So I'm trying to figure out if that blue line is in the right place, I know that I should have 9 edges but not sure if it's correct.
The code
public int getResult(int p1, int p2) {
int result = 0; // 1
if (p1 == 0) { // 2
result += 1; //3
} else {
result += 2; //4
}
if (p2 == 0) { //5
result += 3; //6
} else {
result += 4; //7
}
return result; //8 exit node
}
so 8 nodes and it should have 9 edges, right? Did I do the right thing?
Yes, the blue line is placed correctly because after the 3rd line, your program is going to jump to the 5th line.
The easiest way to compute cyclomatic complexity without drawing any flow diagram is as follows:
Count all the loops in the program for, while, do-while, if. Assign a value of 1 to each loop. Else should not be counted here.
Assign a value of 1 to each switch case. Default case should not be counted here.
Cyclomatic complexity = Total number of loops + 1
In your program, there are 2 if loops, so the cyclomatic complexity would be 3(2+1)
You can cross-check it with the standard formulae available as well which are as below:
C = E-N+2 (9-8+2=3)
OR
C = Number of closed regions + 1 (2+1=3)
According to wikipedia:
M = E − N + 2P,
where
E = the number of edges of the graph.
N = the number of nodes of the graph.
P = the number of connected components.
so:
9 - 8 + 2*1 = 3
Related
Background Information: I solved the N-Queens problem with the C# algorithm below, which returns the total number of solutions given the board of size n x n. It works, but I do not understand why this would be O(n!) time complexity, or if it is a different time complexity. I am also unsure of the space used in the recursion stack (but am aware of the extra space used in the boolean jagged array). I cannot seem to wrap my mind around understanding the time and space complexity of such solutions. Having this understanding would be especially useful during technical interviews, for complexity analysis without the ability to run code.
Preliminary Investigation: I have read several SO posts where the author directly asks the community to provide the time and space complexity of their algorithms. Rather than doing the same and asking for the quick and easy answers, I would like to understand how to calculate the time and space complexity of backtracking algorithms so that I can do so moving forward.
I have also read in numerous locations within and outside of SO that generally, recursive backtracking algorithms are O(n!) time complexity since at each of the n iterations, you look at one less item: n, then n - 1, then n - 2, ... 1. However, I have not found any explanation as to why this is the case. I also have not found any explanation for the space complexity of such algorithms.
Question: Can someone please explain the step-by-step problem-solving approach to identify time and space complexities of recursive backtracking algorithms such as these?
public class Solution {
public int NumWays { get; set; }
public int TotalNQueens(int n) {
if (n <= 0)
{
return 0;
}
NumWays = 0;
bool[][] board = new bool[n][];
for (int i = 0; i < board.Length; i++)
{
board[i] = new bool[n];
}
Solve(n, board, 0);
return NumWays;
}
private void Solve(int n, bool[][] board, int row)
{
if (row == n)
{
// Terminate since we've hit the bottom of the board
NumWays++;
return;
}
for (int col = 0; col < n; col++)
{
if (CanPlaceQueen(board, row, col))
{
board[row][col] = true; // Place queen
Solve(n, board, row + 1);
board[row][col] = false; // Remove queen
}
}
}
private bool CanPlaceQueen(bool[][] board, int row, int col)
{
// We only need to check diagonal-up-left, diagonal-up-right, and straight up.
// this is because we should not have a queen in a later row anywhere, and we should not have a queen in the same row
for (int i = 1; i <= row; i++)
{
if (row - i >= 0 && board[row - i][col]) return false;
if (col - i >= 0 && board[row - i][col - i]) return false;
if (col + i < board[0].Length && board[row - i][col + i]) return false;
}
return true;
}
}
First of all, it's definitely not true that recursive backtracking algorithms are all in O(n!): of course it depends on the algorithm, and it could well be worse. Having said that, the general approach is to write down a recurrence relation for the time complexity T(n), and then try to solve it or at least characterize its asymptotic behaviour.
Step 1: Make the question precise
Are we interested in the worst-case, best-case or average-case? What are the input parameters?
In this example, let us assume we want to analyze the worst-case behaviour, and the relevant input parameter is n in the Solve method.
In recursive algorithms, it is useful (though not always possible) to find a parameter that starts off with the value of the input parameter and then decreases with every recursive call until it reaches the base case.
In this example, we can define k = n - row. So with every recursive call, k is decremented starting from n down to 0.
Step 2: Annotate and strip down the code
No we look at the code, strip it down to just the relevant bits and annotate it with complexities.
We can boil your example down to the following:
private void Solve(int n, bool[][] board, int row)
{
if (row == n) // base case
{
[...] // O(1)
return;
}
for (...) // loop n times
{
if (CanPlaceQueen(board, row, col)) // O(k)
{
[...] // O(1)
Solve(n, board, row + 1); // recurse on k - 1 = n - (row + 1)
[...] // O(1)
}
}
}
Step 3: Write down the recurrence relation
The recurrence relation for this example can be read off directly from the code:
T(0) = 1 // base case
T(k) = k * // loop n times
(O(k) + // if (CanPlaceQueen(...))
T(k-1)) // Solve(n, board, row + 1)
= k T(k-1) + O(k)
Step 4: Solve the recurrence relation
For this step, it is useful to know a few general forms of recurrence relations and their solutions. The relation above is of the general form
T(n) = n T(n-1) + f(n)
which has the exact solution
T(n) = n!(T(0) + Sum { f(i)/i!, for i = 1..n })
which we can easily prove by induction:
T(n) = n T(n-1) + f(n) // by def.
= n((n-1)!(T(0) + Sum { f(i)/i!, for i = 1..n-1 })) + f(n) // by ind. hypo.
= n!(T(0) + Sum { f(i)/i!, for i = 1..n-1 }) + f(n)/n!)
= n!(T(0) + Sum { f(i)/i!, for i = 1..n }) // qed
Now, we don't need the exact solution; we just need the asymptotic behaviour when n approaches infinity.
So let's look at the infinite series
Sum { f(i)/i!, for i = 1..infinity }
In our case, f(n) = O(n), but let's look at the more general case where f(n) is an arbitary polynomial in n (because it will turn out that it really doesn't matter). It is easy to see that the series converges, using the ratio test:
L = lim { | (f(n+1)/(n+1)!) / (f(n)/n!) |, for n -> infinity }
= lim { | f(n+1) / (f(n)(n+1)) |, for n -> infinity }
= 0 // if f is a polynomial
< 1, and hence the series converges
Therefore, for n -> infinity,
T(n) -> n!(T(0) + Sum { f(i)/i!, for i = 1..infinity })
= T(0) n!, if f is a polynomial
Step 5: The result
Since the limit of T(n) is T(0) n!, we can write
T(n) ∈ Θ(n!)
which is a tight bound on the worst-case complexity of your algorithm.
In addition, we've proven that it doesn't matter how much work you do within the for-loop in adddition to the recursive calls, as long as it's polynomial, the complexity stays Θ(n!) (for this form of recurrence relations). (In bold because there are lots of SO answers that get this wrong.)
For a similar analysis with a different form of recurrence relation, see here.
Update
I made a mistake in the annotation of the code (I'll leave it because it is still instructive). Actually, both the loop and the work done within the loop do not depend on k = n - row but on the initial value n (let's call it n0 to make it clear).
So the recurrence relation becomes
T(k) = n0 T(k-1) + n0
for which the exact solution is
T(k) = n0^k (T(0) + Sum { n0^(1-i), for i = 1..k })
But since initially n0 = k, we have
T(k) = k^k (T(0) + Sum { n0^(1-i), for i = 1..k })
∈ Θ(k^k)
which is a bit worse than Θ(k!).
This might be a bit cryptic title but I have a very specific problem. First my current setup
Namely in my card simulator I deal 32 cards to 4 players in sets of 8. So 8 cards per player.
With the 4 standard suits (spades, harts , etc)
My current implementation cycles threw all combinations of 8 out of 32
witch gives me a large number of possibilities.
Namely the first player can have 10518300 different hands be dealt.
The second can then be dealt 735471 different hands.
The third player then 12870 different hands.
and finally the fourth can have only 1
giving me a grand total of 9.9561092e+16 different unique ways to deal a deck of 32 cards to 4 players. if the order of cards doesn’t matter.
On a 4 Ghz processor even with 1 tick per possibility it would take me half a year.
However I would like to simplify this dealing of cards by making the exchange of diamonds, harts and spades. Meaning that dealing of 8 harts to player 1 is equivalent to dealing 8 spades. (note that this doesn’t apply to clubs)
I am looking for a way to generate this. Because this will cut down the possibilities of the first hand by at least a factor of 6. My current implementation is in c++.
But feel free to answer in a different Languages
/** http://stackoverflow.com/a/9331125 */
unsigned cjasMain::nChoosek( unsigned n, unsigned k )
{
//assert(k < n);
if (k > n) return 0;
if (k * 2 > n) k = n-k;
if (k == 0) return 1;
int result = n;
for( int i = 2; i <= k; ++i ) {
result *= (n-i+1);
result /= i;
}
return result;
}
/** [combination c n p x]
* get the [x]th lexicographically ordered set of [r] elements in [n]
* output is in [c], and should be sizeof(int)*[r]
* http://stackoverflow.com/a/794 */
void cjasMain::Combination(int8_t* c,unsigned n,unsigned r, unsigned x){
++x;
assert(x>0);
int i,p,k = 0;
for(i=0;i<r-1;i++){
c[i] = (i != 0) ? c[i-1] : 0;
do {
c[i]++;
p = nChoosek(n-c[i],r-(i+1));
k = k + p;
} while(k < x);
k = k - p;
}
c[r-1] = c[r-2] + x - k;
}
/**http://stackoverflow.com/a/9430993 */
template <unsigned n,std::size_t r>
void cjasMain::Combinations()
{
static_assert(n>=r,"error n needs to be larger then r");
std::vector<bool> v(n);
std::fill(v.begin() + r, v.end(), true);
do
{
for (int i = 0; i < n; ++i)
{
if (!v[i])
{
COUT << (i+1) << " ";
}
}
static int j=0;
COUT <<'\t'<< j++<< "\n";
}
while (std::next_permutation(v.begin(), v.end()));
return;
}
A requirement is that from lexicographical number I can get back the original array.
Even the slightest optimization can help my monto carol simulation I hope.
I've been looking for some simple coding challenges recently, and discovered about Pascal's triangle (here), and I've tried to generate one myself in C/Objective-C. For those that don't know what it is, that link explains it pretty well.
I'm starting to get oddness after the fourth row, and I just can't figure out why.
My output for 5 iterations currently looks like this:
1
1 1
1 2 1
1 3 3 1
4 6 3 1
It should look like this:
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
Here is my code so far. The first loop is just a reset loop (setting all the values to 0). The actual logic happens mostly in the second loop. The third loop is where the values are concatenated and formatted in a string.
I've commented this code much more than I would for myself just to aid readability.
int iterations, i, b, mid, chars, temp;
NSLog(#"Please enter the number of itereations");
scanf("%i",&iterations); // take users input and store it in iterations
// calculate where the first 1 should go.
if (iterations % 2 == 0) mid = (iterations)/2;
else mid = (iterations+1)/2;
chars = iterations*2;
int solutions[iterations][chars];
// reset loop
for (i = 0; i<iterations; i++) {
for (b = 0; b<chars; b++) {
solutions[i][b] = 0;
}
}
solutions[0][mid] = 1; // place the initial 1 in first row
for (int row = 1; row<iterations; row++) {
for (int chi = 0; chi<chars; chi++) {
temp = 0;
if (chi > 0) {
temp += solutions[row-1][chi-1]; // add the one diagonally left
}
if (chi < iterations) {
temp += solutions[row-1][chi+1]; // add the one diagonally right
}
solutions[row][chi] = temp; // set the value
}
}
// printing below...
NSMutableString *result = [[NSMutableString alloc] initWithString:#"\n"];
NSMutableString *rowtmp;
for (i = 0; i<iterations; i++) {
rowtmp = [NSMutableString stringWithString:#""];
for (b = 0; b<chars; b++) {
if (solutions[i][b] != 0) [rowtmp appendFormat:#"%i",solutions[i][b]];
else [rowtmp appendString:#" "]; // replace any 0s with spaces.
}
[result appendFormat:#"%#\n",rowtmp];
}
NSLog(#"%#",result);
[result release];
I have a feeling the problem may be to do with the offset, but I have no idea how to fix it. If anyone can spot where my code is going wrong, that would be great.
It appears (from a brief look) that the original midpoint calculation is incorrect. I think it should simply be:
mid = iterations - 1;
In the example of 5 iterations, the midpoint needs to be at array position 4. Each iteration "moves" one more position to the left. The 2nd iteration (2nd row) would then place a 1 at positions 3 and 5. The 3rd iteration at 2 and 6. The 4th at 1 and 7. And the 5th and last iteration would fill in the 1s at 0 and 8.
Also, the second if statement for the temp addition should be as follows otherwise it reads past the end of the array bounds:
if (chi < iterations - 1) {
I have a program with four different buttons. I want to interchange the position of the buttons randomly. For example: 1 2 3 4 Later: 3 4 1 2 Later: 1 3 2 4
Is there a algorithms for that? The only way I can think is to make a random number from 1 to 24 (24 possibilities) and then code all the possible button postitions.
int foo = arcrandom() % 23;
switch(foo){
case 0:
button1postiton = 100; //just an example
button2position = 200;
button3position = 300;
button4position = 400;
break;
case 2:
button1postiton = 200;
//blablabla and so on and so on
}
But is there a more efficient way?
Thanks!
You could shuffle the buttons or their positions, e.g. with a Fisher-Yates shuffle.
There is code in this website to get a list of all permutations of an array (see method perm2), it is coded for char arrays, but can be modified to do int arrays as well and to other languages as well, then you can use mjv's idea.
http://www.cs.princeton.edu/introcs/23recursion/Permutations.java.html
If in Java, this is what I would try....
Once you get all the possible permutations maybe in a vector, I think you can use a grid bag layout and change the grid constraints, picking one of the elements of the vector randomly. I have not tried this out, but I am thinking along the lines of
Vector permutations = ... //get the permutation using a class similar to the one in the website for an array of ints {0,1,2,3}
//The panel
JPanel pane;
JButton button;
pane.setLayout(new GridBagLayout());
GridBagConstraints c = new GridBagConstraints();
//Choose one permutation at random
int foo = arcrandom() % 23;
int current[] = permutations.get(foo);
//Add the buttons in the chosen order
button = new JButton("Button 1");
c.gridx = current[0];
c.gridy = 0;
pane.add(button, c);
button = new JButton("Button 2");
c.gridx = current[1];
c.gridy = 0;
pane.add(button, c);
button = new JButton("Button 3");
c.gridx = current[2];
c.gridy = 0;
pane.add(button, c);
button = new JButton("Button 4");
c.gridx = current[3];
c.gridy = 0;
pane.add(button, c);
Let me know if this works!
Start with a random number 0 <= r < 24
Start with your first position. Derive rr = r % 4 and r = r / 4. Those are the remainder and quotient respectively after division by 4.
The remainder specifies a position. Swap position 0 with the specified position.
For the next position, derive rr = r % 3 and r = r / 3. Again the remainder specifies a position, this time 0, 1 or 2, but relative to your current position (1).
Swap position 1 with position rr+1.
For the next position, derive rr = r % 2 and r = r / 2. Again the remainder specifies a position, this time 0 or 1, and relative to your current position again (2).
Swap position 2 with position rr+2.
For position 3, there is nothing to do.
Note - for each swap, one possibility is to swap a position with itself. Obviously no swap is needed for that.
This is probably the Fisher-Yates shuffle - I had no idea it had a name until today.
Thanks for all your answers! I used the Fisher-Yates shuffle! I found here a nice tutorial, how to use the algorithm in Objective-C: gorbster.net
How many possible combinations of the variables a,b,c,d,e are possible if I know that:
a+b+c+d+e = 500
and that they are all integers and >= 0, so I know they are finite.
#Torlack, #Jason Cohen: Recursion is a bad idea here, because there are "overlapping subproblems." I.e., If you choose a as 1 and b as 2, then you have 3 variables left that should add up to 497; you arrive at the same subproblem by choosing a as 2 and b as 1. (The number of such coincidences explodes as the numbers grow.)
The traditional way to attack such a problem is dynamic programming: build a table bottom-up of the solutions to the sub-problems (starting with "how many combinations of 1 variable add up to 0?") then building up through iteration (the solution to "how many combinations of n variables add up to k?" is the sum of the solutions to "how many combinations of n-1 variables add up to j?" with 0 <= j <= k).
public static long getCombos( int n, int sum ) {
// tab[i][j] is how many combinations of (i+1) vars add up to j
long[][] tab = new long[n][sum+1];
// # of combos of 1 var for any sum is 1
for( int j=0; j < tab[0].length; ++j ) {
tab[0][j] = 1;
}
for( int i=1; i < tab.length; ++i ) {
for( int j=0; j < tab[i].length; ++j ) {
// # combos of (i+1) vars adding up to j is the sum of the #
// of combos of i vars adding up to k, for all 0 <= k <= j
// (choosing i vars forces the choice of the (i+1)st).
tab[i][j] = 0;
for( int k=0; k <= j; ++k ) {
tab[i][j] += tab[i-1][k];
}
}
}
return tab[n-1][sum];
}
$ time java Combos
2656615626
real 0m0.151s
user 0m0.120s
sys 0m0.012s
The answer to your question is 2656615626.
Here's the code that generates the answer:
public static long getNumCombinations( int summands, int sum )
{
if ( summands <= 1 )
return 1;
long combos = 0;
for ( int a = 0 ; a <= sum ; a++ )
combos += getNumCombinations( summands-1, sum-a );
return combos;
}
In your case, summands is 5 and sum is 500.
Note that this code is slow. If you need speed, cache the results from summand,sum pairs.
I'm assuming you want numbers >=0. If you want >0, replace the loop initialization with a = 1 and the loop condition with a < sum. I'm also assuming you want permutations (e.g. 1+2+3+4+5 plus 2+1+3+4+5 etc). You could change the for-loop if you wanted a >= b >= c >= d >= e.
I solved this problem for my dad a couple months ago...extend for your use. These tend to be one time problems so I didn't go for the most reusable...
a+b+c+d = sum
i = number of combinations
for (a=0;a<=sum;a++)
{
for (b = 0; b <= (sum - a); b++)
{
for (c = 0; c <= (sum - a - b); c++)
{
//d = sum - a - b - c;
i++
}
}
}
This would actually be a good question to ask on an interview as it is simple enough that you could write up on a white board, but complex enough that it might trip someone up if they don't think carefully enough about it. Also, you can also for two different answers which cause the implementation to be quite different.
Order Matters
If the order matters then any solution needs to allow for zero to appear for any of the variables; thus, the most straight forward solution would be as follows:
public class Combos {
public static void main() {
long counter = 0;
for (int a = 0; a <= 500; a++) {
for (int b = 0; b <= (500 - a); b++) {
for (int c = 0; c <= (500 - a - b); c++) {
for (int d = 0; d <= (500 - a - b - c); d++) {
counter++;
}
}
}
}
System.out.println(counter);
}
}
Which returns 2656615626.
Order Does Not Matter
If the order does not matter then the solution is not that much harder as you just need to make sure that zero isn't possible unless sum has already been found.
public class Combos {
public static void main() {
long counter = 0;
for (int a = 1; a <= 500; a++) {
for (int b = (a != 500) ? 1 : 0; b <= (500 - a); b++) {
for (int c = (a + b != 500) ? 1 : 0; c <= (500 - a - b); c++) {
for (int d = (a + b + c != 500) ? 1 : 0; d <= (500 - a - b - c); d++) {
counter++;
}
}
}
}
System.out.println(counter);
}
}
Which returns 2573155876.
One way of looking at the problem is as follows:
First, a can be any value from 0 to 500. Then if follows that b+c+d+e = 500-a. This reduces the problem by one variable. Recurse until done.
For example, if a is 500, then b+c+d+e=0 which means that for the case of a = 500, there is only one combination of values for b,c,d and e.
If a is 300, then b+c+d+e=200, which is in fact the same problem as the original problem, just reduced by one variable.
Note: As Chris points out, this is a horrible way of actually trying to solve the problem.
link text
If they are a real numbers then infinite ... otherwise it is a bit trickier.
(OK, for any computer representation of a real number there would be a finite count ... but it would be big!)
It has general formulae, if
a + b + c + d = N
Then number of non-negative integral solution will be C(N + number_of_variable - 1, N)
#Chris Conway answer is correct. I have tested with a simple code that is suitable for smaller sums.
long counter = 0;
int sum=25;
for (int a = 0; a <= sum; a++) {
for (int b = 0; b <= sum ; b++) {
for (int c = 0; c <= sum; c++) {
for (int d = 0; d <= sum; d++) {
for (int e = 0; e <= sum; e++) {
if ((a+b+c+d+e)==sum) counter=counter+1L;
}
}
}
}
}
System.out.println("counter e "+counter);
The answer in math is 504!/(500! * 4!).
Formally, for x1+x2+...xk=n, the number of combination of nonnegative number x1,...xk is the binomial coefficient: (k-1)-combination out of a set containing (n+k-1) elements.
The intuition is to choose (k-1) points from (n+k-1) points and use the number of points between two chosen points to represent a number in x1,..xk.
Sorry about the poor math edition for my fist time answering Stack Overflow.
Just a test for code block
Just a test for code block
Just a test for code block
Including negatives? Infinite.
Including only positives? In this case they wouldn't be called "integers", but "naturals", instead. In this case... I can't really solve this, I wish I could, but my math is too rusty. There is probably some crazy integral way to solve this. I can give some pointers for the math skilled around.
being x the end result,
the range of a would be from 0 to x,
the range of b would be from 0 to (x - a),
the range of c would be from 0 to (x - a - b),
and so forth until the e.
The answer is the sum of all those possibilities.
I am trying to find some more direct formula on Google, but I am really low on my Google-Fu today...