How to find even sum - python

I need to find the sum of all even numbers below the inserted number. For example if I insert 8 then the sum would be 2+4+6+8=20. If I insert 9 then it also needs to be 20. And it needs to be based on recursion.
This is what I have so far:
def even(a):
if a == 0:
else:
even(a - 1)
even(8)
I cannot figure out what to change under the "if" part for it to give the right outcome

If the function is called with an odd number, n, then you can immediately call again with the number below (an even).
Then if the function is called with an even number return that even number plus the result of summing all the even numbers below this number by calling again with n - 2.
Finally, your base case occurs when n = 0. In this case just return 0.
So we have
def even_sum(n):
if n % 2 == 1: # n is odd
return even_sum(n - 1)
if n == 0:
return 0
return n + even_sum(n - 2)
which works as expected
>>> even_sum(8)
20
>>> even_sum(9)
20
>>> even_sum(0)
0

To design a recursive algorithm, the first thing to wonder is "In what cases can my algorithm return an answer trivially?". In your case, the answer is "If it is called with 0, the algorithm answers 0". Hence, you can write:
def even(n):
if n == 0:
return 0
Now the next question is "Given a particular input, how can I reduce the size of this input, so that it will eventually reach the trivial condition?"
If you have an even number, you want to have this even number + the sum of even numbers below it, which is the result of even(n-2). If you have an odd number, you want to return the sum of even numbers below it. Hence the final version of your function is:
def even(n):
if n == 0 or n == 1:
return 0
if n % 2 == 0:
return n + even(n - 2)
return even(n - 1)

Both with o(n) time complexity
With For loop
num = int(input("Enter a number: ")) # given number to find sum
my_sum = 0
for n in range(num + 1):
if n % 2 == 0:
my_sum += n
print(my_sum)
With recursion
def my_sum(num):
if num == 0:
return 0
if num % 2 == 1:
return my_sum(num - 1)
return num + my_sum(num - 2)
always avoid O(n^2) and greater time complexity

For a recursive solution:
def evenSum(N): return 0 if N < 2 else N - N%2 + evenSum(N-2)
If you were always given an even number as input, you could simply recurse using N + f(N-2).
For example: 8 + ( 6 + (4 + ( 2 + 0 ) ) )
But the odd numbers will require that you strip the odd bit in the calculation (e.g. subtracting 1 at each recursion)
For example: 9-1 + ( 7-1 + ( 5-1 + ( 3-1 + 0 ) ) )
You can achieve this stripping of odd bits by subtracting the modulo 2 of the input value. This subtracts zero for even numbers and one for odd numbers.
adjusting your code
Your approach is recursing by 1, so it will go through both the even and odd numbers down to zero (at which point it must stop recursing and simply return zero).
Here's how you can adjust it:
Return a value of zero when you are given zero as input
Make sure to return the computed value that comes from the next level of recursion (you are missing return in front of your call to even(a-1)
Add the parameter value when it is even but don't add it when it is odd
...
def even(a):
if a == 0 : return 0 # base case, no further recusion
if a%2 == 1 : return even(a-1) # odd number: skip to even number
return a + even(a-1) # even number: add with recursion
# a+even(a-2) would be better
A trick to create a recursive function
An easy way to come up with the structure of a recursive function is to be very optimistic and imagine that you already have one that works. Then determine how you would use the result of that imaginary function to produce the next result. That will be the recursive part of the function.
Finally, find a case where you would know the answer without using the function. That will be your exit condition.
In this case (sum of even numbers), imagine you already have a function magic(x) that gives you the answer for x. How would you use it to find a solution for n given the result of magic(n-1) ?
If n is even, add it to magic(n-1). If n is odd, use magic(n-1) directly.
Now, to find a smaller n where we know the answer without using magic(). Well if n is less than 2 (or zero) we know that magic(n) will return zero so we can give that result without calling it.
So our recursion is "n+magic(n-1) if n is even, else magic(n-1)"
and our stop condition is "zero if n < 2"
Now substitute magic with the name of your function and the magic is done.
For an O(1) solution:
Given that the sum of numbers from 1 to N can be calculated with N*(N+1)//2, you can get half of the sum of even numbers if you use N//2 in the formula. Then multiply the result by 2 to obtain the sum of even numbers.
so (N//2)*(N//2+1) will give the answer directly in O(1) time:
N = 8
print((N//2)*(N//2+1))
# 20
# other examples:
for N in range(10):
print(N,N//2*(N//2+1))
# 0 0
# 1 0
# 2 2
# 3 2
# 4 6
# 5 6
# 6 12
# 7 12
# 8 20
# 9 20
Visually, you can see the progression like this:
1..n : 1 2 3 4 5 6 7 8
∑n : 1 3 6 10 15 21 28 36 n(n+1)/2
n/2 : 0 1 1 2 2 3 3 4
1..n/2 : 1 2 3 4
∑n/2 : 1 3 5 10 half of the result
2∑n/2 : 2 6 10 20 sum of even numbers
So we simply replace N with N//2 in the formula and multiply the result by 2:
N*(N+1)//2 --> replace N with N//2 --> N//2*(N//2+1)//2
N//2*(N//2+1)//2 --> multiply by 2 --> N//2*(N//2+1)
Another way to see it is using Gauss's visualisation of the sum of numbers but using even numbers:
ascending 2 4 6 8 ... N-6 N-4 N-2 N (where N is even)
descending N N-2 N-4 N-6 ... 8 6 4 2
--- --- --- --- --- --- --- ---
totals N+2 N+2 N+2 N+2 ... N+2 N+2 N+2 N+2 (N/2 times N+2)
Because we added the even numbers twice, once in ascending order and once in descending order, the sum of all the totals will be twice the sum of even numbers (we need to divide that sum by 2 to get what we are looking for).
sum of evens: N/2*(N+2)/2 --> N/2*(N/2+1)
The N/2(N/2+1) formulation allows us to supply the formula with an odd number and get the right result by using integer division which absorbs the 'odd bit': N//2(N//2+1)
Recursive O(1) solution
Instead of using the integer division to absorb the odd bit, you could use recursion with the polynomial form of N/2*(N+2)/2: N^2/4 + N/2
def sumEven(n):
if n%2 == 0 : return n**2/4 + n/2 # exit condition
return sumEven(n-1) # recursion
Technically this is recursive although in practice it will never go deeper than 1 level

Try out this.
>>> n = 5
>>> sum(range(0, n+1, 2))

with minimum complexity
# include <stdio.h>
void main()
{
int num, sum, i;
printf("Number: ");
scanf("%d", &num);
i = num;
if (num % 2 != 0)
num = num -1;
sum = (num * (num + 2)) / 4;
printf("The sum of even numbers upto %d is %d\n\n", i, sum);
}
It is a C program and could be used in any language with respective syntax.

And it needs to be based on recursion.
Though you want a recursion one, I still want to share this dp solution with detailed steps to solve this problem.
Dynamic Programming
dp[i] represents the even sum among [0, i] which I denote as nums.
Case1: When i is 0, there is one number 0 in nums. dp[0] is 0.
Case2: When i is 1, there are two numbers 0 and 1 in nums. dp[1] is still 0.
Case3: When i is 2, there are three numbers 0, 1 and 2 in nums. dp[2] is 2.
Case4: When i is greater than 2, there are two more cases
If i is odd, dp[i] = dp[i-1]. Since i is odd, it is the same with [0, i-1].
If i is even, dp[i] = dp[i-2] + i by adding the current even number to the even sum among [0, i-2] (i-1 is odd, so won't be added).
PS. dp[i] = dp[i-1] + i is also ok. The difference is how you initialize dp.
Since we want the even sum among [0, n], we return dp[n]. You can conclude this from the first three cases.
def even_sum(n):
dp = []
# Init
dp.append(0) # dp[0] = 0
dp.append(0) # dp[1] = 0
# DP
for i in range(2, n+1): # n+1 because range(i, j) results [i, j) and you take n into account
if i % 2 == 1: # n is odd
dp.append(dp[i-1]) # dp[i] = dp[i-1]
else: # n is even
dp.append(dp[i-2] + i) # dp[i] = dp[i-2] + i
return dp[-1]

Related

How to find the sum of all the divisors of a number, without including it [closed]

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Finding the sum of all the divisors of a number, without including that particular number that is being divided.
For example if we want to find the divisors of number 6 they would be 1,2,3. Number 6 should be included as it is a divisor, but for this scenario we are not considering it.
Note:- number 3 and 4 are not included since, when you divide number 6 by number 3, there would be a remainder.
A divisor is a number that divides into another without a remainder.
def sum_divisors(n):
sum = 0
# Return the sum of all divisors of n, not including n
return sum
print(sum_divisors(0)) # 0
print(sum_divisors(3)) # Should sum of 1 # 1
print(sum_divisors(36)) # Should sum of 1+2+3+4+6+9+12+18 # 55
print(sum_divisors(102)) # Should be sum of 2+3+6+17+34+51 # 114
I think I got it:
def sum_divisors(n):
return sum([i for i in range(1, n)
if n % i == 0])
print(sum_divisors(0))
print(sum_divisors(3)) # Should sum of 1
print(sum_divisors(36)) # Should sum of 1+2+3+4+6+9+12+18
print(sum_divisors(102)) # Should be sum of 2+3+6+17+34+51
Output
0
1
55
114
You could use a combined filter() and lambda() function
num = 36
div_sum = sum(list(filter(lambda div : num % div == 0 , range(1, num))))
# divisors : [1, 2, 3, 4, 6, 9, 12, 18]
# divisors sum : 55
print('divisors sum : ', div_sum)
We need to find the divisors of the number first, and then sum them. Finding out if a number is a divisor of another number is pretty simple:
if x % y == 0:
is_divisor = True
Modulo - % - returns the remainder of a division. 9 % 2 = 1. 10 % 3 = 1. However, we'll need to know which numbers to check. We could just loop through every number up to our input:
for i in range(n):
if n % i == 0:
is_divisor = True
...but that's too much. For example, we know that the highest possible divisor of n that isn't n itself, is 1/2n if n is even, and lower if it's not. Instead, let's focus on getting each pair, and then check if we should continue. We also need to store all of these numbers, so lets create some lists:
lower_divisors = []
upper_divisors = []
We'll want to know what our current highest lower divisor and lowest highest divisor are, and also know where to start. We can populate the lower divisor list with 1 and the highest with n. We can worry about removing n later.
lower_divisors = [1]
upper_divisors = [n]
continu = True
while continu:
# Let's get the last item in each list
highest_low = lower_divisors[-1]
lowest_high = upper_divisors[-1]
for i in range(highest_low + 1, lowest_high):
if n % i == 0:
lower_divisors.append(i)
upper_divisors.append(n // i)
break
continu = False
# If we've left the loop, we have all of our divisors.
lower_sum = sum(lower_divisors)
higher_sum = sum(upper_divisors) - n #Gotta take it back out!
sm = lower_sum + higher_sum
return sm
That should be a fast way to achieve your goal, without making your computer work harder - but... it's a lot more steps to write. I wrote it in far more steps to make it clear what I was doing and to be readable, but it could definitely be trimmed up a lot.
Upon running the code, the simple method of looping through each number from 1 to n starts to really add up when n is over 8 digits. 9 digit numbers were taking around 12-16 seconds on my machine. The longer approach above returns 32-digit values for n in under 1/10th of a second.

Goldbach Conjecture Verify up to N in Python

I seem to have created an infinite loop in my python code. My aim was to create a function 'check' which uses my previous 'goldbach' function to confirm that every even number greater than 4 and up to the inputted N comply with the Goldbach Conjecture (a pretty pointless procedure, I know, but it's for my assignment). I know that my 'goldbach' function is working well and produces a pair of primes that sum to N for 'good' inputs and (0,0) for 'bad' inputs. I want my check function to return True for all even inputs greater than 4 (as these comply with the conjecture), and False for any odd inputs. However, my code won't run when I try my check function in the console so something has gone wrong - any ideas what it is?
def goldbach(N):
x, y = 0, 0
result = 0
if N % 2 == 0:
prime = odd_primes(N)
while result != N:
for i in range(len(prime)):
if result == N: break
x = prime[i]
for j in range(len(prime)):
y = prime[j]
result = x + y
if result == N: break
return x, y
def check(N):
for n in range(4, N+1):
if n % 2 ==0:
g = goldbach(n)
if g == (0,0):
return False
else:
return True
You're returning immediately after checking the first item in the range. You need to return False as soon as you encounter an item that doesn't match expectations, and return True at the end if all of them match expectations.
If you only want to look at even numbers, use a stride of 2 in the range() function rather than testing each number to see if it's even or odd.
def check(N):
for n in range(4, N+1, 2):
if goldbach(n) == (0, 0):
return False
return True
You don't need the while loop in goldbach(). The two for loops test all combinations of primes. If they don't find a matching pair, there's no reason to restart them.
You can also simplify and optimize your loops. The inner loop only needs to test primes starting from x, because pairs of primes where y < x would have already been tested in an earlier iteration of x.
def goldbach(N):
if N % 2 == 0:
prime = odd_primes(N)
for i, x in enumerate(prime):
for y in prime[i:]:
if x + y == N:
return x, y
return 0, 0
However, I think your code should still work, which suggests that the problem is actually that odd_primes() isn't returning all primes up to N.
I just created a replacement for your function odd_primes(N), to return a list of all the primes less than or equal to N (having 2 in the there doesn't seem to make a difference). Your check() function seems to check all integers between 4 and N inclusive, and return False if it finds something for which no Goldbach sum was found. However, as somebody else pointed out, it also immediately returns True once it finds a pair. So what happens when you run check() is that starts with the number 4, finds that its Goldbach pair is (2,2), then immediately quits the function by returning True, ignoring any other values between 4 and N.
When I replace the return True with a print statement and just add a `return True after the entire loop:
def check(N):
for n in range(4, N+1):
if n % 2 ==0:
g = goldbach(n)
if g == (0,0):
print("No sum found for %d !" % n)
return False
else:
print("%d is equal to %d + %d" % (n, g[0], g[1]))
return True
and then run check(20) for example, I get:
4 is equal to 2 + 2
6 is equal to 3 + 3
8 is equal to 3 + 5
10 is equal to 3 + 7
12 is equal to 5 + 7
14 is equal to 3 + 11
16 is equal to 3 + 13
18 is equal to 5 + 13
20 is equal to 3 + 17
By the way, if you just want to know whether a given even number can be written as the sum of two primes but you don't care what the actual pair of primes is, you could do something like this:
def goldbach(N):
if N % 2 == 0:
primes = odd_primes(N)
# Generate a list of all the i+j sums where j >= i (skipping
# most duplicates this way), and see if N is among the sums.
sums = [i + j for i in primes for j in primes[primes.index(i):]]
return(N in sums)
else:
print("N must be even.")
return(False)

Anyway to inverse factorial function?

So the question comes like this, I'm new to python:
def factorial_cap(num): For positive integer n, the factorial of n (denoted as n!), is the product
of all positive integers from 1 to n inclusive. Implement the function that returns the smallest
positive n such that n! is greater than or equal to argument num.
o Assumption: num will always be a positive integer.
# Examples
# factorial_cap(20) output is 4 since 3!<20 but 4!>20
# factorial_cap(24) output is 4 since 4!=24
# factorial_cap(1) output is 1 since 1!=1
# And here is what I got
def factorial_cap(num):
n = 1
for i in range (1,num+1):
n = n*i
I'm pretty sure this is the right function for factorial def. But I just couldn't figure out, instead of getting the 'total value', how can I just get the right output as I posted example above?
Btw, should I use 'return' at the end of def, or it does not matter in this case?
There needs to be a test for when the current total is greater than or equal to the requested number. So you can use the condition of a while loop to perform that check, and increment a counter, i, that keeps track of the current iteration. Then it's a matter of returning the current value of i that produced the value >= the required number:
def factorial_cap(num):
n = 1
i = 1
while n < num:
i += 1
n *= i
return i
>>> factorial_cap(20)
4
>>> factorial_cap(24)
4
>>> factorial_cap(25)
5
>>> factorial_cap(1)
1
>>> factorial_cap(3628800)
10
You want a return but that isn't n, but i
def factorial_cap(num):
n = 1
i = 0
while True:
i += 1
n = n*i
if n >= num:
break
return i
print(factorial_cap(20))
print(factorial_cap(24))
print(factorial_cap(1))

How to calculate no. of palindroms in a large number interval?

I want to calculate how many numbers are palindrome in large interval data say 10^15
My simple code (python) snippet is:
def count_palindromes(start, end):
count = 0
for i in range(start, end + 1):
if str(i) == str(i)[::-1]:
count += 1
return count
start = 1000 #some initial number
end = 10000000000000 #some other large number
if __name__ == "__main__":
print count_palindromes(start, end)
Its a simple program which checks each number one by one. Its vary time consuming and takes a lot of computer resources.
Is there any other method/technique by which we can count Palindrome numbers? Any Algorithm to use for this?
I want to minimize time taken in producing the output.
When you want to count the numbers having some given property between two limits, it is often useful to solve the somewhat simpler problem
How many numbers with the given property are there between 0 and n?
Keeping one limit fixed can make the problem significantly simpler to tackle. When the simpler problem is solved, you can get the solution to the original problem with a simple subtraction:
countBetween(a,b) = countTo(b) - countTo(a)
or countTo(b ± 1) - countTo(a ± 1), depending on whether the limit is included in countTo and which limits shall be included in countBetween.
If negative limits can occur (not for palindromes, I presume), countTo(n) should be <= 0 for negative n (one can regard the function as an integral with respect to the counting measure).
So let us determine
palindromes_below(n) = #{ k : 0 <= k < n, k is a palindrome }
We get more uniform formulae for the first part if we pretend that 0 is not a palindrome, so for the first part, we do that.
Part 1: How many palindromes with a given number d of digits are there?
The first digit cannot be 0, otherwise it's unrestricted, hence there are 9 possible choices (b-1 for palindromes in an arbitrary base b).
The last digit is equal to the first by the fact that it shall be a palindrome.
The second digit - if d >= 3 - can be chosen arbitrarily and independently from the first. That also determines the penultimate digit.
If d >= 5, one can also freely choose the third digit, and so on.
A moment's thought shows that for d = 2*k + 1 or d = 2*k + 2, there are k digits that can be chosen without restriction, and one digit (the first) that is subject to the restriction that it be non-zero. So there are
9 * 10**k
d-digit palindromes then ((b-1) * b**k for base b).
That's a nice and simple formula. From that, using the formula for a geometric sum, we can easily obtain the number of palindromes smaller than 10n (that is, with at most n digits):
if n is even, the number is
n/2-1 n/2-1
2 * ∑ 9*10**k = 18 * ∑ 10**k = 18 * (10**(n/2) - 1) / (10 - 1) = 2 * (10**(n/2) - 1)
k=0 k=0
if n is odd, the number is
2 * (10**((n-1)/2) - 1) + 9 * 10**((n-1)/2) = 11 * (10**((n-1)/2) - 2
(for general base b, the numbers are 2 * (b**(n/2) - 1) resp. (b+1) * b**((n-1)/2) - 2).
That's not quite as uniform anymore, but still simple enough:
def palindromes_up_to_n_digits(n):
if n < 1:
return 0
if n % 2 == 0:
return 2*10**(n//2) - 2
else:
return 11*10**(n//2) - 2
(remember, we don't count 0 yet).
Now for the remaining part. Given n > 0 with k digits, the palindromes < n are either
palindromes with fewer than k digits, there are palindromes_up_to_n_digits(k-1) of them, or
palindromes with exactly k digits that are smaller than n.
So it remains to count the latter.
Part 2:
Letm = (k-1)//2 and
d[1] d[2] ... d[m] d[m+1] ... d[k]
the decimal representation of n (the whole thing works with the same principle for other bases, but I don't explicitly mention that in the following), so
k
n = ∑ d[j]*10**(k-j)
j=1
For each 1 <= c[1] < d[1], we can choose the m digits c[2], ..., c[m+1] freely to obtain a palindrome
p = c[1] c[2] ... c[m+1] {c[m+1]} c[m] ... c[2] c[1]
(the digit c[m+1] appears once for odd k and twice for even k). Now,
c[1]*(10**(k-1) + 1) <= p < (c[1] + 1)*10**(k-1) <= d[1]*10**(k-1) <= n,
so all these 10**m palindromes (for a given choice of c[1]!) are smaller than n.
Thus there are (d[1] - 1) * 10**m k-digit palindromes whose first digit is smaller than the first digit of n.
Now let us consider the k-digit palindromes with first digit d[1] that are smaller than n.
If k == 2, there is one if d[1] < d[2] and none otherwise. If k >= 3, for each 0 <= c[2] < d[2], we can freely choose the m-1 digits c[3] ... c[m+1] to obtain a palindrome
p = d[1] c[2] c[3] ... c[m] c[m+1] {c[m+1]} c[m] ... c[3] c[2] d[1]
We see p < n:
d[1]*(10**(k-1) + 1) + c[2]*(10**(k-2) + 10)
<= p < d[1]*(10**(k-1) + 1) + (c[2] + 1)*(10**(k-2) + 10)
<= d[1]*(10**(k-1) + 1) + d[2]*(10**(k-2) + 10) <= n
(assuming k > 3, for k == 3 replace 10**(k-2) + 10 with 10).
So that makes d[2]*10**(m-1) k-digit palindromes with first digit d[1] and second digit smaller than d[2].
Continuing, for 1 <= r <= m, there are
d[m+1]*10**(m-r)
k-digit palindromes whose first r digits are d[1] ... d[r] and whose r+1st digit is smaller than d[r+1].
Summing up, there are
(d[1]-1])*10**m + d[2]*10**(m-1) + ... + d[m]*10 + d[m+1]
k-digit palindromes that have one of the first m+1 digits smaller than the corresponding digit of n and all preceding digits equal to the corresponding digit of n. Obviously, these are all smaller than n.
There is one k-digit palindrome p whose first m+1 digits are d[1] .. d[m+1], we must count that too if p < n.
So, wrapping up, and now incorporating 0 too, we get
def palindromes_below(n):
if n < 1:
return 0
if n < 10:
return n # 0, 1, ..., n-1
# General case
dec = str(n)
digits = len(dec)
count = palindromes_up_to_n_digits(digits-1) + 1 # + 1 for 0
half_length = (digits-1) // 2
front_part = dec[0:half_length + 1]
count += int(front_part) - 10**half_length
i, j = half_length, half_length+1
if digits % 2 == 1:
i -= 1
while i >= 0 and dec[i] == dec[j]:
i -= 1
j += 1
if i >= 0 and dec[i] < dec[j]:
count += 1
return count
Since the limits are both to be included in the count for the given problem (unless the OP misunderstood), we then have
def count_palindromes(start, end):
return palindromes_below(end+1) - palindromes_below(start)
for a fast solution:
>>> bench(10**100,10**101-1)
900000000000000000000000000000000000000000000000000 palindromes between
10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
and
99999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999
in 0.000186920166016 seconds
Actually, it's a problem for Google Codejam (which I'm pretty sure you're not supposed to get outside help on) but alas, I'll throw in my 2 cents.
The idea I came up with (but failed to implement) for the large problem was to precompile (generated at runtime, not hardcoded into the source) a list of all palindromic numbers less than 10^15 (there's not very many, it takes like ~60 seconds) then find out how many of those numbers lie between the bounds of each input.
EDIT: This won't work on the 10^100 problem, like you said, that would be a mathematical solution (although there is a pattern if you look, so you'd just need an algorithm to generate all numbers with that pattern)
I presume this is for something like Project Euler... my rough idea would be to generate all numbers up to half the length of your limit (like, if you're going to 99999, go up to 99). Then reverse them, append them to the unreversed one, and potentially add a digit in the middle (for the numbers with odd lengths). You'll might have to do some filtering for duplicates, or weird ones (like if you had a zero at the beginning of the number or sommat) but that should be a lot faster than what you were doing.

Project Euler 5 in Python - How can I optimize my solution?

I've recently been working on Project Euler problems in Python. I am fairly new to Python, and still somewhat new as a programmer.
In any case, I've ran into a speed-related issue coding a solution for problem #5. The problem is,
"2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder. What is the smallest positive number that is evenly divisible by all of the numbers from 1 to 20?"
I've checked around some, and I haven't been able to find anything on this problem pertaining to Python specifically. There were some completed scripts, but I want to avoid looking at other's code in full, if possible, instead wanting to improve my own.
The code I have written runs successfully for the example of 2520 and the range 1 to 10, and should be directly modifiable to work with the question. However, upon running it, I do not get an answer. Presumably, it is a very high number, and the code is not fast enough. Printing the current number being checked seems to support this, reaching several million without getting an answer.
The code, in it's current implementation is as follows:
rangemax = 20
def div_check(n):
for i in xrange(11,rangemax+1):
if n % i == 0:
continue
else:
return False
return True
if __name__ == '__main__':
num = 2
while not div_check(num):
print num
num += 2
print num
I have already made a couple changes which I think should help the speed. For one, for a number to be divisible by all numbers 1 to 20, it must be even, as only even numbers are divisible by 2. Hence, I can increment by 2 instead of 1. Also, although I didn't think of it myself, I found someone point out that a number divisible by 11 to 20 is divisible by 1 to 10. (Haven't checked that one, but it seems reasonable)
The code still, however is not fast enough. What optimisations, either programmatic, or mathematics, can I make to make this code run faster?
Thanks in advance to any who can help.
Taking the advice of Michael Mior and poke, I wrote a solution. I tried to use a few tricks to make it fast.
Since we need a relatively short list of numbers tested, then we can pre-build the list of numbers rather than repeatedly calling xrange() or range().
Also, while it would work to just put the numbers [1, 2, 3, ..., 20] in the list, we can think a little bit, and pull numbers out:
Just take the 1 out. Every integer is evenly divisible by 1.
If we leave the 20 in, there is no need to leave the 2 in. Any integer evenly divisible by 20 is evenly divisible by 2 (but the reverse might not be true). So we leave the 20 and take out the 2, the 4, and the 5. Leave the 19, as it's prime. Leave the 18, but now we can take out the 3 and the 6. If you repeat this process, you wind up with a much shorter list of numbers to try.
We start at 20 and step numbers by 20, as Michael Mior suggested. We use a generator expression inside of all(), as poke suggested.
Instead of a while loop, I used a for loop with xrange(); I think this is slightly faster.
The result:
check_list = [11, 13, 14, 16, 17, 18, 19, 20]
def find_solution(step):
for num in xrange(step, 999999999, step):
if all(num % n == 0 for n in check_list):
return num
return None
if __name__ == '__main__':
solution = find_solution(20)
if solution is None:
print "No answer found"
else:
print "found an answer:", solution
On my computer, this finds an answer in under nine seconds.
EDIT:
And, if we take advice from David Zaslavsky, we realize we can start the loop at 2520, and step by 2520. If I do that, then on my computer I get the correct answer in about a tenth of a second.
I made find_solution() take an argument. Try calling find_solution(2520).
My first answer sped up the original calculation from the question.
Here's another answer that solves it a different way: just find all the prime factors of each number, then multiply them together to go straight to the answer. In other words, this automates the process recommended by poke in a comment.
It finishes in a fraction of a second. I don't think there is a faster way to do this.
I did a Google search on "find prime factors Python" and found this:
http://www.stealthcopter.com/blog/2009/11/python-factors-of-a-number/
From that I found a link to factor.py (written by Mike Hansen) with some useful functions:
https://gist.github.com/weakish/986782#file-factor-py
His functions didn't do quite what I wanted, so I wrote a new one but used his pull_prime_factors() to do the hard work. The result was find_prime_factors() which returns a list of tuples: a prime number, and a count. For example, find_prime_factors(400) returns [(2,4), (5,2)] because the prime factors of 400 are: (2*2*2*2)*(5*5)
Then I use a simple defaultdict() to keep track of how many we have seen so far of each prime factor.
Finally, a loop multiplies everything together.
from collections import defaultdict
from factor import pull_off_factors
pf = defaultdict(int)
_primes = [2,3,5,7,11,13,17,19,23,29]
def find_prime_factors(n):
lst = []
for p in _primes:
n = pull_off_factors(n, p, lst)
return lst
def find_solution(low, high):
for num in xrange(low, high+1):
lst = find_prime_factors(num)
for n, count in lst:
pf[n] = max(pf[n], count)
print "prime factors:", pf
solution = 1
for n, count in pf.items():
solution *= n**count
return solution
if __name__ == '__main__':
solution = find_solution(1, 20)
print "answer:", solution
EDIT: Oh wow, I just took a look at #J.F. Sebastian's answer to a related question. His answer does essentially the same thing as the above code, only far more simply and elegantly. And it is in fact faster than the above code.
Least common multiple for 3 or more numbers
I'll leave the above up, because I think the functions might have other uses in Project Euler. But here's the J.F. Sebastian solution:
def gcd(a, b):
"""Return greatest common divisor using Euclid's Algorithm."""
while b:
a, b = b, a % b
return a
def lcm(a, b):
"""Return lowest common multiple."""
return a * b // gcd(a, b)
def lcmm(*args):
"""Return lcm of args."""
return reduce(lcm, args)
def lcm_seq(seq):
"""Return lcm of sequence."""
return reduce(lcm, seq)
solution = lcm_seq(xrange(1,21))
print "lcm_seq():", solution
I added lcm_seq() but you could also call:
lcmm(*range(1, 21))
Since your answer must be divisible by 20, you can start at 20 and increment by 20 instead of by two. In general, you can start at rangemax and increment by rangemax. This reduces the number of times div_check is called by an order of magnitude.
Break down the number as a prime factorization.
All primes less than 20 are:
2,3,5,7,11,13,17,19
So the bare minimum number that can be divided by these numbers is:
2*3*5*7*11*13*17*19
Composites:
4,6,8,9,10,12,14,15,16,18,20 = 2^2, 2*3, 2^3, 3^2, 2*5, 2^2*3, 2*7, 3*5, 2*3^2, 2^2*5
Starting from the left to see which factors needed:
2^3 to build 4, 8, and 16
3 to build 9
Prime factorization: 2^4 * 3^2 * 5 * 7 * 11 * 13 * 17 * 19 = 232,792,560
I got the solution in 0.066 milliseconds (only 74 spins through a loop) using the following procedure:
Start with smallest multiple for 1, which = 1. Then find the smallest multiple for the next_number_up. Do this by adding the previous smallest multiple to itself (smallest_multiple = smallest_multiple + prev_prod) until next_number_up % smallest_multiple == 0. At this point smallest_multiple is the correct smallest multiple for next_number_up. Then increment next_number_up and repeat until you reach the desired smallest_multiple (in this case 20 times). I believe this finds the solution in roughly n*log(n) time (though, given the way numbers seem to work, it seems to complete much faster than that usually).
For example:
1 is the smallest multiple for 1
Find smallest multiple for 2
Check if previous smallest multiple works 1/2 = .5, so no
previous smallest multiple + previous smallest multiple == 2.
Check if 2 is divisible by 2 - yes, so 2 is the smallest multiple for 2
Find smallest multiple for 3
Check if previous smallest multiple works 2/3 = .667, so no
previous smallest multiple + previous smallest multiple == 4
Check if 4 is divisible by 3 - no
4 + previous smallest multiple == 6
Check if 6 is divisible by 3 - yes, so 6 is the smallest multiple for 3
Find smallest multiple for 4
Check if previous smallest multiple works 6/4 = 1.5, so no
previous smallest multiple + previous smallest multiple == 12
Check if 12 is divisble by 4 - yes, so 12 is the smallest multiple for 4
repeat until 20..
Below is code in ruby implementing this approach:
def smallestMultiple(top)
prod = 1
counter = 0
top.times do
counter += 1
prevprod = prod
while prod % counter != 0
prod = prod + prevprod
end
end
return prod
end
List comprehensions are faster than for loops.
Do something like this to check a number:
def get_divs(n):
divs = [x for x in range(1,20) if n % x == 0]
return divs
You can then check the length of the divs array to see if all the numbers are present.
Two different types of solutions have been posted here. One type uses gcd calculations; the other uses prime factorization. I'll propose a third type, which is based on the prime factorization approach, but is likely to be much faster than prime factorization itself. It relies on a few simple observations about prime powers -- prime numbers raised to some integral exponent. In short, it turns out that the least common multiple of all numbers below some number n is equal to the product of all maximal prime powers below n.
To prove this, we begin by thinking about the properties that x, the least common multiple of all numbers below n, must have, and expressing them in terms of prime powers.
x must be a multiple of all prime powers below n. This is obvious; say n = 20. 2, 2 * 2, 2 * 2 * 2, and 2 * 2 * 2 * 2 are all below 20, so they all must divide x. Likewise, 3 and 3 * 3 are both below n and so both must divide x.
If some number a is a multiple of the prime power p ** e, and p ** e is the maximal power of p below n, then a is also a multiple of all smaller prime powers of p. This is also quite obvious; if a == p * p * p, then a == (p * p) * p.
By the unique factorization theorem, any number m can be expressed as a multiple of prime powers less than m. If m is less than n, then m can be expressed as a multiple of prime powers less than n.
Taken together, the second two observations show that any number x that is a multiple of all maximal prime powers below n must be a common multiple of all numbers below n. By (2), if x is a multiple of all maximal prime powers below n, it is also a multiple of all prime powers below n. So by (3), it is also a multiple of all other numbers below n, since they can all be expressed as multiples of prime powers below n.
Finally, given (1), we can prove that x is also the least common multiple of all numbers below n, because any number less than x could not be a multiple of all maximal prime powers below n, and so could not satisfy (1).
The upshot of all this is that we don't need to factorize anything. We can just generate primes less than n!
Given a nicely optimized sieve of eratosthenes, one can do that very quickly for n below one million. Then all you have to do is find the maximal prime power below n for each prime, and multiply them together.
prime_powers = [get_max_prime_power(p, n) for p in sieve(n)]
result = reduce(operator.mul, prime_powers)
I'll leave writing get_max_prime_power as an exercise. A fast version, combined with the above, can generate the lcm of all numbers below 200000 in 3 seconds on my machine.
The result is a 86871-digit number!
This solution ran pretty quickly for me (imports numpy).
t0 = time.time()
import numpy
ints = numpy.array(range(1,21))
primes = [2,3,5,7,11,13,17,19] # under 20
facts = []
for p in primes:
counter = 0
nums = ints
while any(nums % p == 0):
nums = nums / float(p)
counter += 1
facts.append(counter)
facts = numpy.array(facts)
mults = primes**facts
ans = 1
for m in mults:
ans = m * ans
t1 =time.time()
perf = t1 - t0
print "Problem 5\nAnswer:",ans, "runtime:", perf, "seconds"
"""Problem 5
Answer: 232792560 runtime: 0.00505399703979 seconds"""
Here i have also done using prime factorization way.
#!/usr/bin/env python
import math
def is_prime(num):
if num > 1:
if num == 2:
return True
if num%2 == 0:
return False
for i in range(3, int(math.sqrt(num))+1, 2):
if num%i == 0:
return False
return True
return False
def lcm(number):
prime = []
lcm_value = 1
for i in range(2,number+1):
if is_prime(i):
prime.append(i)
final_value = []
for i in prime:
x = 1
while i**x < number:
x = x + 1
final_value.append(i**(x-1))
for j in final_value:
lcm_value = j * lcm_value
return lcm_value
if __name__ == '__main__':
print lcm(20)
After checking how much time it has taken, it was not bad at all.
root#l-g6z6152:~/learn/project_euler# time python lcm.py
232792560
real 0m0.019s
user 0m0.008s
sys 0m0.004s
I wrote a solution to euler5 that:
Is orders of magnitude faster than most of the solutions here when n=20 (though not all respondents report their time) because it uses no imports (other than to measure time for this answer) and only basic data structures in python.
Scales much better than most other solutions. It will give the answer for n=20 in 6e-05 seconds, or for n=100 in 1 millisec, faster than most of the responses for n=20 listed here.
import time
a=time.clock() # set timer
j=1
factorlist=[]
mydict={}
# change second number to desired number +1 if the question were changed.
for i in range(2,21,1):
numberfactors=[]
num=i
j=2
# build a list of the prime factors
for j in range(j,num+1,1):
counter=0
if i%j==0:
while i%j==0:
counter+=1
numberfactors.append(j)
i=i/j
# add a list of factors to a dictionary, with each prime factor as a key
if j not in mydict:
mydict[j] = counter
# now, if a factor is already present n times, including n times the factor
# won't increase the LCM. So replace the dictionary key with the max number of
# unique factors if and only if the number of times it appears is greater than
# the number of times it has already appeared.
# for example, the prime factors of 8 are 2,2, and 2. This would be replaced
# in the dictionary once 16 were found (prime factors 2,2,2, and 2).
elif mydict[j] < counter:
mydict[j]=counter
total=1
for key, value in mydict.iteritems():
key=int(key)
value=int(value)
total=total*(key**value)
b=time.clock()
elapsed_time=b-a
print total, "calculated in", elapsed_time, "seconds"
returns:
232792560 calculated in 6e-05 seconds
# does not rely on heuristics unknown to all users, for instance the idea that
# we only need to include numbers above 10, etc.
# For all numbers evenly divisible by 1 through 100:
69720375229712477164533808935312303556800 calculated in 0.001335 seconds
Here is program in C language. Cheers
#include <stdio.h>
#include <stdlib.h>
//2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder.
//What is the smallest positive number that is evenly divisible by all of the numbers from 1 to 20?
bez_ost(int q)
{
register br=0;
for( register i=1;i<=20;i++)
if(q%i==0)
br++;
if(br==20)
return 1;
return 0;
}
int main()
{
register j=20;
register ind=0;
while(ind!=1)
{
j++;
if(bez_ost(j))
break;
}
fprintf(stdout,"\nSmallest positive number that is evenlu divisible by all of the numbers from 1 to 20 is: %d\n\a",j);
system("Pause");
}
I've had the same problem. The algorithm seems to be quite slow, but it does work nonetheless.
result = list()
xyz = [x for x in range(11, 21)]
number = [2520]
count = 0
while len(result) == 0:
for n in number:
print n
for x in xyz:
if n % x == 0:
count += 1
elif n % x != 0:
count = 0
break
if count == 10:
result.append(number[0])
elif count != 10:
number[0] += 1
print result
This was the algorithm I made.
How about this? The required number is, after all, the LCM of the given numbers.
def lcm(a,b):
lcm1 = 0
if a == b:
lcm1 = a
else:
if a > b:
greater = a
else:
greater = b
while True:
if greater % a == 0 and greater % b == 0:
lcm1 = greater
break
greater += 1
return lcm1
import time
start_time = time.time()
list_numbers = list(range(2,21))
lcm1 = lcm(list_numbers[0],list_numbers[1])
for i in range(2,len(list_numbers)):
lcm1 = lcm(lcm1,list_numbers[i])
print(lcm1)
print('%0.5f'%(time.time()-start_time))
This code took a full 45 s to get the answer to the actual question! Hope it helps.
import time
primes = [11,13,17,19]
composites = [12,14,15,16,18,20]
def evenlyDivisible(target):
evenly = True
for n in composites:
if target % n > 0:
evenly = False
break
return evenly
step = 1
for p in primes:
step *= p
end = False
number = 0
t1 = time.time()
while not end:
number += step
if evenlyDivisible(number):
end = True
print("Smallest positive evenly divisible number is",number)
t2 = time.time()
print("Time taken =",t2-t1)
Executed in 0.06 seconds
Here is my Python solution, it has 12 iteration so compiled quite fast:
smallest_num = 1
for i in range (1,21):
if smallest_num % i > 0: # If the number is not divisible by i
for k in range (1,21):
if (smallest_num * k) % i == 0: # Find the smallest number divisible by i
smallest_num = smallest_num * k
break
print (smallest_num)
Here's an observation on this problem. Ultimately, it takes 48 iterations to find the solution.
Any number that is divisible by all of the numbers from 1..20 must be divisible by the product of the primes in that range, namely 2, 3, 5, 7, 11, 13, 17, and 19. It cannot be smaller than the product of these primes, so let's use that number, 232,792,560, as the increment, rather than 20, or 2,520, or some other number.
As it turns out, 48 * 232,792,560 is divisible by all numbers 1..20. By the way, the product of all of the non-primes between 1..20 is 66. I haven't quite figured out the relationship between 48 and 66 in this context.
up = int(input('Upper limit: '))
number = list(range(1, up + 1))
n = 1
for i in range(1, up):
n = n * number[i]
for j in range(i):
if number[i] % number[j] == 0:
n = n / number[j]
number[i] = number[i] / number[j]
print(n)
How I can reduce the complexity of this
num = 1
found = False
while not found:
count =0
for i in range(1, 21):
if num %i == 0:
count+=1
if count ==10:
print(num)
found = True
num+=1
Here is the code in C++ to find the solution for this question.
What we have to do is to run a loop from 1 until we got that number so just iterate through the loop and once the number get evenly divisble(remainder 0) flag value dont get change and flag remains 1 and we got that number and break through outer loop and print the answer
#include <bits/stdc++.h>
using namespace std;
int main()
{
ios_base::sync_with_stdio(false);
cin.tie(NULL);
int i,j,flag=1;
for(i=1;;i++) //iterate until we got the number
{
flag=1;
for(j=2;j<=20;j++) //check form 1 to 20 for that number
{
if(i%j!=0) //if the number is not evenly divisible we break loop and
{
flag=0;break; // initilize flag as 0 i.e. that number is not what we want
}
}
if(flag==1) //if any number we got that is evenly divisible i.e. flag value doesnt change we got that number we break through the loop and print the answer
break;
} // after ending of the loop as we jump to next number make flag also 1 again so that again inner loop conditions apply on it
cout<<i;
return 0;
}
A typescript variant that seems to be relatively quick, leveraging recursion and known facts.
describe(`2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder.
What is the smallest positive number that is evenly divisible by all of the numbers from 1 to 20?`,
() => {
it("prooves the example: 10", () => smallestWithoutRemainder(10).should.be.equal(2520));
it("prooves 1", () => smallestWithoutRemainder(1).should.be.equal(1));
it("prooves 2", () => smallestWithoutRemainder(2).should.be.equal(2));
it("prooves 3", () => smallestWithoutRemainder(3).should.be.equal(6));
it("prooves 4", () => smallestWithoutRemainder(4).should.be.equal(12));
it("prooves 5", () => smallestWithoutRemainder(5).should.be.equal(60));
it("prooves 6", () => smallestWithoutRemainder(6).should.be.equal(60));
it("prooves 7", () => smallestWithoutRemainder(7).should.be.equal(420));
it("prooves 8", () => smallestWithoutRemainder(8).should.be.equal(840));
it("prooves 9", () => smallestWithoutRemainder(9).should.be.equal(2520));
it("prooves 12", () => smallestWithoutRemainder(12).should.be.equal(27720));
it("prooves 20", () => smallestWithoutRemainder(20).should.be.equal(232792560));
it("prooves 30", () => smallestWithoutRemainder(30).should.be.equal(2329089562800));
it("prooves 40", () => smallestWithoutRemainder(40).should.be.equal(5342931457063200));
});
let smallestWithoutRemainder = (end: number, interval?: number) => {
// What do we know?
// - at 10, the answer is 2520
// - can't be smaller than the lower multiple of 10
// - must be an interval of the lower multiple of 10
// so:
// - the interval and the start should at least be divisable by 'end'
// - we can recurse and build on the results before it.
if (!interval) interval = end;
let count = Math.floor(end / 10);
if (count == 1) interval = 2520;
else if (count > 1) interval = smallestWithoutRemainder((count - 1) * 10, interval);
for (let i = interval; true; i += interval) {
let failed = false;
for (let j = end; j > 1; j--) {
if (i % j != 0) {
failed = true;
break;
}
}
if (!failed) return i;
}
}
I think this the answer:
primes = [11, 13, 17, 19]
result = 2520
for i in primes:
result *= i
print (result * 2)

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