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How can I find (n!) % m faster than O(n)?
1 <= n <= 1e18
1 <= m <= 1e6
You can easily have O(m) time complexity in the worst case (when m is a prime) and it seems to be good enough since you have m <= 1e6 (while n can be up to 1e18). Note, that when n >= m
n! = 1 * 2 * ... * m * ... * n
^
factorial is divisible by m
and that's why
n! % m == 0 # whenever n >= m
Another implementation detail is that you don't have to compute n! % m as 1 * 2 * ... * n % m but you can do it as ((..(1 % m) * 2 % m) ... * n % m) in order not to deal with huge numbers.
C# code example
private static int Compute(long n, long m) {
if (n >= m)
return 0;
long result = 1;
// result != 0 - we can well get 0 and stop looping when m is not prime
for (long d = 2; d <= n && result != 0; ++d)
result = (result * d) % m;
return result;
}
As explained by Dmitry, you can suppose than m<n. Let p1....pk the list of primes smaller or equal to m. Then m! mod n=(p1^a1.p2^a2....pk^ak)mod n=(p1^a1)mod n.(p2^a2 mod n)....(pk ^ak mod n) (mod n) for some a1.... ak that I'll let you find by yourself.
Using https://en.wikipedia.org/wiki/Modular_exponentiation, you can then compute m! (mod n).
A double ended selection sort, one that swaps both min and max, is claimed to be faster to be an ordinary selection sort, even thought the number of comparisons is the same. I understand that it gets rid of some of the looping, but if the number of comparisons stay the same, how are they faster?
Thanks in advance
Here's implementations of selection sort and double ended selection sort that count comparisons performed.
If you run it, you'll see that double-ended selection sort always performs more comparisons than regular selection sort.
import random
def selsort(xs):
N = len(xs)
comparisons = 0
for i in xrange(N):
m = i
for j in xrange(i+1, N):
comparisons += 1
if xs[j] < xs[m]: m = j
xs[i], xs[m] = xs[m], xs[i]
return comparisons
def deselsort(xs):
N = len(xs)
comparisons = 0
for i in xrange(N//2):
M = m = i
for j in xrange(i+1, N-i):
comparisons += 2
if xs[j] < xs[m]: m = j
if xs[j] >= xs[M]: M = j
xs[i], xs[m] = xs[m], xs[i]
if M == i: M = m
xs[N-i-1], xs[M] = xs[M], xs[N-i-1]
return comparisons
for rr in xrange(1, 30):
xs = range(rr)
random.shuffle(xs)
xs0 = xs[:]
xs1 = xs[:]
print len(xs), selsort(xs0), deselsort(xs1)
assert xs0 == sorted(xs0), xs0
assert xs1 == sorted(xs1), xs1
That's because the number of comparisons for regular selection sort is:
(n-1) + (n-2) + ... + 1 = n(n-1)/2
For double-ended selection sort, the number of comparisons is (for odd n -- the even case is similar)
2(n-1) + 2(n-3) + 2(n-5) + ... + 2
= (n-1)+(n-2)+1 + (n-3)+(n-4)+1 + ... 2+1+1
= ((n-1) + (n-2) + ... + 1) + (n-1)/2
= n(n-1)/2 + (n-1)/2
(Here, I'm rewriting each term 2(n-i) as (n-i) + (n-i-1) + 1)
I am given a formula f(n) where f(n) is defined, for all non-negative integers, as:
f(0) = 1
f(1) = 1
f(2) = 2
f(2n) = f(n) + f(n + 1) + n (for n > 1)
f(2n + 1) = f(n - 1) + f(n) + 1 (for n >= 1)
My goal is to find, for any given number s, the largest n where f(n) = s. If there is no such n return None. s can be up to 10^25.
I have a brute force solution using both recursion and dynamic programming, but neither is efficient enough. What concepts might help me find an efficient solution to this problem?
I want to add a little complexity analysis and estimate the size of f(n).
If you look at one recursive call of f(n), you notice, that the input n is basically divided by 2 before calling f(n) two times more, where always one call has an even and one has an odd input.
So the call tree is basically a binary tree where always the half of the nodes on a specific depth k provides a summand approx n/2k+1. The depth of the tree is log₂(n).
So the value of f(n) is in total about Θ(n/2 ⋅ log₂(n)).
Just to notice: This holds for even and odd inputs, but for even inputs the value is about an additional summand n/2 bigger. (I use Θ-notation to not have to think to much about some constants).
Now to the complexity:
Naive brute force
To calculate f(n) you have to call f(n) Θ(2log₂(n)) = Θ(n) times.
So if you want to calculate the values of f(n) until you reach s (or notice that there is no n with f(n)=s) you have to calculate f(n) s⋅log₂(s) times, which is in total Θ(s²⋅log(s)).
Dynamic programming
If you store every result of f(n), the time to calculate a f(n) reduces to Θ(1) (but it requires much more memory). So the total time complexity would reduce to Θ(s⋅log(s)).
Notice: Since we know f(n) ≤ f(n+2) for all n, you don't have to sort the values of f(n) and do a binary search.
Using binary search
Algorithm (input is s):
Set l = 1 and r = s
Set n = (l+r)/2 and round it to the next even number
calculate val = f(n).
if val == s then return n.
if val < s then set l = n
else set r = n.
goto 2
If you found a solution, fine. If not: try it again but round in step 2 to odd numbers. If this also does not return a solution, no solution exists at all.
This will take you Θ(log(s)) for the binary search and Θ(s) for the calculation of f(n) each time, so in total you get Θ(s⋅log(s)).
As you can see, this has the same complexity as the dynamic programming solution, but you don't have to save anything.
Notice: r = s does not hold for all s as an initial upper limit. However, if s is big enough, it holds. To be save, you can change the algorithm:
check first, if f(s) < s. If not, you can set l = s and r = 2s (or 2s+1 if it has to be odd).
Can you calculate the value of f(x) which x is from 0 to MAX_SIZE only once time?
what i mean is : calculate the value by DP.
f(0) = 1
f(1) = 1
f(2) = 2
f(3) = 3
f(4) = 7
f(5) = 4
... ...
f(MAX_SIZE) = ???
If the 1st step is illegal, exit. Otherwise, sort the value from small to big.
Such as 1,1,2,3,4,7,...
Now you can find whether exists n satisfied with f(n)=s in O(log(MAX_SIZE)) time.
Unfortunately, you don't mention how fast your algorithm should be. Perhaps you need to find some really clever rewrite of your formula to make it fast enough, in this case you might want to post this question on a mathematics forum.
The running time of your formula is O(n) for f(2n + 1) and O(n log n) for f(2n), according to the Master theorem, since:
T_even(n) = 2 * T(n / 2) + n / 2
T_odd(n) = 2 * T(n / 2) + 1
So the running time for the overall formula is O(n log n).
So if n is the answer to the problem, this algorithm would run in approx. O(n^2 log n), because you have to perform the formula roughly n times.
You can make this a little bit quicker by storing previous results, but of course, this is a tradeoff with memory.
Below is such a solution in Python.
D = {}
def f(n):
if n in D:
return D[n]
if n == 0 or n == 1:
return 1
if n == 2:
return 2
m = n // 2
if n % 2 == 0:
# f(2n) = f(n) + f(n + 1) + n (for n > 1)
y = f(m) + f(m + 1) + m
else:
# f(2n + 1) = f(n - 1) + f(n) + 1 (for n >= 1)
y = f(m - 1) + f(m) + 1
D[n] = y
return y
def find(s):
n = 0
y = 0
even_sol = None
while y < s:
y = f(n)
if y == s:
even_sol = n
break
n += 2
n = 1
y = 0
odd_sol = None
while y < s:
y = f(n)
if y == s:
odd_sol = n
break
n += 2
print(s,even_sol,odd_sol)
find(9992)
This recursive in every iteration for 2n and 2n+1 is increasing values, so if in any moment you will have value bigger, than s, then you can stop your algorithm.
To make effective algorithm you have to find or nice formula, that will calculate value, or make this in small loop, that will be much, much, much more effective, than your recursion. Your recursion is generally O(2^n), where loop is O(n).
This is how loop can be looking:
int[] values = new int[1000];
values[0] = 1;
values[1] = 1;
values[2] = 2;
for (int i = 3; i < values.length /2 - 1; i++) {
values[2 * i] = values[i] + values[i + 1] + i;
values[2 * i + 1] = values[i - 1] + values[i] + 1;
}
And inside this loop add condition of possible breaking it with success of failure.
The F series is defined as
F(0) = 1
F(1) = 1
F(i) = i * F(i - 1) * F(i - 2) for i > 1
The task is to find the number of different divisors for F(i)
This question is from Timus . I tried the following Python but it surely gives a time limit exceeded. This bruteforce approach will not work for a large input since it will cause integer overflow as well.
#!/usr/bin/env python
from math import sqrt
n = int(raw_input())
def f(n):
global arr
if n == 0:
return 1
if n == 1:
return 1
a = 1
b = 1
for i in xrange(2, n + 1):
k = i * a * b
a = b
b = k
return b
x = f(n)
cnt = 0
for i in xrange(1, int(sqrt(x)) + 1):
if x % i == 0:
if x / i == i:
cnt += 1
else:
cnt += 2
print cnt
Any optimization?
EDIT
I have tried the suggestion, and rewrite the solution: (not storing the F(n) value directly, but a list of factors)
#!/usr/bin/env python
#from math import sqrt
T = 10000
primes = range(T)
primes[0] = False
primes[1] = False
primes[2] = True
primes[3] = True
for i in xrange(T):
if primes[i]:
j = i + i
while j < T:
primes[j] = False
j += i
p = []
for i in xrange(T):
if primes[i]:
p.append(i)
n = int(raw_input())
def f(n):
global p
if n == 1:
return 1
a = dict()
b = dict()
for i in xrange(2, n + 1):
c = a.copy()
for y in b.iterkeys():
if c.has_key(y):
c[y] += b[y]
else:
c[y] = b[y]
k = i
for y in p:
d = 0
if k % y == 0:
while k % y == 0:
k /= y
d += 1
if c.has_key(y):
c[y] += d
else:
c[y] = d
if k < y: break
a = b
b = c
k = 1
for i in b.iterkeys():
k = k * (b[i] + 1) % (1000000007)
return k
print f(n)
And it still gives TL5, not faster enough, but this solves the problem of overflow for value F(n).
First see this wikipedia article on the divisor function. In short, if you have a number and you know its prime factors, you can easily calculate the number of divisors (get SO to do TeX math):
$n = \prod_{i=1}^r p_i^{a_i}$
$\sigma_x(n) = \prod_{i=1}^{r} \frac{p_{i}^{(a_{i}+1)x}-1}{p_{i}^x-1}$
Anyway, it's a simple function.
Now, to solve your problem, instead of keeping F(n) as the number itself, keep it as a set of prime factors and exponent sizes. Then the function that calculates F(n) simply takes the two sets for F(n-1) and F(n-2), sums the exponents of the same prime factors in both sets (assuming zero for nonexistent ones) and additionally adds the set of prime factors and exponent sizes for the number i. This means that you need another simple1 function to find the prime factors of i.
Computing F(n) this way, you just need to apply the above formula (taken from Wikipedia) to the set and there's your value. Note also that F(n) can quickly get very large. This solution also avoids usage of big-num libraries (since no prime factor nor its exponent is likely to go beyond 4 billion2).
1 Of course this is not so simple for arbitrarily large i, otherwise we wouldn't have any form of security right now, but for your application it should be simple enough.
2 Well it might. If you happen to figure out a simple formula answering your question given any n, then large ns would also be possible in the test case, for which this algorithm is likely going to give a time limit exceeded.
That is a fun problem.
The F(n) grow extremely fast. Since F(n) <= F(n+1) for all n, we have
F(n+2) > F(n)²
for all n, and thus
F(n) > 2^(2^(n/2-1))
for n > 2. That crude estimate already shows that one cannot store these numbers for any but the smallest n. By that F(100) requires more than (2^49) bits of storage, and 128 GB are only 2^40 bits. Actually, the prime factorisation of F(100) is
*Fiborial> fiborials !! 100
[(2,464855623252387472061),(3,184754360086075580988),(5,56806012190322167100)
,(7,20444417903078359662),(11,2894612619136622614),(13,1102203323977318975)
,(17,160545601976374531),(19,61312348893415199),(23,8944533909832252),(29,498454445374078)
,(31,190392553955142),(37,10610210054141),(41,1548008760101),(43,591286730489)
,(47,86267571285),(53,4807526976),(59,267914296),(61,102334155),(67,5702887),(71,832040)
,(73,317811),(79,17711),(83,2584),(89,144),(97,3)]
and that would require about 9.6 * 10^20 (roughly 2^70) bits - a little less than half of them are trailing zeros, but even storing the numbers à la floating point numbers with a significand and an exponent doesn't bring the required storage down far enough.
So instead of storing the numbers themselves, one can consider the prime factorisation. That also allows an easier computation of the number of divisors, since
k k
divisors(n) = ∏ (e_i + 1) if n = ∏ p_i^e_i
i=1 i=1
Now, let us investigate the prime factorisations of the F(n) a little. We begin with the
Lemma: A prime p divides F(n) if and only if p <= n.
That is easily proved by induction: F(0) = F(1) = 1 is not divisible by any prime, and there are no primes <= 1.
Now suppose that n > 1 and
A(k) = The prime factors of F(k) are exactly the primes <= k
holds for k < n. Then, since
F(n) = n * F(n-1) * F(n-2)
the set prime factors of F(n) is the union of the sets of prime factors of n, F(n-1) and F(n-2).
By the induction hypothesis, the set of prime factors of F(k) is
P(k) = { p | 1 < p <= k, p prime }
for k < n. Now, if n is composite, all prime factors of n are samller than n, hence the set of prime factors of F(n) is P(n-1), but since n is not prime, P(n) = P(n-1). If, on the other hand, n is prime, the set of prime factors of F(n) is
P(n-1) ∪ {n} = P(n)
With that, let us see how much work it is to track the prime factorisation of F(n) at once, and update the list/dictionary for each n (I ignore the problem of finding the factorisation of n, that doesn't take long for the small n involved).
The entry for the prime p appears first for n = p, and is then updated for each further n, altogether it is created/updated N - p + 1 times for F(N). Thus there are
∑ (N + 1 - p) = π(N)*(N+1) - ∑ p ≈ N²/(2*log N)
p <= N p <= N
updates in total. For N = 10^6, about 3.6 * 10^10 updates, that is way more than can be done in the allowed time (0.5 seconds).
So we need a different approach. Let us look at one prime p alone, and follow the exponent of p in the F(n).
Let v_p(k) be the exponent of p in the prime factorisation of k. Then we have
v_p(F(n)) = v_p(n) + v_p(F(n-1)) + v_p(F(n-2))
and we know that v_p(F(k)) = 0 for k < p. So (assuming p is not too small to understand what goes on):
v_p(F(n)) = v_p(n) + v_p(F(n-1)) + v_p(F(n-2))
v_p(F(p)) = 1 + 0 + 0 = 1
v_p(F(p+1)) = 0 + 1 + 0 = 1
v_p(F(p+2)) = 0 + 1 + 1 = 2
v_p(F(p+3)) = 0 + 2 + 1 = 3
v_p(F(p+4)) = 0 + 3 + 2 = 5
v_p(F(p+5)) = 0 + 5 + 3 = 8
So we get Fibonacci numbers for the exponents, v_p(F(p+k)) = Fib(k+1) - for a while, since later multiples of p inject further powers of p,
v_p(F(2*p-1)) = 0 + Fib(p-1) + Fib(p-2) = Fib(p)
v_p(F(2*p)) = 1 + Fib(p) + Fib(p-1) = 1 + Fib(p+1)
v_p(F(2*p+1)) = 0 + (1 + Fib(p+1)) + Fib(p) = 1 + Fib(p+2)
v_p(F(2*p+2)) = 0 + (1 + Fib(p+2)) + (1 + Fib(p+1)) = 2 + Fib(p+3)
v_p(F(2*p+3)) = 0 + (2 + Fib(p+3)) + (1 + Fib(p+2)) = 3 + Fib(p+4)
but the additional powers from 2*p also follow a nice Fibonacci pattern, and we have v_p(F(2*p+k)) = Fib(p+k+1) + Fib(k+1) for 0 <= k < p.
For further multiples of p, we get another Fibonacci summand in the exponent, so
n/p
v_p(F(n)) = ∑ Fib(n + 1 - k*p)
k=1
-- until n >= p², because multiples of p² contribute two to the exponent, and the corresponding summand would have to be multiplied by 2; for multiples of p³, by 3 etc.
One can also split the contributions of multiples of higher powers of p, so one would get one Fibonacci summand due to it being a multiple of p, one for it being a multiple of p², one for being a multiple of p³ etc, that yields
n/p n/p² n/p³
v_p(F(n)) = ∑ Fib(n + 1 - k*p) + ∑ Fib(n + 1 - k*p²) + ∑ Fib(n + 1 - k*p³) + ...
k=1 k=1 k=1
Now, in particular for the smaller primes, these sums have a lot of terms, and computing them that way would be slow. Fortunately, there is a closed formula for sums of Fibonacci numbers whose indices are an arithmetic progression, for 0 < a <= s
m
∑ Fib(a + k*s) = (Fib(a + (m+1)*s) - (-1)^s * Fib(a + m*s) - (-1)^a * Fib(s - a) - Fib(a)) / D(s)
k=0
where
D(s) = Luc(s) - 1 - (-1)^s
and Luc(k) is the k-th Lucas number, Luc(k) = Fib(k+1) + Fib(k-1).
For our purposes, we only need the Fibonacci numbers modulo 10^9 + 7, then the division must be replaced by a multiplication with the modular inverse of D(s).
Using these facts, the number of divisors of F(n) modulo 10^9+7 can be computed in the allowed time for n <= 10^6 (about 0.06 seconds on my old 32-bit box), although with Python, on the testing machines, further optimisations might be necessary.
Here is the problem that tagged as dynamic-programming (Given a number N, find the number of ways to write it as a sum of two or more consecutive integers) and example 15 = 7+8, 1+2+3+4+5, 4+5+6
I solved with math like that :
a + (a + 1) + (a + 2) + (a + 3) + ... + (a + k) = N
(k + 1)*a + (1 + 2 + 3 + ... + k) = N
(k + 1)a + k(k+1)/2 = N
(k + 1)*(2*a + k)/2 = N
Then check that if N divisible by (k+1) and (2*a+k) then I can find answer in O(sqrt(N)) time
Here is my question how can you solve this by dynamic-programming ? and what is the complexity (O) ?
P.S : excuse me, if it is a duplicate question. I searched but I can find
The accepted answer was great but the better approach wasn't clearly presented. Posting my java code as below for reference. It might be quite verbose, but explains the idea more clearly. This assumes that the consecutive integers are all positive.
private static int count(int n) {
int i = 1, j = 1, count = 0, sum = 1;
while (j<n) {
if (sum == n) { // matched, move sub-array section forward by 1
count++;
sum -= i;
i++;
j++;
sum +=j;
} else if (sum < n) { // not matched yet, extend sub-array at end
j++;
sum += j;
} else { // exceeded, reduce sub-array at start
sum -= i;
i++;
}
}
return count;
}
We can use dynamic programming to calculate the sums of 1+2+3+...+K for all K up to N. sum[i] below represents the sum 1+2+3+...+i.
sum = [0]
for i in 1..N:
append sum[i-1] + i to sum
With these sums we can quickly find all sequences of consecutive integers summing to N. The sum i+(i+1)+(i+2)+...j is equal to sum[j] - sum[i] + 1. If the sum is less than N, we increment j. If the sum is greater than N, we increment i. If the sum is equal to N, we increment our counter and both i and j.
i = 0
j = 0
count = 0
while j <= N:
cur_sum = sum[j] - sum[i] + 1
if cur_sum == N:
count++
if cur_sum <= N:
j++
if cur_sum >= N:
i++
There are better alternatives than using this dynamic programming solution though. The sum array can be calculated mathematically using the formula k(k+1)/2, so we could calculate it on-the-fly without need for the additional storage. Even better though, since we only ever shift the end-points of the sum we're working with by at most 1 in each iteration, we can calculate it even more efficiently on the fly by adding/subtracting the added/removed values.
i = 0
j = 0
sum = 0
count = 0
while j <= N:
cur_sum = sum[j] - sum[i] + 1
if cur_sum == N:
count++
if cur_sum <= N:
j++
sum += j
if cur_sum >= N:
sum -= i
i++
For odd N, this problem is equivalent to finding the number of divisors of N not exceeding sqrt(N). (For even N, there is a couple of twists.) That task takes O(sqrt(N)/ln(N)) if you have access to a list of primes, O(sqrt(N)) otherwise.
I don't see how dynamic programming can help here.
In order to solve the problem we will try all sums of consecutive integers in [1, M], where M is derived from M(M+1)/2 = N.int count = 0
for i in [1,M]
for j in [i, M]
s = sum(i, j) // s = i + (i+1) + ... + (j-1) + j
if s == N
count++
if s >= N
break
return count
Since we do not want to calculate sum(i, j) in every iteration from scratch we'll use a technique known as "memoization". Let's create a matrix of integers sum[M+1][M+1] and set sum[i][j] to i + (i+1) + ... + (j-1) + j.for i in [1, M]
sum[i][i] = i
int count = 0
for i in [1, M]
for j in [i + 1, M]
sum[i][j] = sum[i][j-1] + j
if sum[i][j] == N
count++
if sum[i][j] >= N
break
return count
The complexity is obviously O(M^2), i.e. O(N)
1) For n >= 0 an integer, the sum of integers from 0 to n is n*(n+1)/2. This is classic : write this sum first like this :
S = 0 + 1 + ... + n
and then like this :
S = n + (n-1) + ... + 0
You see that 2*S is equal to (0+n) + (1 + n-1)) + ... + (n+0) = (n+1)n, so that S = n(n+1)/2 indeed. (Well known but is prefered my answer to be self contained).
2) From 1, if we note cons(m,n) the sum m+(m+1)+...(n-1)+n the consecutive sum of integers between posiive (that is >=0) such that 1<=m<=n we see that :
cons(m,n) = (0+1+...+n) - (0+1+...+(m-1)) which gives from 1 :
cons(m,n) = n*(n+1)/ - m(m-1)/2
3) The question is then recasted into the following : in how many ways can we write N in the form N = cons(m,n) with m,n integers such that 1<=m<=n ? If we have N = cons(m,n), this is equivalent to m^2 - m + (2N -n^2 -n) = 0, that is, the real polynomial T^2 - m + (2N -n^2 -n) has a real root, m : its discriminant delta must then be a square. But we have :
delta = 1 - 3*(2*N - n^2 - n)
And this delta is an integer which must be a square. There exists therefore an integer M such that :
delta = 1 - 3*(2*N - n^2 - n) = M^2
that is
M^2 = 1 - 6*N + n(n+1)
n(n+1) is always dividible by 2 (it's for instance 2 times our S from the beginning, but here is a more trivial reason, among to consecutive integers, one must be even) and therefore M^2 is odd, implying that M must be odd.
4) Rewrite or previous equation as :
n^2 + n + (1-6*N - M^2) = 0
This show that the real polynomial X^2 + X + (1-6*N - M^2) has a real zero, n : its discriminant gamma must therefore be a square, but :
gamma = 1 - 4*(1-6*N-M^2)
and this must be a square, so that here again, there exist an integer G such that
G^2 = 1 - 4*(1-6*N-M^2)
G^2 = 1 + 4*(2*N + m*(m-1))
which shows that, as M is odd, G is odd also.
5) Substracting M^2 = 1 - 4*(2*N - n*(n+1)) to G^2 = 1 + 4*(2*N + m*(m-1))) yields to :
G^2 - M^2 = 4*(2*N + m*(m-1)) + 4*(2*N -n*(n+1))
= 16*N + 4*( m*(m-1) - n*(n+1) )
= 16*N - 8*N (because N = cons(m,n))
= 8*N
And finally this can be rewritten as :
(G-M)*(G+M) = 8*N, that is
[(G-M)/2]*[(G+M)/2] = 2*N
where (G-M)/2 and (G+M)/2 are integers (G-M and G+M are even since G and M are odd)
6) Thus, at each manner to write N as cons(m,n), we can associate a way (and only one way, as M and G are uniquely determined) to factor 2*N into the product x*y, with x = (G-M)/2 and y = (G+M)/2 where G and M are two odd integers. Since G = x + y and M = -x + y, as G and M are odd, we see that x and y should have opposite parities. Thus among x and y, one is even and the other is odd. Thus 2*N = x*y where among x and y, one is even and the other is odd. Lets c be the odd one among x and y, and d be the even one. Then 2*N = c*d, thus N = c*(d/2). So c is and odd number dividing N, and is uniquely determined by N, as soon as N = cons(m,n). Reciprocally, as soon as N has an odd divisor, one can reverse engineer all this stuff to find n and m.
7) *Conclusion : there exist a one to one correspondance between the number of ways of writing N = cons(m,n) (which is the number of ways of writing N as sum of consecutive integers, as we have seen) and the number of odd divisors of N.*
8) Finally, the number we are looking for is the number of odd divisors of n. I guess that solving this one by DP or whatever is easier than solving the previous one.
When you think it upside down (Swift)...
func cal(num : Int) -> Int {
let halfVal = Double(Double(num)/2.0).rounded(.up)
let endval = Int((halfVal/2).rounded(.down))
let halfInt : Int = Int(halfVal)
for obj in (endval...halfInt).reversed() {
var sum : Int = 0
for subVal in (1...obj).reversed() {
sum = sum + subVal
if sum > num {
break
}
if sum == num {
noInt += 1
break
}
}
}
return noInt
}