return 0;
}
}

First think of a mathematical solution.
Smart numbers form a sequence:
a0 = 1
an+1 = n + an
This gives a function for smart numbers:
f(x) = ax² + bx + c
f(x + 1) = f(x) + x = ...
So the problem is to find for a given y a matching x.
You can do this by a binary search.
int isSmart(int n) {
int xlow = 1;
int xhigh = n; // Exclusive. For n == 0 return 1.
while (xlow < xhigh) {
int x = (xlow + xhigh)/2;
int y = f(x);
if (y == n) {
return 1;
}
if (y < n) {
xlow = x + 1;
} else {
xhigh = x;
}
}
return 0;
}
Yet smarter would be to use the solution for x and look whether it is an integer:
ax² + bx + c' = 0 where c' = c - n
x = ...

I was playing around with this and I noticed something. The smart numbers are
1 2 4 7 11 16 22 29 ...
If you subtract one you get
0 1 3 6 10 15 21 28 ...
0 1 2 3 4 5 6 7 ...
The above sequence happens to be the sum of the first n numbers starting with 0 which is n*(n+1)/2. So add 1 to that and you get a smart number.
Since n and n+1 are next door to each other you can derive them by reversing the process.
Take 29, subtract 1 = 28, * 2 = 56. The sqrt(56) rounded up is 8. So the 8th smart number (counting from 0) is 29.
Using that information you can detect a smart number without a loop by simply reversing the process.
public static int isSmart(int v) {
int vv = (v-1)*2;
int sq = (int)Math.sqrt(vv);
int chk = (sq*(sq+1))/2 + 1;
return (chk == v) ? 1 : 0;
}
Using a version which supports longs have verified this against the iterative process from 1 to 10,000,000,000.

Java - Assistance with understanding codility code

For the PermCheck codility test, I coded one solution (please see below) but it only really solved the example given in the codility test because there are only a few values in the array and small values. I also added code below which scored 100%, which is code I found on the internet. That code looks very different from mine and I couldn't work out how he/she was able to get the answer. Could someone please explain the code step by step and how it results in the answer please.
Codility Test:
PermCheck
Check whether array A is a permutation.
A non-empty zero-indexed array A consisting of N integers is given.
A permutation is a sequence containing each element from 1 to N once, and only once.
For example, array A such that:
A[0] = 4
A[1] = 1
A[2] = 3
A[3] = 2
is a permutation, but array A such that:
A[0] = 4
A[1] = 1
A[2] = 3
is not a permutation, because value 2 is missing.
The goal is to check whether array A is a permutation.
Write a function:
class Solution {
public int solution(int[] A);
}
that, given a zero-indexed array A, returns 1 if array A is a permutation and 0 if it is not.
For example, given array A such that:
A[0] = 4
A[1] = 1
A[2] = 3
A[3] = 2
the function should return 1.
Given array A such that:
A[0] = 4
A[1] = 1
A[2] = 3
the function should return 0.
Assume that:
N is an integer within the range [1..100'000];
Each element of array A is an integer within the range [1..1'000'000'000].
Complexity:
Expected worst-case time complexity is O(N)
Expected worst-case space complexity is O(N), beyond input storage (not counting the storage required for input arguments).
Elements of input arrays can be modified.
100% Score Solution (found from internet):
public static final int NOT_PERMUTATION = 0;
public static final int PERMUTATION = 1;
// (4,1,3,2) = 1
// (4,1,3) = 0
// (1) = 1
// () = 1
// (2) = 0
public int PermSolution(int[] A) {
// write your code in Java SE 8
int[] mark = new int[A.length + 1];
int counter = 0;
for (int i = 0; i < A.length; ++i) {
int value = A[i];
if(value >= mark.length) {
return NOT_PERMUTATION;
}
if(mark[value] == 0) {
mark[value]=1;
++counter;
} else {
return NOT_PERMUTATION;
}
}
return counter == A.length ? PERMUTATION : NOT_PERMUTATION;
}
My Solution:
public int PermSolution(int[] A)
{
int perm = 1;
Arrays.sort(A);
if (A[0] != 1) return 0;
for (int i = 0; i < A.length; i++)
{
if (A[i] + 1 == A[i + 1])
{
return perm;
}
if (A[i] + 1 != A[i + 1])
{
return 0;
}
}
return perm;
}

Using Arrays.sort() is kind of original, that's not how I would have done it though.
To comment your code, it's probably isn't working because of this : return perm;
Let's say you have this Array which is not a permutation:
A[0] = 4
A[1] = 1
A[2] = 2
One you execute Arrays.sort(A), you'll have this :
A[0] = 1
A[1] = 2
A[2] = 4
Now let's execute your code :
if (A[0] != 1) return 0;
A[0] is indeed equal to 1
Next, for i==0 we have :
if (A[i] + 1 == A[i + 1])
{
return perm;
}
A[i] + 1 is equal 2 and A[i+1] is also equal to 2
the condition being true, you execute a return perm; and thus, you end your execution with a return 1.
Actually, as long as your array contains 1 and 2, this function will always return 1
For it to work, you'll have to check all of the array before actually returning a value.
This should work :
public int PermSolution(int[] A)
{
int perm = 1;
Arrays.sort(A);
if (A[0] != 1) return 0;
for (int i = 0; i < A.length; i++)
{
if (A[i] + 1 != A[i + 1])
{
return 0;
}
}
return perm;
}
To optimize it even further, this should work as well :
public int PermSolution(int[] A)
{
Arrays.sort(A);
for (int i = 0; i < A.length; i++)
{
if (A[i] != i+1)
{
return 0;
}
}
return 1;
}

Why don't we avoid Arrays.sort(A) in order to gain the computation efficiency by below:
public static int PermSolution(int[] A)
{
int len=A.length;
if(len==1)
return A[0]==1 ? 1 : 0;
BitSet set=new BitSet(len+2);
for (int i = 0; i < len; i++)
{
if(A[i]>len || set.get(A[i]))
return 0;
set.set(A[i]);
}
return set.nextClearBit(1)==(len+1) ? 1 : 0;
}

Here is some solution that I have developed. Not sure about constraints, if someone can help to test it against them. Thanks people!
private static int solution(int[] arr) {
int perm=1;
boolean b=false;
Arrays.sort(arr);
int i=0;
while (i<=arr.length) {
if(i < arr.length-2)
b = arr[i+1]-1 == (arr[i]);
if(b) {
System.out.println("if " + i);
i++;
perm=1;
}else {
System.out.println("else " + i);
perm = 0;
break;
}
}
return perm;
}

CodeChef Array Transform Program

Here's the Problem Statement :
Given n numbers, you can perform the following operation any number of
times : Choose any subset of the
numbers, none of which are 0.
Decrement the numbers in the subset by
1, and increment the numbers not in
the subset by K. Is it possible to
perform operations such that all
numbers except one of them become 0 ?
Input : The first line contains the
number of test cases T. 2*T lines
follow, 2 for each case. The first
line of a test case contains the
numbers n and K. The next line
contains n numbers, a_1...a_n. Output
: Output T lines, one corresponding to
each test case. For a test case,
output "YES" if there is a sequence of
operations as described, and "NO"
otherwise.
Sample Input :
3
2 1
10 10
3 2
1 2 2
3 2
1 2 3
Sample Output :
YES
YES
NO
Constraints :
1 <= T <= 1000
2 <= n <= 100
1 <= K <= 10
0 <= a_i <= 1000
& here's my code :
import java.util.*;
public class ArrayTransform {
public static void main(String args[]) {
Scanner sc = new Scanner(System.in);
int no_of_tests = sc.nextInt();
int size;
int a[] = new int[100];
boolean yes;
int j;
int k;
for (int i = 0; i < no_of_tests; i++) {
size = sc.nextInt();
k = sc.nextInt();
for (j = 0; j < size; j++) {
a[j] = sc.nextInt();
}
yes = is_possible(a, size, k + 1);
if (yes)
System.out.println("YES\n");
else
System.out.println("NO\n");
}
}
static boolean is_possible(int a[], int size, int k_1) {
int count = 0;
int m[] = { -1, -1 };
int mod;
for (int i = 0; i < size; i++) {
mod = a[i] % k_1;
if (m[0] != mod && m[1] != mod) {
if (m[0] == -1)
m[0] = mod;
else if (m[1] == -1)
m[1] = mod;
else
return false;
}
}
return true;
}
}

if (m[0] != mod && m[1] != mod)
Here instead of && there should be ||. Only one of the m's need to match the mod.