This repository hosts a Python implementation of a Sudoku solver leveraging NumPy. The solver endeavors to address the vacant cells of a Sudoku puzzle utilizing fundamental constraint propagation methodologies .
The Sudoku solver provided here attempts to solve Sudoku puzzles by iteratively filling in empty cells based on valid constraints. It employs a straightforward algorithm to identify potential values for each cell by considering the current state of the rows, columns, and 3x3 subgrids.
- Python 3.x
- NumPy
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Clone the repository:
git clone https://github.com/Mugta1/sudokusolver.git cd sudokusolver -
Verify NumPy installation:
pip install numpy
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Execute the solver:
python sudokusolver.py
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Input Validation: Initial scrutiny ensures the integrity of the Sudoku grid, confirming each row comprises precisely 9 elements.
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Potential Values Calculation: For each empty cell (designated by 0), the program computes feasible values by assessing the current row, column, and 3x3 subgrid states.
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Constraint Propagation: Intersection of potential values from the row, column, and 3x3 subgrid determines a unique value, if available, which is then assigned to the cell.
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Iterative Process: Steps 2 and 3 iteratively execute until all cells are filled or no further deterministic assignments are feasible.
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Output: The solved Sudoku grid is printed upon completion. It also prints the time used and the number of times the loop ran to solve the sudoku.
- Optimization: Harness NumPy to condense codebase and bolster performance.
- Graphical User Interface: Integrate a GUI for enhanced user interaction.
Contributions are encouraged! Should you identify areas for enhancement or detect bugs, please initiate an issue or submit a pull request.
This project is licensed under the MIT License. Refer to the LICENSE file for further details.