I need to calculate power of a power. For example: 3^2^n . You can think n as input but this example is not the same thing as 9^n. I write a algorithm using loops but now I need to write recursive one. I couldn't find an efficient way to write it.
I went ahead and implemented this in Ruby, which is pretty darn close to pseudocode and has the added benefit of being testable. Since Ruby also has arbitrary precision integer arithmetic, the following code works with non-trivial arguments.
This implementation is based on the old trick of squaring the base and raising it to half the specified power when the exponent is even, so the recursive stack grows logarithmically rather than linearly in powers. This was inspired by Ilya's answer, but I found that the y > 1 and n > 1 case is not correct, leading me to use the recursive call within a recursive call implemented in the elif n > 1 line below:
def powpow(x, y, n)
if y == 0
return 1
elsif y == 1 || n == 0
return x
elsif n > 1
return powpow(x, powpow(y, n, 1), 1)
elsif y.even?
return powpow(x * x, y / 2, 1)
else
return x * powpow(x * x, y / 2, 1)
end
end
p powpow(3,2,5) # => 1853020188851841
I was able to confirm that result directly:
irb(main):001:0> 2**5
=> 32
irb(main):002:0> 3**32
=> 1853020188851841
Let's say x^(y^n) = powpow(x, y, n) with y and n >= 1
If y > 1 and n > 1, powpow(x, y, n) = powpow(x, y, 1) * powpow(x, y, n-1) (getting closer to the result)
If y > 1 and n = 1, powpow(x, y, 1) = x * powpow(x, y-1, 1) (getting closer)
If y = 1 and n = 1, powpow(x, 1, 1) = x (solved)
That's less efficient than a loop, but it's recursive. Is that what you're aiming for ...?
EDIT as #pjs has pointed out, the first case should be:
powpow(x, y, 1) = powpow(x, powpow(y, n, 1), 1)
public class Power {
int ans = 1;
int z = 1;
int x = 1;
int pow1(int b, int c) {
if (c > 1) {
pow1(b, c - 1);
}
ans = ans * b;
return ans;
}
void pow(int a, int b, int c) {
x = pow1(b, c);
ans = a;
pow1(a, x - 1);
}
public static void main(String[] args) {
Power p = new Power();
p.pow(3, 2, 3);
System.out.println(p.ans);
}
}
Reccursive Approach
We can compute power(x, y) efficiently in complexity O(log y) using the following reccurnce :
power(x, y) :
if y is 0 : return 1
if y is even :
return square( power(x, y / 2))
else :
return square( power(x, (y - 1) / 2 ) * x
Using master theorem we can compute the complexity of above procedure to be O(log y) (Similar case as that of binary search.)
Now, if we use the above procedure to compute 3 ^ (2 ^ n).
We can see that (2 ^ n) will be computed in O(log n) and 3 ^ k. Where k = 2 ^ n, will be computed in O(log k) = O(log (2 ^ n)) = O(n).
So using the binary exponentiation trick sequentially we can solve this using a complexity of O(n).
Iterative approach
Idea : Suppose we have calculated 3 ^ (2 ^ x). Then we can easily calculated 3 ^ (2 ^ (x + 1)) by just squaring 3 ^ (2 ^ x) as :
( 3 ^ (2 ^ x) ) * ( 3 ^ (2 ^ x) ) = 3 ^ ( (2 ^ x) + (2 ^ x) )
= 3 ^ ( 2 * (2 ^ x) )
= 3 ^ ( (2 ^ (x + 1) )
So, if we start with 3 ^ (2 ^ 0), in n steps we can reach on to 3 ^ (2 ^ n) :
def solve(n):
ans = 3 ^ (2 ^ 0) = 3
for i in range(0, n) :
ans = square(ans)
return ans
Clearly the complexity of the above solution is also O(n).
Related
How to find the n-th term in a sequence with following recurrence relation for a given n?
F(n) = 2 * b * F(n – 1) – F(n – 2), F(0) = a, F(1) = b
where a and b are constants.
The value of N is quite large (1 ≤ n ≤ 1012) and so matrix exponentiation is required.
Here is my code for it; ll is a typedef for long long int, and value is to be taken modulo r.
void multiply(ll F[2][2], ll M[2][2])
{
ll x = ((F[0][0] * M[0][0]) % r + (F[0][1] * M[1][0]) % r) % r;
ll y = ((F[0][0] * M[0][1]) % r + (F[0][1] * M[1][1]) % r) % r;
ll z = ((F[1][0] * M[0][0]) % r + (F[1][1] * M[1][0]) % r) % r;
ll w = ((F[1][0] * M[0][1]) % r + (F[1][1] * M[1][1]) % r) % r;
F[0][0] = x;
F[0][1] = y;
F[1][0] = z;
F[1][1] = w;
}
void power(ll F[2][2], ll n, ll b)
{
if (n == 0 || n == 1)
return;
ll M[2][2] = {{2 * b, -1}, {1, 0}};
power(F, n / 2,b);
multiply(F, F);
if (n % 2 != 0)
multiply(F, M);
}
ll rec(ll n, ll b, ll a)
{
ll F[2][2] = {{2 * b, -1}, {1, 0}};
if (n == 0)
return a;
if (n == 1)
return b;
power(F, n - 1,b);
return F[0][0] % r;
}
However I am facing problems getting required value in all cases, that is I am getting Wrong Answer (WA) verdict for some cases.
Could anyone help me with this question and point out the mistake in this code so I can tackle these kind of problems myself afterward?
P.S. First timer here. Apologies if I did something incorrectly and missed out on anything.
Technical:
Perhaps you are asked to find the value res modulo r so that 0 <= res < r.
However, by using -1 in the matrix, you can actually get negative intermediate and final values. The reason is that, in most programming languages, the modulo operation actually uses division rounded towards zero, and so produces a result in the range -r < res < r (example link).
Try either of the following:
Change that -1 to r - 1, so that all intermediate values remain non-negative.
Fix the final result by returning (F[0][0] + r) % r instead of just F[0][0] % r.
Formula:
Your formula looks wrong. Logically, your rec function says that nothing except F(0) depends on a, which is obviously wrong.
Recall why and how we use the matrix in the first place:
( F(n) ) = ( 2b -1 ) * ( F(n-1) )
( F(n-1) ) ( 1 0 ) ( F(n-2) )
Here, we get a 2x1 vector by multiplying a 2x2 matrix and a 2x1 vector. We then look at its top element and have, by multiplication rules,
F(n) = 2b * F(n-1) + (-1) * F(n-2)
The point is, we can take the power of the matrix to get the following:
( F(n) ) = ( 2b -1 ) ^{n-1} * ( F(1) )
( F(n-1) ) ( 1 0 ) ( F(0) )
By the same argument, we have
F(n) = X * F(1) + Y * F(0)
where X and Y are the top row of the matrix:
( 2b -1 ) ^{n-1} = ( X Y )
( 1 0 ) ( Z T )
So F[0][0] % r is not the answer, really.
The real answer looks like
(F[0][0] * b + F[0][1] * a) % r
If we can have negative intermediate values (see point 1 above), the result is still from -r to r instead of from 0 to r. To fix it, we can add one more r and take the modulo once again:
((F[0][0] * b + F[0][1] * a) % r + r) % r
Possible reason for WA is, you return a or b without doing any mod.
Try it.
if (n == 0)
return a%r;
if (n == 1)
return b%r;
If you are still getting WA, please give some test cases or problem link.
The code implements an example of a Pollard rho() function for finding a factor of a positive integer, n. I've examined some of the code in the Julia "Primes" package that runs rapidly in an attempt to speedup the pollard_rho() function, all to no avail. The code should execute n = 1524157897241274137 in approximately 100 mSec to 30 Sec (Erlang, Haskell, Mercury, SWI Prolog) but takes about 3 to 4 minutes on JuliaBox, IJulia, and the Julia REPL. How can I make this go fast?
pollard_rho(1524157897241274137) = 1234567891
__precompile__()
module Pollard
export pollard_rho
function pollard_rho{T<:Integer}(n::T)
f(x::T, r::T, n) = rem(((x ^ T(2)) + r), n)
r::T = 7; x::T = 2; y::T = 11; y1::T = 11; z::T = 1
while z == 1
x = f(x, r, n)
y1 = f(y, r, n)
y = f(y1, r, n)
z = gcd(n, abs(x - y))
end
z >= n ? "error" : z
end
end # module
There are quite a few problems with type instability here.
Don't return either the string "error" or a result; instead explicitly call error().
As Chris mentioned, x and r ought to be annotated to be of type T, else they will be unstable.
There also seems to be a potential problem with overflow. A solution is to widen in the squaring step before truncating back to type T.
function pollard_rho{T<:Integer}(n::T)
f(x::T, r::T, n) = rem(Base.widemul(x, x) + r, n) % T
r::T = 7; x::T = 2; y::T = 11; y1::T = 11; z::T = 1
while z == 1
x = f(x, r, n)
y1 = f(y, r, n)
y = f(y1, r, n)
z = gcd(n, abs(x - y))
end
z >= n ? error() : z
end
After making these changes the function will run as fast as you could expect.
julia> #btime pollard_rho(1524157897241274137)
4.128 ms (0 allocations: 0 bytes)
1234567891
To find these problems with type instability, use the #code_warntype macro.
This is a question given in this presentation. Dynamic Programming
now i have implemented the algorithm using recursion and it works fine for small values. But when n is greater than 30 it becomes really slow.The presentation mentions that for large values of n one should consider something similar to
the matrix form of Fibonacci numbers .I am having trouble undestanding how to use the matrix form of Fibonacci numbers to come up with a solution.Can some one give me some hints or pseudocode
Thanks
Yes, you can use the technique from fast Fibonacci implementations to solve this problem in time O(log n)! Here's how to do it.
Let's go with your definition from the problem statement that 1 + 3 is counted the same as 3 + 1. Then you have the following recurrence relation:
A(0) = 1
A(1) = 1
A(2) = 1
A(3) = 2
A(k+4) = A(k) + A(k+1) + A(k+3)
The matrix trick here is to notice that
| 1 0 1 1 | |A( k )| |A(k) + A(k-2) + A(k-3)| |A(k+1)|
| 1 0 0 0 | |A(k-1)| | A( k ) | |A( k )|
| 0 1 0 0 | |A(k-2)| = | A(k-1) | = |A(k-1)|
| 0 0 1 0 | |A(k-3)| | A(k-2) | = |A(k-2)|
In other words, multiplying a vector of the last four values in the series produces a vector with those values shifted forward by one step.
Let's call that matrix there M. Then notice that
|A( k )| |A(k+2)|
|A(k-1)| |A(k+1)|
M^2 |A(k-2)| = |A( k )|
|A(k-3)| |A(k-1)|
In other words, multiplying by the square of this matrix shifts the series down two steps. More generally:
|A( k )| | A(k+n) |
|A(k-1)| |A(k-1 + n)|
M^n |A(k-2)| = |A(k-2 + n)|
|A(k-3)| |A(k-3 + n)|
So multiplying by Mn shifts the series down n steps. Now, if we want to know the value of A(n+3), we can just compute
|A(3)| |A(n+3)|
|A(2)| |A(n+2)|
M^n |A(1)| = |A(n+1)|
|A(0)| |A(n+2)|
and read off the top entry of the vector! This can be done in time O(log n) by using exponentiation by squaring. Here's some code that does just that. This uses a matrix library I cobbled together a while back:
#include "Matrix.hh"
#include <cstdint>
#include <iomanip>
#include <iostream>
#include <algorithm>
using namespace std;
/* Naive implementations of A. */
uint64_t naiveA(int n) {
if (n == 0) return 1;
if (n == 1) return 1;
if (n == 2) return 1;
if (n == 3) return 2;
return naiveA(n-1) + naiveA(n-3) + naiveA(n-4);
}
/* Constructs and returns the giant matrix. */
Matrix<4, 4, uint64_t> M() {
Matrix<4, 4, uint64_t> result;
fill(result.begin(), result.end(), uint64_t(0));
result[0][0] = 1;
result[0][2] = 1;
result[0][3] = 1;
result[1][0] = 1;
result[2][1] = 1;
result[3][2] = 1;
return result;
}
/* Constructs the initial vector that we multiply the matrix by. */
Vector<4, uint64_t> initVec() {
Vector<4, uint64_t> result;
result[0] = 2;
result[1] = 1;
result[2] = 1;
result[3] = 1;
return result;
}
/* O(log n) time for raising a matrix to a power. */
Matrix<4, 4, uint64_t> fastPower(const Matrix<4, 4, uint64_t>& m, int n) {
if (n == 0) return Identity<4, uint64_t>();
auto half = fastPower(m, n / 2);
if (n % 2 == 0) return half * half;
else return half * half * m;
}
/* Fast implementation of A(n) using matrix exponentiation. */
uint64_t fastA(int n) {
if (n == 0) return 1;
if (n == 1) return 1;
if (n == 2) return 1;
if (n == 3) return 2;
auto result = fastPower(M(), n - 3) * initVec();
return result[0];
}
/* Some simple test code showing this in action! */
int main() {
for (int i = 0; i < 25; i++) {
cout << setw(2) << i << ": " << naiveA(i) << ", " << fastA(i) << endl;
}
}
Now, how would this change if 3 + 1 and 1 + 3 were treated as equivalent? This means that we can think about solving this problem in the following way:
Let A(n) be the number of ways to write n as a sum of 1s, 3s, and 4s.
Let B(n) be the number of ways to write n as a sum of 1s and 3s.
Let C(n) be the number of ways to write n as a sum of 1s.
We then have the following:
A(n) = B(n) for all n ≤ 3, since for numbers in that range the only options are to use 1s and 3s.
A(n + 4) = A(n) + B(n + 4), since your options are either (1) use a 4 or (2) not use a 4, leaving the remaining sum to use 1s and 3s.
B(n) = C(n) for all n ≤ 2, since for numbers in that range the only options are to use 1s.
B(n + 3) = B(n) + C(n + 3), sine your options are either (1) use a 3 or (2) not use a 3, leaving the remaining sum to use only 1s.
C(0) = 1, since there's only one way to write 0 as a sum of no numbers.
C(n+1) = C(n), since the only way to write something with 1s is to pull out a 1 and write the remaining number as a sum of 1s.
That's a lot to take in, but do notice the following: we ultimately care about A(n), and to evaluate it, we only need to know the values of A(n), A(n-1), A(n-2), A(n-3), B(n), B(n-1), B(n-2), B(n-3), C(n), C(n-1), C(n-2), and C(n-3).
Let's imagine, for example, that we know these twelve values for some fixed value of n. We can learn those twelve values for the next value of n as follows:
C(n+1) = C(n)
B(n+1) = B(n-2) + C(n+1) = B(n-2) + C(n)
A(n+1) = A(n-3) + B(n+1) = A(n-3) + B(n-2) + C(n)
And the remaining values then shift down.
We can formulate this as a giant matrix equation:
A( n ) A(n-1) A(n-2) A(n-3) B( n ) B(n-1) B(n-2) C( n )
| 0 0 0 1 0 0 1 1 | |A( n )| = |A(n+1)|
| 1 0 0 0 0 0 0 0 | |A(n-1)| = |A( n )|
| 0 1 0 0 0 0 0 0 | |A(n-2)| = |A(n-1)|
| 0 0 1 0 0 0 0 0 | |A(n-3)| = |A(n-2)|
| 0 0 0 0 0 0 1 1 | |B( n )| = |B(n+1)|
| 0 0 0 0 1 0 0 0 | |B(n-1)| = |B( n )|
| 0 0 0 0 0 1 0 0 | |B(n-2)| = |B(n-1)|
| 0 0 0 0 0 0 0 1 | |C( n )| = |C(n+1)|
Let's call this gigantic matrix here M. Then if we compute
|2| // A(3) = 2, since 3 = 3 or 3 = 1 + 1 + 1
|1| // A(2) = 1, since 2 = 1 + 1
|1| // A(1) = 1, since 1 = 1
M^n |1| // A(0) = 1, since 0 = (empty sum)
|2| // B(3) = 2, since 3 = 3 or 3 = 1 + 1 + 1
|1| // B(2) = 1, since 2 = 1 + 1
|1| // B(1) = 1, since 1 = 1
|1| // C(3) = 1, since 3 = 1 + 1 + 1
We'll get back a vector whose first entry is A(n+3), the number of ways to write n+3 as a sum of 1's, 3's, and 4's. (I've actually coded this up to check it - it works!) You can then use the technique for computing Fibonacci numbers using a matrix to a power efficiently that you saw with Fibonacci numbers to solve this in time O(log n).
Here's some code doing that:
#include "Matrix.hh"
#include <cstdint>
#include <iomanip>
#include <iostream>
#include <algorithm>
using namespace std;
/* Naive implementations of A, B, and C. */
uint64_t naiveC(int n) {
return 1;
}
uint64_t naiveB(int n) {
return (n < 3? 0 : naiveB(n-3)) + naiveC(n);
}
uint64_t naiveA(int n) {
return (n < 4? 0 : naiveA(n-4)) + naiveB(n);
}
/* Constructs and returns the giant matrix. */
Matrix<8, 8, uint64_t> M() {
Matrix<8, 8, uint64_t> result;
fill(result.begin(), result.end(), uint64_t(0));
result[0][3] = 1;
result[0][6] = 1;
result[0][7] = 1;
result[1][0] = 1;
result[2][1] = 1;
result[3][2] = 1;
result[4][6] = 1;
result[4][7] = 1;
result[5][4] = 1;
result[6][5] = 1;
result[7][7] = 1;
return result;
}
/* Constructs the initial vector that we multiply the matrix by. */
Vector<8, uint64_t> initVec() {
Vector<8, uint64_t> result;
result[0] = 2;
result[1] = 1;
result[2] = 1;
result[3] = 1;
result[4] = 2;
result[5] = 1;
result[6] = 1;
result[7] = 1;
return result;
}
/* O(log n) time for raising a matrix to a power. */
Matrix<8, 8, uint64_t> fastPower(const Matrix<8, 8, uint64_t>& m, int n) {
if (n == 0) return Identity<8, uint64_t>();
auto half = fastPower(m, n / 2);
if (n % 2 == 0) return half * half;
else return half * half * m;
}
/* Fast implementation of A(n) using matrix exponentiation. */
uint64_t fastA(int n) {
if (n == 0) return 1;
if (n == 1) return 1;
if (n == 2) return 1;
if (n == 3) return 2;
auto result = fastPower(M(), n - 3) * initVec();
return result[0];
}
/* Some simple test code showing this in action! */
int main() {
for (int i = 0; i < 25; i++) {
cout << setw(2) << i << ": " << naiveA(i) << ", " << fastA(i) << endl;
}
}
This is a very interesting sequence. It is almost but not quite the order-4 Fibonacci (a.k.a. Tetranacci) numbers. Having extracted the doubling formulas for Tetranacci from its companion matrix, I could not resist doing it again for this very similar recurrence relation.
Before we get into the actual code, some definitions and a short derivation of the formulas used are in order. Define an integer sequence A such that:
A(n) := A(n-1) + A(n-3) + A(n-4)
with initial values A(0), A(1), A(2), A(3) := 1, 1, 1, 2.
For n >= 0, this is the number of integer compositions of n into parts from the set {1, 3, 4}. This is the sequence that we ultimately wish to compute.
For convenience, define a sequence T such that:
T(n) := T(n-1) + T(n-3) + T(n-4)
with initial values T(0), T(1), T(2), T(3) := 0, 0, 0, 1.
Note that A(n) and T(n) are simply shifts of each other. More precisely, A(n) = T(n+3) for all integers n. Accordingly, as elaborated by another answer, the companion matrix for both sequences is:
[0 1 0 0]
[0 0 1 0]
[0 0 0 1]
[1 1 0 1]
Call this matrix C, and let:
a, b, c, d := T(n), T(n+1), T(n+2), T(n+3)
a', b', c', d' := T(2n), T(2n+1), T(2n+2), T(2n+3)
By induction, it can easily be shown that:
[0 1 0 0]^n = [d-c-a c-b b-a a]
[0 0 1 0] [ a d-c c-b b]
[0 0 0 1] [ b b+a d-c c]
[1 1 0 1] [ c c+b b+a d]
As seen above, for any n, C^n can be fully determined from its rightmost column alone. Furthermore, multiplying C^n with its rightmost column produces the rightmost column of C^(2n):
[d-c-a c-b b-a a][a] = [a'] = [a(2d - 2c - a) + b(2c - b)]
[ a d-c c-b b][b] [b'] [ a^2 + c^2 + 2b(d - c)]
[ b b+a d-c c][c] [c'] [ b(2a + b) + c(2d - c)]
[ c c+b b+a d][d] [d'] [ b^2 + d^2 + 2c(a + b)]
Thus, if we wish to compute C^n for some n by repeated squaring, we need only perform matrix-vector multiplication per step instead of the full matrix-matrix multiplication.
Now, the implementation, in Python:
# O(n) integer additions or subtractions
def A_linearly(n):
a, b, c, d = 0, 0, 0, 1 # T(0), T(1), T(2), T(3)
if n >= 0:
for _ in range(+n):
a, b, c, d = b, c, d, a + b + d
else: # n < 0
for _ in range(-n):
a, b, c, d = d - c - a, a, b, c
return d # because A(n) = T(n+3)
# O(log n) integer multiplications, additions, subtractions.
def A_by_doubling(n):
n += 3 # because A(n) = T(n+3)
if n >= 0:
a, b, c, d = 0, 0, 0, 1 # T(0), T(1), T(2), T(3)
else: # n < 0
a, b, c, d = 1, 0, 0, 0 # T(-1), T(0), T(1), T(2)
# Unroll the final iteration to avoid computing extraneous values
for i in reversed(range(1, abs(n).bit_length())):
w = a*(2*(d - c) - a) + b*(2*c - b)
x = a*a + c*c + 2*b*(d - c)
y = b*(2*a + b) + c*(2*d - c)
z = b*b + d*d + 2*c*(a + b)
if (n >> i) & 1 == 0:
a, b, c, d = w, x, y, z
else: # (n >> i) & 1 == 1
a, b, c, d = x, y, z, w + x + z
if n & 1 == 0:
return a*(2*(d - c) - a) + b*(2*c - b) # w
else: # n & 1 == 1
return a*a + c*c + 2*b*(d - c) # x
print(all(A_linearly(n) == A_by_doubling(n) for n in range(-1000, 1001)))
Because it was rather trivial to code, the sequence is extended to negative n in the usual way. Also provided is a simple linear implementation to serve as a point of reference.
For n large enough, the logarithmic implementation above is 10-20x faster than directly exponentiating the companion matrix with numpy, by a simple (i.e. not rigorous, and likely flawed) timing comparison. And by my estimate, it would still take ~100 years to compute A(10**12)! Even though the algorithm above has room for improvement, that number is simply too large. On the other hand, computing A(10**12) mod M for some M is much more attainable.
A direct relation to Lucas and Fibonacci numbers
It turns out that T(n) is even closer to the Fibonacci and Lucas numbers than it is to Tetranacci. To see this, note that the characteristic polynomial for T(n) is x^4 - x^3 - x - 1 = 0 which factors into (x^2 - x - 1)(x^2 + 1) = 0. The first factor is the characteristic polynomial for Fibonacci & Lucas! The 4 roots of (x^2 - x - 1)(x^2 + 1) = 0 are the two Fibonacci roots, phi and psi = 1 - phi, and i and -i--the two square roots of -1.
The closed-form expression or "Binet" formula for T(n) will have the general form:
T(n) = U(n) + V(n)
U(n) = p*(phi^n) + q*(psi^n)
V(n) = r*(i^n) + s*(-i)^n
for some constant coefficients p, q, r, s.
Using the initial values for T(n), solving for the coefficients, applying some algebra, and noting that the Lucas numbers have the closed-form expression: L(n) = phi^n + psi^n, we can derive the following relations:
L(n+1) - L(n) L(n-1) F(n) + F(n-2)
U(n) = ------------- = -------- = ------------
5 5 5
where L(n) is the n'th Lucas number with L(0), L(1) := 2, 1 and F(n) is the n'th Fibonacci number with F(0), F(1) := 0, 1. And we also have:
V(n) = 1 / 5 if n = 0 (mod 4)
| -2 / 5 if n = 1 (mod 4)
| -1 / 5 if n = 2 (mod 4)
| 2 / 5 if n = 3 (mod 4)
Which is ugly, but trivial to code. Note that the numerator of V(n) can also be succinctly expressed as cos(n*pi/2) - 2sin(n*pi/2) or (3-(-1)^n) / 2 * (-1)^(n(n+1)/2), but we use the piece-wise definition for clarity.
Here's an even nicer, more direct identity:
T(n) + T(n+2) = F(n)
Essentially, we can compute T(n) (and therefore A(n)) by using Fibonacci & Lucas numbers. Theoretically, this should be much more efficient than the Tetranacci-like approach.
It is known that the Lucas numbers can computed more efficiently than Fibonacci, therefore we will compute A(n) from the Lucas numbers. The most efficient, simple Lucas number algorithm I know of is one by L.F. Johnson (see his 2010 paper: Middle and Ripple, fast simple O(lg n) algorithms for Lucas Numbers). Once we have a Lucas algorithm, we use the identity: T(n) = L(n - 1) / 5 + V(n) to compute A(n).
# O(log n) integer multiplications, additions, subtractions
def A_by_lucas(n):
n += 3 # because A(n) = T(n+3)
offset = (+1, -2, -1, +2)[n % 4]
L = lf_johnson_2010_middle(n - 1)
return (L + offset) // 5
def lf_johnson_2010_middle(n):
"-> n'th Lucas number. See [L.F. Johnson 2010a]."
#: The following Lucas identities are used:
#:
#: L(2n) = L(n)^2 - 2*(-1)^n
#: L(2n+1) = L(2n+2) - L(2n)
#: L(2n+2) = L(n+1)^2 - 2*(-1)^(n+1)
#:
#: The first and last identities are equivalent.
#: For the unrolled iteration, the following is also used:
#:
#: L(2n+1) = L(n)*L(n+1) - (-1)^n
#:
#: Since this approach uses only square multiplications per loop,
#: It turns out to be slightly faster than standard Lucas doubling,
#: which uses 1 square and 1 regular multiplication.
if n >= 0:
a, b, sign = 2, 1, +1 # L(0), L(1), (-1)^0
else: # n < 0
a, b, sign = -1, 2, -1 # L(-1), L(0), (-1)^(-1)
# unroll the last iteration to avoid computing unnecessary values
for i in reversed(range(1, abs(n).bit_length())):
a = a*a - 2*sign # L(2k)
c = b*b + 2*sign # L(2k+2)
b = c - a # L(2k+1)
sign = +1
if (n >> i) & 1:
a, b = b, c
sign = -1
if n & 1:
return a*b - sign
else:
return a*a - 2*sign
You may verify that A_by_lucas produces the same results as the previous A_by_doubling function, but is roughly 5x faster. Still not fast enough to compute A(10**12) in any reasonable amount of time!
You can easily improve your current recursion implementation by adding memoization which makes the solution fast again. C# code:
// Dictionary to store computed values
private static Dictionary<int, long> s_Solutions = new Dictionary<int, long>();
private static long Count134(int value) {
if (value == 0)
return 1;
else if (value <= 0)
return 0;
long result;
// Improvement: Do we have the value computed?
if (s_Solutions.TryGetValue(value, out result))
return result;
result = Count134(value - 4) +
Count134(value - 3) +
Count134(value - 1);
// Improvement: Store the value computed for future use
s_Solutions.Add(value, result);
return result;
}
And so you can easily call
Console.Write(Count134(500));
The outcome (which takes about 2 milliseconds) is
3350159379832610737
Example :
A=5, B=2, N=12
Then let x=2, y=1, so 12 - (5(2) + 2(1)) = 0.
Another example:
A=5, B=4, N=12
Here x=1, y=1 is the best possible. Note x=2, y=0 would be better except that x=0 is not allowed.
I'm looking for something fast.
Note it's sufficient to find the value of Ax+By. It's not necessary to give x or y explicitly.
If gcd(A,B)|N, then N is your maximal value. Otherwise, it's the greatest multiple of gcd(A,B) that's smaller than N. Using 4x+2y=13 as an example, that value is gcd(4,2)*6=12 realized by 4(2)+2(2)=12 (among many solutions).
As a formula, your maximal value is Floor(N/gcd(A,B))*gcd(A,B).
Edit: If both x and y must be positive, this may not work. However, won't even be a solution if A+B>N. Here's an algorithm for you...
from math import floor, ceil
def euclid_wallis(m, n):
col1 = [1, 0, m]
col2 = [0, 1, n]
while col2[-1] != 0:
f = -1 * (col1[-1] // col2[-1])
col2, col1 = [x2 * f + x1 for x1, x2 in zip(col1, col2)], col2
return col1, col2
def positive_solutions(A, B, N):
(x, y, gcf), (cx, cy, _) = euclid_wallis(A, B)
f = N // gcf
while f > 0:
fx, fy, n = f*x, f*y, f*gcf
k_min = (-fx + 0.) / cx
k_max = (-fy + 0.) / cy
if cx < 0:
k_min, k_max = k_max, k_min
if floor(k_min) + 1 <= ceil(k_max) - 1:
example_k = int(floor(k_min) + 1)
return fx + cx * example_k, fy + cy * example_k, n
if k_max <= 1:
raise Exception('No solution - A: {}, B: {}, N: {}'.format(A, B, N))
f -= 1
print positive_solutions(5, 4, 12) # (1, 1, 9)
print positive_solutions(2, 3, 6) # (1, 1, 5)
print positive_solutions(23, 37, 238) # (7, 2, 235)
A brute-force O(N^2 / A / B) algorithm, implemented in plain Python3:
import math
def axby(A, B, N):
return [A * x + B * y
for x in range(1, 1 + math.ceil(N / A))
for y in range(1, 1 + math.ceil(N / B))
if (N - A * x - B * y) >= 0]
def bestAxBy(A, B, N):
return min(axby(A, B, N), key=lambda x: N - x)
This matched your examples:
In [2]: bestAxBy(5, 2, 12)
Out[2]: 12 # 5 * (2) + 2 * (1)
In [3]: bestAxBy(5, 4, 12)
Out[3]: 9 # 5 * (1) + 4 * (1)
Have no idea what algorithm that might be, but I think you need something like that (C#)
static class Program
{
static int solve( int a, int b, int N )
{
if( a <= 0 || b <= 0 || N <= 0 )
throw new ArgumentOutOfRangeException();
if( a + b > N )
return -1; // Even x=1, y=1 still more then N
int x = 1;
int y = ( N - ( x * a ) ) / b;
int zInitial = a * x + b * y;
int zMax = zInitial;
while( true )
{
x++;
y = ( N - ( x * a ) ) / b;
if( y <= 0 )
return zMax; // With that x, no positive y possible
int z = a * x + b * y;
if( z > zMax )
zMax = z; // Nice, found better
if( z == zInitial )
return zMax; // x/y/z are periodical, returned where started, meaning no new values are expected
}
}
static void Main( string[] args )
{
int r = solve( 5, 4, 12 );
Console.WriteLine( "{0}", r );
}
}
I have three numbers x, y , z.
For a range between numbers x and y.
How can i find the total numbers whose % with z is 0 i.e. how many numbers between x and y are divisible by z ?
It can be done in O(1): find the first one, find the last one, find the count of all other.
I'm assuming the range is inclusive. If your ranges are exclusive, adjust the bounds by one:
find the first value after x that is divisible by z. You can discard x:
x_mod = x % z;
if(x_mod != 0)
x += (z - x_mod);
find the last value before y that is divisible by y. You can discard y:
y -= y % z;
find the size of this range:
if(x > y)
return 0;
else
return (y - x) / z + 1;
If mathematical floor and ceil functions are available, the first two parts can be written more readably. Also the last part can be compressed using math functions:
x = ceil (x, z);
y = floor (y, z);
return max((y - x) / z + 1, 0);
if the input is guaranteed to be a valid range (x >= y), the last test or max is unneccessary:
x = ceil (x, z);
y = floor (y, z);
return (y - x) / z + 1;
(2017, answer rewritten thanks to comments)
The number of multiples of z in a number n is simply n / z
/ being the integer division, meaning decimals that could result from the division are simply ignored (for instance 17/5 => 3 and not 3.4).
Now, in a range from x to y, how many multiples of z are there?
Let see how many multiples m we have up to y
0----------------------------------x------------------------y
-m---m---m---m---m---m---m---m---m---m---m---m---m---m---m---
You see where I'm going... to get the number of multiples in the range [ x, y ], get the number of multiples of y then subtract the number of multiples before x, (x-1) / z
Solution: ( y / z ) - (( x - 1 ) / z )
Programmatically, you could make a function numberOfMultiples
function numberOfMultiples(n, z) {
return n / z;
}
to get the number of multiples in a range [x, y]
numberOfMultiples(y) - numberOfMultiples(x-1)
The function is O(1), there is no need of a loop to get the number of multiples.
Examples of results you should find
[30, 90] ÷ 13 => 4
[1, 1000] ÷ 6 => 166
[100, 1000000] ÷ 7 => 142843
[777, 777777777] ÷ 7 => 111111001
For the first example, 90 / 13 = 6, (30-1) / 13 = 2, and 6-2 = 4
---26---39---52---65---78---91--
^ ^
30<---(4 multiples)-->90
I also encountered this on Codility. It took me much longer than I'd like to admit to come up with a good solution, so I figured I would share what I think is an elegant solution!
Straightforward Approach 1/2:
O(N) time solution with a loop and counter, unrealistic when N = 2 billion.
Awesome Approach 3:
We want the number of digits in some range that are divisible by K.
Simple case: assume range [0 .. n*K], N = n*K
N/K represents the number of digits in [0,N) that are divisible by K, given N%K = 0 (aka. N is divisible by K)
ex. N = 9, K = 3, Num digits = |{0 3 6}| = 3 = 9/3
Similarly,
N/K + 1 represents the number of digits in [0,N] divisible by K
ex. N = 9, K = 3, Num digits = |{0 3 6 9}| = 4 = 9/3 + 1
I think really understanding the above fact is the trickiest part of this question, I cannot explain exactly why it works.
The rest boils down to prefix sums and handling special cases.
Now we don't always have a range that begins with 0, and we cannot assume the two bounds will be divisible by K.
But wait! We can fix this by calculating our own nice upper and lower bounds and using some subtraction magic :)
First find the closest upper and lower in the range [A,B] that are divisible by K.
Upper bound (easier): ex. B = 10, K = 3, new_B = 9... the pattern is B - B%K
Lower bound: ex. A = 10, K = 3, new_A = 12... try a few more and you will see the pattern is A - A%K + K
Then calculate the following using the above technique:
Determine the total number of digits X between [0,B] that are divisible by K
Determine the total number of digits Y between [0,A) that are divisible by K
Calculate the number of digits between [A,B] that are divisible by K in constant time by the expression X - Y
Website: https://codility.com/demo/take-sample-test/count_div/
class CountDiv {
public int solution(int A, int B, int K) {
int firstDivisible = A%K == 0 ? A : A + (K - A%K);
int lastDivisible = B%K == 0 ? B : B - B%K; //B/K behaves this way by default.
return (lastDivisible - firstDivisible)/K + 1;
}
}
This is my first time explaining an approach like this. Feedback is very much appreciated :)
This is one of the Codility Lesson 3 questions. For this question, the input is guaranteed to be in a valid range. I answered it using Javascript:
function solution(x, y, z) {
var totalDivisibles = Math.floor(y / z),
excludeDivisibles = Math.floor((x - 1) / z),
divisiblesInArray = totalDivisibles - excludeDivisibles;
return divisiblesInArray;
}
https://codility.com/demo/results/demoQX3MJC-8AP/
(I actually wanted to ask about some of the other comments on this page but I don't have enough rep points yet).
Divide y-x by z, rounding down. Add one if y%z < x%z or if x%z == 0.
No mathematical proof, unless someone cares to provide one, but test cases, in Perl:
#!perl
use strict;
use warnings;
use Test::More;
sub multiples_in_range {
my ($x, $y, $z) = #_;
return 0 if $x > $y;
my $ret = int( ($y - $x) / $z);
$ret++ if $y%$z < $x%$z or $x%$z == 0;
return $ret;
}
for my $z (2 .. 10) {
for my $x (0 .. 2*$z) {
for my $y (0 .. 4*$z) {
is multiples_in_range($x, $y, $z),
scalar(grep { $_ % $z == 0 } $x..$y),
"[$x..$y] mod $z";
}
}
}
done_testing;
Output:
$ prove divrange.pl
divrange.pl .. ok
All tests successful.
Files=1, Tests=3405, 0 wallclock secs ( 0.20 usr 0.02 sys + 0.26 cusr 0.01 csys = 0.49 CPU)
Result: PASS
Let [A;B] be an interval of positive integers including A and B such that 0 <= A <= B, K be the divisor.
It is easy to see that there are N(A) = ⌊A / K⌋ = floor(A / K) factors of K in interval [0;A]:
1K 2K 3K 4K 5K
●········x········x··●·····x········x········x···>
0 A
Similarly, there are N(B) = ⌊B / K⌋ = floor(B / K) factors of K in interval [0;B]:
1K 2K 3K 4K 5K
●········x········x········x········x···●····x···>
0 B
Then N = N(B) - N(A) equals to the number of K's (the number of integers divisible by K) in range (A;B]. The point A is not included, because the subtracted N(A) includes this point. Therefore, the result should be incremented by one, if A mod K is zero:
N := N(B) - N(A)
if (A mod K = 0)
N := N + 1
Implementation in PHP
function solution($A, $B, $K) {
if ($K < 1)
return 0;
$c = floor($B / $K) - floor($A / $K);
if ($A % $K == 0)
$c++;
return (int)$c;
}
In PHP, the effect of the floor function can be achieved by casting to the integer type:
$c = (int)($B / $K) - (int)($A / $K);
which, I think, is faster.
Here is my short and simple solution in C++ which got 100/100 on codility. :)
Runs in O(1) time. I hope its not difficult to understand.
int solution(int A, int B, int K) {
// write your code in C++11
int cnt=0;
if( A%K==0 or B%K==0)
cnt++;
if(A>=K)
cnt+= (B - A)/K;
else
cnt+=B/K;
return cnt;
}
(floor)(high/d) - (floor)(low/d) - (high%d==0)
Explanation:
There are a/d numbers divisible by d from 0.0 to a. (d!=0)
Therefore (floor)(high/d) - (floor)(low/d) will give numbers divisible in the range (low,high] (Note that low is excluded and high is included in this range)
Now to remove high from the range just subtract (high%d==0)
Works for integers, floats or whatever (Use fmodf function for floats)
Won't strive for an o(1) solution, this leave for more clever person:) Just feel this is a perfect usage scenario for function programming. Simple and straightforward.
> x,y,z=1,1000,6
=> [1, 1000, 6]
> (x..y).select {|n| n%z==0}.size
=> 166
EDIT: after reading other's O(1) solution. I feel shamed. Programming made people lazy to think...
Division (a/b=c) by definition - taking a set of size a and forming groups of size b. The number of groups of this size that can be formed, c, is the quotient of a and b. - is nothing more than the number of integers within range/interval ]0..a] (not including zero, but including a) that are divisible by b.
so by definition:
Y/Z - number of integers within ]0..Y] that are divisible by Z
and
X/Z - number of integers within ]0..X] that are divisible by Z
thus:
result = [Y/Z] - [X/Z] + x (where x = 1 if and only if X is divisible by Y otherwise 0 - assuming the given range [X..Y] includes X)
example :
for (6, 12, 2) we have 12/2 - 6/2 + 1 (as 6%2 == 0) = 6 - 3 + 1 = 4 // {6, 8, 10, 12}
for (5, 12, 2) we have 12/2 - 5/2 + 0 (as 5%2 != 0) = 6 - 2 + 0 = 4 // {6, 8, 10, 12}
The time complexity of the solution will be linear.
Code Snippet :
int countDiv(int a, int b, int m)
{
int mod = (min(a, b)%m==0);
int cnt = abs(floor(b/m) - floor(a/m)) + mod;
return cnt;
}
here n will give you count of number and will print sum of all numbers that are divisible by k
int a = sc.nextInt();
int b = sc.nextInt();
int k = sc.nextInt();
int first = 0;
if (a > k) {
first = a + a/k;
} else {
first = k;
}
int last = b - b%k;
if (first > last) {
System.out.println(0);
} else {
int n = (last - first)/k+1;
System.out.println(n * (first + last)/2);
}
Here is the solution to the problem written in Swift Programming Language.
Step 1: Find the first number in the range divisible by z.
Step 2: Find the last number in the range divisible by z.
Step 3: Use a mathematical formula to find the number of divisible numbers by z in the range.
func solution(_ x : Int, _ y : Int, _ z : Int) -> Int {
var numberOfDivisible = 0
var firstNumber: Int
var lastNumber: Int
if y == x {
return x % z == 0 ? 1 : 0
}
//Find first number divisible by z
let moduloX = x % z
if moduloX == 0 {
firstNumber = x
} else {
firstNumber = x + (z - moduloX)
}
//Fist last number divisible by z
let moduloY = y % z
if moduloY == 0 {
lastNumber = y
} else {
lastNumber = y - moduloY
}
//Math formula
numberOfDivisible = Int(floor(Double((lastNumber - firstNumber) / z))) + 1
return numberOfDivisible
}
public static int Solution(int A, int B, int K)
{
int count = 0;
//If A is divisible by K
if(A % K == 0)
{
count = (B / K) - (A / K) + 1;
}
//If A is not divisible by K
else if(A % K != 0)
{
count = (B / K) - (A / K);
}
return count;
}
This can be done in O(1).
Here you are a solution in C++.
auto first{ x % z == 0 ? x : x + z - x % z };
auto last{ y % z == 0 ? y : y - y % z };
auto ans{ (last - first) / z + 1 };
Where first is the first number that ∈ [x; y] and is divisible by z, last is the last number that ∈ [x; y] and is divisible by z and ans is the answer that you are looking for.