Codeforces735D Taxes(哥德巴赫猜想)

本文介绍了一个税收优化问题,通过应用哥德巴赫猜想来解决如何将整数n分解以达到最少的税收支出。针对不同类型的整数(素数、偶数和奇数),提出了具体的解决方案。

题意:已知n元需缴税为n的最大因子x元。现通过将n元分成k份的方式来减少缴税。问通过这种处理方式需缴纳的税费。

分析:

1、若n为素数,不需分解,可得1

2、若n为偶数,由哥德巴赫猜想:一个大于2的偶数可以分解成两个素数的和,可得2

3、若n为奇数,n-2为素数,则为2,否则为3。(因为若为奇数要拆,不能拆成1+偶数,至少拆为2+奇数,若此奇数为素数,则输出为2,否则继续拆成3+偶数,那么结果为3(充分利用哥德巴赫猜想的结论))

#pragma comment(linker, "/STACK:102400000, 102400000")
#include<cstdio>
#include<cstring>
#include<cstdlib>
#include<cctype>
#include<cmath>
#include<iostream>
#include<sstream>
#include<iterator>
#include<algorithm>
#include<string>
#include<vector>
#include<set>
#include<map>
#include<stack>
#include<deque>
#include<queue>
#include<list>
#define Min(a, b) ((a < b) ? a : b)
#define Max(a, b) ((a < b) ? b : a)
typedef long long ll;
typedef unsigned long long llu;
const int INT_INF = 0x3f3f3f3f;
const int INT_M_INF = 0x7f7f7f7f;
const ll LL_INF = 0x3f3f3f3f3f3f3f3f;
const ll LL_M_INF = 0x7f7f7f7f7f7f7f7f;
const int dr[] = {0, 0, -1, 1};
const int dc[] = {-1, 1, 0, 0};
const int MOD = 1e9 + 7;
const double pi = acos(-1.0);
const double eps = 1e-8;
const int MAXN = 100000 + 10;
const int MAXT = 10000 + 10;
using namespace std;
bool judge_prime(int n){
    for(int i = 2; i <= sqrt(n + 0.5); ++i){
        if(n % i == 0){
            return false;
        }
    }
    return true;
}
int main(){
    int n;
    while(scanf("%d", &n) == 1){
        if(judge_prime(n)){
            printf("1\n");
            continue;
        }
        if(n % 2 == 0){
            printf("2\n");
            continue;
        }
        if(n & 1){
            if(judge_prime(n - 2)){
                printf("2\n");
                continue;
            }
            else{
                printf("3\n");
                continue;
            }
        }
    }
    return 0;
}

 

转载于:https://www.cnblogs.com/tyty-Somnuspoppy/p/6114066.html

### Codeforces 1487D Problem Solution The problem described involves determining the maximum amount of a product that can be created from given quantities of ingredients under an idealized production process. For this specific case on Codeforces with problem number 1487D, while direct details about this exact question are not provided here, similar problems often involve resource allocation or limiting reagent type calculations. For instance, when faced with such constraints-based questions where multiple resources contribute to producing one unit of output but at different ratios, finding the bottleneck becomes crucial. In another context related to crafting items using various materials, it was determined that the formula `min(a[0],a[1],a[2]/2,a[3]/7,a[4]/4)` could represent how these limits interact[^1]. However, applying this directly without knowing specifics like what each array element represents in relation to the actual requirements for creating "philosophical stones" as mentioned would require adjustments based upon the precise conditions outlined within 1487D itself. To solve or discuss solutions effectively regarding Codeforces' challenge numbered 1487D: - Carefully read through all aspects presented by the contest organizers. - Identify which ingredient or component acts as the primary constraint towards achieving full capacity utilization. - Implement logic reflecting those relationships accurately; typically involving loops, conditionals, and possibly dynamic programming depending on complexity level required beyond simple minimum value determination across adjusted inputs. ```cpp #include <iostream> #include <vector> using namespace std; int main() { int n; cin >> n; vector<long long> a(n); for(int i=0;i<n;++i){ cin>>a[i]; } // Assuming indices correspond appropriately per problem statement's ratio requirement cout << min({a[0], a[1], a[2]/2LL, a[3]/7LL, a[4]/4LL}) << endl; } ``` --related questions-- 1. How does identifying bottlenecks help optimize algorithms solving constrained optimization problems? 2. What strategies should contestants adopt when translating mathematical formulas into code during competitive coding events? 3. Can you explain why understanding input-output relations is critical before implementing any algorithmic approach? 4. In what ways do prefix-suffix-middle frameworks enhance model training efficiency outside of just tokenization improvements? 5. Why might adjusting sample proportions specifically benefit models designed for tasks requiring both strong linguistic comprehension alongside logical reasoning skills?
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