# COUNTWAY - Editorial

Author and Editorialist: Lalit Kundu
Tester: Kamil Debowski

medium-hard

### PREREQUISITES:

combinatorics, fast-fourier transform, divide and conquer

### PROBLEM:

Given an array of integers A_1, A_2, ..., A_N and an integer K, count number of ways to choose a multiset(i.e. a set with duplicate elements) of size K. Two multisets are different iff count of any element is different in both.

### QUICK EXPLANATION:

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Answer is coefficient of x^K in polynomial \dfrac{(1 - x^{f_1 + 1})*(1 - x^{f_2 + 1})*....*(1 - x^{f_k + 1})}{(1-x)^{k}}, where f_1, f_2, ..., f_k are frequencies of k distinct elements present in the array A. Numerator of this polynomial can be evaluated using any fast polynomial multiplication algorithm since it’s degree is N.

### EXPLANATION:

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Theorem 1:
It’s a popular result using generating functions that number of integral solutions to equation x_1 + x_2, ..., + x_r = N, where x_i \le l_i are coefficient of x^N in (1 + x + x^2 + ... + x^{l_1})*1 + x + x^2 + ... + x^{l_2})*...*1 + x + x^2 + ... + x^{l_r}), which we re-write as \dfrac{(1 - x^{l_1 + 1})*(1 - x^{l_2 + 1})*....*(1 - x^{l_r + 1})}{(1-x)^{r}}.

Theorem 2:
Coefficient of x^k in (1-x)^{-N} is C(N + k - 1, k), where C(i, j) is number of ways to choose j distinct elements from a set of i distinct elements. This can be proved using Taylor series expansion.

We’ll avoid the proof here, but you can follow above link to explore more.

Now, assume we have k distinct elements in the array with frequencies f_1, f_2, ..., f_k. Let’s say x_i is the frequency of i^{th} distinct element in chosen multiset, then x_1 + x_2 + ... + x_k = K, where x_i \le f_i. So, from above theorem, we need to find coefficient of x^K in polynomial \dfrac{(1 - x^{f_1 + 1})*(1 - x^{f_2 + 1})*....*(1 - x^{f_k + 1})}{(1-x)^{k}}. Note we can evaulate the numerator since it’s degree is O(f_1 + f_2 + ..., f_k) = O(N). Let’s say we use FFT to multiply two polynomials of degree p in O(p \text{ log } p). If we start multiplying these k terms in numerator in the given order, it could still take O(N^2) worst case. Here comes in the idea of divide and conquer. At each step we break down numerator into two parts with k/2 terms each, solve them recursively and multiply to find the polymial. This approach takes O(N \text{log}^{2} N) worst case.

For multiplication of two polymials you can use FTT or even Karatsuba. For further reading on these algorithms you can read this.

### AUTHOR’S, TESTER’S SOLUTIONS:

3 Likes

Can you provide me with a link to a tutorial for Fast Fourier transform with implementation details? It would be of great help to start with…

2 Likes

I did Π(1+…+xi^fi) and got ac. Is there any advantage in doing Π (1−xi^(fi+1))/(1-x)

In what order do you take the polynomials?
I thought of always multiplying the two polynomials with smallest degrees.
But what is it’s complexity? Is it n log^2n?

1 Like

I used the code from here: https://www.hackerrank.com/challenges/emma-and-sum-of-products/editorial

1 Like

I did the same too but had to multiply the polynomials in increasing order of degrees. This solution: https://www.codechef.com/viewsolution/11936211 uses the same fft code as mine but doesn’t multiply the polynomials in increasing order of degree and still gets ac really fast. Any idea what is happening here? For reference, here is my solution: https://www.codechef.com/viewsolution/11938697

The complexity is N * log N instead of N log ^ 2 N, using the idea described in editorial. This is because, last step of FFT multiplication would be most costly, enough, to ignore previous steps.

The complexity is N * log N instead of N log ^ 2 N, using the idea described in editorial. This is because, last step of FFT multiplication would be most costly, enough, to ignore previous steps.

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