问题 E: Shortest Distance (20)

本文介绍了一种高效计算高速公路任意两个出口间最短距离的方法。通过预处理数据减少时间复杂度,避免了多次遍历数组的问题。

题目描述

The task is really simple: given N exits on a highway which forms a simple cycle, you are supposed to tell the shortest distance between any pair of exits.

输入

Each input file contains one test case. For each case, the first line contains an integer N (in [3, 105]), followed by N integer distances D1 D2 ... DN, where Di is the distance between the i-th and the (i+1)-st exits, and DN is between the N-th and the 1st exits. All the numbers in a line are separated by a space. The second line gives a positive integer M (<=104), with M lines follow, each contains a pair of exit numbers, provided that the exits are numbered from 1 to N. It is guaranteed that the total round trip distance is no more than 107.

输出

For each test case, print your results in M lines, each contains the shortest distance between the corresponding given pair of exits.

样例输入

5 1 2 4 14 9
3
1 3
2 5
4 1

样例输出

3
10
7

此题主要难点在于控制好时间复杂度,若在输入每个数字对再去数组中逐一遍历分别计算顺时针和逆时针的和取其最小值,需要多个for循环遍历,若n的规模过大则无法在短时内解决此问题。

书中提供的思路的妙处在于在输入数组时就预先进行计算,将每一个元素到第一个元素之间的和计算出来存入数组。在输入数字对进行结果计算时,仅需取出预先处理好的数组中的数据作差即可。

这种思路同样体现了牺牲空间复杂度来提高时间复杂度的思想。在本文最后还附上了自己之前写的时间复杂度过高的程序代码,可从中汲取教训。

#include<cstdio>
#include<algorithm>
using namespace std;
int main(){
	const int Nmax=100005;
	int dis[Nmax],a[Nmax],n,m,sum=0;
	scanf("%d",&n);
	for(int i=0;i<n;i++){
		scanf("%d",a+i);
		dis[i]=sum;
		sum+=a[i];
	}
	scanf("%d",&m);
	for(int i=0;i<m;i++){
		int x,y,ahead=0,back=0;
		scanf("%d%d",&x,&y);
		if(x>y)swap(x,y);
		ahead+=(dis[y-1]-dis[x-1]);
		back+=(sum-dis[y-1]+dis[x-1]);
		if(ahead>back)printf("%d\n",back);
		else printf("%d\n",ahead);
	}
	return 0;
}

时间复杂度过大的算法如下:

#include<cstdio>
int main(){
	int n,a[100000];
	scanf("%d",&n);
	for(int i=0;i<n;i++)scanf("%d",a+i);
	int m,b,c,sum_ahead=0,sum_back=0;
	scanf("%d",&m);
	for(int i=0;i<m;i++){
		scanf("%d%d",&b,&c);
		if(((c+n-b)%n)<(n-(c+n-b)%n)){
			for(int j=0,k=b-1;j<(c+n-b)%n;j++){
				sum_ahead+=a[k];
				k++;
				if(k==n)k=0;
			}
			for(int j=0,k=b-1;j<n-(c+n-b)%n;j++){
				k--;
				if(k==-1)k=n-1;
				sum_back+=a[k];
				if(sum_back>sum_ahead){
					printf("%d\n",sum_ahead);
					break;
				}
			}
			if(sum_back<sum_ahead)printf("%d\n",sum_back);
		}else{
			for(int j=0,k=b-1;j<n-(c+n-b)%n;j++){
				k--;
				if(k==-1)k=n-1;
				sum_back+=a[k];					
			}
			for(int j=0,k=b-1;j<(c+n-b)%n;j++){
				sum_ahead+=a[k];
				k++;
				if(k==n)k=0;
				if(sum_ahead>sum_back){
					printf("%d\n",sum_back);
					break;
				}
			}
			if(sum_ahead<sum_back)printf("%d\n",sum_ahead);
		}						
		sum_back=0;
		sum_ahead=0;
	}
	return 0;
}

import heapq def dijkstra(graph, start): shortest_paths = {vertex: float("inf") for vertex in graph}#vertex: float("inf"):将节点距离设为无穷,for vertex in graph:遍历所有节点 shortest_paths[start] = 0#将起点距离设为0 predecessors = {vertex: None for vertex in graph}# priority_queue = [(0,start)] while priority_queue: current_distance, current_vertex = heapq.heappop(priority_queue) if current_distance > shortest_paths[current_vertex]: continue; for neighbor, weight in graph[current_vertex].items(): distance = current_distance + weight if distance < shortest_paths[neighbor]: shortest_paths[neighbor] = distance predecessors[neighbor] = current_vertex heapq.heappush(priority_queue, (distance,neighbor)) return shortest_paths, predecessors graph = { "A": {"B": 4, "C": 5}, "B": {"A": 4, "C": 11, "D": 9, "E": 7}, "C": {"A": 5, "B": 11, "E": 3}, "D": {"B": 9, "E": 13, "F": 2}, "E": {"B": 7, "C": 3, "D": 13, "F": 6}, "F": {"D": 2, "E": 6} } shortest_paths, predecessors = dijkstra(graph, "A") #打印最短路径 print("shortest_paths") for vertex, distance in shortest_paths.items(): print(f"{vertex}: {distance}") print("predecessors:") for vertex, distance in predecessors.items(): print(f"{vertex}: {distance}") def reconstruct_path(predecessor, start, end): path = [] while end: path.append(end) end = predecessors[end] return path[::-1] if path[-1] == start else [] print("Shortest paths from A to F:", reconstruct_path(predecessors,"A", "F")) 我要详细的运行过程
10-16
import heapq def dijkstra(graph, start): shortest_paths = {vertex: float("inf") for vertex in graph}#vertex: float("inf"):将节点距离设为无穷,for vertex in graph:遍历所有节点 shortest_paths[start] = 0#将起点距离设为0,初始{'A': 0, 'B': inf, 'C': inf, 'D': inf, 'E': inf, 'F': inf} predecessors = {vertex: None for vertex in graph}# priority_queue = [(0,start)]#初始为[(0, 'A')] while priority_queue: current_distance, current_vertex = heapq.heappop(priority_queue) if current_distance > shortest_paths[current_vertex]: continue for neighbor, weight in graph[current_vertex].items():#graph[current_vertex].items():返回邻居及权重,并分别赋给neighbor与weight distance = current_distance + weight if distance < shortest_paths[neighbor]: shortest_paths[neighbor] = distance predecessors[neighbor] = current_vertex heapq.heappush(priority_queue, (distance,neighbor)) return shortest_paths, predecessors graph = { "A": {"B": 4, "C": 5}, "B": {"A": 4, "C": 11, "D": 9, "E": 7}, "C": {"A": 5, "B": 11, "E": 3}, "D": {"B": 9, "E": 13, "F": 2}, "E": {"B": 7, "C": 3, "D": 13, "F": 6}, "F": {"D": 2, "E": 6} } shortest_paths, predecessors = dijkstra(graph, "A") #打印A到各节点的最短路径 print("shortest_paths") for vertex, distance in shortest_paths.items(): print(f"{vertex}: {distance}") #打印各节点的前驱 print("predecessors:") for vertex, distance in predecessors.items(): print(f"{vertex}: {distance}") def reconstruct_path(predecessor, start, end): path = [] while end: path.append(end) end = predecessors[end] return path[::-1] if path[-1] == start else [] print("Shortest paths from A to F:", reconstruct_path(predecessors, "A", "F"))
10-17
import heapq def dijkstra(graph, start): shortest_paths = {vertex: float("inf") for vertex in graph}#vertex: float("inf"):将节点距离设为无穷,for vertex in graph:遍历所有节点 shortest_paths[start] = 0 predecessors = {vertex: None for vertex in graph} priority_queue = [(0,start)]#初始为[(0, 'A')] while priority_queue: current_distance, current_vertex = heapq.heappop(priority_queue)#优先弹出最小的节点,如第一次弹出(0,‘A),后续(4,’B')与(5,’C')进入则弹出(4,’B') if current_distance > shortest_paths[current_vertex]: continue for neighbor, weight in graph[current_vertex].items():#graph[current_vertex].items():返回邻居及距离,并分别赋给neighbor与weight distance = current_distance + weight#此时距离A的距离 if distance < shortest_paths[neighbor]:#判断路径距离,当已到该有路径大于目前到该节点路径时,更新到A的距离及前置节点 shortest_paths[neighbor] = distance#更新距离 predecessors[neighbor] = current_vertex#更新前置节点 heapq.heappush(priority_queue, (distance,neighbor))#更新优先队列 return shortest_paths, predecessors#返回哥哥节点到A的最短距离,及其前置节点 #打印A到F的最短路径 def reconstruct_path(predecessor, start, end): path = [] while end: path.append(end) end = predecessors[end] return path[::-1] if path[-1] == start else []#path[::-1] 表示步长为-1的反向遍历,且在path最后一个元素等于start时返回 # graph = { "A": {"B": 4, "C": 5}, "B": {"A": 4, "C": 11, "D": 9, "E": 7}, "C": {"A": 5, "B": 11, "E": 3}, "D": {"B": 9, "E": 13, "F": 2}, "E": {"B": 7, "C": 3, "D": 13, "F": 6}, "F": {"D": 2, "E": 6} } shortest_paths, predecessors = dijkstra(graph, "A") #打印A到各节点的最短路径 print("shortest_paths") for vertex, distance in shortest_paths.items(): print(f"{vertex}: {distance}") #打印各节点的前驱 print("predecessors:") for vertex, distance in predecessors.items(): print(f"{vertex}: {distance}") print("Shortest paths from A to F:", reconstruct_path(predecessors, "A", "F"))distance = current_distance + weight是什么意思
最新发布
10-22
评论 1
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