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Factorial for number N is N x N-1 x N-2 X N- (N-1). e.g. if you need to calculate factorial for 5 then compute 5 x 4 x 3 x 2 x 1.
P(n,r)=(n!)/(r!(n-r)!)This would give you the number of possible permutations.n factorial over r factorial times n minus r factorial
int factorial(int n) { int i; int f=1; for(i=2;i<=n;++i) f*=i; return f; }
It is n! or n factorial.
n! = 1*2* ... * (n-1)*n
The time complexity of an algorithm with a factorial time complexity of O(n!) is O(n!).
Factorial for number N is N x N-1 x N-2 X N- (N-1). e.g. if you need to calculate factorial for 5 then compute 5 x 4 x 3 x 2 x 1.
P(n,r)=(n!)/(r!(n-r)!)This would give you the number of possible permutations.n factorial over r factorial times n minus r factorial
int factorial(int n) { int i; int f=1; for(i=2;i<=n;++i) f*=i; return f; }
An example in Java, to compute 10!: int factorial = 1; for(int i = 1; i < 11; i++) { factorial *= i; }
Oh, what a lovely question! To compute the sum of the squares of N numbers, you can create a simple algorithm. Start by initializing a variable to hold the sum, then loop through each number, square it, and add it to the sum. Once you've done this for all N numbers, you'll have the sum of their squares. Just like painting a happy little tree, take your time and enjoy the process.
Factorial (n) = n * Factorial (n-1) for all positive values n given Factorial (1) = Factorial (0) = 1. Pseudo-code: Function: factorial, f Argument: positive number, n IF n<=1 THEN RETURN 1 ELSE RETURN n * f(n-1) END IF
#include <iostream> using namespace std; int main() { int i, number=0, factorial=1; // User input must be an integer number between 1 and 10 while(number<1 number>10) { cout << "Enter integer number (1-10) = "; cin >> number; } // Calculate the factorial with a FOR loop for(i=1; i<=number; i++) { factorial = factorial*i; } // Output result cout << "Factorial = " << factorial << endl;
// returns n! int fact(int n) { int f_n = 1; for(int i = n; i > 1; --i) { f_n *= n; } return f_n; }
It is not except when n = 1.
what is the value of negative n factorial ?
The time complexities associated with the algorithm being used include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, O(n2) for quadratic time, and O(n!) for factorial time. These complexities represent how the algorithm's performance scales with the input size.