Optimizing the Sudoku Solver

Now that we have implemented the basic Sudoku solver algorithm using backtracking, let's explore some possible optimizations to improve its performance.

One optimization strategy is to identify the most constrained cells in the puzzle, i.e., the cells with the fewest number of possible candidate values. By prioritizing the most constrained cells during the backtracking process, we can potentially reduce the overall number of iterations required.

Another optimization technique is known as the 'maximum constraint value' heuristic. Instead of choosing the next empty cell based on its position in the puzzle, we select the cell that has the maximum number of constraints on its candidate values. By prioritizing these cells, we can potentially find solutions more quickly.

These are just a couple of examples of optimization techniques that can be applied to the Sudoku solver algorithm. The specific optimization strategy to be used depends on the characteristics of the puzzle and the desired performance goals.

Your Task: Implement one or more optimizations in the provided C++ code snippet to improve the performance of the Sudoku solver algorithm.

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#include <iostream>

using namespace std;

​

int main() {

  // Replace with your optimized Sudoku solver algorithm

}