如果你对动态规划不熟悉,望转到该篇 \color{red}{如果你对动态规划不熟悉,望转到该篇~}如果你对动态规划不熟悉,望转到该篇 

肝了好多天-动态规划十连-超细腻解析|刷题打卡

这是一道比较简单的题目???????????? \color{green}{这是一道比较简单的题目???? ???? ???? ~}这是一道比较简单的题目???????????? 

什么题可以选择动态规划来做?

1.计数

  • 有多少种方式走到右下角
  • 有多少种方法选出k个数是的和是sum

2.求最大值最小值

  • 从左上角走到右下角路径的最大数字和
  • 最长上升子序列长度

3.求存在性

  • 取石子游戏,先手是否必胜
  • 能不能选出k个数使得和是sum

4.综合运用

  • 动态规划 + hash
  • 动态规划 + 递归
  • ...

leecode 221. 最大正方形

在一个由 '0' 和 '1' 组成的二维矩阵内,找到只包含 '1' 的最大正方形,并返回其面积。

示例 1:

动态规划-最大正方形_后端

输入:matrix = [["1","0","1","0","0"],["1","0","1","1","1"],["1","1","1","1","1"],["1","0","0","1","0"]]

输出:4

动态规划-最大正方形_后端_02

输入:matrix = [["0","1"],["1","0"]]

输出:1

示例 3:

输入:matrix = [["0"]]

输出:0

提示:

m == matrix.length

n == matrix[i].length

1 <= m, n <= 300

matrix[i][j] 为 '0' 或 '1'


--

棒!???????????? \color{green}{棒!???? ???? ???? ~}棒!???????????? 

参考代码

GO语言版

func maximalSquare(matrix [][]byte) int {
    dp := make([][]int, len(matrix))
    maxSide := 0
    for i := 0; i < len(matrix); i++ {
        dp[i] = make([]int, len(matrix[i]))
        for j := 0; j < len(matrix[i]); j++ {
            dp[i][j] = int(matrix[i][j] - '0')
            if dp[i][j] == 1 {
                maxSide = 1
            }
        }
    }

    for i := 1; i < len(matrix); i++ {
        for j := 1; j < len(matrix[i]); j++ {
            if dp[i][j] == 1 {
                dp[i][j] = min(min(dp[i-1][j], dp[i][j-1]), dp[i-1][j-1]) + 1
                if dp[i][j] > maxSide {
                    maxSide = dp[i][j]
                }
            }
        }
    }
    return maxSide * maxSide
}

func min(x, y int) int {
    if x < y {
        return x
    }
    return y
}





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NICE太简单啦???????????? \color{red}{NICE太简单啦???? ???? ???? ~}NICE太简单啦???????????? 

java版

class Solution {
    public int maximalSquare(char[][] matrix) {
        int maxSide = 0;
        if (matrix == null || matrix.length == 0 || matrix[0].length == 0) {
            return maxSide;
        }
        int rows = matrix.length, columns = matrix[0].length;
        int[][] dp = new int[rows][columns];
        for (int i = 0; i < rows; i++) {
            for (int j = 0; j < columns; j++) {
                if (matrix[i][j] == '1') {
                    if (i == 0 || j == 0) {
                        dp[i][j] = 1;
                    } else {
                        dp[i][j] = Math.min(Math.min(dp[i - 1][j], dp[i][j - 1]), dp[i - 1][j - 1]) + 1;
                    }
                    maxSide = Math.max(maxSide, dp[i][j]);
                }
            }
        }
        int maxSquare = maxSide * maxSide;
        return maxSquare;
    }
}


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