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Finding three numbers whose sum is divisible by a given number

How can I search through an array and find every combination of three values whose sum is divisible by a given number(x) in java.
In other words, every combination where (n1+n2+n3) % x == 0.
I know this would be a simple solution using a triple for loop but I need something with a time complexity of O(N^2).
Any idea's?

1 - Hash every (element % x) in the list.
2 - Sum every pair of elements in the list (lets call the sum y) and do modded_y = y % x.
3 - Check if x - modded_y is in the hash table. If it is and it's not one of the other 2 numbers then you found a possible combination. Keep iterating and hashing the combinations you found so that they don't repeat.
This is called a meet in the middle strategy.
It's complexity is O(n + (n^2 * 1)) = O(n^2)

I will call the given array A1.
1) create two structs
struct pair{
int first;
int second;
bool valid;
}
struct third{
int value;
int index;
}
2) using a nested loop, initialize an array B1 of all possible Pairs.
3) Loop through B1. if (A1[B1[i].first] + A1[B1[i].second])%x==0 then set B1[i].valid to true
4) create an array A3 of Thirds that stores the index and value of every element from A1 that is divisible by x.
5) using a nested loop, go through each element of A3 and each element of B1. If B1.valid = true
print A1[B1[i].first] and A1[B1[i].second] with an element from A1[A3.index].
that should give you all combinations without using any triple loops.

Find a matrix which satisfies certain constraints

Another description of the problem: Compute a matrix which satisfies certain constraints
Given a function whose only argument is a 4x4 matrix (int[4][4] matrix), determine the maximal possible output (return value) of that function.
The 4x4 matrix must satisfy the following constraints:
All entries are integers between -10 and 10 (inclusively).
It must be symmetrix, entry(x,y) = entry(y,x).
Diagonal entries must be positive, entry(x,x) > 0.
The sum of all 16 entries must be 0.
The function must only sum up values of the matrix, nothing fancy.
My question:
Given such a function which sums up certain values of a matrix (matrix satisfies above constraints), how do I find the maximal possible output/return value of that function?
For example:
/* The function sums up certain values of the matrix,
a value can be summed up multiple or 0 times. */
// for this example I arbitrarily chose values at (0,0), (1,2), (0,3), (1,1).
int exampleFunction(int[][] matrix) {
int a = matrix[0][0];
int b = matrix[1][2];
int c = matrix[0][3];
int d = matrix[1][1];
return a+b+c+d;
}
/* The result (max output of the above function) is 40,
it can be achieved by the following matrix: */
0. 1. 2. 3.
0. 10 -10 -10 10
1. -10 10 10 -10
2. -10 10 1 -1
3. 10 -10 -1 1
// Another example:
// for this example I arbitrarily chose values at (0,3), (0,1), (0,1), (0,4), ...
int exampleFunction2(int[][] matrix) {
int a = matrix[0][3] + matrix[0][1] + matrix[0][1];
int b = matrix[0][3] + matrix[0][3] + matrix[0][2];
int c = matrix[1][2] + matrix[2][1] + matrix[3][1];
int d = matrix[1][3] + matrix[2][3] + matrix[3][2];
return a+b+c+d;
}
/* The result (max output of the above function) is -4, it can be achieved by
the following matrix: */
0. 1. 2. 3.
0. 1 10 10 -10
1. 10 1 -1 -10
2. 10 -1 1 -1
3. -10 -10 -1 1
I don't know where to start. Currently I'm trying to estimate the number of 4x4 matrices which satisfy the constraints, if the number is small enough the problem could be solved by brute force.
Is there a more general approach?
Can the solution to this problem be generalized such that it can be easily adapted to arbitrary functions on the given matrix and arbitrary constraints for the matrix?

You can try to solve this using linear programming techniques.
The idea is to express the problem as some inequalities, some equalities, and a linear objective function and then call a library to optimize the result.
Python code:
import scipy.optimize as opt
c = [0]*16
def use(y,x):
c[y*4+x] -= 1
if 0:
use(0,0)
use(1,2)
use(0,3)
use(1,1)
else:
use(0,3)
use(0,1)
use(0,1)
use(0,3)
use(0,3)
use(0,2)
use(1,2)
use(2,1)
use(3,1)
use(1,3)
use(2,3)
use(3,2)
bounds=[ [-10,10] for i in range(4*4) ]
for i in range(4):
bounds[i*4+i] = [1,10]
A_eq = [[1] * 16]
b_eq = [0]
for x in range(4):
for y in range(x+1,4):
D = [0]*16
D[x*4+y] = 1
D[y*4+x] = -1
A_eq.append(D)
b_eq.append(0)
r = opt.linprog(c,A_eq=A_eq,b_eq=b_eq,bounds=bounds)
for y in range(4):
print r.x[4*y:4*y+4]
print -r.fun
This prints:
[ 1. 10. -10. 10.]
[ 10. 1. 8. -10.]
[-10. 8. 1. -10.]
[ 10. -10. -10. 1.]
16.0
saying that the best value for your second case is 16, with the given matrix.
Strictly speaking you are wanting integer solutions. Linear programming solves this type of problem when the inputs can be any real values, while integer programming solves this type when the inputs must be integers.
In your case you may well find that the linear programming method already provides integer solutions (it does for the two given examples). When this happens, it is certain that this is the optimal answer.
However, if the variables are not integral you may need to find an integer programming library instead.

Sort the elements in the matrix in descending order and store in an array.Iterate through the elements in the array one by one
and add it to a variable.Stop iterating at the point when adding an element to variable decrease its value.The value stored in the variable gives maximum value.
maxfunction(matrix[][])
{
array(n)=sortDescending(matrix[][]);
max=n[0];
i=1;
for i to n do
temp=max;
max=max+n[i];
if(max<temp)
break;
return max;
}

You need to first consider what matrices will satisfy the rules. The 4 numbers on the diagonal must be positive, with the minimal sum of the diagonal being 4 (four 1 values), and the maximum being 40 (four 10 values).
The total sum of all 16 items is 0 - or to put it another way, sum(diagnoal)+sum(rest-of-matrix)=0.
Since you know that sum(diagonal) is positive, that means that sum(rest-of-matrix) must be negative and equal - basically sum(diagonal)*(-1).
We also know that the rest of the matrix is symmetrical - so you're guaranteed the sum(rest-of-matrix) is an even number. That means that the diagonal must also be an even number, and the sum of the top half of the matrix is exactly half the diagonal*(-1).
For any given function, you take a handful of cells and sum them. Now you can consider the functions as fitting into categories. For functions that take all 4 cells from the diagonal only, the maximum will be 40. If the function takes all 12 cells which are not the diagonal, the maximum is -4 (negative minimal diagonal).
Other categories of functions that have an easy answer:
1) one from the diagonal and an entire half of the matrix above/below the diagonal - the max is 3. The diagonal cell will be 10, the rest will be 1, 1, 2 (minimal to get to an even number) and the half-matrix will sum at -7.
2) two cells of the diagonal and half a matrix - the max is 9. the two diagonal cells are maximised to two tens, the remaining cells are 1,1 - and so the half matrix sums at -11.
3) three cells from the diagonal and half a matrix - the max is 14.
4) the entire diagonal and half the matrix - the max is 20.
You can continue with the categories of selecting functions (using some from the diagonal and some from the rest), and easily calculating the maximum for each category of selecting function. I believe they can all be mapped.
Then the only step is to put your new selecting function in the correct category and you know the maximum.

How to convert large integer number to binary?

Sorry for a possible duplicate post, I saw many similar topics here but none was exactly I needed. Before actually posting a question I want to explicitly state that this question is NOT A HOMEWORK.
So the question is: how to convert a large integer number into binary representation? The integer number is large enough to fit in primitive type (Java long cannot be used). An input might be represented as a string format or as an array of digits. Disclaimer, This is not going to be a solution of production level, so I don't want to use BigInteger class. Instead, I want to implement an algorithm.
So far I ended up with the following approach:
Input and output values represented as strings. If the last digit of input is even, I prepend the output with "0", otherwise - with "1". After that, I replace input with input divided by 2. I use another method - divideByTwo for an arithmetical division. This process runs in a loop until input becomes "0" or "1". Finally, I prepend input to the output. Here's the code:
Helper Method
/**
* #param s input integer value in string representation
* #return the input divided by 2 in string representation
**/
static String divideByTwo(String s)
{
String result = "";
int dividend = 0;
int quotent = 0;
boolean dividendIsZero = false;
while (s.length() > 0)
{
int i = 1;
dividend = Character.getNumericValue(s.charAt(0));
while (dividend < 2 && i < s.length())
{
if (dividendIsZero) {result += "0";}
dividend = Integer.parseInt(s.substring(0, ++i));
}
quotent = dividend / 2;
dividend -= quotent * 2;
dividendIsZero = (dividend == 0);
result += Integer.toString(quotent);
s = s.substring(i);
if (!dividendIsZero && s.length() != 0)
{
s = Integer.toString(dividend) + s;
}
}
return result;
}
Main Method
/**
* #param s the integer in string representation
* #return the binary integer in string representation
**/
static String integerToBinary(String s)
{
if (!s.matches("[0-9]+"))
{
throw new IllegalArgumentException(s + " cannot be converted to integer");
}
String result = "";
while (!s.equals("0") && !s.equals("1"))
{
int lastDigit = Character.getNumericValue(s.charAt(s.length()-1));
result = lastDigit % 2 + result; //if last digit is even prepend 0, otherwise 1
s = divideByTwo(s);
}
return (s + result).replaceAll("^0*", "");
}
As you can see, the runtime is O(n^2). O(n) for integerToBinary method and O(n) for divideByTwo that runs inside the loop. Is there a way to achieve a better runtime? Thanks in advance!

Try this:
new BigDecimal("12345678901234567890123456789012345678901234567890").toString(2);
Edit:
For making a big-number class, you may want to have a look at my post about this a week ago. Ah, the question was by you, never mind.
The conversion between different number systems in principle is a repeated "division, remainder, multiply, add" operation. Let's look at an example:
We want to convert 123 from decimal to a base 3 number. What do we do?
Take the remainder modulo 3 - prepend this digit to the result.
Divide by 3.
If the number is bigger than 0, continue with this number at step 1
So it looks like this:
123 % 3 == 0. ==> The last digit is 0.
123 / 3 == 41.
41 % 3 == 2 ==> The second last digit is 2.
41 / 3 == 13
13 % 3 == 1 ==> The third digit is 1.
13 / 3 == 4
4 % 3 == 1 ==> The fourth digit is 1 again.
4 / 3 == 1
1 % 3 == 1 ==> The fifth digit is 1.
So, we have 11120 as the result.
The problem is that for this you need to have already some kind of division by 3 in decimal format, which is usually not the case if you don't implement your number in a decimal-based format (like I did in the answer to your last question linked above).
But it works for converting from your internal number format to any external format.
So, let's look at how we would do the inverse calculation, from 11120 (base 3) to its decimal equivalent. (Base 3 is here the placeholder for an arbitrary radix, Base 10 the placeholder for your internal radix.) In principle, this number can be written as this:
1 * 3^4 + 1 * 3^3 + 1*3^2 + 2*3^1 + 0*3^0
A better way (faster to calculate) is this:
((((1 * 3) + 1 )*3 + 1 )*3 + 2)*3 + 0
1
3
4
12
13
39
41
123
123
(This is known as Horner scheme, normally used for calculating values of polynomials.)
You can implement this in the number scheme you are implementing, if you know how to represent the input radix (and the digits) in your target system.
(I just added such a calculation to my DecimalBigInt class, but you may want to do the calculations directly in your internal data structure instead of creating a new object (or even two) of your BigNumber class for every decimal digit to be input.)

Among the simple methods there are two possible approaches (all numbers that appear here decimal)
work in decimal and divide by 2 in each step as you outlined in the question
work in binary and multiply by 10 in each step for example 123 = ((1 * 10) + 2) * 10 + 3
If you are working on a binary computer the approach 2 may be easier.
See for example this post for a more in-depth discussion of the topic.

In wikipedia, it is said:
For very large numbers, these simple methods are inefficient because
they perform a large number of multiplications or divisions where one
operand is very large. A simple divide-and-conquer algorithm is more
effective asymptotically: given a binary number, it is divided by
10^k, where k is chosen so that the quotient roughly equals the
remainder; then each of these pieces is converted to decimal and the
two are concatenated. Given a decimal number, it can be split into two
pieces of about the same size, each of which is converted to binary,
whereupon the first converted piece is multiplied by 10^k and added to
the second converted piece, where k is the number of decimal digits in
the second, least-significant piece before conversion.
I have tried, this method is faster than conventional one for numbers larger than 10,000 digits.

Printing PowerSet with help of bit position

Googling around for a while to find subsets of a String, i read wikipedia and it mentions that
.....For the whole power set of S we get:
{ } = 000 (Binary) = 0 (Decimal)
{x} = 100 = 4
{y} = 010 = 2
{z} = 001 = 1
{x, y} = 110 = 6
{x, z} = 101 = 5
{y, z} = 011 = 3
{x, y, z} = 111 = 7
Is there a possible way to implement this through program and avoid recursive algorithm which uses string length?
What i understood so far is that, for a String of length n, we can run from 0 to 2^n - 1 and print characters for on bits.
What i couldn't get is how to map those on bits with the corresponding characters in the most optimized manner
PS : checked thread but couldnt understood this and c++ : Power set generated by bits

The idea is that a power set of a set of size n has exactly 2^n elements, exactly the same number as there are different binary numbers of length at most n.
Now all you have to do is create a mapping between the two and you don't need a recursive algorithm. Fortunately with binary numbers you have a real intuitive and natural mapping in that you just add a character at position j in the string to a subset if your loop variable has bit j set which you can easily do with getBit() I wrote there (you can inline it but for you I made a separate function for better readability).
P.S. As requested, more detailed explanation on the mapping:
If you have a recursive algorithm, your flow is given by how you traverse your data structure in the recursive calls. It is as such a very intuitive and natural way of solving many problems.
If you want to solve such a problem without recursion for whatever reason, for instance to use less time and memory, you have the difficult task of making this traversal explicit.
As we use a loop with a loop variable which assumes a certain set of values, we need to make sure to map each value of the loop variable, e.g. 42, to one element, in our case a subset of s, in a way that we have a bijective mapping, that is, we map to each subset exactly once. Because we have a set the order does not matter, so we just need whatever mapping that satisfies these requirements.
Now we look at a binary number, e.g. 42 = 32+8+2 and as such in binary with the position above:
543210
101010
We can thus map 42 to a subset as follows using the positions:
order the elements of the set s in any way you like but consistently (always the same in one program execution), we can in our case use the order in the string
add an element e_j if and only if the bit at position j is set (equal to 1).
As each number has at least one digit different from any other, we always get different subsets, and thus our mapping is injective (different input -> different output).
Our mapping is also valid, as the binary numbers we chose have at most the length equal to the size of our set so the bit positions can always be assigned to an element in the set. Combined with the fact that our set of inputs is chosen to have the same size (2^n) as the size of a power set, we can follow that it is in fact bijective.
import java.util.HashSet;
import java.util.Set;
public class PowerSet
{
static boolean getBit(int i, int pos) {return (i&1<<pos)>0;}
static Set<Set<Character>> powerSet(String s)
{
Set<Set<Character>> pow = new HashSet<>();
for(int i=0;i<(2<<s.length());i++)
{
Set<Character> subSet = new HashSet<>();
for(int j=0;j<s.length();j++)
{
if(getBit(i,j)) {subSet.add(s.charAt(j));}
}
pow.add(subSet);
}
return pow;
}
public static void main(String[] args)
{System.out.println(powerSet("xyz"));}
}

Here is easy way to do it (pseudo code) :-
for(int i=0;i<2^n;i++) {
char subset[];
int k = i;
int c = 0;
while(k>0) {
if(k%2==1) {
subset.add(string[c]);
}
k = k/2;
c++;
}
print subset;
}
Explanation :- The code divides number by 2 and calculates remainder which is used to convert number to binary form. Then as you know only selects index in string which has 1 at that bit number.

truncated binary logarithm

I have a question about this problem, and any help would be great!
Write a program that takes one integer N as an
argument and prints out its truncated binary logarithm [log2 N]. Hint: [log2 N] = l is the largest integer ` such that
2^l <= N.
I got this much down:
int N = Integer.parseInt(args[0]);
double l = Math.log(N) / Math.log(2);
double a = Math.pow(2, l);
But I can't figure out how to truncate l while keeping 2^l <= N
Thanks
This is what i have now:
int N = Integer.parseInt(args[0]);
int i = 0; // loop control counter
int v = 1; // current power of two
while (Math.pow(2 , i) <= N) {
i = i + 1;
v = 2 * v;
}
System.out.println(Integer.highestOneBit(N));
This prints out the integer that is equal to 2^i which would be less than N. My test still comes out false and i think that is because the question is asking to print the i that is the largest rather than the N. So when i do
Integer.highestOneBit(i)
the correct i does not print out. For example if i do: N = 38 then the highest i should be 5, but instead it prints out 4.
Then i tried this:
int N = Integer.parseInt(args[0]);
int i; // loop control counter
for (i= 0; Math.pow(2 , i) == N; i++) {
}
System.out.println(Integer.highestOneBit(i));
Where if i make N = 2 i should print out to be 1, but instead it is printing out 0.
I've tried a bunch of things on top of that, but cant get what i am doing wrong. Help would be greatly appreciated. Thanks

I believe the answer you're looking for here is based on the underlying notion of how a number is actually stored in a computer, and how that can be used to your advantage in a problem such as this.
Numbers in a computer are stored in binary - a series of ones and zeros where each column represents a power of 2:
(Above image from http://www.mathincomputers.com/binary.html - see for more info on binary)
The zeroth power of 2 is over on the right. So, 01001, for example, represents the decimal value 2^0 + 2^3; 9.
This storage format, interestingly, gives us some additional information about the number. We can see that 2^3 is the highest power of 2 that 9 is made up of. Let's imagine it's the only power of two it contains, by chopping off all the other 1's except the highest. This is a truncation, and results in this:
01000
You'll now notice this value represents 8, or 2^3. Taking it down to basics, lets now look at what log base 2 really represents. It's the number that you raise 2 to the power of to get the thing your finding the log of. log2(8) is 3. Can you see the pattern emerging here?
The position of the highest bit can be used as an approximation to it's log base 2 value.
2^3 is the 3rd bit over in our example, so a truncated approximation to log base 2(9) is 3.
So the truncated binary logarithm of 9 is 3. 2^3 is less than 9; This is where the less than comes from, and the algorithm to find it's value simply involves finding the position of the highest bit that makes up the number.
Some more examples:
12 = 1100. Position of the highest bit = 3 (starting from zero on the right). Therefore the truncated binary logarithm of 12 = 3. 2^3 is <= 12.
38 = 100110. Position of the highest bit = 5. Therefore the truncated binary logarithm of 38 = 5. 2^5 is <= 38.
This level of pushing bits around is known as bitwise operations in Java.
Integer.highestOneBit(n) returns essentially the truncated value. So if n was 9 (1001), highestOneBit(9) returns 8 (1000), which may be of use.
A simple way of finding the position of that highest bit of a number involves doing a bitshift until the value is zero. Something a little like this:
// Input number - 1001:
int n=9;
int position=0;
// Cache the input number - the loop destroys it.
int originalN=n;
while( n!=0 ){
position++; // Also position = position + 1;
n = n>>1; // Shift the bits over one spot (Overwriting n).
// 1001 becomes 0100, then 0010, then 0001, then 0000 on each iteration.
// Hopefully you can then see that n is zero when we've
// pushed all the bits off.
}
// Position is now the point at which n became zero.
// In your case, this is also the value of your truncated binary log.
System.out.println("Binary log of "+originalN+" is "+position);