# [Study Notes] Lagrangian Interpolation

The first step in learning polynomials.

### References:

attack's luogu blog

oi wiki Lagrangian interpolation

Apocryphal's luogu blog

## 1. Introduction to Lagrangian Interpolation

question:

luogu P4781 [template] Lagrangian interpolation

Solution 1: Gaussian elimination

Obviously, there are infinite polynomials of $$\deg \geqslant n$$, because according to Gaussian elimination, there must be free elements.

List these $$n$$ points directly to the equation system and solve it with Gaussian elimination.

After finding the polynomial, you can find $$f(k)$$.

Time complexity$$O(n^3).$$

Solution 2: Lagrangian Interpolation

Give a basis function for a point $$(x_i,y_i)$$:

$g(k)=\prod_{j\not=i}^{1\leqslant j\leqslant n} \dfrac{k-x_j}{x_i-x_j}$

It is easy to find:$$\forall j\not=i,g (x_j)=0.$$ Because there is always$$k=x_j$$ in the cumulative multiplication, so that$$k-x_j=x_j-x_j=0,g (x_j)=0.$$

For $$j=i,g(x_j)=1.$$because each item in the cumulative product is$$\dfrac{x_i-x_j}{x_i-x_j}=1,g(x_j)=1.\ ) So our polynomial can be expressed as: $f(k)=\sum_{i=1}^{n}y_i\times\prod_{j\not=i}^{1\leqslant j\leqslant n} \dfrac{k-x_j}{x_i-x_j}$ In this way\(\forall(x_i,y_i),f(x_i)=y_i$$, you can also find$$f(k).$$

Probably because the inverse element is required, the time complexity is $$O(n^2+n\log n)=O(n^2).$$

$$\tt{code:}$$

#include <bits/stdc++.h>
#define ll long long
using namespace std;
int n;
const ll mod = 998244353;
ll x, y, k, ans;
ll ksm(ll s1, ll s2) {
if(!s2) return 1;
if(s2 & 1) return s1 * ksm(s1, s2 - 1) % mod;
ll ret = ksm(s1, s2 / 2);
return ret * ret % mod;
}
int main() {
scanf("%d%lld", &n, &k);
for(int i = 1; i <= n; i++) scanf("%lld%lld", &x[i], &y[i]);
for(int i = 1; i <= n; i++) {
ll sum = 1, mul = 1;
for(int j = 1; j <= n; j++) {
if(i == j) continue;
sum *= (k - x[j]); sum %= mod; sum += mod; sum %= mod;
mul *= (x[i] - x[j]); mul %= mod; mul += mod; mul %= mod;
}
sum *= ksm(mul, mod-2); sum %= mod;
ans += (sum * y[i]) % mod; ans %= mod;
}
printf("%lld\n", ans);
return 0;
}


## 2. Extension of Lagrangian interpolation method

question:

Same as above, adding $$x_i$$ to the limit of continuous value.

solution:

Suppose $$x_i\in[1,n],$$ is formulated as:

$f(k)=\sum_{i=1}^{n}y_i\times\prod_{j\not=i}^{1\leqslant j\leqslant n} \dfrac{k-j}{i-j}$

It is possible to maintain $$num=\prod_{1\leqslant j\leqslant n}k-j,$$ so the numerator is partially reduced to $$\dfrac{num}{k-i},$$ because doing so requires an inverse element, with a log, Very bad, so you can preprocess the prefix and $$pre_i$$ and the suffix and $$suf_i.$$

For the denominator, it can be transformed into the form of $$1\times 2\times...\times i-1\times (-1)\times (-2)\times...\times(i-n)$$.

In essence, it is a factorial, |denominator|$$=fac_{i-1}\times fac_{n-i},$$ and then deal with the positive and negative.

This plus some $$O(n)$$ preprocessing $$pre,suf,fac,inv...$$, we only need $$O(n)$$ to complete.

## 3. Specific application of Lagrange interpolation & Ex amp les

[example 1] CF622F The Sum of the k-th Powers

The classic model of Lagrangian interpolation, that is to find $$\sum_{i=1}^n i^k$$.

This is a polynomial of degree $$k+1$$, prove it perceptually:

Consider a polynomial of degree $$n$$$$g(x)=a_nx^n+a_{n-1}x^{n-1}+...+a_0$$.

Its first-order difference$$\Delta g(x)=g(x+1)-g(x)=a_n(x+1)^n-a_nx^n+...$$.

Through the binomial theorem, we find that the $$n$$ item will be eliminated at this time, namely:

Each time a difference is made, the degree of the polynomial is reduced by one.

Let$$f(x)=\sum_{i=1}^ni^k,$$ then$$\Delta f(x)=f(x+1)-f(x)=(x+1)^ k,$$ is a polynomial of degree $$k$$.

So $$f(x)$$ is $$k+1$$ degree polynomial.

At this point, we only need to determine $$k+2$$ points and do an interpolation to find $$f(n)$$.

Because $$i$$ is continuous, the Lagrange interpolation extension in 2.2 can be used to achieve $$O(k\log k).$$

$$i^k$$ can also be obtained linearly, so I won't explain it here.

$$\tt{code:}$$

#include <bits/stdc++.h>

#define ll long long

using namespace std;

const int N = 1e6;
const ll mod = 1e9 + 7;

ll n, k, fac[N+11], pre[N+11], suf[N+11], ans;

ll ksm(ll s1, ll s2) {
if(!s2) return 1ll;
if(s2 % 2) return s1 * ksm(s1, s2-1) % mod;
ll ret = ksm(s1, s2/2);
return ret * ret % mod;
}

int main() {
scanf("%lld%lld", &n, &k);
ll sum = 0;
fac = 1;
for(int i = 1; i <= k+2; i++)
fac[i] = fac[i-1] * (ll)i % mod;
pre = suf[k+3] = 1;
for(int i = 1; i <= k+2; i++)
pre[i] = pre[i-1] * (n - i) % mod;
for(int i = k+2; i >= 1; i--)
suf[i] = suf[i+1] * (n - i) % mod;
for(int i = 1; i <= k+2; i++) {
sum += ksm(i, k); sum %= mod;
ll num = pre[i-1] * suf[i+1] % mod;
ll fm = fac[i-1] * fac[k+2-i] % mod;
num *= sum; num %= mod;
num *= ksm(fm, mod-2); num %= mod;
if((k+2-i) % 2 == 0) ans += num;
else ans -= num;
ans %= mod; ans = (ans + mod) % mod;
}
printf("%lld\n", ans);
return 0;
}


Problem transformation: first find the number of $$k,$$ that is "blasphemy". Ignore the $$a_i$$ greater than $$n$$ (although I don't know if there are any), it is easy to find that one "desecration" can make all monsters with continuous HP die, so $$k=m+0?1: (\exists a_i=1).$$

And because the blood volume of the monsters is different from each other, we can convert the calculated value into $$\sum_{i=1}^n i^k,$$, so it is similar to [Example 1].

The code will not be released.

Posted by slindstr on Wed, 03 Aug 2022 01:34:27 +0930