HDU6198 number number number

number number number

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 132    Accepted Submission(s): 90


Problem Description
We define a sequence  F :

  F0=0,F1=1 ;
  Fn=Fn1+Fn2 (n2) .

Give you an integer  k , if a positive number  n  can be expressed by
n=Fa1+Fa2+...+Fak  where  0a1a2ak , this positive number is  mjfgood . Otherwise, this positive number is  mjfbad .
Now, give you an integer  k , you task is to find the minimal positive  mjfbad  number.
The answer may be too large. Please print the answer modulo 998244353.
 

Input
There are about 500 test cases, end up with EOF.
Each test case includes an integer  k  which is described above. ( 1k109 )
 

Output
For each case, output the minimal  mjfbad  number mod 998244353.
 

Sample Input
  
1
 

Sample Output
  
4
 

Source

——————————————————————————————————
题目的意思是在斐波那契序列中找出k个数从凑出的数是good的,不能就是bad的,给出k,求最小的bad数
思路:找规律发现这个数是斐波那契第2*k+3项-1,数据较大矩阵快速幂搞定
#include <iostream>
#include <cstdio>
#include <cstring>
#include <string>
#include <algorithm>
#include <cmath>
#include <map>
#include <cmath>
#include <set>
#include <stack>
#include <queue>
#include <vector>
#include <bitset>
#include <functional>

using namespace std;

#define LL long long
const int INF = 0x3f3f3f3f;
const LL mod=998244353;

struct Matrix
{
    LL v[5][5];
    Matrix()
    {
        memset(v,0,sizeof v);
    }
} dan;

Matrix mul(Matrix a,Matrix b,int d)
{
    Matrix ans;
    for(int i=0; i<d; i++)
    {
        for(int j=0; j<d; j++)
        {
            for(int k=0; k<d; k++)
            {
                ans.v[i][j]+=(a.v[i][k]*b.v[k][j])%mod;
                ans.v[i][j]%=mod;
            }
        }
    }
    return ans;
}

Matrix pow(Matrix a,int k,int d)
{
    Matrix ans=dan;
    while(k)
    {
        if(k&1) ans=mul(ans,a,d);
        k>>=1;
        a=mul(a,a,d);
    }
    return ans;
}

int main()
{
    int k;
    while(~scanf("%d",&k))
    {
        dan.v[0][0]=1,dan.v[0][1]=0;
        Matrix a;
        a.v[0][0]=a.v[0][1]=a.v[1][0]=1,a.v[1][1]=0;
        Matrix ans= pow(a,2*k+2,2);
        printf("%lld\n",((ans.v[0][0]-1)%mod+mod)%mod);
    }
    return 0;
}



### 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.
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值