108 - Maximum Sum UVA

 Maximum Sum 

Background

A problem that is simple to solve in one dimension is often much more difficult to solve in more than one dimension. Consider satisfying a boolean expression in conjunctive normal form in which each conjunct consists of exactly 3 disjuncts. This problem (3-SAT) is NP-complete. The problem 2-SAT is solved quite efficiently, however. In contrast, some problems belong to the same complexity class regardless of the dimensionality of the problem.

The Problem

Given a 2-dimensional array of positive and negative integers, find the sub-rectangle with the largest sum. The sum of a rectangle is the sum of all the elements in that rectangle. In this problem the sub-rectangle with the largest sum is referred to as the maximal sub-rectangle. A sub-rectangle is any contiguous sub-array of size tex2html_wrap_inline33 or greater located within the whole array. As an example, the maximal sub-rectangle of the array:

displaymath35

is in the lower-left-hand corner:

displaymath37

and has the sum of 15.

Input and Output

The input consists of an tex2html_wrap_inline39 array of integers. The input begins with a single positive integer N on a line by itself indicating the size of the square two dimensional array. This is followed by tex2html_wrap_inline43 integers separated by white-space (newlines and spaces). These tex2html_wrap_inline43 integers make up the array in row-major order (i.e., all numbers on the first row, left-to-right, then all numbers on the second row, left-to-right, etc.). N may be as large as 100. The numbers in the array will be in the range [-127, 127].

The output is the sum of the maximal sub-rectangle.

Sample Input

4
0 -2 -7  0 9  2 -6  2
-4  1 -4  1 -1
8  0 -2

Sample Output

15

本题可以利用前缀和解决问题:
对于给定的矩阵grid[m][n]不妨设sum[x][y]代表
以(x,y)为右下角
(0,0)为左上角

的矩形
那么有:sum[x][y] = sum[x-1][y]+sum[x][y-1]-sum[x-1][y-1]+grid[x][y];
sum[0][y]=sum[x][0]=0;
代码如下:
#include <stdio.h>
#include <math.h>
#include <algorithm>
#include <string.h>
#include <stdlib.h>
using namespace std;
int num[110][110];
int sum[110][110];
int main()
{
    int n;
    while(scanf("%d",&n)==1)
    {
        int i,j;
        for(i=1;i<=n;++i)
          for(j=1;j<=n;++j)
            scanf("%d",&num[i][j]);
        for(i = 0;i<=n;++i)
        {
            sum[0][i] = 0;
            sum[i][0] = 0;
        }
        for(i = 1;i<=n;++i)
           for(j = 1;j<=n;++j)
             sum[i][j] = sum[i-1][j] + sum[i][j-1] - sum[i-1][j-1] + num[i][j];
        int maxn = num[1][1];
        for(i = n;i>=1;--i)
           for(j = n;j>=1;--j)
             for(int k = 0;k<=i;++k)
               for(int l = 0;l<=j;++l)
                  {
                      int temp = sum[i][j] - sum[k][j] - sum[i][l] +sum[k][l];
                      maxn = max(temp,maxn);
                  }
         printf("%d\n",maxn);
    }
    return 0;

}






                
### 关于最大子数组和扩展问题 对于求解多个不重叠的最大子数组之和的问题,可以基于动态规划的思想来实现。给定一个整数数组 `nums` 和正整数 `m` 表示要找到的非空连续子数组的数量,目标是在这些子数组互不相交的情况下使它们各自的元素总和最大化。 #### 动态规划方法解析 定义二维数组 `dp[i][j]` 来表示前 `i` 个数字中选取 `j+1` 个不重叠子序列所能获得的最大和: - 当只考虑单个子数组时 (`j=0`) ,这退化成经典的Kadane's Algorithm问题[^1]。 ```java public int maxSubArray(int[] nums) { int cur = nums[0]; int max = cur; for (int i = 1; i < nums.length; i++) { cur = Math.max(nums[i], cur + nums[i]); if (max < cur) max = cur; } return max; } ``` - 对于多组子数组的情况,则需要额外的状态转移方程来进行处理。状态转移逻辑如下: - 如果当前选择新开辟一段作为第 `(j+1)` 组的一部分; - 或者延续之前已经存在的某段作为新的结尾部分继续增长; 具体实现上可以通过遍历每一个可能的位置并尝试更新最优解的方式完成计算过程。 #### Java 实现代码 下面是一个完整的Java程序用于解决问题 F - Max Sum Plus Plus: ```java import java.util.Arrays; class Solution { public static void main(String args[]) { int m = 2; // Number of subarrays to find int[] nums = {-2, 1, -3, 4, -1, 2, 1, -5, 4}; System.out.println(maxSumOfMSubArrays(m, nums)); } private static int maxSumOfMSubArrays(int k, int[] a){ int n = a.length; int[][] dp = new int[n][k]; // Initialize first row with single-subarray maximum sums. dp[0][0] = a[0]; for (int j = 1; j < k; ++j) dp[j][0] = Integer.MIN_VALUE; for (int i = 1; i < n; ++i) { dp[i][0] = Math.max(dp[i - 1][0] + a[i], a[i]); for (int j = 1; j <= Math.min(i,k); ++j) { dp[i][j] = Math.max( dp[i - 1][j], ((i >= 1)? dp[i - 1][j - 1]: 0) + a[i] ); } } // Find the largest sum among all possibilities at last column. return Arrays.stream(dp).mapToInt(row -> row[k-1]).max().getAsInt(); } } ```
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