Gym - 101142F Folding

本文深入探讨了一种基于纸张折叠的算法,详细解释了如何通过特定的数学逻辑将任意大小的纸张精确折成目标尺寸。算法的核心在于理解每次折叠实际上是在缩小边长,且目标长度需位于特定范围内。文章提供了C++实现代码,展示了如何计算折叠次数以达到所需尺寸,同时强调了最优化策略——选择较短边进行折叠通常更高效。

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思路:

因为只能沿与边平行的线折,所以每次折叠相当于缩小两条边中的一条边,而如果当前一条边长为x则折叠后最小为(x+1)/2。所以只要目标长度在[(x+1)/2,x)范围内都能通过一次折叠得到。按照这个思路就很容易求得把长为A的边折成B所需要的次数了。

需要注意的是最后结果并不一定是长边折长边;短边折短边最少。

代码:

#include <bits/stdc++.h>

using namespace std;

int Solved(long long t1,long long t2){
	int num = 0;
	if(t1 < t2)return 0x3f3f3f3f;
	while(t1 > t2){
		t1 = (t1+1)/2;
		++num;
	}
	return num;
}

int main(){
	
	freopen("folding.in","r",stdin);
	freopen("folding.out","w",stdout);
	long long W,H,w,h;
	while(scanf("%lld %lld %lld %lld",&W,&H,&w,&h) == 4){
		if(max(W,H)<max(w,h) || min(W,H)<min(w,h)){
			printf("-1\n");
			continue;
		}
		printf("%d\n",min(Solved(W,w)+Solved(H,h),Solved(W,h)+Solved(H,w)));
	}
	
	return 0;
} 

 

### Gym-Gazebo Library for Robotics Simulation and Reinforcement Learning The **gym-gazebo** library is designed as a comprehensive toolset for roboticists, integrating simulation environments, middleware systems like ROS or ROS 2, and advanced machine learning paradigms such as reinforcement learning into one cohesive framework[^1]. This combination allows researchers and developers to design, test, and refine behavioral algorithms for robots within simulated conditions before deploying them onto real-world hardware. #### Key Features of gym-gazebo - It leverages the power of Gazebo—a widely-used physics simulator—to provide realistic simulations of robot dynamics. - The integration with ROS/ROS 2 enables seamless communication between different components involved in both simulation and physical deployment scenarios. - By incorporating reinforcement learning methodologies, it facilitates training models through trial-and-error processes while optimizing performance metrics over time. An updated version called **Gym-Ignition**, introduced later by some contributors working along similar lines but using Ignition instead of classic GAZEBOSimulations offers reproducible experiments which are crucial when comparing results across studies involving deep reinforcement learning applications specifically tailored towards autonomous vehicles among other domains mentioned earlier under references four & five respectively.[^4] Here’s how you can install `gym-gazebo` via pip command typically used inside Python virtual environments: ```bash pip install gym-gazebo ``` For more customized installations depending upon specific versions required alongside compatible dependencies including particular releases associated either directly from source repositories hosted generally at GitHub pages maintained actively throughout recent years until now; refer official documentation links provided usually after each release announcement posts found easily searching terms related above citations given previously too!
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