130. Surrounded Regions

本文详细介绍了如何使用深度优先搜索(DFS)和广度优先搜索(BFS)解决二维矩阵中被‘X’包围的‘O’区域问题。通过将边界上的‘O’及其相连的‘O’标记为特殊符号,再将剩余的‘O’转换为‘X’,最后恢复边界‘O’区域,实现了对被包围区域的捕获。

130. Surrounded Regions


Given a 2D board containing ‘X’ and ‘O’ (the letter O), capture all regions surrounded by ‘X’.

A region is captured by flipping all 'O’s into 'X’s in that surrounded region.

Example:

X X X X
X O O X
X X O X
X O X X

After running your function, the board should be:

X X X X
X X X X
X X X X
X O X X

Explanation:

Surrounded regions shouldn’t be on the border, which means that any ‘O’ on the border of the board are not flipped to ‘X’. Any ‘O’ that is not on the border and it is not connected to an ‘O’ on the border will be flipped to ‘X’. Two cells are connected if they are adjacent cells connected horizontally or vertically.

https://www.cnblogs.com/higerzhang/p/4149040.html

思路:
http://www.cnblogs.com/grandyang/p/4555831.html
Explanation当中透露了思路。我们的目标是找到所有与某一个在边界的’O’相连的‘O’,可以用dfs或者bfs来做。dfs容易造成栈溢出,需要限定搜索的边界来避免。

方法1: DFS

class Solution {
public:
    void solve(vector<vector<char>>& board) {
        if (board.size() == 0 || board[0].size() == 0) return;
        int m = board.size();
        int n = board[0].size();
        for (int i = 0; i < m; i++){
            for (int j = 0; j < n; j++)
                if ((i == 0 || i == board.size() - 1||j ==0 || j == board[0].size() - 1) &&  board[i][j] == 'O')
                    dfs(board, i, j);
        }
        
        
        for (int i = 0; i < m; i++){
            for (int j = 0; j < n; j++){
                if (board[i][j] == 'O')
                    board[i][j] = 'X';
                if (board[i][j] == '$')
                    board[i][j] = 'O';
            }
        }
        return;
        
    }
    
    void dfs(vector<vector<char>> & board, int i, int j){
        if (board[i][j] == 'O'){
            int m = board.size();
            int n = board[0].size();
            board[i][j] = '$';
        
        if (i > 0 && board[i - 1][j] == 'O'){
            dfs(board, i - 1, j);
        }
        
        if (i < m - 1 && board[i + 1][j] == 'O'){
            dfs(board, i + 1, j);
        }
        
        if (j > 0 && board[i][j - 1] == 'O'){
            dfs(board, i, j - 1);
        }
        
        if (j < n - 1 && board[i][j + 1] == 'O'){
            dfs(board, i, j + 1);
        }
        return;
        }
    }
};

方法2: BFS

我们也可以使用迭代的解法,但是整体的思路还是一样的,我们在找到边界上的O后,然后利用队列queue进行BFS查找和其相连的所有O,然后都标记上美元号。最后的处理还是先把所有的O变成X,然后再把美元号变回O即可,参见代码如下:


class Solution {
public:
    void solve(vector<vector<char>>& board) {
        if (board.size() == 0 || board[0].size() == 0) return;
        int m = board.size();
        int n = board[0].size();
        
        queue<pair<int, int>> myQueue;
        
        for (int i = 0; i < m; i++){
            for (int j = 0; j < n; j++)
                if ((i == 0 || i == board.size() - 1||j ==0 || j == board[0].size() - 1) &&  board[i][j] == 'O'){
                    myQueue.push(make_pair(i, j));
                }          
        }
        
        // call bfs
        bfs(board, myQueue);
        
        // final flip
        for (int i = 0; i < m; i++){
            for (int j = 0; j < n; j++){
                if (board[i][j] == 'O')
                    board[i][j] = 'X';
                if (board[i][j] == '$')
                    board[i][j] = 'O';
            }
        }
        return;
    }
    
    void bfs(vector<vector<char>> & board, queue<pair<int, int>> & q){
        int m = board.size();
        int n = board[0].size();
        
        while (!q.empty()){
            int i = q.front().first;
            int j = q.front().second;
            q.pop();
           
            board[i][j] = '$';
            if (i > 0 && board[i - 1][j] == 'O'){
               q.push(make_pair(i - 1, j));
             }
        
        if (i < m - 1 && board[i + 1][j] == 'O'){
           q.push(make_pair(i + 1, j));
        }
        
        if (j > 0 && board[i][j - 1] == 'O'){
            q.push(make_pair(i, j - 1));
        }
        
        if (j < n - 1 && board[i][j + 1] == 'O'){
           q.push(make_pair(i, j + 1));
        }
        }
        return;
    }
};
### LeetCode Top 100 Popular Problems LeetCode provides an extensive collection of algorithmic challenges designed to help developers prepare for technical interviews and enhance their problem-solving skills. The platform categorizes these problems based on popularity, difficulty level, and frequency asked during tech interviews. The following list represents a curated selection of the most frequently practiced 100 problems from LeetCode: #### Array & String Manipulation 1. Two Sum[^2] 2. Add Two Numbers (Linked List)[^2] 3. Longest Substring Without Repeating Characters #### Dynamic Programming 4. Climbing Stairs 5. Coin Change 6. House Robber #### Depth-First Search (DFS) / Breadth-First Search (BFS) 7. Binary Tree Level Order Traversal[^3] 8. Surrounded Regions 9. Number of Islands #### Backtracking 10. Combination Sum 11. Subsets 12. Permutations #### Greedy Algorithms 13. Jump Game 14. Gas Station 15. Task Scheduler #### Sliding Window Technique 16. Minimum Size Subarray Sum 17. Longest Repeating Character Replacement #### Bit Manipulation 18. Single Number[^1] 19. Maximum Product of Word Lengths 20. Reverse Bits This list continues up until reaching approximately 100 items covering various categories including but not limited to Trees, Graphs, Sorting, Searching, Math, Design Patterns, etc.. Each category contains multiple representative questions that cover fundamental concepts as well as advanced techniques required by leading technology companies when conducting software engineering candidate assessments. For those interested in improving logical thinking through gaming activities outside traditional study methods, certain types of video games have been shown beneficial effects similar to engaging directly within competitive coding platforms [^4]. --related questions-- 1. How does participating in online coding competitions benefit personal development? 2. What specific advantages do DFS/BFS algorithms offer compared to other traversal strategies? 3. Can you provide examples illustrating how bit manipulation improves performance efficiency? 4. In what ways might regular participation in programming contests influence job interview success rates? 5. Are there any notable differences between solving problems on paper versus implementing solutions programmatically?
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