LeetCode_37 Sudoku Solver

本文介绍了一种通过回溯法解决数独谜题的算法,详细解释了如何通过遍历空格并尝试填充数字来逐步求解数独,确保最终得到唯一解。

Original problem: 37 Sudoku Solver 这里写链接内容
Write a program to solve a Sudoku puzzle by filling the empty cells.
Empty cells are indicated by the character ‘.’.
You may assume that there will be only one unique solution.

Related problem: 36 Valid Sudoku 这里写链接内容
Analysis: This problem is a little bit different from very typical backtracking problems though its process is executed in a very trivial backtracking way: Try each character ‘1’~’9’ in where the sudoku is marked by ‘.’, if one succeed at some step, then go into next step similarly. After all blanks have been filled with digit characters and the sudoku is valid, keep that sudoku and return it as the final result.

The key point here is to control the process, and make it stop when we find a right solution, otherwise the current correct solution would be erased if we do not stop the backtracking process immediately.

So we use a boolean helper here, inside the helper function, we go through all positions, if at some step, we find all digit chars can not make the sudoku success, we return false. After scanning all the blocks in the sudoku grid, we return true which means we did not see any place that digit chars would fail the sudoku. Here is the code for this problem:

public class Solution {
    public void solveSudoku(char[][] board) {
        helper(board);
    }

    private boolean helper(char[][] board){
        for(int ii = 0; ii < 9; ii++){
            for(int jj = 0; jj < 9; jj++){
                if(board[ii][jj] == '.'){
                    for(char c = '1'; c <= '9'; c++){
                        if(isValid(board, ii, jj, c)){
                            board[ii][jj] = c;
                            if( helper(board) ) return true;
                            else board[ii][jj] = '.';
                        }
                    }
                    return false;
                }
            }
        }
        return true;
    }

    private boolean isValid(char[][] board, int i, int j, char c){
        for(int ii = 0; ii < 9; ii++){
            if(board[ii][j] == c) return false;
        }
        for(int jj = 0; jj < 9; jj++){
            if(board[i][jj] == c) return false;
        }
        for(int ii = (i/3)*3; ii < (i/3)*3+3; ii++){
            for(int jj = (j/3)*3; jj < (j/3)*3+3; jj++){
                if(board[ii][jj] == c) return false;
            }
        }
        return true;
    }
}
### LeetCode Problem 37: Sudoku Solver #### Problem Description The task involves solving a partially filled Sudoku puzzle. The input is represented as a two-dimensional integer array `board` where each element can be either a digit from '1' to '9' or '.' indicating empty cells. #### Solution Approach To solve this problem, one approach uses backtracking combined with depth-first search (DFS). This method tries placing numbers between 1 and 9 into every cell that contains '.', checking whether it leads to a valid solution by ensuring no conflicts arise within rows, columns, and subgrids[^6]. ```cpp void solveSudoku(vector<vector<char>>& board) { backtrack(board); } bool backtrack(vector<vector<char>> &board){ for(int row = 0; row < 9; ++row){ for(int col = 0; col < 9; ++col){ if(board[row][col] != '.') continue; for(char num='1';num<='9';++num){ if(isValidPlacement(board,row,col,num)){ placeNumber(num,board,row,col); if(backtrack(board)) return true; removeNumber(num,board,row,col); } } return false; } } return true; } ``` In the provided code snippet: - A function named `solveSudoku()` initiates the process. - Within `backtrack()`, nested loops iterate over all positions in the grid looking for unassigned spots denoted by '.' - For any such spot found, attempts are made to insert digits ranging from '1' through '9'. - Before insertion, validation checks (`isValidPlacement`) ensure compliance with Sudoku rules regarding uniqueness per row/column/subgrid constraints. - If inserting a number results in reaching a dead end without finding a complete solution, removal occurs before trying another possibility. This algorithm continues until filling out the entire board correctly or exhausting possibilities when returning failure status upward along recursive calls stack frames. --related questions-- 1. How does constraint propagation improve efficiency while solving puzzles like Sudoku? 2. Can genetic algorithms provide alternative methods for tackling similar combinatorial problems effectively? 3. What optimizations could enhance performance further beyond basic DFS/backtracking techniques used here?
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