UVa OJ 108 - Maximum Sum (最大和)

Time limit: 3.000 seconds

 

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 1 × 1 or greater located within the whole array. As an example, the maximal sub-rectangle of the array:

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

is in the lower-left-hand corner:

9	2
-4	1
-1	8

and has the sum of 15.

 

Input and Output

The input consists of an N × N 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 N² integers separated by white-space (newlines and spaces). These N² 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

 

Analysis

多维（含二维）的最大子集问题是NP问题，只能用暴力搜索解决。在搜索的过程中，如果不注意保留以前的结果，将会使复杂度大大增加。我的算法是由行开始，顺序如下：

(r₁), (r₁, r₂), ..., (r₁, r₂, ..., r_n),

(r₂), (r₂, r₃), ..., (r₂, r₃, ..., r_n),

....

(r_n-1), (r_n-1, r_n),

(r_n)

在每一行组的循环中，列组的遍例顺序也是类似。这样有一个极大的好处，就是可以保留各列的和，当子集递增到下一行时，直接将原有各列和加上新一行的值就得到了新的子集。在列中也可以同样处理，而先行后列可以顺序访问原数据矩阵而无需跳转，可以加快内存访问速度，避免页面错误（这是操作系统层面的内容，单就算法而言可以不管）。

 

Solution

#include <iostream>
#include <stdlib.h>
using namespace std;
int main(void) {
	int aMat[100][100], int aColSum[100], nDem = 100, nMaxSum = -128, nTemp;
	cin >> nDem;
	for (int i = 0; i < nDem; ++i) {
		for (int j = 0; j < nDem; ++j) {
			cin >> nTemp;
			aMat[i][j] = nTemp;
		}
	}
	for (int nRowBeg = 0; nRowBeg < nDem; ++nRowBeg) {
		memset(aColSum, 0, sizeof(aColSum));
		for (int nRowEnd = nRowBeg; nRowEnd < nDem; ++nRowEnd) {
			int *pCol = (int*)(aMat[nRowEnd]);
			for (int nCol = 0; nCol < nDem; aColSum[nCol] += pCol[nCol++]);
			for (int nColBeg = 0; nColBeg < nDem; ++nColBeg) {
				int nSum = 0;
				for (int nColEnd = nColBeg; nColEnd < nDem; ++nColEnd) {
					nSum += aColSum[nColEnd];
					nMaxSum = nSum > nMaxSum ? nSum : nMaxSum;
				}
			}
		}
	}
	cout << nMaxSum << endl;
	return 0;
}