Codeforces-545D. Queue

本文介绍了一个通过调整服务队列中顾客顺序来最大化顾客满意度的问题。针对每个顾客的服务时间和等待时间,通过算法实现顾客顺序的最佳排列,使得不满顾客的数量降到最低。
                          D. Queue
                 time limit per test1 second
               memory limit per test256 megabytes
                     inputstandard input
                    outputstandard output

Little girl Susie went shopping with her mom and she wondered how to improve service quality.

There are n people in the queue. For each person we know time ti needed to serve him. A person will be disappointed if the time he waits is more than the time needed to serve him. The time a person waits is the total time when all the people who stand in the queue in front of him are served. Susie thought that if we swap some people in the queue, then we can decrease the number of people who are disappointed.

Help Susie find out what is the maximum number of not disappointed people can be achieved by swapping people in the queue.

Input
The first line contains integer n (1 ≤ n ≤ 105).

The next line contains n integers ti (1 ≤ ti ≤ 109), separated by spaces.

Output
Print a single number — the maximum number of not disappointed people in the queue.

Examples
input
5
15 2 1 5 3
output
4
Note
Value 4 is achieved at such an arrangement, for example: 1, 2, 3, 5, 15. Thus, you can make everything feel not disappointed except for the person with time 5.

#include<iostream>
#include<algorithm>
using namespace std;
int a[100005];
int main()
{
    int n;
    scanf("%d", &n);
    for (int i = 0; i < n; i++)
        scanf("%d", &a[i]);
    sort(a, a + n);
    long long sum = 0;
    int maximum = 0;
    for (int i = 0; i < n; i++)
    {
        if (a[i] >= sum)
        {
            maximum++;
            sum += a[i];
        }
    }
    printf("%d\n", maximum);
    return 0;
}
关于 Codeforces Round 1014 Div. 2 的具体题解和比赛详情并未在当前提供的引用中提及。然而,可以基于类似的竞赛结构以及常见的算法问题类型提供一些推测性的分析。 通常情况下,Codeforces 比赛中的题目会涉及多种经典算法领域,例如动态规划、贪心策略、图论、字符串处理等。以下是可能适用于该轮次的一些常见问题类型的解答框架: ### 动态规划 (Dynamic Programming) 如果某道题目涉及到最优子结构性质,则可以通过构建状态转移方程来解决问题。例如,在某些背包类问题中,定义 `dp[i][j]` 表示前 i 件物品容量为 j 时的最大价值[^3]。 ```python def knapsack(values, weights, capacity): n = len(values) dp = [[0]*(capacity+1) for _ in range(n+1)] for i in range(1,n+1): for w in range(capacity+1): if weights[i-1] <= w: dp[i][w] = max(dp[i-1][w], values[i-1]+dp[i-1][w-weights[i-1]]) else: dp[i][w] = dp[i-1][w] return dp[n][capacity] values = [60, 100, 120] weights = [10, 20, 30] capacity = 50 print(knapsack(values, weights, capacity)) ``` ### 贪心算法 (Greedy Algorithm) 当面对资源分配等问题时,采用局部最优策略往往能够达到全局最佳效果。比如安排会议时间表以最大化会议室利用率的情形下,按照结束时间排序并依次选取不冲突的区间即可实现目标[^4]。 ### 图论 (Graph Theory) 对于连通性和最短路徑计算方面的需求,Dijkstra 和 Floyd-Warshall 是两种非常实用的方法。前者适合单源最短路径查询;后者则能解决多源场景下的需求[^5]。 ```python import heapq def dijkstra(graph, start_node): distances = {node: float('inf') for node in graph} distances[start_node] = 0 priority_queue = [(0,start_node)] while priority_queue: current_distance, current_vertex = heapq.heappop(priority_queue) if current_distance > distances[current_vertex]: continue for neighbor, weight in graph[current_vertex].items(): distance = current_distance + weight if distance < distances[neighbor]: distances[neighbor] = distance heapq.heappush(priority_queue,(distance,neighbor)) graph = { 'A': {'B': 1,'C': 4}, 'B': {'A': 1,'C': 2,'D': 6}, 'C': {'A': 4,'B': 2,'D': 3}, 'D': {'B': 6,'C': 3} } dijkstra(graph, 'A') ``` 尽管上述内容并非直接针对 Codeforces Round 1014 Div. 2 提供的具体解决方案,但它涵盖了比赛中可能出现的核心知识点和技术手段。 ####
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
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