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I am having difficulty deciding what the time complexity of Euclid's greatest common denominator algorithm is. This algorithm in pseudo-code is:
function gcd(a, b)
while b ≠ 0
t := b
b := a mod b
a := t
return a
It seems to depend on a and b. My thinking is that the time complexity is O(a % b). Is that correct? Is there a better way to write that?
One trick for analyzing the time complexity of Euclid's algorithm is to follow what happens over two iterations:
a', b' := a % b, b % (a % b)
Now a and b will both decrease, instead of only one, which makes the analysis easier. You can divide it into cases:
Tiny A: 2a <= b
Tiny B: 2b <= a
Small A: 2a > b but a < b
Small B: 2b > a but b < a
Equal: a == b
Now we'll show that every single case decreases the total a+b by at least a quarter:
Tiny A: b % (a % b) < a and 2a <= b, so b is decreased by at least half, so a+b decreased by at least 25%
Tiny B: a % b < b and 2b <= a, so a is decreased by at least half, so a+b decreased by at least 25%
Small A: b will become b-a, which is less than b/2, decreasing a+b by at least 25%.
Small B: a will become a-b, which is less than a/2, decreasing a+b by at least 25%.
Equal: a+b drops to 0, which is obviously decreasing a+b by at least 25%.
Therefore, by case analysis, every double-step decreases a+b by at least 25%. There's a maximum number of times this can happen before a+b is forced to drop below 1. The total number of steps (S) until we hit 0 must satisfy (4/3)^S <= A+B. Now just work it:
(4/3)^S <= A+B
S <= lg[4/3](A+B)
S is O(lg[4/3](A+B))
S is O(lg(A+B))
S is O(lg(A*B)) //because A*B asymptotically greater than A+B
S is O(lg(A)+lg(B))
//Input size N is lg(A) + lg(B)
S is O(N)
So the number of iterations is linear in the number of input digits. For numbers that fit into cpu registers, it's reasonable to model the iterations as taking constant time and pretend that the total running time of the gcd is linear.
Of course, if you're dealing with big integers, you must account for the fact that the modulus operations within each iteration don't have a constant cost. Roughly speaking, the total asymptotic runtime is going to be n^2 times a polylogarithmic factor. Something like n^2 lg(n) 2^O(log* n). The polylogarithmic factor can be avoided by instead using a binary gcd.
The suitable way to analyze an algorithm is by determining its worst case scenarios.
Euclidean GCD's worst case occurs when Fibonacci Pairs are involved.
void EGCD(fib[i], fib[i - 1]), where i > 0.
For instance, let's opt for the case where the dividend is 55, and the divisor is 34 (recall that we are still dealing with fibonacci numbers).
As you may notice, this operation costed 8 iterations (or recursive calls).
Let's try larger Fibonacci numbers, namely 121393 and 75025. We can notice here as well that it took 24 iterations (or recursive calls).
You can also notice that each iterations yields a Fibonacci number. That's why we have so many operations. We can't obtain similar results only with Fibonacci numbers indeed.
Hence, the time complexity is going to be represented by small Oh (upper bound), this time. The lower bound is intuitively Omega(1): case of 500 divided by 2, for instance.
Let's solve the recurrence relation:
We may say then that Euclidean GCD can make log(xy) operation at most.
There's a great look at this on the wikipedia article.
It even has a nice plot of complexity for value pairs.
It is not O(a%b).
It is known (see article) that it will never take more steps than five times the number of digits in the smaller number. So the max number of steps grows as the number of digits (ln b). The cost of each step also grows as the number of digits, so the complexity is bound by O(ln^2 b) where b is the smaller number. That's an upper limit, and the actual time is usually less.
See here.
In particular this part:
Lamé showed that the number of steps needed to arrive at the greatest common divisor for two numbers less than n is
So O(log min(a, b)) is a good upper bound.
Here's intuitive understanding of runtime complexity of Euclid's algorithm. The formal proofs are covered in various texts such as Introduction to Algorithms and TAOCP Vol 2.
First think about what if we tried to take gcd of two Fibonacci numbers F(k+1) and F(k). You might quickly observe that Euclid's algorithm iterates on to F(k) and F(k-1). That is, with each iteration we move down one number in Fibonacci series. As Fibonacci numbers are O(Phi ^ k) where Phi is golden ratio, we can see that runtime of GCD was O(log n) where n=max(a, b) and log has base of Phi. Next, we can prove that this would be the worst case by observing that Fibonacci numbers consistently produces pairs where the remainders remains large enough in each iteration and never become zero until you have arrived at the start of the series.
We can make O(log n) where n=max(a, b) bound even more tighter. Assume that b >= a so we can write bound at O(log b). First, observe that GCD(ka, kb) = GCD(a, b). As biggest values of k is gcd(a,c), we can replace b with b/gcd(a,b) in our runtime leading to more tighter bound of O(log b/gcd(a,b)).
Here is the analysis in the book Data Structures and Algorithm Analysis in C by Mark Allen Weiss (second edition, 2.4.4):
Euclid's algorithm works by continually computing remainders until 0 is reached. The last nonzero remainder is the answer.
Here is the code:
unsigned int Gcd(unsigned int M, unsigned int N)
{
unsigned int Rem;
while (N > 0) {
Rem = M % N;
M = N;
N = Rem;
}
Return M;
}
Here is a THEOREM that we are going to use:
If M > N, then M mod N < M/2.
PROOF:
There are two cases. If N <= M/2, then since the remainder is smaller
than N, the theorem is true for this case. The other case is N > M/2.
But then N goes into M once with a remainder M - N < M/2, proving the
theorem.
So, we can make the following inference:
Variables M N Rem
initial M N M%N
1 iteration N M%N N%(M%N)
2 iterations M%N N%(M%N) (M%N)%(N%(M%N)) < (M%N)/2
So, after two iterations, the remainder is at most half of its original value. This would show that the number of iterations is at most 2logN = O(logN).
Note that, the algorithm computes Gcd(M,N), assuming M >= N.(If N > M, the first iteration of the loop swaps them.)
Worst case will arise when both n and m are consecutive Fibonacci numbers.
gcd(Fn,Fn−1)=gcd(Fn−1,Fn−2)=⋯=gcd(F1,F0)=1 and nth Fibonacci number is 1.618^n, where 1.618 is the Golden ratio.
So, to find gcd(n,m), number of recursive calls will be Θ(logn).
The worst case of Euclid Algorithm is when the remainders are the biggest possible at each step, ie. for two consecutive terms of the Fibonacci sequence.
When n and m are the number of digits of a and b, assuming n >= m, the algorithm uses O(m) divisions.
Note that complexities are always given in terms of the sizes of inputs, in this case the number of digits.
Gabriel Lame's Theorem bounds the number of steps by log(1/sqrt(5)*(a+1/2))-2, where the base of the log is (1+sqrt(5))/2. This is for the the worst case scenerio for the algorithm and it occurs when the inputs are consecutive Fibanocci numbers.
A slightly more liberal bound is: log a, where the base of the log is (sqrt(2)) is implied by Koblitz.
For cryptographic purposes we usually consider the bitwise complexity of the algorithms, taking into account that the bit size is given approximately by k=loga.
Here is a detailed analysis of the bitwise complexity of Euclid Algorith:
Although in most references the bitwise complexity of Euclid Algorithm is given by O(loga)^3 there exists a tighter bound which is O(loga)^2.
Consider; r0=a, r1=b, r0=q1.r1+r2 . . . ,ri-1=qi.ri+ri+1, . . . ,rm-2=qm-1.rm-1+rm rm-1=qm.rm
observe that: a=r0>=b=r1>r2>r3...>rm-1>rm>0 ..........(1)
and rm is the greatest common divisor of a and b.
By a Claim in Koblitz's book( A course in number Theory and Cryptography) is can be proven that: ri+1<(ri-1)/2 .................(2)
Again in Koblitz the number of bit operations required to divide a k-bit positive integer by an l-bit positive integer (assuming k>=l) is given as: (k-l+1).l ...................(3)
By (1) and (2) the number of divisons is O(loga) and so by (3) the total complexity is O(loga)^3.
Now this may be reduced to O(loga)^2 by a remark in Koblitz.
consider ki= logri +1
by (1) and (2) we have: ki+1<=ki for i=0,1,...,m-2,m-1 and ki+2<=(ki)-1 for i=0,1,...,m-2
and by (3) the total cost of the m divisons is bounded by: SUM [(ki-1)-((ki)-1))]*ki for i=0,1,2,..,m
rearranging this: SUM [(ki-1)-((ki)-1))]*ki<=4*k0^2
So the bitwise complexity of Euclid's Algorithm is O(loga)^2.
For the iterative algorithm, however, we have:
int iterativeEGCD(long long n, long long m) {
long long a;
int numberOfIterations = 0;
while ( n != 0 ) {
a = m;
m = n;
n = a % n;
numberOfIterations ++;
}
printf("\nIterative GCD iterated %d times.", numberOfIterations);
return m;
}
With Fibonacci pairs, there is no difference between iterativeEGCD() and iterativeEGCDForWorstCase() where the latter looks like the following:
int iterativeEGCDForWorstCase(long long n, long long m) {
long long a;
int numberOfIterations = 0;
while ( n != 0 ) {
a = m;
m = n;
n = a - n;
numberOfIterations ++;
}
printf("\nIterative GCD iterated %d times.", numberOfIterations);
return m;
}
Yes, with Fibonacci Pairs, n = a % n and n = a - n, it is exactly the same thing.
We also know that, in an earlier response for the same question, there is a prevailing decreasing factor: factor = m / (n % m).
Therefore, to shape the iterative version of the Euclidean GCD in a defined form, we may depict as a "simulator" like this:
void iterativeGCDSimulator(long long x, long long y) {
long long i;
double factor = x / (double)(x % y);
int numberOfIterations = 0;
for ( i = x * y ; i >= 1 ; i = i / factor) {
numberOfIterations ++;
}
printf("\nIterative GCD Simulator iterated %d times.", numberOfIterations);
}
Based on the work (last slide) of Dr. Jauhar Ali, the loop above is logarithmic.
Yes, small Oh because the simulator tells the number of iterations at most. Non Fibonacci pairs would take a lesser number of iterations than Fibonacci, when probed on Euclidean GCD.
At every step, there are two cases
b >= a / 2, then a, b = b, a % b will make b at most half of its previous value
b < a / 2, then a, b = b, a % b will make a at most half of its previous value, since b is less than a / 2
So at every step, the algorithm will reduce at least one number to at least half less.
In at most O(log a)+O(log b) step, this will be reduced to the simple cases. Which yield an O(log n) algorithm, where n is the upper limit of a and b.
I have found it here
I am having difficulty deciding what the time complexity of Euclid's greatest common denominator algorithm is. This algorithm in pseudo-code is:
function gcd(a, b)
while b ≠ 0
t := b
b := a mod b
a := t
return a
It seems to depend on a and b. My thinking is that the time complexity is O(a % b). Is that correct? Is there a better way to write that?
One trick for analyzing the time complexity of Euclid's algorithm is to follow what happens over two iterations:
a', b' := a % b, b % (a % b)
Now a and b will both decrease, instead of only one, which makes the analysis easier. You can divide it into cases:
Tiny A: 2a <= b
Tiny B: 2b <= a
Small A: 2a > b but a < b
Small B: 2b > a but b < a
Equal: a == b
Now we'll show that every single case decreases the total a+b by at least a quarter:
Tiny A: b % (a % b) < a and 2a <= b, so b is decreased by at least half, so a+b decreased by at least 25%
Tiny B: a % b < b and 2b <= a, so a is decreased by at least half, so a+b decreased by at least 25%
Small A: b will become b-a, which is less than b/2, decreasing a+b by at least 25%.
Small B: a will become a-b, which is less than a/2, decreasing a+b by at least 25%.
Equal: a+b drops to 0, which is obviously decreasing a+b by at least 25%.
Therefore, by case analysis, every double-step decreases a+b by at least 25%. There's a maximum number of times this can happen before a+b is forced to drop below 1. The total number of steps (S) until we hit 0 must satisfy (4/3)^S <= A+B. Now just work it:
(4/3)^S <= A+B
S <= lg[4/3](A+B)
S is O(lg[4/3](A+B))
S is O(lg(A+B))
S is O(lg(A*B)) //because A*B asymptotically greater than A+B
S is O(lg(A)+lg(B))
//Input size N is lg(A) + lg(B)
S is O(N)
So the number of iterations is linear in the number of input digits. For numbers that fit into cpu registers, it's reasonable to model the iterations as taking constant time and pretend that the total running time of the gcd is linear.
Of course, if you're dealing with big integers, you must account for the fact that the modulus operations within each iteration don't have a constant cost. Roughly speaking, the total asymptotic runtime is going to be n^2 times a polylogarithmic factor. Something like n^2 lg(n) 2^O(log* n). The polylogarithmic factor can be avoided by instead using a binary gcd.
The suitable way to analyze an algorithm is by determining its worst case scenarios.
Euclidean GCD's worst case occurs when Fibonacci Pairs are involved.
void EGCD(fib[i], fib[i - 1]), where i > 0.
For instance, let's opt for the case where the dividend is 55, and the divisor is 34 (recall that we are still dealing with fibonacci numbers).
As you may notice, this operation costed 8 iterations (or recursive calls).
Let's try larger Fibonacci numbers, namely 121393 and 75025. We can notice here as well that it took 24 iterations (or recursive calls).
You can also notice that each iterations yields a Fibonacci number. That's why we have so many operations. We can't obtain similar results only with Fibonacci numbers indeed.
Hence, the time complexity is going to be represented by small Oh (upper bound), this time. The lower bound is intuitively Omega(1): case of 500 divided by 2, for instance.
Let's solve the recurrence relation:
We may say then that Euclidean GCD can make log(xy) operation at most.
There's a great look at this on the wikipedia article.
It even has a nice plot of complexity for value pairs.
It is not O(a%b).
It is known (see article) that it will never take more steps than five times the number of digits in the smaller number. So the max number of steps grows as the number of digits (ln b). The cost of each step also grows as the number of digits, so the complexity is bound by O(ln^2 b) where b is the smaller number. That's an upper limit, and the actual time is usually less.
See here.
In particular this part:
Lamé showed that the number of steps needed to arrive at the greatest common divisor for two numbers less than n is
So O(log min(a, b)) is a good upper bound.
Here's intuitive understanding of runtime complexity of Euclid's algorithm. The formal proofs are covered in various texts such as Introduction to Algorithms and TAOCP Vol 2.
First think about what if we tried to take gcd of two Fibonacci numbers F(k+1) and F(k). You might quickly observe that Euclid's algorithm iterates on to F(k) and F(k-1). That is, with each iteration we move down one number in Fibonacci series. As Fibonacci numbers are O(Phi ^ k) where Phi is golden ratio, we can see that runtime of GCD was O(log n) where n=max(a, b) and log has base of Phi. Next, we can prove that this would be the worst case by observing that Fibonacci numbers consistently produces pairs where the remainders remains large enough in each iteration and never become zero until you have arrived at the start of the series.
We can make O(log n) where n=max(a, b) bound even more tighter. Assume that b >= a so we can write bound at O(log b). First, observe that GCD(ka, kb) = GCD(a, b). As biggest values of k is gcd(a,c), we can replace b with b/gcd(a,b) in our runtime leading to more tighter bound of O(log b/gcd(a,b)).
Here is the analysis in the book Data Structures and Algorithm Analysis in C by Mark Allen Weiss (second edition, 2.4.4):
Euclid's algorithm works by continually computing remainders until 0 is reached. The last nonzero remainder is the answer.
Here is the code:
unsigned int Gcd(unsigned int M, unsigned int N)
{
unsigned int Rem;
while (N > 0) {
Rem = M % N;
M = N;
N = Rem;
}
Return M;
}
Here is a THEOREM that we are going to use:
If M > N, then M mod N < M/2.
PROOF:
There are two cases. If N <= M/2, then since the remainder is smaller
than N, the theorem is true for this case. The other case is N > M/2.
But then N goes into M once with a remainder M - N < M/2, proving the
theorem.
So, we can make the following inference:
Variables M N Rem
initial M N M%N
1 iteration N M%N N%(M%N)
2 iterations M%N N%(M%N) (M%N)%(N%(M%N)) < (M%N)/2
So, after two iterations, the remainder is at most half of its original value. This would show that the number of iterations is at most 2logN = O(logN).
Note that, the algorithm computes Gcd(M,N), assuming M >= N.(If N > M, the first iteration of the loop swaps them.)
Worst case will arise when both n and m are consecutive Fibonacci numbers.
gcd(Fn,Fn−1)=gcd(Fn−1,Fn−2)=⋯=gcd(F1,F0)=1 and nth Fibonacci number is 1.618^n, where 1.618 is the Golden ratio.
So, to find gcd(n,m), number of recursive calls will be Θ(logn).
The worst case of Euclid Algorithm is when the remainders are the biggest possible at each step, ie. for two consecutive terms of the Fibonacci sequence.
When n and m are the number of digits of a and b, assuming n >= m, the algorithm uses O(m) divisions.
Note that complexities are always given in terms of the sizes of inputs, in this case the number of digits.
Gabriel Lame's Theorem bounds the number of steps by log(1/sqrt(5)*(a+1/2))-2, where the base of the log is (1+sqrt(5))/2. This is for the the worst case scenerio for the algorithm and it occurs when the inputs are consecutive Fibanocci numbers.
A slightly more liberal bound is: log a, where the base of the log is (sqrt(2)) is implied by Koblitz.
For cryptographic purposes we usually consider the bitwise complexity of the algorithms, taking into account that the bit size is given approximately by k=loga.
Here is a detailed analysis of the bitwise complexity of Euclid Algorith:
Although in most references the bitwise complexity of Euclid Algorithm is given by O(loga)^3 there exists a tighter bound which is O(loga)^2.
Consider; r0=a, r1=b, r0=q1.r1+r2 . . . ,ri-1=qi.ri+ri+1, . . . ,rm-2=qm-1.rm-1+rm rm-1=qm.rm
observe that: a=r0>=b=r1>r2>r3...>rm-1>rm>0 ..........(1)
and rm is the greatest common divisor of a and b.
By a Claim in Koblitz's book( A course in number Theory and Cryptography) is can be proven that: ri+1<(ri-1)/2 .................(2)
Again in Koblitz the number of bit operations required to divide a k-bit positive integer by an l-bit positive integer (assuming k>=l) is given as: (k-l+1).l ...................(3)
By (1) and (2) the number of divisons is O(loga) and so by (3) the total complexity is O(loga)^3.
Now this may be reduced to O(loga)^2 by a remark in Koblitz.
consider ki= logri +1
by (1) and (2) we have: ki+1<=ki for i=0,1,...,m-2,m-1 and ki+2<=(ki)-1 for i=0,1,...,m-2
and by (3) the total cost of the m divisons is bounded by: SUM [(ki-1)-((ki)-1))]*ki for i=0,1,2,..,m
rearranging this: SUM [(ki-1)-((ki)-1))]*ki<=4*k0^2
So the bitwise complexity of Euclid's Algorithm is O(loga)^2.
For the iterative algorithm, however, we have:
int iterativeEGCD(long long n, long long m) {
long long a;
int numberOfIterations = 0;
while ( n != 0 ) {
a = m;
m = n;
n = a % n;
numberOfIterations ++;
}
printf("\nIterative GCD iterated %d times.", numberOfIterations);
return m;
}
With Fibonacci pairs, there is no difference between iterativeEGCD() and iterativeEGCDForWorstCase() where the latter looks like the following:
int iterativeEGCDForWorstCase(long long n, long long m) {
long long a;
int numberOfIterations = 0;
while ( n != 0 ) {
a = m;
m = n;
n = a - n;
numberOfIterations ++;
}
printf("\nIterative GCD iterated %d times.", numberOfIterations);
return m;
}
Yes, with Fibonacci Pairs, n = a % n and n = a - n, it is exactly the same thing.
We also know that, in an earlier response for the same question, there is a prevailing decreasing factor: factor = m / (n % m).
Therefore, to shape the iterative version of the Euclidean GCD in a defined form, we may depict as a "simulator" like this:
void iterativeGCDSimulator(long long x, long long y) {
long long i;
double factor = x / (double)(x % y);
int numberOfIterations = 0;
for ( i = x * y ; i >= 1 ; i = i / factor) {
numberOfIterations ++;
}
printf("\nIterative GCD Simulator iterated %d times.", numberOfIterations);
}
Based on the work (last slide) of Dr. Jauhar Ali, the loop above is logarithmic.
Yes, small Oh because the simulator tells the number of iterations at most. Non Fibonacci pairs would take a lesser number of iterations than Fibonacci, when probed on Euclidean GCD.
At every step, there are two cases
b >= a / 2, then a, b = b, a % b will make b at most half of its previous value
b < a / 2, then a, b = b, a % b will make a at most half of its previous value, since b is less than a / 2
So at every step, the algorithm will reduce at least one number to at least half less.
In at most O(log a)+O(log b) step, this will be reduced to the simple cases. Which yield an O(log n) algorithm, where n is the upper limit of a and b.
I have found it here
I need to execute a loop
while (X) do Y
for a N times, which is a large number. While Y, the loop body, is rather quick, the test X takes up about 70% of the runtime.
I can calculate the number of loop iterations N beforehand so instead of using X as the condition, a simple For-Loop would be possible.
for (i=1 to N) do Y
However, N might exceed the maximum value an integer can store on the machine, so this is not an option. As an alternative, I proposed to use a floating point variable F instead. As N is large, it is likely that F cannot be exactly equal to N. Thus, I calculate F to be the largest floating point number smaller than N. This allows me to run
for (i=1 to F) do Y
while (X) do Y
Most of the iterations will not need to test X everytime, only the last N-F do.
The question is: How would I implement a for-Loop from 1 to F? Simply increasing a counter or decreasing F by 1 in each step would not work, as the numerical error would grow too large. My current solution is:
for (while F > MAXINT)
for (i=1 to MAXINT)
do Y
F -= MAXINT
while (X) do Y
Is there a better way to solve this problem?
What do you mean by numerical error? Floating point counting is exact within its precision.
Here are the maximum values representable by integers exactly using the following data types:
uint32max = 4294967295
uint64max = 18446744073709551615
float32intmax = 16777216
float64intmax = 9007199254740992
Every integer from 0 to the max is exactly representable without numerical error.
As you can see, the largest count is available with uint64. Next comes float64, then uint32 and finally float32.
What happens when you increment uint32=4294967295 ? 0.
What happens when you increment float32=16777216 ? 16777216.
Which is the better behavior?
Have you thought about using a 2-dimensional loop? If N ism the maximum count for a 1-dimensional loop, then N x N is the maximum for a 2-dimensional loop, etc. so that if you maximum count is less than MAXUINT x MAXUINT, then decompose you number N such that:
N == M * MAXUINT + R;
where
M = N / MAXUINT;
R = N - M * MAXUINT;
then
for (i = M; i--;) for (j = MAXUINT; j--;) DoStuff();
for (i = R; i--;) DoStuff();
If MAXUINT*MAXUINT is not a large enough count for you, you can add 3-, 4-, ... -dimensional loops.
I'm working on a cryptographic exercise, and I'm trying to calculate (2n-1)mod p where p is a prime number
What would be the best approach to do this? I'm working with C so 2n-1 becomes too large to hold when n is large
I came across the equation (a*b)modp=(a(bmodp))modp, but I'm not sure this applies in this case, as 2n-1 may be prime (or I'm not sure how to factorise this)
Help much appreciated.
A couple tips to help you come up with a better way:
Don't use (a*b)modp=(a(bmodp))modp to compute 2n-1 mod p, use it to compute 2n mod p and then subtract afterward.
Fermat's little theorem can be useful here. That way, the exponent you actually have to deal with won't exceed p.
You mention in the comments that n and p are 9 or 10 digits, or something. If you restrict them to 32 bit (unsigned long) values, you can find 2^n mod p with a simple (binary) modular exponentiation:
unsigned long long u = 1, w = 2;
while (n != 0)
{
if ((n & 0x1) != 0)
u = (u * w) % p; /* (mul-rdx) */
if ((n >>= 1) != 0)
w = (w * w) % p; /* (sqr-rdx) */
}
r = (unsigned long) u;
And, since (2^n - 1) mod p = r - 1 mod p :
r = (r == 0) ? (p - 1) : (r - 1);
If 2^n mod p = 0 - which doesn't actually occur if p > 2 is prime - but we might as well consider the general case - then (2^n - 1) mod p = -1 mod p.
Since the 'common residue' or 'remainder' (mod p) is in [0, p - 1], we add a some multiple of p so that it is in this range.
Otherwise, the result of 2^n mod p was in [1, p - 1], and subtracting 1 will be in this range already. It's probably better expressed as:
if (r == 0)
r = p - 1; /* -1 mod p */
else
r = r - 1;
To take modulus you somehow must have 2^n-1 or you will move in a different direction of algorithms, interesting but seperate direction somehow, so i recommend you to use big int concept as it will be easy... make a structure and implement a big value in small values, e.g.
struct bigint{
int lowerbits;
int upperbits;
}
decomposition of the statement also has solution like 2^n = (2^n-4 * 2^4 )-1%p decompose and seperatly handle them, that will be quite algorithmic then
To compute 2^n - 1 mod p, you can use exponentiation by squaring after first removing any multiple of (p - 1) from n (since a^{p-1} = 1 mod p). In pseudo-code:
n = n % (p - 1)
result = 1
pow = 2
while n {
if n % 2 {
result = (result * pow) % p
}
pow = (pow * pow) % p
n /= 2
}
result = (result + p - 1) % p
I came across the answer that I am posting here, when solving one of the mathematical problems on HackerRank, and it has worked for all the given test cases given there.
If you restrict n and p to 64 bit (unsigned long) values, then here is the mathematical approach :
2^n - 1 can be written as 1*[ (2^n - 1)/(2 - 1) ]
If you look at this carefully, this is the sum of the GP 1 + 2 + 4 + .. + 2^(n-1)
And voila, we know that (a+b)%m = ( (a%m) + (b%m) )%m
If you have a confusion whether the above relation is true or not for addition, you can google for it or you can check this link : http://www.inf.ed.ac.uk/teaching/courses/dmmr/slides/13-14/Ch4.pdf
So, now we can apply the above mentioned relation to our GP, and you would have your answer!!
That is,
(2^n - 1)%p is equivalent to ( 1 + 2 + 4 + .. + 2^(n-1) )%p and now apply the given relation.
First, focus on 2n mod p because you can always subtract one at the end.
Consider the powers of two. This is a sequence of numbers produced by repeatedly multiplying by two.
Consider the modulo operation. If the number is written in base p, you're just grabbing the last digit. Higher digits can be thrown away.
So at some point(s) in the sequence, you get a two-digit number (a 1 in the p's place), and your task is really just to get rid of the first digit (subtract p) when that happens.
Stopping here conceptually, the brute-force approach would be something like this:
uint64_t exp2modp( uint64_t n, uint64_t p ) {
uint64_t ret = 1;
uint64_t limit = p / 2;
n %= p; // Apply Fermat's Little Theorem.
while ( n -- ) {
if ( ret >= limit ) {
ret *= 2;
ret -= p;
} else {
ret *= 2;
}
}
return ret;
}
Unfortunately, this still takes forever for large n and p, and I can't think of any better number theory offhand.
If you have a multiplication facility which can compute (p-1)^2 without overflow, then you can use an analogous algorithm using repeated squaring with a modulo after each square operation, and then take the product of the series of square residuals, again with a modulo after each multiplication.
step 1. x= shifting 1 n times and then subtract 1
step 2.result = logical and operation of x and p
Optimized way to handle the value of n^n (1 ≤ n ≤ 10^9)
I used long long int but it's not good enough as the value might be (1000^1000)
Searched and found the GMP library http://gmplib.org/ and BigInt class but don't wanna use them. I am looking for some numerical method to handle this.
I need to print the first and last k (1 ≤ k ≤ 9) digits of n^n
For the first k digits I am getting it like shown below (it's bit ugly way of doing it)
num = pow(n,n);
while(num){
arr[i++] = num%10;
num /= 10;
digit++;
}
while(digit > 0){
j=digit;
j--;
if(count<k){
printf("%lld",arr[j]);
count++;
}
digit--;
}
and for last k digits am using num % 10^k like below.
findk=pow(10,k);
lastDigits = num % findk;
enter code here
maximum value of k is 9. so i need only 18 digits at max.
I am think of getting those 18 digits without really solving the complete n^n expression.
Any idea/suggestion??
// note: Scope of use is limited.
#include <stdio.h>
long long powerMod(long long a, long long d, long long n){
// a ^ d mod n
long long result = 1;
while(d > 0){
if(d & 1)
result = result * a % n;
a = (a * a) % n;
d >>=1;
}
return result;
}
int main(void){
long long result = powerMod(999, 999, 1000000000);//999^999 mod 10^9
printf("%lld\n", result);//499998999
return 0;
}
Finding the Least Significant Digits (last k digits) are easy because of the property of modular arithmetic, which says: (n*n)%m == (n%m * n%m)%m, so the code shown by BLUEPIXY which followed exponentiation by squaring method will work well for finding k LSDs.
Now, Most Significant Digits (1st k digits) of N^N can be found in this way:
We know,
N^N = 10^(N log N)
So if you calculate N log (N) you will get a number of this format xxxx.yyyy, now we have to use this number as a power of 10, it is easily understandable that xxxx or integer part of the number will add xxxx zeros after 10, which is not important for you! That means, if you calculate 10^0.yyyy, you will get those significants digits you are looking for.
So the solution will be something like this:
double R = N * log10 (N);
R = R - (long long) R; //so taking only the fractional part
double V = pow(10, R);
int powerK = 1;
for (int i=0; i<k; i++) powerK *=10;
V *= powerK;
//Now Print the 1st K digits from V
Why don't you want to use bigint libraries?
bignum arithmetic is very hard to do right and efficiently. You could still get a PhD by working on that subject.
Fist, bigint arithmetic have non-trivial algorithmics
Then, bigint implementations usually need some machine instructions (like add with carry) which are not easily accessible in plain C.
For your specific problem (first and last few digits of NN) you'll better also reason on paper (using arithmetic theorems) to lower the complexity. I am not an expert, but I guess that still remains intractable, perhaps with a complexity worse than O(N)