[UVa 12166] 修改天平(Equilibrium Mobile)

解决UVa 12166问题,探讨如何调整深度不超过16的二叉树结构的秤砣重量以达到平衡。关键在于确定一个叶节点的重量能决定整棵树的重量分布,重复的重量意味着相应节点无需修改。目标是找到最大重复次数,从而求得最少修改的秤砣数。

Judge:https://vjudge.net/problem/UVA-12166
题意:给一棵深度不超过16的二叉树,代表一个天平。每根杆都悬挂在中间,每个秤砣的重量已知。至少需要修改多少个秤砣的重量才能使天平平衡?


此题比较难想。

首先,一个明显的事实:要使天平平衡,左右两边重量必须相等。
从叶节点考虑,如果左叶节点的重量已经确定,那么右叶节点的重量也就确定了(等于左叶节点的重量),于是根节点的重量即可看成是两倍的叶节点重量。
这样,一层一层,根节点重量也就确定了。
所以,只要确定了一个叶节点的重量,根节点的重量即可确定。
得出结论:对于每一个叶节点重量,对应着一个根节点重量。当然,可能会有重复。

重复是什么意思?如果你把一个叶节点的重量作为基点,开始修改其他叶节点,这时候会发现有一个节点的重量已经是要修改的值,显然是不需要修改此节点的。
因此,若一个重量对应着m个叶节点,把一个叶节点作为基点修改时,(m-1)个叶节点是不需要修改的。
得出结论:对于每一个重量,需要修改的叶节点数量是“叶节点数量 - 重复的数量”。

所以,找“重复的数量”的最大值就可以了。

#include <iostream>
#include <map>
#include <string>
using namespace std;

map<long long, int> g_mapTime;
int g_iLeafNum;

void Cacl(string str)
{
	int iDepth = 0;
	for (int i = 1; i <= str.length(); i++)
	{
		if (str[i - 1] == '[')	iDepth++;
		else if (str[i - 1] == ']')	iDepth--;
		else if (isdigit(str[i - 1]))
		{
			g_iLeafNum++;
			
			long long iTmp = str[i - 1] - '0';
			for (i++; true; i++)
			{
				if (!isdigit(str[i - 1]))
					break;
				iTmp = iTmp * 10 + str[i - 1] - '0';
			}
			i--;
			
			iTmp = iTmp * (1 << iDepth);
			g_mapTime[iTmp]++;
		}
	}
}

int main()
{
	int iDataTot;
	cin >> iDataTot;
	
	for (int iData = 1; iData <= iDataTot; iData++)
	{
		string str;
		cin >> str;
		
		g_mapTime.clear();
		g_iLeafNum = 0;
		
		Cacl(str);
		
		int iMax = -1;
		for (map<long long, int>::iterator i = g_mapTime.begin(); i != g_mapTime.end(); i++)
			iMax = (iMax > i->second ? iMax : i->second);
		cout << g_iLeafNum - iMax << endl;
	}
	
	return 0;
}


### Deep Equilibrium Modeling in Machine Learning Deep equilibrium (DEQ) models represent a class of neural networks that focus on finding fixed points rather than stacking layers sequentially. These models are particularly useful when dealing with problems where depth plays an essential role but comes at significant computational costs. In DEQs, instead of explicitly constructing deep architectures layer-by-layer, the network computes solutions as implicit functions defined through fixed-point iterations. The core idea behind DEQ lies in solving equations such as \( f(x; \theta) = x \), where \( x \) represents the output and \( \theta \) denotes parameters learned during training. This approach allows for efficient memory usage since no intermediate activations need storage across multiple layers[^1]. Training involves optimizing objectives based on these fixed points while ensuring stability conditions hold true throughout computation processes. In terms of implementation within machine learning frameworks like PyTorch or TensorFlow: ```python import torch from scipy.optimize import root_scalar def deq_forward(z_init, func, theta): """Forward pass function.""" def eqn(z): return z - func(z, theta) sol = root_scalar(eqn, bracket=[-1e3, 1e3], method='brentq') if not sol.converged: raise RuntimeError('Fixed point iteration did not converge.') return sol.root class DEQLayer(torch.nn.Module): def __init__(self, func): super().__init__() self.func = func def forward(self, inputs): outputs = deq_forward(inputs, self.func, self.theta) return outputs ``` This code snippet demonstrates how one might implement a basic version of a DEQ layer leveraging Python's `scipy` library alongside PyTorch tensors. Note this example simplifies actual implementations found in research papers which often include additional considerations regarding numerical precision and convergence guarantees. Regarding reinforcement learning contexts specifically mentioned earlier via references provided about dueling networks & value estimation techniques [^4][^3]: One could extend traditional Q-learning algorithms incorporating ideas from DEQ theory into policy evaluation steps aiming towards more stable representations over time without needing explicit temporal difference updates per se given proper formulations aligning well under certain assumptions concerning environment dynamics along Markov Decision Processes definitions typically utilized therein scenarios involving sequential decision makings tasks among others aspects potentially explored further depending upon specific application requirements accordingly then too!
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