Codeforces 460C Present

Little beaver is a beginner programmer, so informatics is his favorite subject. Soon his informatics teacher is going to have a birthday and the beaver has decided to prepare a present for her. He planted n flowers in a row on his windowsill and started waiting for them to grow. However, after some time the beaver noticed that the flowers stopped growing. The beaver thinks it is bad manners to present little flowers. So he decided to come up with some solutions.

There are m days left to the birthday. The height of the i-th flower (assume that the flowers in the row are numbered from 1 to n from left to right) is equal to ai at the moment. At each of the remaining m days the beaver can take a special watering and water w contiguous flowers (he can do that only once at a day). At that each watered flower grows by one height unit on that day. The beaver wants the height of the smallest flower be as large as possible in the end. What maximum height of the smallest flower can he get?

Input
The first line contains space-separated integers n, m and w (1 ≤ w ≤ n ≤ 105; 1 ≤ m ≤ 105). The second line contains space-separated integers a1, a2, …, an (1 ≤ ai ≤ 109).

Output
Print a single integer — the maximum final height of the smallest flower.

Sample test(s)
input
6 2 3
2 2 2 2 1 1
output
2
input
2 5 1
5 8
output
9

解题思路:二分枚举这个最小高度,然后对于相应的高度,我们判断验证是否存在一种浇花的方案使得满足条件。验证方案是否可行的时候我们需要借用树状数组进行优化,代码如下。

#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <iostream>
#include <string>
#include <vector>
#include <algorithm>
using namespace std;
const int maxn = 100010;
int tree[maxn];
int a[maxn];
int n, m, w;

int lowbit(int x) {
    return x & (-x);
}

void add(int x, int c) {
    for(int i = x; i > 0; i -= lowbit(i)) {
        tree[i] += c;
    }
}

int sum(int x) {
    int s = 0;
    for(int i = x; i <= n; i += lowbit(i)) {
        s += tree[i];
    }
    return s;
}


bool check(int x) {

    memset(tree, 0, sizeof(tree));
    for(int i = 1; i <= n; ++i) {
        add(i-1, -a[i]);
        add(i, a[i]);
    }
    int tot = m;
    for(int i = 1; i <= n; ++i) {
        int t = sum(i);
        if(t >= x) {
            continue;
        } else {
            if(tot < x - t) {
                return false;
            } else {
                if(x - t == 0) continue;
                tot -= (x - t);
                add(i - 1, -(x-t));
                add(min(i+w-1, n), x-t);
            }
        }
    }
    return true;
}

int main() {

    //freopen("aa.in", "r", stdin);
    int l, r, mid;
    int ans = 0;
    int maxh = 0, minh = 0x3f3f3f3f;
    scanf("%d %d %d", &n, &m, &w);
    for(int i = 1; i <= n; ++i) {
        scanf("%d", &a[i]);
        maxh = max(maxh, a[i]);
        minh = min(minh, a[i]);
    }
    l = minh;
    r = maxh + m;
    while(l <= r) {
        mid = (l + r) / 2;
        if(check(mid)) {
            ans = max(ans, mid);
            l = mid + 1;
        } else {
            r = mid - 1;
        }
    }
    printf("%d\n", ans);
    return 0;
}
内容概要:本文介绍了MATLAB实现DBN-RBF深度置信网络结合RBF神经网络多输入单输出回归预测的详细项目实例。项目旨在通过深度置信网络(DBN)和径向基函数神经网络(RBF)的结合,设计出一种高效的回归预测模型,以应对高维数据和非线性关系的挑战。DBN用于无监督特征提取,RBF用于快速回归,两者结合显著提升了预测精度和模型泛化能力。文中详细描述了项目的背景、目标、挑战、解决方案、模型架构、代码实现、GUI设计、性能评估及未来改进方向。 适合人群:具备一定编程基础,对机器学习和深度学习有一定了解的研发人员,尤其是从事金融预测、医疗健康、智能制造等领域的工程师和技术人员。 使用场景及目标:①解决高维数据的特征提取难题,提升非线性回归的拟合精度;②通过无监督学习与快速训练能力的结合,提高模型的预测精度和泛化能力;③应用于金融预测、医疗健康、智能制造等多个领域,提供高效的回归预测工具;④通过实时数据流处理和GPU加速推理,确保系统在实时应用中的快速响应。 其他说明:此项目不仅提供了详细的理论分析和代码实现,还涵盖了系统架构设计、模型部署与应用、安全性与用户隐私保护等方面的全面指导。通过结合其他深度学习模型、多任务学习、增量学习等技术,项目具备广阔的扩展性和应用前景。系统还支持自动化CI/CD管道、API服务与业务集成、前端展示与结果导出等功能,确保了系统的高可用性和易用性。
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