• POJ 2976 Dropping tests(01分数规划入门)


    Dropping tests
    Time Limit: 1000MS   Memory Limit: 65536K
    Total Submissions: 11367   Accepted: 3962

    Description

    In a certain course, you take n tests. If you get ai out of bi questions correct on test i, your cumulative average is defined to be

    .

    Given your test scores and a positive integer k, determine how high you can make your cumulative average if you are allowed to drop any k of your test scores.

    Suppose you take 3 tests with scores of 5/5, 0/1, and 2/6. Without dropping any tests, your cumulative average is . However, if you drop the third test, your cumulative average becomes .

    Input

    The input test file will contain multiple test cases, each containing exactly three lines. The first line contains two integers, 1 ≤ n ≤ 1000 and 0 ≤ k < n. The second line contains n integers indicating ai for all i. The third line contains n positive integers indicating bi for all i. It is guaranteed that 0 ≤ ai ≤ bi ≤ 1, 000, 000, 000. The end-of-file is marked by a test case with n = k = 0 and should not be processed.

    Output

    For each test case, write a single line with the highest cumulative average possible after dropping k of the given test scores. The average should be rounded to the nearest integer.

    Sample Input

    3 1
    5 0 2
    5 1 6
    4 2
    1 2 7 9
    5 6 7 9
    0 0

    Sample Output

    83
    100

    Hint

    To avoid ambiguities due to rounding errors, the judge tests have been constructed so that all answers are at least 0.001 away from a decision boundary (i.e., you can assume that the average is never 83.4997).

    Source

    题目链接:POJ 2976

    题意:给你N个物品,每一个物品有它的属性ai与bi,求丢掉K个物品后使留下来的N-K个物品的${Sigma a_i} over {Sigma b_i}$最大化。

    如果没听过01分数规划可以先看这篇文章:传送门1,传送门2,然后我想说的是其中对$r={{Sigma a_i*x_i} over {Sigma b_i*x_i}}$的移项变形可以得到:$0={Sigma a_i*x_i}-r*{Sigma b_i*x_i}$

    然后就是这里搞了一会儿才弄明白(数学渣没办法),想一想是不是很想熟悉的二次函数$y=ax^2+bx+c$的形式,有时候我们也利用$0=ax^2+bx+c$来推导二次函数,可以发现后者是前者的特殊情况,当y=0时前者便成了后者,或者说后者求出来的解是在自变量轴上的交点。那么上面那个函数同理,若把0改成F(r),则可以发现这是一个图像,由于bixi大于等于0,因此至少是关于r递减,又由于这个变量又受x_i集合的取值影响,这个图像实际上就像我们要使得$0={Sigma a_i*x_i}-r*{Sigma b_i*x_i}$成立,显然需要当$r=r_i$的时候$F(r)=0$,但是这样的取值$r_i$可以有很多个,那么我们要找到最靠右的那个点作为答案即可。当然这题还用到了贪心的思想,因为取哪个是随意的,当然取每一次取最优的

    代码:

    #include <stdio.h>
    #include <iostream>
    #include <algorithm>
    #include <cstdlib>
    #include <sstream>
    #include <numeric>
    #include <cstring>
    #include <bitset>
    #include <string>
    #include <deque>
    #include <stack>
    #include <cmath>
    #include <queue>
    #include <set>
    #include <map>
    using namespace std;
    #define INF 0x3f3f3f3f
    #define LC(x) (x<<1)
    #define RC(x) ((x<<1)+1)
    #define MID(x,y) ((x+y)>>1)
    #define CLR(arr,val) memset(arr,val,sizeof(arr))
    #define FAST_IO ios::sync_with_stdio(false);cin.tie(0);
    typedef pair<int, int> pii;
    typedef long long LL;
    const double PI = acos(-1.0);
    const int N = 1010;
    const double eps = 1e-5;
    double a[N], b[N], d[N];
    
    int main(void)
    {
        int n, k, i;
        while (~scanf("%d%d", &n, &k) && (n | k))
        {
            for (i = 0; i < n; ++i)
                scanf("%lf", a + i);
            for (i = 0; i < n; ++i)
                scanf("%lf", b + i);
            double L = 0, R = 1e9 + 7, ans = 0;
            int res = n - k;
            while (fabs(R - L) >= eps)
            {
                double mid = (L + R) / 2.0;
                for (i = 0; i < n; ++i)
                    d[i] = a[i] - mid * b[i];
                sort(d, d + n, greater<double>());
                double temp = 0;
                for (i = 0; i < res; ++i)
                    temp += d[i];
                if (temp > 0)
                {
                    L = mid;
                    ans = mid;
                }
                else
                    R = mid;
            }
            printf("%.0f
    ", 100.0 * ans);
        }
        return 0;
    }
  • 相关阅读:
    推荐系统多样性指标衡量
    deepfm代码参考
    tf多值离散embedding方法
    样本加权
    tensorflow 分布式搭建
    优化器
    协同过滤代码
    NLP
    双线性ffm
    各种总结
  • 原文地址:https://www.cnblogs.com/Blackops/p/6545302.html
Copyright © 2020-2023  润新知