HDU-problem-1754

I Hate It

Description

很多学校流行一种比较的习惯。老师们很喜欢询问,从某某到某某当中,分数最高的是多少。
这让很多学生很反感。
不管你喜不喜欢,现在需要你做的是,就是按照老师的要求,写一个程序,模拟老师的询问。当然,老师有时候需要更新某位同学的成绩。


Input

本题目包含多组测试,请处理到文件结束。
在每个测试的第一行,有两个正整数 N 和 M ( 0<N<=200000,0<M<5000 ),分别代表学生的数目和操作的数目。
学生ID编号分别从1编到N。
第二行包含N个整数,代表这N个学生的初始成绩,其中第i个数代表ID为i的学生的成绩。
接下来有M行。每一行有一个字符 C (只取'Q'或'U') ,和两个正整数A,B。
当C为'Q'的时候,表示这是一条询问操作,它询问ID从A到B(包括A,B)的学生当中,成绩最高的是多少。
当C为'U'的时候,表示这是一条更新操作,要求把ID为A的学生的成绩更改为B。


Output

对于每一次询问操作,在一行里面输出最高成绩。

思路:简单的静态线段树构造,更新,查找

代码:
#include<stdio.h>
#include<iostream>
using namespace std;
#define len 200001
int tree[len];
struct Tree{
	int l;
	int r;
	int max;
}T[len*5];

int build(int l,int r,int x)  //建树
{
	T[x].l=l;
	T[x].r=r;
	if(l==r)
	{
		T[x].max=tree[l];
		return T[x].max;
	}
	int mid=(l+r)/2;
	build(l,mid,2*x);
	build(mid+1,r,2*x+1);
	return T[x].max=max(T[2*x].max,T[2*x+1].max);
}

void updata(int a,int b,int k)
{
	if(T[k].r>=a&&T[k].l<=a)
	{
		T[k].max=max(T[k].max,b);
		updata(a,b,2*k);
		updata(a,b,2*k+1);
	}
	else
		return;
}

int question(int a,int b,int m)  //询问【a,b】的最大值
{
	if(T[m].l==a&&T[m].r==b)    //T[m]在【a,b】内
	{
		return T[m].max; 
	}
	else if(T[2*m].r>=b)
		return question(a,b,2*m);
	else if(T[2*m+1].l<=a)
		return question(a,b,2*m+1);
	else{
		return max(question(a,T[2*m].r,2*m),question(T[2*m+1].l,b,2*m+1));
	}
}

int main()
{
	int n,m,i;
	while(scanf("%d%d",&n,&m)!=EOF)
	{
		for(i=1;i<=n;i++)
		{
			scanf("%d",&tree[i]);
		}
		build(1,n,1);   //开始建树
		char c;
		int a,b;
		for(int i=0;i<m;i++)
		{
			cin>>c>>a>>b; //这里我尝试scanf输入c、a、b失败了。哪位大佬会的话留言告知下,谢谢!
			if(c=='U')//改变分数
			{
				updata(a,b,1);
			}
			else if(c=='Q'){
			cout<<question(a,b,1)<<endl;
			}
		}
	}
	return 0;
}

                   
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### HDU 1466 Problem Description and Solution The problem **HDU 1466** involves calculating the expected number of steps to reach a certain state under specific conditions. The key elements include: #### Problem Statement Given an interactive scenario where operations are performed on numbers modulo \(998244353\), one must determine the expected number of steps required to achieve a particular outcome. For this type of problem, dynamic programming (DP) is often employed as it allows breaking down complex problems into simpler subproblems that can be solved iteratively or recursively with memoization techniques[^1]. In more detail, consider the constraints provided by similar problems such as those found in references like HDU 6327 which deals with random sequences using DP within given bounds \((1 \leq T \leq 10, 4 \leq n \leq 100)\)[^2]. These types of constraints suggest iterative approaches over small ranges might work efficiently here too. Additionally, when dealing with large inputs up to \(2 \times 10^7\) as seen in reference materials related to counting algorithms [^4], efficient data structures and optimization strategies become crucial for performance reasons. However, directly applying these methods requires understanding how they fit specifically into solving the expectation value calculation involved in HDU 1466. For instance, if each step has multiple outcomes weighted differently based on probabilities, then summing products of probability times cost across all possible states until convergence gives us our answer. To implement this approach effectively: ```python MOD = 998244353 def solve_expectation(n): dp = [0] * (n + 1) # Base case initialization depending upon problem specifics for i in range(1, n + 1): total_prob = 0 # Calculate transition probabilities from previous states for j in transitions_from(i): # Placeholder function representing valid moves prob = calculate_probability(j) next_state_cost = get_next_state_cost(j) dp[i] += prob * (next_state_cost + dp[j]) % MOD total_prob += prob dp[i] %= MOD # Normalize current state's expectation due to accumulated probability mass if total_prob != 0: dp[i] *= pow(total_prob, MOD - 2, MOD) dp[i] %= MOD return dp[n] # Example usage would depend heavily on exact rules governing transitions between states. ``` This code snippet outlines a generic framework tailored towards computing expectations via dynamic programming while adhering strictly to modular arithmetic requirements specified by the contest question format.
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