Develop Reference

how many times will this loop execute

I was just wondering how many times a nested loop like this would run
int sum = 0;
for(int i = 0; i < total; i++) {
for(int j = i + 1; j < total; j++) {
for(int k = j; k < total; k++) {
sum++;
}
}
}
System.out.println(sum);
I can easily see the output of sum, but I would like to be able to mathematically calculate the total of sum with any number for total.

TL;DR
The loop will be executed for ((total ^ 3) - total) / 6 times and hence that will be the value of sum at the end of the loop.
int sum = 0;
for(int i = 0; i < total; i++) {
for(int j = i + 1; j < total; j++) {
for(int k = j; k < total; k++) {
sum++;
}
}
}
It is easy to see that the outer loop runs for a total times. The second loop is the trickier one
Let's try to work this out
i = 0, j runs from 1..total - 1
i = 1, j runs from 2..total - 1
i = 2, j runs from 3..total - 1
...
i = total - 2, j runs from total - 1 ..total - 1 (will only run once)
i = total - 1, inner loop does not execute as the loop termination condition is true.
The third loop is dependent on the second inner loop - k runs from j..total - 1
Let us take total as 6
j runs from 1..5 -> k runs for 5 times (j = 1) + 4 times(j = 2) + 3 times(j = 3)+ 2 times(j = 4) + 1 time(j = 4)
(Showing a minified version for others)
2..5 -> 4+3+2+1
3..5 3+2+1
4..5 2+1
5..5 1
Which is
1 + 2 + 3 + 4 + 5+
1 + 2 + 3 + 4 +
1 + 2 + 3 +
1 + 2 +
1
Generally,
1 + 2 + 3 + .. n +
1 + 2 + 3 +..n - 1+
1 + 2 + 3 +..n - 2+
1 + 2 + 3 +
1 + 2 +
1
This boils down to the sum
n * (n - 1)) / 2
For all values of n ranging from 1 to total
This can be verified with the below
int res = 0;
for (int i = 1; i <= total; i++) {
res += (i * (i - 1))/2;
}
res will be equal to your sum.
Mathematically, the above is
((total ^ 3) - total) / 6
Derivation:
References:
Sums of the First n Natural Numbers
Sum of the Squares of the First n Natural Numbers

The first iteration of the middle loop adds
total-1 + total-2 + ... + 1
to the sum.
The second iteration of the middle loop adds
total-2 + total - 3 + ... + 1
to the sum
The last iteration of the middle loop adds
1
to the sum.
If you sum all of these terms, you get
(total - 1) * 1 + (total - 2) * 2 + (total - 3) * 3 + ... + 2 * (total - 2) + 1 * (total - 1)
It's been a while since I studied math, so I don't remember if there's a simpler formula for this expression.
For example, if total is 10, you get:
9 * 1 + 8 * 2 + 7 * 3 + 6 * 4 + 5 * 5 + 4 * 6 + 3 * 7 + 2 * 8 + 1 * 9 =
9 + 16 + 21 + 24 + 25 + 24 + 21 + 16 + 9 =
165

It needs only a little bit knowledge of programming.Actually logic that is running behind is only computational kind of thing.
let's say:
total=10,sum=0
- when i is 0:
That time j is initialised with 1(i+1) and k as well. So k will lead us to execute the loop 9 times and and as j is incremented ,it will lead us to execute sum statement 8 times and 7 times and further 6 times till 1 time. (9+8+7+6+5+4+3+2+1=45 times.)
- when i is 1:
That time j is initialised with 2 and k as well.So sum statement is going to execute 8 times and then 7 times and then 6 times till 1.
(8+7+6+5+4+3+2+1=36 times).
- when i is 2:
Same thing happens repeatedly but starting with difference number ,so this time (7+6+5+4+3+2+1=28)
So this sequence continues until there is significance of occuring the condition with trueness.
This happens till i is 9.
So the final answer is 1+3+6+10+15+21+28+36+45=165.

The equation is like below
and the k is equal to total :

outermost loop runs 'total' number of times.
for each outer loop , middle loop runs 'total-i' times.
i.e total * total+total * (total-1)+total * (total-2)....total * 1
= total*(total+total-1+total-2...1)
= total*(1+2+3....total)
= total*(sum of first 'total' natural numbers)
= total*(total*(total+1)/2)
now the innermost loop also runs 'total-j' times for each middle loop
i.e
total*(total*(total+1)/2)*(total+(total-1)+(total-2)....+1)
= total*(total*(total+1)/2)*(1+2+3....+total)
= total*(total*(total+1)/2)* (sum of first 'total' natural numbers)
= total*(total*(total+1)/2) * (total*(total+1)/2)..
So finally you will get something close to
total * (total*(total+1)/2) * (total*(total+1)/2).
Sorry there's a correction as #andreas mentioned innermost and middle loops run only till total-i-1 times
in which case it will be the sum of first (total-1) no.s which should be (total-1)*total/2 so the final output should be
total * (total*(total-1)/2) * (total*(total-1)/2) .

As we know, the sum of an arithmetic progression is:
The inner-most loop will loop for
times, which is a function to j.
You sum it up and get a function to i, aka:
You sum it up again and get a function to total, aka:
For Mathematica users, the result is:
f[x_]:=Sum[Sum[x-j,{j,i+1,x-1}],{i,0,x-1}]
From here, we can see it more clearly, and the FINAL result is:
where x is total.

That function will loop (total/6)*(total*total - 1) times
The snippet bellow just verifies that
var total = 100
var sum = 0;
for(var i = 0; i < total; i++) {
for(var j = i + 1; j < total; j++) {
for(var k = j; k < total; k++) {
sum++;
}
}
}
function calc(n) {
return n*(n-1)*(n+1)/6
}
console.log("sum: "+sum)
console.log("calc: "+calc(total))

If we run this loop 100 times and generate a data set, then graph it, we get this:
Now, this graph is clearly a cubic. So we can do a solve using the cubic equation of ax^3+bx^2+cx+d.
Using 4 points, the values of them all are:
So the full equation is
y=x^3/6-x/6
y=x(x^2/6-1/6)
y=(x/6)(x^2-1)
Interactive Graph:
<iframe src="https://www.desmos.com/calculator/61whd83djd?embed" width="500px" height="500px" style="border: 1px solid #ccc" frameborder=0></iframe>

A simple loop like this:
for (i = a; i < b; i ++) {
....
}
runs b-a iterations (i takes the values: a, a+1, a+2... b-2, b-1) if a < b and 0 iterations otherwise. We will assume below that a < b always.
Its number of iterations can be compute using the simple maths formula:
Applying the formula to your code
We start with the innermost loop:
for(int k = j; k < t; k++) {
sum++;
}
Its number of iterations is:
Using the formula above, the value of U is (t-1)-j+1 which means:
U = t - j
Adding the middle loop
Adding the middle loop, the number of iterations becomes:
The terms of the second sum are t-(i+1), t-(i+2), ... t-(t-2), t-(t-1).
By solving the parentheses and putting them in the reverse order they can be written as:
1, 2, ... t-i-2, t-i-1.
Let p = t - j. The second sum now becomes:
It is the sum of the first t-i-1 natural numbers and its value is:
Adding the outer loop
Adding the outer loop the sum becomes:
On the last sum, the expression (t - i) starts with t (when i = 0), continues with t-1 (when i = 1) and it keeps decreasing until it reaches 1 (when i = t - 1). By replacing q = t - i, the last sum becomes:
The last expression subtracts the sum of the first n natural numbers from the sum of the first n square numbers. Its value is:
Now it's easy to simplify the expression:
The final answer
The number of iterations of the posted code is:

Magic square code loop

This is the code for a method which creates a magic square. n is the length of the square. It has to look like:
static int[][] magicSquare(int n) {
int[][] square=new int[n][n];
I don't understand this k=(k+1)%n; especially, why is it %n ?? Doesn´t that put k to 1 every loop again?
for (int i=0; i<n; i++){
in k=i;
for (int j=0; j<n; j++){
square[i][j]=k+1;
k=(k+1)%n;
1 2 3 4
2 3 4 1
3 4 1 2
4 1 2 3

The % in Java is used for modular division. Whenever the operator is applied the right-hand operand will be subtracted as many times as it can from the left-hand operand and what's left will be the output. You can easily check it by dividing the left-hand operand by the right-hand operand and take the leftover as an integer. In the case of a%b it will be like
a - (a/b)*b.
here are some examples:
10 % 4 = 2 // 2*4 = 8 + 2 = 10
10 % 5 = 0 // 2*5 = 10 + 0 = 10
0 % 4 = 0 // result here is 0 thus 0*4 = 0 + 0 = 0
// if you try to extract 4 from 0, you will not succeed and what's left will be returned (which was originally 0 and it's still 0)...
In your case:
k = (k + 1) % n;
is assuring that the value of k will never exceed 4, thus if it is dividable by 4 then it will be divided and the leftover will be written there. In the case when k is exactly 4 you will have the value of 0 written down into k but since you are always adding k + 1 it is writing the value of 1.
For beginners I do recommend to print the values you are interested in and observe how do the data migrate. Here I've added some printlns for you just to get the idea. Run the code and test it yourself. I do believe the things are going to be a bit cleaner.
public static void main(String[] args) {
int n = 4;
int[][] square = new int[n][n];
System.out.println("--------------");
for (int i = 0; i < n; i++) {
int k = i;
System.out.println("Filling magic cube line " + (i + 1) + ". The k variable will start from " + i + "."); // i initial value is 0 so we add 1 to it just to get the proper line number.
for (int j = 0; j < n; j++) {
System.out.println("Filling array index [" + i + "][" + j + "] = " + (k + 1)); // array indexes start from 0 aways and end at array.length - 1, so in case of n = 4, last index in array is 3.
square[i][j] = k + 1; // add 1 to k so the value will be normalized (no 0 entry and last entry should be equal to n).
k = (k + 1) % n; // reset k if it exceeds n value.
}
System.out.println("--------------");
}
Arrays.stream(square).forEach(innerArray -> {
Arrays.stream(innerArray).forEach(System.out::print);
System.out.println();
});
}
You could always play around and refactor the code as follows:
public static void main(String[] args) {
int n = 4;
int[][] square = new int[n][n];
System.out.println("--------------");
for (int i = 1; i <= n; i++) {
int k = i;
System.out.println("Filling magic cube line " + i + ". The k variable will start from " + i + ".");
for (int j = 0; j < n; j++) {
System.out.println("Filling array index [" + (i - 1) + "][" + (j - 1) + "] = " + k); // array indexes start from 0 aways and end at array.length - 1, so in case of n = 4, last index in array is 3. Subtract both i and j with 1 to get the proper array indexes.
square[i - 1][j - 1] = k;
k = (k + 1) % n; // reset k if it exceeds n value.
}
System.out.println("--------------");
}
Arrays.stream(square).forEach(innerArray -> {
Arrays.stream(innerArray).forEach(System.out::print);
System.out.println();
});
}
Remember that the array's indexing starts from 0 and ends at length - 1. In the case of 4, the first index is 0 and the last one is 3. Here is the diff of two implementations, try to see how does the indexes and values depends both on the control variables i and j.
https://www.diffchecker.com/x5lIWi4A
In the first case i and j both start from 0 and are growing till they it's values are both less than n, and in the second example they start from 1 and are growing till they are equal to n. I hope it's getting clearer now. Cheers

Running Time of this Simple Program - Time Complexity

I am trying to figure out what the time complexity of this simple program is, but I can't seem to understand what would be the best way to do this.
I have written down the time complexity side by side for each line
1 public int fnA (int n) {
2 int sum = 0; O(1)
3 for (int i = 0; i < n; i++) { O(n)
4 int j = i; O(n)
5 int product = 1; O(1)
6
7 while (j > 1) { O(n)
8 product ∗= j; O(log n)
9 j = j / 2; O(log n)
10 }
11 sum += product; O(1)
12 }
13 return sum; O(1)
14 }
Am I correct to assume these running times and that the final running time is: O(n)
If not, would somebody be able to explain where it is I am going wrong?
Overall:
1 + n + n + 1 + n + logn + logn + 1 + 1
= 3n + 2logn + 4
Final: O(n)

Time complexity for that algorithm is O(NlogN).
The for loop is executed N times (from 0 to N).
The while loop is executed logN times since your are dividing the number to half each time.
Since your are executing the while inside the for, your are executing a logN operation N times, from there it is the O(NlogN).
All remaining operations (assign, multiplication, division, sum) you can assume that takes O(1)

The crux of the above program is the while loop and it is the defining factor and rest of the lines will not have complexity more than O(n) and assuming that arithmetic operations will run in O(1) time.
while (j > 1) {
product ∗= j;
j = j / 2;
}
The above loop will have a run time of O(log(j)) and j is varying from 1 to n, so its the series...
-> O(log(1) + log(2) + log(3) + log(4).....log(n))
-> O(log(1*2*3*4...*n))
-> O(log(n!))
and O(log(n!)) is equal to O(n log(n))
For the proof for above refer this

No for every i, there is logn loop running and hence for n elements the total complexity is nlogn.
Since you know that the following loop takes logn .
while (j > 1) {
product ∗= j;
j = j / 2;
}
Now this particular loop is executed for every i. And so this will be executed n times. So it becomes nlogn.

To start with, you could count all operations. For example:
1 public int fnA (int n) {
2 int sum = 0; 1
3 for (int i = 0; i < n; i++) {
4 int j = i; n
5 int product = 1; n
6
7 while (j > 1) {
8 product ∗= j; ?
9 j = j / 2; ?
10 }
11 sum += product; n
12 }
13 return sum; 1
14 }
Now we could do the counting: which sums up to: 2 + 3n + nlog(n)
In a lot of programs the counting is more complex and usually has one outstanding higher order term, for example: 2+3n+2n2. When talking about performance we really care about when n is large, because when n is small, the sum is small anyway. When n is large, higher order term drawf the rest, so in this example 2n2 is really the term that matters. So that's the concept of tilde approximation.
With that in mind, usually one could quickly identify the portion of code that gets executed most often and use its count to represent overall time complexity. In example given by OP, it would look like this:
for (int i = 0; i < n; i++) {
for (int j = i; j > 1; j /= 2)
product *= j;
}
which gives ∑log2n. Usually the counting involves discrete mathamatics, one trick I have learned is to just replace with it integral and do caculus: ∫ log2n = nlog(n)

Split number into several numbers

I wrote a programm to get the cross sum of a number:
So when i type in 3457 for example it should output 3 + 4 + 5 + 7. But somehow my logik wont work. When i type in 68768 for example i get 6 + 0 + 7. But when i type in 97999 i get the correct output 9 + 7 + 9. I know that i have could do this task easily with diffrent methods but i tried to use loops . Here is my code: And thanks to all
import Prog1Tools.IOTools;
public class Aufgabe {
public static void main(String[] args){
System.out.print("Please type in a number: ");
int zahl = IOTools.readInteger();
int ten_thousand = 0;
int thousand = 0;
int hundret = 0;
for(int i = 0; i < 10; i++){
if((zahl / 10000) == i){
ten_thousand = i;
zahl = zahl - (ten_thousand * 10000);
}
for(int f = 0; f < 10; f++){
if((zahl / 1000) == f){
thousand = f;
zahl = zahl - (thousand * 1000);
}
for(int z = 0; z < 10; z++){
if((zahl / 100) == z){
hundret = z;
}
}
}
}
System.out.println( ten_thousand + " + " + thousand + " + " + hundret);
}
}

Is this what you want?
String s = Integer.toString(zahl);
for (int i = 0; i < s.length() - 1; i++) {
System.out.println(s.charAt(i) + " + ");
}
System.out.println(s.charAt(s.length()-1);

The problem with the code you've presented is that you have the inner loops nested. Instead, you should finish iterating over each loop before starting with the next one.
What's happening at the moment with 68768 is when the outer for loop gets to i=6, the ten_thousand term gets set to 6 and the inner loops proceed to the calculation of the 'thousand' and 'hundred' terms - and does set those as you expect (and leaving zahl equal to 768 - notice that you don't decrease zahl at the hundreds stage)
But then the outer loop continues looping, this time with i=7. With zahl=768, zahl/1000 = 0' so the 'thousand' term gets set to 0. The hundred term always gets reset to 7 with zahl=768.
The 97999 works because the thousand term is set on the final iteration of the 'i' loop, so never gets reset.
The remedy is to not nest the inner loops - and it'll perform a lot better too!

You should do something like this
input = 56789;
int sum = 0;
int remainder = input % 10 // = 9;
sum += remainder // now sum is sum + remainder
input /= 10; // this makes the input 5678
...
// repeat the process
To loop it, use a while loop instead of a for loop. This a great example of when to use a while loop. If this is for a class, it will show your understanding of when to use while loops: when the number of iterations is unknown, but is based on a condition.
int sum = 0;
while (input/10 != 0) {
int remainder = input % 10;
sum += remainder;
input /= 10;
}
// this is all you really need

Your sample is a little bit complicated. To extract the tenthousand, thousand and the hundreds you can simply do this:
private void testFunction(int zahl) {
int tenThousand = (zahl / 10000) % 10;
int thousand = (zahl / 1000) % 10;
int hundred = (zahl / 100) % 10;
System.out.println(tenThousand + "+" + thousand + "+" + hundred);
}
Bit as many devs reported you should convert it to string and process character by character.

Sum of factorials

So I need to output a sum of factorials like 1!+2!...+n!=sum I found a way to get a single factorial but I don't know how to sum them together. This is my attempt at doing so:
System.out.println("Ievadiet ciparu");
Scanner in = new Scanner(System.in);
n = in.nextInt();
if ( n < 0 )
System.out.println("Ciparam jabut pozitivam.");
else
{
while (x>2){
for ( c = 1 ; c <= n ; c++ )
fact = fact*c;
sum=sum+fact;
n=n-1;
if (n==0) break;
}
System.out.println("Faktorialu summa "+sum);

Rather than have a loop 1-n and calculate each factorial elsewhere, I would accumulate the sum as you calculate the factorials - ie have two local variables; one for factorial and one for the sum:
long factorial = 1, sum = 0;
for (int i = 1; i <= n; i++) {
factorial *= i;
sum += factorial;
}
When tested with n = 5, sum is 153, which is correct: 1 + 2 + 6 + 24 + 120
Your problem was that the sum was outside the loop - you just needed braces like here.
Also, your while loop condition x < 2 will never change, so either the loop will never execute (if x > 1) or the loop will never terminate, because x is not changed within the loop.

hmmm my search for finding a recursive(via recursive method calling) version of these code still getting nowhere
`public static long factorialSum(long n){
long x = n;
for(int i = 1; i < n; i++){
x = (n-i)*(1+x);
}
return x;
}`
if you just look at the problem more closely you'll see you can do it in linear time, the trick is in (n-1)! + n! = (n-1)!*(1 + n), to understand this more deeply i recommend add (n-2)! just to see how it grows.