解题报告 之 UVA116 Unidirectional TSP

本文详细介绍了如何解决UVA116 Unidirectional TSP问题,即在一个网格中,从左往右寻找一条路径,使得路径的权重(矩阵中的数值之和)最小。通过动态规划的方法,引入阶段概念,逐步求解每个阶段的最优路径,并最终找到全局最优解。此外,文章还讨论了如何在路径选择时考虑字典序最小的问题。

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解题报告 之 UVA116 Unidirectional TSP


Description

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Background

Problems that require minimum paths through some domain appear in many different areas of computer science. For example, one of the constraints in VLSI routing problems is minimizing wire length. The Traveling Salesperson Problem (TSP) -- finding whether all the cities in a salesperson's route can be visited exactly once with a specified limit on travel time -- is one of the canonical examples of an NP-complete problem; solutions appear to require an inordinate amount of time to generate, but are simple to check.

This problem deals with finding a minimal path through a grid of points while traveling only from left to right.

The Problem

Given an tex2html_wrap_inline352 matrix of integers, you are to write a program that computes a path of minimal weight. A path starts anywhere in column 1 (the first column) and consists of a sequence of steps terminating in column n (the last column). A step consists of traveling from column i to column i+1 in an adjacent (horizontal or diagonal) row. The first and last rows (rows 1 and m) of a matrix are considered adjacent, i.e., the matrix ``wraps'' so that it represents a horizontal cylinder. Legal steps are illustrated below.

picture25

The weight of a path is the sum of the integers in each of the n cells of the matrix that are visited.

For example, two slightly different tex2html_wrap_inline366 matrices are shown below (the only difference is the numbers in the bottom row).

picture37

The minimal path is illustrated for each matrix. Note that the path for the matrix on the right takes advantage of the adjacency property of the first and last rows.

The Input

The input consists of a sequence of matrix specifications. Each matrix specification consists of the row and column dimensions in that order on a line followed by tex2html_wrap_inline376 integers where m is the row dimension and n is the column dimension. The integers appear in the input in row major order, i.e., the first nintegers constitute the first row of the matrix, the second n integers constitute the second row and so on. The integers on a line will be separated from other integers by one or more spaces. Note: integers are not restricted to being positive. There will be one or more matrix specifications in an input file. Input is terminated by end-of-file.

For each specification the number of rows will be between 1 and 10 inclusive; the number of columns will be between 1 and 100 inclusive. No path's weight will exceed integer values representable using 30 bits.

The Output

Two lines should be output for each matrix specification in the input file, the first line represents a minimal-weight path, and the second line is the cost of a minimal path. The path consists of a sequence of n integers (separated by one or more spaces) representing the rows that constitute the minimal path. If there is more than one path of minimal weight the path that is lexicographically smallest should be output.

Sample Input

5 6
3 4 1 2 8 6
6 1 8 2 7 4
5 9 3 9 9 5
8 4 1 3 2 6
3 7 2 8 6 4
5 6
3 4 1 2 8 6
6 1 8 2 7 4
5 9 3 9 9 5
8 4 1 3 2 6
3 7 2 1 2 3
2 2
9 10 9 10

Sample Output

1 2 3 4 4 5
16
1 2 1 5 4 5
11
1 1
19


题目大意:给一个m行n列的整数矩阵,从第一列任意一位置出发,每一步向右走,向右上走,或者向右下走。求走到最右边为止,求路径和最小的一条路径。注意最后一行的上一行是第一行,第一行的下一行是最后一行。


分析:明显是DP,引入阶段的概念,将每一列作为一个阶段,dp[i][j]表示走到i,j时到达最右还需要和的最小值。则边界条件是dp[i][n-1]=num[i][n-1] (行列从0开始),转移方程为dp[i][j]=min(dp[(i-1+m)%m][j+1], dp[i][j+1], dp[(i+1)%m][j+1])+num[i][j]。最后再遍历第一列的所有可能取最小值即可。


至于字典序最小,直接从上到下考虑即可。先将(i-1+m)%m, i, (i+1)%m 排序即可。


下面上代码:

#include <iostream>
#include <algorithm>
#define INF 0x3f3f3f3f
using namespace std;

int m, n;
int num[15][110];
int dp[15][110];
int Next[15][110];
int first=0,ans=INF;

int DP(int i, int j)
{
	if (dp[i][j] < INF) return dp[i][j];
	if (j == n - 1) return dp[i][j] = num[i][j];
	int row[3] = { i, i - 1, i + 1 };
	if (i - 1 < 0) row[1] = m - 1;
	if (i + 1 >= m) row[2] = 0;

	sort(row, row + 3);
	for (int k = 0; k < 3; k++)
	{
		int v = DP(row[k], j + 1) + num[i][j];
		if (v < dp[i][j])
		{
			dp[i][j] = v;
			Next[i][j] = row[k];
		}
	}

	return dp[i][j];
}

int main()
{
	while (cin>>m>>n)
	{
		ans = INF;
		first = 0;
		for (int i = 0; i < m;i++)
		for (int j = 0; j < n; j++)
		{
			cin >> num[i][j];
			dp[i][j] = INF;
		}
		
		for (int i = 0; i < m; i++)
		{
			int tem=DP(i, 0);
			if (tem < ans)
			{
				ans = tem;
				first = i;
			}

		}

		for (int j = 0, i = first; j < n; i = Next[i][j], j++)
		{
			cout << i+1;
			if (j!= n - 1)
				cout << " ";
			else
				cout << endl;
		}
		cout << ans << endl;
		

	}
	return 0;
}


就是这样!




在MATLAB中实现单向空间后方交会(Unidirectional Space Positioning,通常用于GPS或类似定位系统)的过程可以分为以下几个步骤: 1. **数据获取**: 首先,你需要从GPS接收机或其他卫星定位设备收集实时的卫星信号测量数据,包括伪随机码相位观测值(Pseudorange Observations)和多普勒频移(Doppler Shifts)。 2. **初始化**: 确定初始估计,如用户的位置、速度以及卫星状态。这可能是基于历史位置信息或是通过其他手段得到的粗略估计。 3. **模型建立**: 设计适合单向定位的数学模型,比如利用最小二乘法,构建一个包含观测误差项的方程组。对于单向定位,你通常只关注距离信息而不考虑方向。 4. **迭代求解**: 使用MATLAB的优化函数,例如`lsqnonlin`或者`fmincon`,对观测数据中的参数(如用户位置和卫星坐标)进行非线性最小化,不断调整这些参数直到达到最小残差。 5. **解算结果**: 当求解收敛后,你会得到用户的经度、纬度和高度等精确位置信息。 6. **评估和处理**: 检查解是否合理,比如检查残差是否过大、是否存在异常值,必要时进行滤波或平滑处理。 ```matlab % 示例代码片段 % 假设你有观测数据observedData 和已知卫星状态satellitePositions initialGuess = [lon0; lat0; height0]; % 初始猜测位置 [xSol, fval, exitflag] = lsqnonlin(@modelFunction, initialGuess, observedData, satellitePositions); position = xSol(1:2); % 提取经纬度 function residual = modelFunction(x, obsData, satPos) % 定义并计算模型残差 % 这里假设x = [lon; lat; height] % ... end %
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