I want calculate
(a^n % k - b^m %k)%k
But a^n and b^m can be very large
Bigmod(bigmod(a^n)-bigmod(b^m)) ?
I have tried to calculate bigmod(a^n) - bigmod(b^m) and then used bigmod for the subtraction result then I realized it gave a wrong answer!
is there any to calculate this ?
#include<cstdio>
using namespace std;
template<class T>T big_mod(T n,T p,T m)
{
if(p==0)
return (T)1;
T x=big_mod(n,p/2,m);
x=(x*x)%m;
if(p&1)
x=(x*n)%m;
return x;
}
int main()
{
long long int a=37,b=26,m=10,n=20,mod=1000000008,x,y,z;
x=big_mod(a,m,mod);
y=big_mod(b,n,mod);
z=((x%mod-y%mod)%mod);
cout<<z;
}
How can i calculate bigmod(bigmod(a^n)-bigmod(b^m)) ?
Let your modulus be k. Your expression is equivalent to:
((a^n) % k - (b^m) % k + k) % k
You need to add k because the subtraction can lead to a negative result. This will make it positive, without affecting the result, since k % k == 0.
To compute (x^y) % k, use the exponentiation by squaring algorithm and make sure you take the modulo at each step:
x^y % k = ((x^(y / 2))^2) % k if y is even
(x*x^(y - 1)) % k else
For your code, assuming everything else works, you just need to change this line:
z=((x%mod-y%mod)%mod);
to this:
z=((x%mod-y%mod+mod)%mod);
Related
I want to compute nCk mod m with following constraints:
n<=10^18
k<=10^5
m=10^9+7
I have read this article:
Calculating Binomial Coefficient (nCk) for large n & k
But here value of m is 1009. Hence using Lucas theorem, we need only to calculate 1009*1009 different values of aCb where a,b<=1009
How to do it with above constraints.
I cannot make a array of O(m*k) space complexity with given constraints.
Help!
The binominal coefficient of (n, k) is calculated by the formula:
(n, k) = n! / k! / (n - k)!
To make this work for large numbers n and k modulo m observe that:
Factorial of a number modulo m can be calculated step-by-step, in
each step taking the result % m. However, this will be far too slow with n up to 10^18. So there are faster methods where the complexity is bounded by the modulo, and you can use some of those.
The division (a / b) mod m is equal to (a * b^-1) mod m, where b^-1 is the inverse of b modulo m (that is, (b * b^-1 = 1) mod m).
This means that:
(n, k) mod m = (n! * (k!)^-1 * ((n - k)!)^-1) mod m
The inverse of a number can be efficiently found using the Extended Euclidean algorithm. Assuming you have the factorial calculation sorted out, the rest of the algorithm is straightforward, just watch out for integer overflows on multiplication. Here's reference code that works up to n=10^9. To handle for larger numbers the factorial computation should be replaced with a more efficient algorithm and the code should be slightly adapted to avoid integer overflows, but the main idea will remain the same:
#define MOD 1000000007
// Extended Euclidean algorithm
int xGCD(int a, int b, int &x, int &y) {
if (b == 0) {
x = 1;
y = 0;
return a;
}
int x1, y1, gcd = xGCD(b, a % b, x1, y1);
x = y1;
y = x1 - (long long)(a / b) * y1;
return gcd;
}
// factorial of n modulo MOD
int modfact(int n) {
int result = 1;
while (n > 1) {
result = (long long)result * n % MOD;
n -= 1;
}
return result;
}
// multiply a and b modulo MOD
int modmult(int a, int b) {
return (long long)a * b % MOD;
}
// inverse of a modulo MOD
int inverse(int a) {
int x, y;
xGCD(a, MOD, x, y);
return x;
}
// binomial coefficient nCk modulo MOD
int bc(int n, int k)
{
return modmult(modmult(modfact(n), inverse(modfact(k))), inverse(modfact(n - k)));
}
Just use the fact that
(n, k) = n! / k! / (n - k)! = n*(n-1)*...*(n-k+1)/[k*(k-1)*...*1]
so you actually have just 2*k=2*10^5 factors. For the inverse of a number you can use suggestion of kfx since your m is prime.
First, you don't need to pre-compute and store all the possible aCb values! they can be computed per case.
Second, for the special case when (k < m) and (n < m^2), the Lucas theorem easily reduces to the following result:
(n choose k) mod m = ((n mod m) choose k) mod m
then since (n mod m) < 10^9+7 you can simply use the code proposed by #kfx.
We want to compute nCk (mod p). I'll handle when 0 <= k <= p-2, because Lucas's theorem handles the rest.
Wilson's theorem states that for prime p, (p-1)! = -1 (mod p), or equivalently (p-2)! = 1 (mod p) (by division).
By division: (k!)^(-1) = (p-2)!/(k!) = (p-2)(p-3)...(k+1) (mod p)
Thus, the binomial coefficient is n!/(k!(n-k)!) = n(n-1)...(n-k+1)/(k!) = n(n-1)...(n-k+1)(p-2)(p-3)...(k+1) (mod p)
Voila. You don't have to do any inverse computations or anything like that. It's also fairly easy to code. A couple optimizations to consider: (1) you can replace (p-2)(p-3)... with (-2)(-3)...; (2) nCk is symmetric in the sense that nCk = nC(n-k) so choose the half that requires you to do less computations.
I want to compute nCk mod m with following constraints:
n<=10^18
k<=10^5
m=10^9+7
I have read this article:
Calculating Binomial Coefficient (nCk) for large n & k
But here value of m is 1009. Hence using Lucas theorem, we need only to calculate 1009*1009 different values of aCb where a,b<=1009
How to do it with above constraints.
I cannot make a array of O(m*k) space complexity with given constraints.
Help!
The binominal coefficient of (n, k) is calculated by the formula:
(n, k) = n! / k! / (n - k)!
To make this work for large numbers n and k modulo m observe that:
Factorial of a number modulo m can be calculated step-by-step, in
each step taking the result % m. However, this will be far too slow with n up to 10^18. So there are faster methods where the complexity is bounded by the modulo, and you can use some of those.
The division (a / b) mod m is equal to (a * b^-1) mod m, where b^-1 is the inverse of b modulo m (that is, (b * b^-1 = 1) mod m).
This means that:
(n, k) mod m = (n! * (k!)^-1 * ((n - k)!)^-1) mod m
The inverse of a number can be efficiently found using the Extended Euclidean algorithm. Assuming you have the factorial calculation sorted out, the rest of the algorithm is straightforward, just watch out for integer overflows on multiplication. Here's reference code that works up to n=10^9. To handle for larger numbers the factorial computation should be replaced with a more efficient algorithm and the code should be slightly adapted to avoid integer overflows, but the main idea will remain the same:
#define MOD 1000000007
// Extended Euclidean algorithm
int xGCD(int a, int b, int &x, int &y) {
if (b == 0) {
x = 1;
y = 0;
return a;
}
int x1, y1, gcd = xGCD(b, a % b, x1, y1);
x = y1;
y = x1 - (long long)(a / b) * y1;
return gcd;
}
// factorial of n modulo MOD
int modfact(int n) {
int result = 1;
while (n > 1) {
result = (long long)result * n % MOD;
n -= 1;
}
return result;
}
// multiply a and b modulo MOD
int modmult(int a, int b) {
return (long long)a * b % MOD;
}
// inverse of a modulo MOD
int inverse(int a) {
int x, y;
xGCD(a, MOD, x, y);
return x;
}
// binomial coefficient nCk modulo MOD
int bc(int n, int k)
{
return modmult(modmult(modfact(n), inverse(modfact(k))), inverse(modfact(n - k)));
}
Just use the fact that
(n, k) = n! / k! / (n - k)! = n*(n-1)*...*(n-k+1)/[k*(k-1)*...*1]
so you actually have just 2*k=2*10^5 factors. For the inverse of a number you can use suggestion of kfx since your m is prime.
First, you don't need to pre-compute and store all the possible aCb values! they can be computed per case.
Second, for the special case when (k < m) and (n < m^2), the Lucas theorem easily reduces to the following result:
(n choose k) mod m = ((n mod m) choose k) mod m
then since (n mod m) < 10^9+7 you can simply use the code proposed by #kfx.
We want to compute nCk (mod p). I'll handle when 0 <= k <= p-2, because Lucas's theorem handles the rest.
Wilson's theorem states that for prime p, (p-1)! = -1 (mod p), or equivalently (p-2)! = 1 (mod p) (by division).
By division: (k!)^(-1) = (p-2)!/(k!) = (p-2)(p-3)...(k+1) (mod p)
Thus, the binomial coefficient is n!/(k!(n-k)!) = n(n-1)...(n-k+1)/(k!) = n(n-1)...(n-k+1)(p-2)(p-3)...(k+1) (mod p)
Voila. You don't have to do any inverse computations or anything like that. It's also fairly easy to code. A couple optimizations to consider: (1) you can replace (p-2)(p-3)... with (-2)(-3)...; (2) nCk is symmetric in the sense that nCk = nC(n-k) so choose the half that requires you to do less computations.
I'm trying to solve the following problem:
A rectangular paper sheet of M*N is to be cut down into squares such that:
The paper is cut along a line that is parallel to one of the sides of the paper.
The paper is cut such that the resultant dimensions are always integers.
The process stops when the paper can't be cut any further.
What is the minimum number of paper pieces cut such that all are squares?
Limits: 1 <= N <= 100 and 1 <= M <= 100.
Example: Let N=1 and M=2, then answer is 2 as the minimum number of squares that can be cut is 2 (the paper is cut horizontally along the smaller side in the middle).
My code:
cin >> n >> m;
int N = min(n,m);
int M = max(n,m);
int ans = 0;
while (N != M) {
ans++;
int x = M - N;
int y = N;
M = max(x, y);
N = min(x, y);
}
if (N == M && M != 0)
ans++;
But I am not getting what's wrong with this approach as it's giving me a wrong answer.
I think both the DP and greedy solutions are not optimal. Here is the counterexample for the DP solution:
Consider the rectangle of size 13 X 11. DP solution gives 8 as the answer. But the optimal solution has only 6 squares.
This thread has many counter examples: https://mathoverflow.net/questions/116382/tiling-a-rectangle-with-the-smallest-number-of-squares
Also, have a look at this for correct solution: http://int-e.eu/~bf3/squares/
I'd write this as a dynamic (recursive) program.
Write a function which tries to split the rectangle at some position. Call the function recursively for both parts. Try all possible splits and take the one with the minimum result.
The base case would be when both sides are equal, i.e. the input is already a square, in which case the result is 1.
function min_squares(m, n):
// base case:
if m == n: return 1
// minimum number of squares if you split vertically:
min_ver := min { min_squares(m, i) + min_squares(m, n-i) | i ∈ [1, n/2] }
// minimum number of squares if you split horizontally:
min_hor := min { min_squares(i, n) + min_squares(m-i, n) | i ∈ [1, m/2] }
return min { min_hor, min_ver }
To improve performance, you can cache the recursive results:
function min_squares(m, n):
// base case:
if m == n: return 1
// check if we already cached this
if cache contains (m, n):
return cache(m, n)
// minimum number of squares if you split vertically:
min_ver := min { min_squares(m, i) + min_squares(m, n-i) | i ∈ [1, n/2] }
// minimum number of squares if you split horizontally:
min_hor := min { min_squares(i, n) + min_squares(m-i, n) | i ∈ [1, m/2] }
// put in cache and return
result := min { min_hor, min_ver }
cache(m, n) := result
return result
In a concrete C++ implementation, you could use int cache[100][100] for the cache data structure since your input size is limited. Put it as a static local variable, so it will automatically be initialized with zeroes. Then interpret 0 as "not cached" (as it can't be the result of any inputs).
Possible C++ implementation: http://ideone.com/HbiFOH
The greedy algorithm is not optimal. On a 6x5 rectangle, it uses a 5x5 square and 5 1x1 squares. The optimal solution uses 2 3x3 squares and 3 2x2 squares.
To get an optimal solution, use dynamic programming. The brute-force recursive solution tries all possible horizontal and vertical first cuts, recursively cutting the two pieces optimally. By caching (memoizing) the value of the function for each input, we get a polynomial-time dynamic program (O(m n max(m, n))).
This problem can be solved using dynamic programming.
Assuming we have a rectangle with width is N and height is M.
if (N == M), so it is a square and nothing need to be done.
Otherwise, we can divide the rectangle into two other smaller one (N - x, M) and (x,M), so it can be solved recursively.
Similarly, we can also divide it into (N , M - x) and (N, x)
Pseudo code:
int[][]dp;
boolean[][]check;
int cutNeeded(int n, int m)
if(n == m)
return 1;
if(check[n][m])
return dp[n][m];
check[n][m] = true;
int result = n*m;
for(int i = 1; i <= n/2; i++)
int tmp = cutNeeded(n - i, m) + cutNeeded(i,m);
result = min(tmp, result);
for(int i = 1; i <= m/2; i++)
int tmp = cutNeeded(n , m - i) + cutNeeded(n,i);
result = min(tmp, result);
return dp[n][m] = result;
Here is a greedy impl. As #David mentioned it is not optimal and is completely wrong some cases so dynamic approach is the best (with caching).
def greedy(m, n):
if m == n:
return 1
if m < n:
m, n = n, m
cuts = 0
while n:
cuts += m/n
m, n = n, m % n
return cuts
print greedy(2, 7)
Here is DP attempt in python
import sys
def cache(f):
db = {}
def wrap(*args):
key = str(args)
if key not in db:
db[key] = f(*args)
return db[key]
return wrap
#cache
def squares(m, n):
if m == n:
return 1
xcuts = sys.maxint
ycuts = sys.maxint
x, y = 1, 1
while x * 2 <= n:
xcuts = min(xcuts, squares(m, x) + squares(m, n - x))
x += 1
while y * 2 <= m:
ycuts = min(ycuts, squares(y, n) + squares(m - y, n))
y += 1
return min(xcuts, ycuts)
This is essentially classic integer or 0-1 knapsack problem that can be solved using greedy or dynamic programming approach. You may refer to: Solving the Integer Knapsack
I need to find n!%1000000009.
n is of type 2^k for k in range 1 to 20.
The function I'm using is:
#define llu unsigned long long
#define MOD 1000000009
llu mulmod(llu a,llu b) // This function calculates (a*b)%MOD caring about overflows
{
llu x=0,y=a%MOD;
while(b > 0)
{
if(b%2 == 1)
{
x = (x+y)%MOD;
}
y = (y*2)%MOD;
b /= 2;
}
return (x%MOD);
}
llu fun(int n) // This function returns answer to my query ie. n!%MOD
{
llu ans=1;
for(int j=1; j<=n; j++)
{
ans=mulmod(ans,j);
}
return ans;
}
My demand is such that I need to call the function 'fun', n/2 times. My code runs too slow for values of k around 15. Is there a way to go faster?
EDIT:
In actual I'm calculating 2*[(i-1)C(2^(k-1)-1)]*[((2^(k-1))!)^2] for all i in range 2^(k-1) to 2^k. My program demands (nCr)%MOD caring about overflows.
EDIT: I need an efficient way to find nCr%MOD for large n.
The mulmod routine can be speeded up by a large factor K.
1) '%' is overkill, since (a + b) are both less than N.
- It's enough to evaluate c = a+b; if (c>=N) c-=N;
2) Multiple bits can be processed at once; see optimization to "Russian peasant's algorithm"
3) a * b is actually small enough to fit 64-bit unsigned long long without overflow
Since the actual problem is about nCr mod M, the high level optimization requires using the recurrence
(n+1)Cr mod M = (n+1)nCr / (n+1-r) mod M.
Because the left side of the formula ((nCr) mod M)*(n+1) is not divisible by (n+1-r), the division needs to be implemented as multiplication with the modular inverse: (n+r-1)^(-1). The modular inverse b^(-1) is b^(M-1), for M being prime. (Otherwise it's b^(phi(M)), where phi is Euler's Totient function.)
The modular exponentiation is most commonly implemented with repeated squaring, which requires in this case ~45 modular multiplications per divisor.
If you can use the recurrence
nC(r+1) mod M = nCr * (n-r) / (r+1) mod M
It's only necessary to calculate (r+1)^(M-1) mod M once.
Since you are looking for nCr for multiple sequential values of n you can make use of the following:
(n+1)Cr = (n+1)! / ((r!)*(n+1-r)!)
(n+1)Cr = n!*(n+1) / ((r!)*(n-r)!*(n+1-r))
(n+1)Cr = n! / ((r!)*(n-r)!) * (n+1)/(n+1-r)
(n+1)Cr = nCr * (n+1)/(n+1-r)
This saves you from explicitly calling the factorial function for each i.
Furthermore, to save that first call to nCr you can use:
nC(n-1) = n //where n in your case is 2^(k-1).
EDIT:
As Aki Suihkonen pointed out, (a/b) % m != a%m / b%m. So the method above so the method above won't work right out of the box. There are two different solutions to this:
1000000009 is prime, this means that a/b % m == a*c % m where c is the inverse of b modulo m. You can find an explanation of how to calculate it here and follow the link to the Extended Euclidean Algorithm for more on how to calculate it.
The other option which might be easier is to recognize that since nCr * (n+1)/(n+1-r) must give an integer, it must be possible to write n+1-r == a*b where a | nCr and b | n+1 (the | here means divides, you can rewrite that as nCr % a == 0 if you like). Without loss of generality, let a = gcd(n+1-r,nCr) and then let b = (n+1-r) / a. This gives (n+1)Cr == (nCr / a) * ((n+1) / b) % MOD. Now your divisions are guaranteed to be exact, so you just calculate them and then proceed with the multiplication as before. EDIT As per the comments, I don't believe this method will work.
Another thing I might try is in your llu mulmod(llu a,llu b)
llu mulmod(llu a,llu b)
{
llu q = a * b;
if(q < a || q < b) // Overflow!
{
llu x=0,y=a%MOD;
while(b > 0)
{
if(b%2 == 1)
{
x = (x+y)%MOD;
}
y = (y*2)%MOD;
b /= 2;
}
return (x%MOD);
}
else
{
return q % MOD;
}
}
That could also save some precious time.
What is fastest way to calculate the first n satisfying the equation
a^n mod m = 1
Here a,n,m can be prime or composite
mod : is the modulus operator
What is wrong with the direct way:
int mod_order(int m, int a) {
for(int n = 1, an = a; n != m; n++, an = an * a % m) if(an % m == 1) return n;
return -1;
}
If gcd(a,m)>1, then there is no such n. (Obvious)
Otherwise, if m is prime, n=m-1. (Proof)
Otherwise (and as more general case), n=ф(m), where ф is Euler's totient function. (Proof)
As you can see, computing ф(m) is essentially the same as factorization of m. This can be done in sqrt(m) time or faster, depending on how convoluted is the algorithm you use. Simple one:
int phi(m){
if(m==1) return 1;
for(int d=2; d*d<m; ++d){
if(m%d != 0) continue;
int deg = 1; long acc=1;
for(; m%(acc*d)==0; ++deg) acc*=d;
acc /= d;
return phi(m/acc)*acc*(d-1)/d;
}
return m-1;
}
Upd: My bad. a^(ф(m)) = 1 (mod m), but there can be lesser value of n (for a=1, n=1, no difference what m is; for a=14, m=15, n=2). n is divisor of ф(m), but efficiently computing least possible n seems to be tricky. Task can be divided, by using this theorem (minimal n is least common multiple for all degrees for respective remainders). But when m is prime or has big enough prime divisor, and there is only one a (as opposed to computing n for many different a with the same m), we're kind of out of options. You may want to look at 1, 2.