For a "full" solver that works on any $N$, the most robust approach is to use a Reduction Method (reducing the $N \times N \times N$ cube to a $3 \times 3 \times 3$ state) combined with the Kociemba algorithm for the final solve.
Here's a high-level overview of the Kociemba algorithm: nxnxn rubik 39scube algorithm github python full
def apply_move(self, move): # Apply a move to the cube if move == 'U': # Rotate top face clockwise self.cube[:, :, 0, :] = np.rot90(self.cube[:, :, 0, :], -1) elif move == 'D': # Rotate bottom face clockwise self.cube[:, :, -1, :] = np.rot90(self.cube[:, :, -1, :], -1) # ... implement other moves ...state and then applying an optimal solver like Kociemba's. Below is an overview of the technical landscape for implementing this in Python using existing GitHub frameworks. Core Algorithm: Reduction Method For a "full" solver that works on any
Implemented as:
Now go ahead—clone, solve, and push the boundaries of NxNxN cubing with Python! state and then applying an optimal solver like Kociemba's
We solve centers layer by layer using commutators. For example, a 3-cycle of center pieces: