codeforces 1303D Fill The Bag 贪心

探讨了如何通过贪心策略解决背包填充问题,利用二进制思想优化解题过程,确保最少的操作次数将盒子装满背包。

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https://vjudge.net/problem/CodeForces-1303D
在这里插入图片描述题目大意:你有一个大小为 n n n的背包, m m m个盒子,第 i i i个盒子的大小为 a i a_i ai,保证 a i a_i ai均为 2 2 2的幂次,每次操作可以选取任意一个盒子并把它分成两个大小相等的盒子,问经过多少次操作后你可以用其中一部分盒子填满背包,若无解输出 − 1 -1 1

思路:贪心,考虑 n n n的二进制的第 i i i位,要么由现在已经有的盒子凑出 2 i 2^i 2i,要么划分 a j > 2 i a_j>2^i aj>2i的盒子,因为前者对答案没有贡献,所以优先使用这种方法,如果凑不出来的话只能采用后者,为了使答案最小我们肯定要找到 > 2 i >2^i >2i且最小的 a j a_j aj进行划分;如果这两种方法都不可行的话,说明无解。

#include<bits/stdc++.h>
#define INF 0x3f3f3f3f
using namespace std;
typedef long long ll;

const int maxn=1e5+5;

ll n;
int t,m,a[maxn],cnt[70];
//cnt[i]记录大小为2^i的盒子的个数
int main()
{
    scanf("%d",&t);
    while(t--)
    {
        memset(cnt,0,sizeof(cnt));
        ll sum=0;
        scanf("%lld%d",&n,&m);
        for(int i=0;i<m;i++)
        {
            scanf("%d",&a[i]);
            sum+=a[i];
        }
        if(sum<n)
            printf("-1\n");
        else if(sum==n)
            printf("0\n");
        else
        {
            int ans=0;
            bool flag=1;
            for(int i=0;i<m;i++)
                cnt[(int)(log2(a[i])+0.5)]++;
            for(ll i=1,j=0;i<=n&&flag;i<<=1,++j)
            {
                if(n&i)
                {
                    if(cnt[j])//用已经有的盒子
                        --cnt[j];
                    else
                    {
                        bool tag=0;
                        for(int k=j+1;k<60;k++)//找到一个盒子进行划分
                        {
                            if(cnt[k])
                            {
                                tag=1,--cnt[k],ans+=k-j;
                                while(--k>j)
                                    ++cnt[k];
                                break;
                            }
                        }
                        flag=tag;
                    }
                }
                cnt[j+1]+=cnt[j]>>1;//两个小盒子合成一个大盒子
            }
            if(!flag)
                ans=-1;
            printf("%d\n",ans);
        }
    }
    return 0;
}

### 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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