[堆][贪心]cf228cFox and Box Accumulation

本文介绍了一种关于箱堆叠的算法问题,旨在利用不同强度的箱子构建尽可能少的堆叠,以解决Fox Ciel的问题。文章详细阐述了解决方案,并提供了一段使用C++实现的代码。

C. Fox and Box Accumulation
time limit per test
1 second
memory limit per test
256 megabytes
input
standard input
output
standard output

Fox Ciel has n boxes in her room. They have the same size and weight, but they might have different strength. The i-th box can hold at most xi boxes on its top (we'll call xi the strength of the box).

Since all the boxes have the same size, Ciel cannot put more than one box directly on the top of some box. For example, imagine Ciel has three boxes: the first has strength 2, the second has strength 1 and the third has strength 1. She cannot put the second and the third box simultaneously directly on the top of the first one. But she can put the second box directly on the top of the first one, and then the third box directly on the top of the second one. We will call such a construction of boxes a pile.

Fox Ciel wants to construct piles from all the boxes. Each pile will contain some boxes from top to bottom, and there cannot be more than xi boxes on the top of i-th box. What is the minimal number of piles she needs to construct?

Input

The first line contains an integer n (1 ≤ n ≤ 100). The next line contains n integers x1, x2, ..., xn (0 ≤ xi ≤ 100).

Output

Output a single integer — the minimal possible number of piles.

Sample test(s)
Input
3
0 0 10
Output
2
Input
5
0 1 2 3 4
Output
1
Input
4
0 0 0 0
Output
4
Input
9
0 1 0 2 0 1 1 2 10
Output
3
Note

In example 1, one optimal way is to build 2 piles: the first pile contains boxes 1 and 3 (from top to bottom), the second pile contains only box 2.

In example 2, we can build only 1 pile that contains boxes 1, 2, 3, 4, 5 (from top to bottom).

`
假如没有相同大小的,我们依次选择v,v-1,v-2...1是最优的,这样没有浪费。
假如有相同大小的,我们仍先选择v,v-1,v-2...1。当其不连续的时候,若此序列只有n个,那我们必定可以插入v-n个箱子进去,显然我们要插入v-n个较大的。当其连续时,可以证明这样仍然是最优的,因为如果把其中v,v-1...v',v'-1...1换成v,v-1...v',v'...1的话,个数不能增加,但是剩下的选择的空间变小了。
经过一轮放置后,若没有放完,则继续按照这样的方式放置。

这道题显然用堆实现最方面,但是当时没有想到。

#include <cstdio>
#include <functional>
#include <algorithm>
using std::sort;
using std::greater;

int ptr[110];
int num[110];
int start[110];

int min(int a,int b,int c)
{
	if (a < b && a < c)
		return a;
	if (b < c)
		return b;
	return c;
}

int main()
{
	int n;
	scanf("%d",&n);
	for (int i=1;i<=n;i++)
	{
		scanf("%d",num+i);
	}
	sort(num+1,num+1+n,greater<int>());
	num[0] = 0x3f3f3f3f;
	num[n+1] = 0x3f3f3f3f;
	for (int i=1;i<=n;i++)
	{
		if (num[i]!=num[i-1])
		{
			ptr[++ptr[0]] = i;
			start[ptr[0]] = i;
		}
	}
	start[ptr[0]+1] = n+1;
	int grp = 0;
	int used= 0;
	while (1)
	{
		if (used == n)
			break;
		int left = -0x3f3f3f3f;
		grp ++;
		for (int i=1;i<=ptr[0];i++)
		{
			if (ptr[i] == -1)
				continue;
			if (left == -0x3f3f3f3f)
				left = num[ptr[i]];
			else
				left --;
			used ++;
			if (num[ptr[i]]==num[ptr[i]+1])
				ptr[i] ++;
			else
				ptr[i] = -1;
		}
		if (left <= 0)
			continue;
		for (int i=ptr[0];left>0 && i>=1;i--)
		{
			if (ptr[i] == -1)
				continue;
			int chose = min(num[ptr[i]],start[i+1]-ptr[i],left);
			left -= chose;
			used += chose;
			if (num[ptr[i]]==num[ptr[i]+chose])
				ptr[i] += chose;
			else
				ptr[i] = -1;
		}
	}
	printf("%d",grp);
	return 0;
}


内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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