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I am trying to solve a problem which is described below,
Given value of f(0) and k , which are integers.
I need to find value of f( T ). where T<=1010
Recursive function is,
f(n) = 2*f(n-1) , if 4*f(n-1) <=k
k - ( 2*f(n-1) ) , if 4*f(n-1) > k
My efforts,
#include<iostream>
using namespace std;
int main(){
long k,f0,i;
cin>>k>>f0;
long operation ;
cin>>operation;
long answer=f0;
for(i=1;i<=operation;i++){
answer=(4*answer <= k )?(2*answer):(k-(2*answer));
}
cout<<answer;
return 0;
}
My code gives me right answer. But, The code will run 1010 time in worst case that gives me Time Limit Exceed. I need more efficient solution for this problem. Please help me. I don't know the correct algorithm.
If 2f(0) < k then you can compute this function in O(log n) time (using exponentiation by squaring modulo k).
r = f(0) * 2^n mod k
return 2 * r >= k ? k - r : r
You can prove this by induction. The induction hypothesis is that 0 <= f(n) < k/2, and that the above code fragment computes f(n).
Here's a Python program which checks random test cases, comparing a naive implementation (f) with an optimized one (g).
def f(n, k, z):
r = z
for _ in xrange(n):
if 4*r <= k:
r = 2 * r
else:
r = k - 2 * r
return r
def g(n, k, z):
r = (z * pow(2, n, k)) % k
if 2 * r >= k:
r = k - r
return r
import random
errs = 0
while errs < 20:
k = random.randrange(100, 10000000)
n = random.randrange(100000)
z = random.randrange(k//2)
a1 = f(n, k, z)
a2 = g(n, k, z)
if a1 != a2:
print n, k, z, a1, a2
errs += 1
print '.',
Can you use methmetical solution before progamming and compulating?
Actually,
f(n) = f0*2^(n-1) , if f(n-1)*4 <= k
k - f0*2^(n-1) , if f(n-1)*4 > k
thus, your code will write like this:
condition = f0*pow(2, operation-2)
answer = condition*4 =< k? condition*2: k - condition*2
For a simple loop, your answer looks pretty tight; one could optimise a little bit using answer<<2 instead of 4*answer, and answer<<1 for 2*answer, but quite possibly your compiler is already doing that. If you're blowing the time with this, it might be necessary to reduce the loop itself somehow.
I can't figure out a mathematical pattern that #Shannon was going for, but I'm thinking we could exploit the fact that this function will sooner or later cycle. If the cycle is short enough, then we could short the loop by just getting the answer at the same point in the cycle.
So let's get some cycle detection equipment in the form of Brent's algorithm, and see if we can cut the loop to reasonable levels.
def brent(f, x0):
# main phase: search successive powers of two
power = lam = 1
tortoise = x0
hare = f(x0) # f(x0) is the element/node next to x0.
while tortoise != hare:
if power == lam: # time to start a new power of two?
tortoise = hare
power *= 2
lam = 0
hare = f(hare)
lam += 1
# Find the position of the first repetition of length λ
mu = 0
tortoise = hare = x0
for i in range(lam):
# range(lam) produces a list with the values 0, 1, ... , lam-1
hare = f(hare)
# The distance between the hare and tortoise is now λ.
# Next, the hare and tortoise move at same speed until they agree
while tortoise != hare:
tortoise = f(tortoise)
hare = f(hare)
mu += 1
return lam, mu
f0 = 2
k = 198779
t = 10000000000
def f(x):
if 4 * x <= k:
return 2 * x
else:
return k - 2 * x
lam, mu = brent(f, f0)
t2 = t
if t >= mu + lam: # if T is past the cycle's first loop,
t2 = (t - mu) % lam + mu # find the equivalent place in the first loop
x = f0
for i in range(t2):
x = f(x)
print("Cycle start: %d; length: %d" % (mu, lam))
print("Equivalent result at index: %d" % t2)
print("Loop iterations skipped: %d" % (t - t2))
print("Result: %d" % x)
As opposed to the other proposed answers, this approach actually could use a memo array to speed up the process, since the start of the function is actually calculated multiple times (in particular, inside brent), or it may be irrelevant, depending on how big the cycle happens to be.
The algorithm you proposed already has O(n).
To come up with more efficient algorithms, there is not that much direction we can go about. Some typical options we have
1.Decease the coefficients of the linear term( but I doubt it would make a difference in this case
2.Change to O(Logn)(typically use some sort of divide and conquer technique)
3.Change to O(1)
In this case, we can do the last one.
The recursion function is a piece-wise function
f(n) = 2*f(n-1) , if 4*f(n-1) <=k
k - ( 2*f(n-1) ) , if 4*f(n-1) > k
Let's tackle it by case:
case 1: if 4*f(n-1) <= k (1)(assuming the starting index is zero)
this is a obvious a geometry series
a_n = 2*a_n-1
Therefore, have the formula
Sn = 2^(n-1)f(0) ----()
Case 2: if 4*f(n-1) > k (2), we have
a_n = -2a_n-1 + k
Assuming, a_j is the element in the sequence which just satisfy condition (2)
Nestedly sub in an_1 to the formula, you will obtain the equation
an = k -2k +4k -8k... +(-2)^(n-j)* a_j
k -2k 4k -8... is another gemo series
Sn = k*(1-2^(n-j))/(1-2) ---gemo series sum formula with starting value k and ratio = -2
Therefore, we have a formula for an in the case 2
an = k * (1-2^(n-j))/(1-2) + (-2)^(n-j) * a_j ----（**）
All we left to do it to find aj which just dissatisfy condition (1) and satisfy (2)
This can be obtained in constant time again using the formula we have for case 1:
find n such that, 4*an = 4*Sn = 4*2^(n-1)*f(0)
solve for n: 4*2^(n-1)*f(0) = k, if n is not integer, take ceiling of n
In my first attempt to solve this question, I had wrong assumption that the value of the sequence is monotonically increasing but in fact the sequence might jump between case 1 and case 2. Therefore, there might not be constant algorithm to solve the problem.
However, we can use utilize the result above to skip iterative update complexity.
The overall algorithm will look something like:
start with T, K, and f(0)
compute n that make the condition switch using either (*) or (**)
update f(0) with f(n), update T - n
repeat
terminate when T-n = 0(the last iteration might over compute causing T-n<0, therefore, you need to go back a little bit if that happen)
Create a map that can store your results. Before finding f(n) check in that map, if solution is already existed or not.
If exists, use that solution.
Otherwise find it, store it for future use.
For C++:
Definition:
map<long,long>result;
Insertion:
result[key]=value
Accessing:
value=result[key];
Checking:
map<long,long>::iterator it=result.find(key);
if(it==result.end())
{
//key was not found, find the solution and insert into result
}
else
{
return result[key];
}
Use above technique for better solution.
I have given a Number A where 1<=A<=10^6 and a Number K. I have to find the all the numbers between 1 to A where A%i==k and i is 1<=i<=A. Is there any better solution than looping
Simple Solution
for(int i=1;i<=A;i++)
if(A%i==k) count++;
Is there any better solution than iterating all the numbers between 1 to A
The expression A % i == k is equivalent to A == n * i + k for any integer value of n that gives a value of A within the stated bounds.
This can be rearranged as n * i = A - k, and can be solved by finding all the factors of A - k that are multiples of i (where k < i <= A).
Here are a couple of examples:
A = 100, k = 10
F = factor_list(A-k) = factor_list(90) = [1,2,3,5,6,9,10,15,18,30,45,90]
(discard all factors less than or equal to k)
Result: [15,18,30,45,90]
A = 288, k = 32
F = [2,4,8,16,32,64,128,256]
Result: [64,128,256]
If A - k is prime, then there is either one solution (A-k) or none (if A-k <= k).
Is there an efficient algorithm to compute the smallest integer N such that N! is divisible by p^k where p is a relatively small prime number and k, a very large integer. In other words,
factorial(N) mod p^k == 0
If, given N and p, I wanted to find how many times p divides into N!, I would use the well-known formula
k = Sum(floor(N/p^i) for i=1,2,...
I've done brute force searches for small values of k but that approach breaks down very quickly as k increases and there doesn't appear to be a pattern that I can extrapolate to larger values.
Edited 6/13/2011
Using suggestions proposed by Fiver and Hammar, I used a quasi-binary search to solve the problem but not quite in the manner they suggested. Using a truncated version of the second formula above, I computed an upper bound on N as the product of k and p (using just the first term). I used 1 as the lower bound. Using the classic binary search algorithm, I computed the midpoint between these two values and calculated what k would be using this midpoint value as N in the second formula, this time with all the terms being used.
If the computed k was too small, I adjusted the lower bound and repeated. Too big, I first tested to see if k computed at midpoint-1 was smaller than the desired k. If so, midpoint was returned as the closest N. Otherwise, I adjusted the highpoint and repeated.
If the computed k were equal, I tested whether the value at midpoint-1 was equal to the value at midpoint. If so, I adjusted the highpoint to be the midpoint and repeated. If midpoint-1 was less than the desired k, the midpoint was returned as the desired answer.
Even with very large values for k (10 or more digits), this approach works O(n log(n)) speeds.
OK this is kind of fun.
Define f(i) = (p^i - 1) / (p - 1)
Write k in a kind of funny "base" where the value of position i is this f(i).
You do this from most-significant to least-significant digit. So first, find the largest j such that f(j) <= k. Then compute the quotient and remainder of k / f(j). Store the quotient as q_j and the remainder as r. Now compute the quotient and remainder of r / f(j-1). Store the quotient as q_{j-1} and the remainder as r again. Now compute the quotient and remainder of r / f(j-2). And so on.
This generates a sequence q_j, q_{j-1}, q_{j-2}, ..., q_1. (Note that the sequence ends at 1, not 0.) Then compute q_j*p^j + q_{j-1}*p^(j-1) + ... q_1*p. That's your N.
Example: k = 9, p = 3. So f(i) = (3^i - 1) / 2. f(1) = 1, f(2) = 4, f(3) = 13. So the largest j with f(j) <= 9 is i = 2 with f(2) = 4. Take the quotient and remainder of 9 / 4. That's a quotient of 2 (which is the digit in our 2's place) and remainder of 1.
For that remainder of 1, find the quotient and remainder of 1 / f(1). Quotient is 1, remainder is zero, so we are done.
So q_2 = 2, q_1 = 1. 2*3^2 + 1*3^1 = 21, which is the right N.
I have an explanation on paper for why this works, but I am not sure how to communicate it in text... Note that f(i) answers the question, "how many factors of p are there in (p^i)!". Once you find the largest i,j such that j*f(i) is less than k, and realize what you are really doing is finding the largest j*p^i less than N, the rest kind of falls out of the wash. In our p=3 example, for instance, we get 4 p's contributed by the product of 1-9, 4 more contributed by the product of 10-18, and one more contributed by 21. Those first two are just multiples of p^2; f(2) = 4 is telling us that each multiple of p^2 contributes 4 more p's to the product.
[update]
Code always helps to clarify. Save the following perl script as foo.pl and run it as foo.pl <p> <k>. Note that ** is Perl's exponentiation operator, bdiv computes a quotient and remainder for BigInts (unlimited-precision integers), and use bigint tells Perl to use BigInts everywhere.
#!/usr/bin/env perl
use warnings;
use strict;
use bigint;
#ARGV == 2
or die "Usage: $0 <p> <k>\n";
my ($p, $k) = map { Math::BigInt->new($_) } #ARGV;
sub f {
my $i = shift;
return ($p ** $i - 1) / ($p - 1);
}
my $j = 0;
while (f($j) <= $k) {
$j++;
}
$j--;
my $N = 0;
my $r = $k;
while ($r > 0) {
my $val = f($j);
my ($q, $new_r) = $r->bdiv($val);
$N += $q * ($p ** $j);
$r = $new_r;
$j--;
}
print "Result: $N\n";
exit 0;
Using the formula you mentioned, the sequence of k values given fixed p and N = 1,2... is non-decreasing. This means you can use a variant of binary search to find N given the desired k.
Start with N = 1, and calculate k.
Double N until k is greater or equal than your desired k to get an upper bound.
Do a binary search on the remaining interval to find your k.
Why don't you try binary search for the answer, using the second formula you mentioned?
You only need to consider values for N, for which p divides N, because if it doesn't, then N! and (N-1)! are divided by the same power of p, so N can't be the smallest one.
Consider
I = (pn)!
and ignore prime factors other than p. The result looks like
I = pn * pn-1 * pn-2 * ... * p * 1
I = pn + (n-1) + (n-2) + ... 2 + 1
I = p(n2 +n)/2
So we're trying to find the smallest n such that
(n2 +n)/2 >= k
which if I remember the quadratic equation right gives us
N = pn, where n >= (sqrt(1+8k) -1)/2
(P.S. Does anyone know how to show the radical symbol in markdown?)
EDIT:
This is wrong. Let me see if I can salvage it...
To test whether a number is prime or not, why do we have to test whether it is divisible only up to the square root of that number?
If a number n is not a prime, it can be factored into two factors a and b:
n = a * b
Now a and b can't be both greater than the square root of n, since then the product a * b would be greater than sqrt(n) * sqrt(n) = n. So in any factorization of n, at least one of the factors must be smaller than the square root of n, and if we can't find any factors less than or equal to the square root, n must be a prime.
Let's say m = sqrt(n) then m × m = n. Now if n is not a prime then n can be written as n = a × b, so m × m = a × b. Notice that m is a real number whereas n, a and b are natural numbers.
Now there can be 3 cases:
a > m ⇒ b < m
a = m ⇒ b = m
a < m ⇒ b > m
In all 3 cases, min(a, b) ≤ m. Hence if we search till m, we are bound to find at least one factor of n, which is enough to show that n is not prime.
Because if a factor is greater than the square root of n, the other factor that would multiply with it to equal n is necessarily less than the square root of n.
Suppose n is not a prime number (greater than 1). So there are numbers a and b such that
n = ab (1 < a <= b < n)
By multiplying the relation a<=b by a and b we get:
a^2 <= ab
ab <= b^2
Therefore: (note that n=ab)
a^2 <= n <= b^2
Hence: (Note that a and b are positive)
a <= sqrt(n) <= b
So if a number (greater than 1) is not prime and we test divisibility up to square root of the number, we will find one of the factors.
It's all really just basic uses of Factorization and Square Roots.
It may appear to be abstract, but in reality it simply lies with the fact that a non-prime-number's maximum possible factorial would have to be its square root because:
sqrroot(n) * sqrroot(n) = n.
Given that, if any whole number above 1 and below or up to sqrroot(n) divides evenly into n, then n cannot be a prime number.
Pseudo-code example:
i = 2;
is_prime = true;
while loop (i <= sqrroot(n))
{
if (n % i == 0)
{
is_prime = false;
exit while;
}
++i;
}
Let's suppose that the given integer N is not prime,
Then N can be factorized into two factors a and b , 2 <= a, b < N such that N = a*b.
Clearly, both of them can't be greater than sqrt(N) simultaneously.
Let us assume without loss of generality that a is smaller.
Now, if you could not find any divisor of N belonging in the range [2, sqrt(N)], what does that mean?
This means that N does not have any divisor in [2, a] as a <= sqrt(N).
Therefore, a = 1 and b = n and hence By definition, N is prime.
...
Further reading if you are not satisfied:
Many different combinations of (a, b) may be possible. Let's say they are:
(a1, b1), (a2, b2), (a3, b3), ..... , (ak, bk). Without loss of generality, assume ai < bi, 1<= i <=k.
Now, to be able to show that N is not prime it is sufficient to show that none of ai can be factorized further. And we also know that ai <= sqrt(N) and thus you need to check till sqrt(N) which will cover all ai. And hence you will be able to conclude whether or not N is prime.
...
So to check whether a number N is Prime or not.
We need to only check if N is divisible by numbers<=SQROOT(N). This is because, if we factor N into any 2 factors say X and Y, ie. N=XY.
Each of X and Y cannot be less than SQROOT(N) because then, XY < N
Each of X and Y cannot be greater than SQROOT(N) because then, X*Y > N
Therefore one factor must be less than or equal to SQROOT(N) ( while the other factor is greater than or equal to SQROOT(N) ).
So to check if N is Prime we need only check those numbers <= SQROOT(N).
Let's say we have a number "a", which is not prime [not prime/composite number means - a number which can be divided evenly by numbers other than 1 or itself. For example, 6 can be divided evenly by 2, or by 3, as well as by 1 or 6].
6 = 1 × 6 or 6 = 2 × 3
So now if "a" is not prime then it can be divided by two other numbers and let's say those numbers are "b" and "c". Which means
a=b*c.
Now if "b" or "c" , any of them is greater than square root of "a "than multiplication of "b" & "c" will be greater than "a".
So, "b" or "c" is always <= square root of "a" to prove the equation "a=b*c".
Because of the above reason, when we test if a number is prime or not, we only check until square root of that number.
Given any number n, then one way to find its factors is to get its square root p:
sqrt(n) = p
Of course, if we multiply p by itself, then we get back n:
p*p = n
It can be re-written as:
a*b = n
Where p = a = b. If a increases, then b decreases to maintain a*b = n. Therefore, p is the upper limit.
Update: I am re-reading this answer again today and it became clearer to me more. The value p does not necessarily mean an integer because if it is, then n would not be a prime. So, p could be a real number (ie, with fractions). And instead of going through the whole range of n, now we only need to go through the whole range of p. The other p is a mirror copy so in effect we halve the range. And then, now I am seeing that we can actually continue re-doing the square root and doing it to p to further half the range.
Let n be non-prime. Therefore, it has at least two integer factors greater than 1. Let f be the smallest of n's such factors. Suppose f > sqrt n. Then n/f is an integer ≤ sqrt n, thus smaller than f. Therefore, f cannot be n's smallest factor. Reductio ad absurdum; n's smallest factor must be ≤ sqrt n.
Any composite number is a product of primes.
Let say n = p1 * p2, where p2 > p1 and they are primes.
If n % p1 === 0 then n is a composite number.
If n % p2 === 0 then guess what n % p1 === 0 as well!
So there is no way that if n % p2 === 0 but n % p1 !== 0 at the same time.
In other words if a composite number n can be divided evenly by
p2,p3...pi (its greater factor) it must be divided by its lowest factor p1 too.
It turns out that the lowest factor p1 <= Math.square(n) is always true.
Yes, as it was properly explained above, it's enough to iterate up to Math.floor of a number's square root to check its primality (because sqrt covers all possible cases of division; and Math.floor, because any integer above sqrt will already be beyond its range).
Here is a runnable JavaScript code snippet that represents a simple implementation of this approach – and its "runtime-friendliness" is good enough for handling pretty big numbers (I tried checking both prime and not prime numbers up to 10**12, i.e. 1 trillion, compared results with the online database of prime numbers and encountered no errors or lags even on my cheap phone):
function isPrime(num) {
if (num % 2 === 0 || num < 3 || !Number.isSafeInteger(num)) {
return num === 2;
} else {
const sqrt = Math.floor(Math.sqrt(num));
for (let i = 3; i <= sqrt; i += 2) {
if (num % i === 0) return false;
}
return true;
}
}
<label for="inp">Enter a number and click "Check!":</label><br>
<input type="number" id="inp"></input>
<button onclick="alert(isPrime(+document.getElementById('inp').value) ? 'Prime' : 'Not prime')" type="button">Check!</button>
To test the primality of a number, n, one would expect a loop such as following in the first place :
bool isPrime = true;
for(int i = 2; i < n; i++){
if(n%i == 0){
isPrime = false;
break;
}
}
What the above loop does is this : for a given 1 < i < n, it checks if n/i is an integer (leaves remainder 0). If there exists an i for which n/i is an integer, then we can be sure that n is not a prime number, at which point the loop terminates. If for no i, n/i is an integer, then n is prime.
As with every algorithm, we ask : Can we do better ?
Let us see what is going on in the above loop.
The sequence of i goes : i = 2, 3, 4, ... , n-1
And the sequence of integer-checks goes : j = n/i, which is n/2, n/3, n/4, ... , n/(n-1)
If for some i = a, n/a is an integer, then n/a = k (integer)
or n = ak, clearly n > k > 1 (if k = 1, then a = n, but i never reaches n; and if k = n, then a = 1, but i starts form 2)
Also, n/k = a, and as stated above, a is a value of i so n > a > 1.
So, a and k are both integers between 1 and n (exclusive). Since, i reaches every integer in that range, at some iteration i = a, and at some other iteration i = k. If the primality test of n fails for min(a,k), it will also fail for max(a,k). So we need to check only one of these two cases, unless min(a,k) = max(a,k) (where two checks reduce to one) i.e., a = k , at which point a*a = n, which implies a = sqrt(n).
In other words, if the primality test of n were to fail for some i >= sqrt(n) (i.e., max(a,k)), then it would also fail for some i <= n (i.e., min(a,k)). So, it would suffice if we run the test for i = 2 to sqrt(n).
Given two ranges of positive integers x: [1 ... n] and y: [1 ... m] and random real R from 0 to 1, I need to find the pair of elements (i,j) from x and y such that x_i / y_j is closest to R.
What is the most efficient way to find this pair?
Using Farey sequence
This is a simple and mathematically beautiful algorithm to solve this: run a binary search, where on each iteration the next number is given by the mediant formula (below). By the properties of the Farey sequence that number is the one with the smallest denominator within that interval. Consequently this sequence will always converge and never 'miss' a valid solution.
In pseudocode:
input: m, n, R
a_num = 0, a_denom = 1
b_num = 1, b_denom = 1
repeat:
-- interestingly c_num/c_denom is already in reduced form
c_num = a_num + b_num
c_denom = a_denom + b_denom
-- if the numbers are too big, return the closest of a and b
if c_num > n or c_denom > m then
if R - a_num/a_denom < b_num/b_denom - R then
return a_num, a_denom
else
return b_num, b_denom
-- adjust the interval:
if c_num/c_denom < R then
a_num = c_num, a_denom = c_denom
else
b_num = c_num, b_denom = c_denom
goto repeat
Even though it's fast on average (my educated guess that it's O(log max(m,n))), it can still be slow if R is close to a fraction with a small denominator. For example finding an approximation to 1/1000000 with m = n = 1000000 will take a million iterations.
The standard approach to approximating reals with rationals is computing the continued fraction series (see [1]). Put a limit on the nominator and denominator while computing parts of the series, and the last value before you break the limits is a fraction very close to your real number.
This will find a very good approximation very fast, but I'm not sure this will always find a closest approximation. It is known that
any convergent [partial value of the continued fraction expansion] is nearer to the continued fraction than any other fraction whose denominator is less than that of the convergent
but there may be approximations with larger denominator (still below your limit) that are better approximations, but are not convergents.
[1] http://en.wikipedia.org/wiki/Continued_fraction
Given that R is a real number such that 0 <= R <= 1, integers x: [1 ... n] and integers y: [1 ... m]. It is assumed that n <= m, since if n > m then x[n]/y[m] will be greater than 1, which cannot be the closest approximation to R.
Therefore, the best approximation of R with the denominator d will be either floor(R*d) / d or ceil(R*d) / d.
The problem can be solved in O(m) time and O(1) space (in Python):
from __future__ import division
from random import random
from math import floor
def fractionize(R, n, d):
error = abs(n/d - R)
return (n, d, error) # (numerator, denominator, absolute difference to R)
def better(a, b):
return a if a[2] < b[2] else b
def approximate(R, n, m):
best = (0, 1, R)
for d in xrange(1, m+1):
n1 = min(n, int(floor(R * d)))
n2 = min(n, n1 + 1) # ceil(R*d)
best = better(best, fractionize(R, n1, d))
best = better(best, fractionize(R, n2, d))
return best
if __name__ == '__main__':
def main():
R = random()
n = 30
m = 100
print R, approximate(R, n, m)
main()
Prolly get flamed, but a lookup might be best where we compute all of the fractional values for each of the possible values.. So a simply indexing a 2d array indexed via the fractional parts with the array element containing the real equivalent. I guess we have discrete X and Y parts so this is finite, it wouldnt be the other way around.... Ahh yeah, the actual searching part....erm reet....
Rather than a completely brute force search, do a linear search over the shortest of your lists, using round to find the best match for each element. Maybe something like this:
best_x,best_y=(1,1)
for x in 1...n:
y=max(1,min(m,round(x/R)))
#optional optimization (if you have a fast gcd)
if gcd(x,y)>1:
continue
if abs(R-x/y)<abs(R-bestx/besty):
best_x,best_y=(x,y)
return (best_x,best_y)
Not at all sure whether the gcd "optimization" will ever be faster...
The Solution:
You can do this O(1) space and O(m log(n)) time:
there is no need to create any list to search,
The pseudo code may be is buggy but the idea is this:
r: input number to search.
n,m: the ranges.
for (int i=1;i<=m;i++)
{
minVal = min(Search(i,1,n,r), minVal);
}
//x and y are start and end of array:
decimal Search(i,x,y,r)
{
if (i/x > r)
return i/x - r;
decimal middle1 = i/Cill((x+y)/2);
decimal middle2 = i/Roof((x+y)/2);
decimal dist = min(middle1,middle2)
decimal searchResult = 100000;
if( middle > r)
searchResult = Search (i, x, cill((x+y)/2),r)
else
searchResult = Search(i, roof((x+y)/2), y,r)
if (searchResult < dist)
dist = searchResult;
return dist;
}
finding the index as home work to reader.
Description: I think you can understand what's the idea by code, but let trace one of a for loop:
when i=1:
you should search within bellow numbers:
1,1/2,1/3,1/4,....,1/n
you check the number with (1,1/cill(n/2)) and (1/floor(n/2), 1/n) and doing similar binary search on it to find the smallest one.
Should do this for loop for all items, so it will be done m time. and in each time it takes O(log(n)). this function can improve by some mathematical rules, but It will be complicated, I skip it.
If the denominator of R is larger than m then use the Farey method (which the Fraction.limit_denominator method implements) with a limit of m to get a fraction a/b where b is smaller than m else let a/b = R. With b <= m, either a <= n and you are done or else let M = math.ceil(n/R) and re-run the Farey method.
def approx2(a, b, n, m):
from math import ceil
from fractions import Fraction
R = Fraction(a, b)
if R < Fraction(1, m):
return 1, m
r = R.limit_denominator(m)
if r.numerator > n:
M = ceil(n/R)
r = R.limit_denominator(M)
return r.numerator, r.denominator
>>> approx2(113, 205, 50, 200)
(43, 78)
It might be possible to just run the Farey method once using a limiting denominator of min(ceil(n/R), m) but I am not sure about that:
def approx(a, b, n, m):
from math import ceil
from fractions import Fraction
R = Fraction(a, b)
if R < Fraction(1, m):
return 1, m
r = R.limit_denominator(min(ceil(n/R), m))
return r.numerator, r.denominator