Codeforces 438 E 小朋友和二叉树

这篇博客探讨了快速幂运算在解决二叉树权值和问题中的应用,通过生成函数和多项式求根的方法,实现了复杂度为O(n+mlog2m)的空间和时间效率解决方案。文章详细阐述了算法原理,并给出了C语言实现代码。

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题面
根据题意,设 f t f_t ft 表示权值和为 t t t 的二叉树数量,枚举根节点的权值和右子树的权值,则有:
f t = ∑ i = 1 n ∑ j = 0 t − c i f t − c i − j f j f_t=\sum_{i=1}^n \sum_{j=0}^{t-c_i} f_{t-c_i-j}f_j ft=i=1nj=0tciftcijfj
g i g_i gi 表示是否存在权值为 i i i 的点,有则 g i g_i gi 1 1 1,否则为 0 0 0。对上式进行改写:
f t = ∑ i = 1 t ∑ j = 0 t − i g i f j f t − i − j f_t=\sum_{i=1}^t \sum_{j=0}^{t-i} g_i f_j f_{t-i-j} ft=i=1tj=0tigifjftij
F ( x ) , G ( x ) F(x),G(x) F(x),G(x) 分别为 f , g f,g f,g 的生成函数。由于初始时 f ( 0 ) = 1 f(0)=1 f(0)=1,因此在模 x m + 1 x^{m+1} xm+1 意义下:
F ( x ) ≡ G ( x ) F ( x ) F ( x ) + 1 F(x) \equiv G(x)F(x)F(x) +1 F(x)G(x)F(x)F(x)+1
解一元二次方程得到:
F ( x ) = 1 ± 1 − 4 G ( x ) 2 G ( x ) F(x)=\frac{1 \pm \sqrt{1-4G(x)}}{2G(x)} F(x)=2G(x)1±14G(x)
分子有理化,得到:
F ( x ) = 2 1 ± 1 − 4 G ( x ) F(x)=\frac{2}{1 \pm \sqrt{1-4G(x)}} F(x)=1±14G(x) 2
由于 g 0 = 0 , f 0 = 1 g_0=0,f_0=1 g0=0,f0=1,确定 F ( x ) F(x) F(x) 的取值:
F ( x ) = 2 1 + 1 − 4 G ( x ) F(x)=\frac{2}{1+\sqrt{1-4G(x)}} F(x)=1+14G(x) 2
多项式开方即可。
时间复杂度 O ( n + m l o g 2 m ) O(n+mlog_2m) O(n+mlog2m),空间复杂度 O ( m ) O(m) O(m)

#include<stdio.h>
#define R register int
#define L long long
#define I inline
#define N 262144
#define P 998244353
int c[N],b[N];
I void Swap(int&x,int&y){
	int tem=x;
	x=y;
	y=tem;
}
I int Add(int x,int y){
	x+=y;
	return x>=P?x-P:x;
}
I int Div2(int x){
	return((x&1)==1?x+P:x)>>1;
}
I int PowMod(int x,int y){
	int s=1;
	while(y!=0){
		if((y&1)==1){
			s=(L)s*x%P;
		}
		x=(L)x*x%P;
		y>>=1;
	}
	return s;
}
I void NTT(int*A,int len,const short type){
	int tem=0;
	for(R i=0;i!=len;i++){
		if(i<tem){
			Swap(A[i],A[tem]);
		}
		R j=len;
		do{
			j>>=1;
			tem^=j;
		}while(tem<j);
	}
	static int w[N];
	w[0]=1;
	for(R i=1;i!=len;i<<=1){
		tem=i<<1;
		int omg=PowMod(3,P-1+(P-1)*type/tem);
		for(R j=1;j!=i;j++){
			w[j]=(L)w[j-1]*omg%P;
		}
		for(R j=0;j!=len;j+=tem){
			for(R k=j;k!=i+j;k++){
				int t1=A[k],t2=(L)A[i+k]*w[k-j]%P;
				A[k]=Add(t1,t2);
				A[i+k]=Add(t1,P-t2);
			}
		}
	}
	if(type==-1){
		tem=PowMod(len,P-2);
		for(R i=0;i!=len;i++){
			A[i]=(L)A[i]*tem%P;
		}
	}
}
I void PolyInverse(int*A,int*B,const int len){
	static int tem[N];
	B[0]=PowMod(A[0],P-2);
	for(R i=1;i!=len;i<<=1){
		int p=i<<2;
		NTT(B,p,1);
		for(R j=0;j!=i<<1;j++){
			tem[j]=A[j];
		}
		NTT(tem,p,1);
		for(R j=0;j!=p;j++){
			B[j]=(P+2-(L)tem[j]*B[j]%P)*B[j]%P;
		}
		NTT(B,p,-1);
		for(R j=i<<1;j!=p;j++){
			B[j]=tem[j]=0;
		}
	}
	for(R i=0;i!=len<<1;i++){
		tem[i]=0;
	}
}
I void PolySqrt(int*A,int*B,const int len){
	static int C[N],D[N];
	B[0]=1;
	for(R i=1;i!=len;i<<=1){
		for(R j=0;j!=i<<1;j++){
			C[j]=A[j];
			D[j]=0;
		}
		for(R j=i<<1;j!=i<<2;j++){
			D[j]=C[j]=0;
		}
		NTT(C,i<<2,1);
		PolyInverse(B,D,i<<1);
		NTT(B,i<<2,1);
		NTT(D,i<<2,1);
		for(R j=0;j!=i<<2;j++){
			B[j]=Div2(((L)C[j]*D[j]+B[j])%P);
		}
		NTT(B,i<<2,-1);
		for(R j=i<<1;j!=i<<2;j++){
			B[j]=0;
		}
	}
}
int main(){
	int n,m,x;
	scanf("%d%d",&n,&m);
	for(R i=0;i!=n;i++){
		scanf("%d",&x);
		c[x]=P-4;
	}
	c[0]=1;
	x=1;
	while(x<=m){
		x<<=1;
	}
	PolySqrt(c,b,x);
	b[0]=2;
	for(R i=0;i!=x;i++){
		c[i]=0;
	}
	PolyInverse(b,c,x);
	for(R i=1;i<=m;i++){
		x=c[i]<<1;
		printf("%d\n",x>=P?x-P:x);
	}
	return 0;
}
### Codeforces 887E Problem Solution and Discussion The problem **887E - The Great Game** on Codeforces involves a strategic game between two players who take turns to perform operations under specific rules. To tackle this challenge effectively, understanding both dynamic programming (DP) techniques and bitwise manipulation is crucial. #### Dynamic Programming Approach One effective method to approach this problem utilizes DP with memoization. By defining `dp[i][j]` as the optimal result when starting from state `(i,j)` where `i` represents current position and `j` indicates some status flag related to previous moves: ```cpp #include <bits/stdc++.h> using namespace std; const int MAXN = ...; // Define based on constraints int dp[MAXN][2]; // Function to calculate minimum steps using top-down DP int minSteps(int pos, bool prevMoveType) { if (pos >= N) return 0; if (dp[pos][prevMoveType] != -1) return dp[pos][prevMoveType]; int res = INT_MAX; // Try all possible next positions and update 'res' for (...) { /* Logic here */ } dp[pos][prevMoveType] = res; return res; } ``` This code snippet outlines how one might structure a solution involving recursive calls combined with caching results through an array named `dp`. #### Bitwise Operations Insight Another critical aspect lies within efficiently handling large integers via bitwise operators instead of arithmetic ones whenever applicable. This optimization can significantly reduce computation time especially given tight limits often found in competitive coding challenges like those hosted by platforms such as Codeforces[^1]. For detailed discussions about similar problems or more insights into solving strategies specifically tailored towards contest preparation, visiting forums dedicated to algorithmic contests would be beneficial. Websites associated directly with Codeforces offer rich resources including editorials written after each round which provide comprehensive explanations alongside alternative approaches taken by successful contestants during live events. --related questions-- 1. What are common pitfalls encountered while implementing dynamic programming solutions? 2. How does bit manipulation improve performance in algorithms dealing with integer values? 3. Can you recommend any online communities focused on discussing competitive programming tactics? 4. Are there particular patterns that frequently appear across different levels of difficulty within Codeforces contests?
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