Sudoku Solver Aniket Awati

Aditya Shevade

Sudoku is a very popular board game these days. It's being assessed
as an NP-complete problem which has made a magnet out of it attracting
programming talent. Many hugely efficient Sudoku Solvers have been
constructed till date. Still there is always a room for optimization,
variations and implementations.

Logical Solver

I am not going to go into the rules of Sudoku in this document. The
assignment already lists those rules. It is assumed here that the
reader is already familiar with the rules. Every Sudoku solver has to
check validity of a number in a blank cell numerous times. The process
involves checking if the number is already present in every row, column
and the block where that cell resides. If the value is valid, it may be
put there but we cannot be sure unless we make sure none of the other
values can contend for the same cell. This means for every validation,
solver has to check in 9*3=27 cells for each number. Or a whopping 243
checks every cell which is a lot. So if this number can be reduced,
overall speed of the solvers will increase proportionately.

As it turns out, we can reduce the number of checks at the expense of 
some memory. We index the board using 3 2D arrays. The indexes are 
updated every time an assignment is made. There are separate indexes
(or what we refer to as tags) for rows, columns and blocks on the board.

Consider the cell (4, 5) that is row 4 and column 5. Say that number is 8.
Now, the tagRow contains the state of the rows. This is the 4th row and
the number 8 is present there. So we make tagRow[4][8] = True. Similarly
tagCol[5][8] would be true and this cells falls in the 4th block so
tagBlk[4][8] would also be true.

Now suppose we want to check if the number 8 can be put at location (4, 1).
Originally we would have had to check entire 4th row then the 1st column
and finally 3rd block for the number 8. Now however, in the best case
scenario where you check row first, all the code does is checks tagRow[4][8]
(since it wants to know if the number 8 is already in 4th row). Since the
value of the tag is true, the program does not have to check further.

Now, the algorithm is very simple. All the tags are filled first with the
values of the cells already filled in the problem. Then it goes on checking
the numbers from 1 to 9 for all rows and columns. If it finds the value is
valid, it sets a flag. Once it has checked for all the 9 values and has only
found one valid value, the value is valid and we can assign it (also called 
a singles value or a singleton). If, however, multiple values can be true in
any cell (after checking with the row, column and block tags), the algorithm
skips it.

It goes on recursively checking the values, if it finds the value to be valid,
it sets the cell and assigns the tags and goes into next iteration. Now, if
there is any iteration where no value is valid that means we have no more
singletons present in the puzzle.

At this point the logical solver can no longer solve the puzzle further. This
is where the code breaks out of the loop and goes to the backtrack solver.

Backtrack Solver

Most puzzles with a single solution can be solved with the logical solver.
Traditionally that means almost all the puzzles can be solved with this
puzzle. However, nowadays to increase the complexity, the puzzles are
designed in a way where they can have multiple solutions. This makes
solving them more challenging.

In the backtrack solver, we again make use of the same tags which were
used for logical solving. The algorithm assumes the first valid value to
be true. It assigns the tags and tries to solve the puzzle logically. If
it cannot solve the puzzle that means our initial assumption was wrong.
All the tags are reset and the next valid value for previous cell is used.
If that also fails to solve the puzzle, we go one more step back and so on.

The advantage here is again the tags. We are only checking 3 values in
backtracking instead of 27 for each cell. This makes slight improvement in
the code. The backtrack goes advancing column by column and then the next
row. If it finally reaches the last row and column (final cell) and finds a
valid value that means the puzzle is solved. If there is no valid value at
the last position then the algorithm again backtracks.


As already mentioned above, the singleton logical solver is good enough
to take care of almost all the easy and medium level puzzles and even
some hard level puzzles. For others it can fill anywhere from 70% to 90%
of the board at which point the remaining cells are filled by the
backtrack algorithm.

Backtrack algorithm fills the first value it finds to be valid. As we
already mentioned, there were 2 puzzles in the set which have multiple
solutions. In that case, this solver finds the first value it can come
up with. If, instead of checking for valid numbers from 1 to 9, it checks
them from 9 to 1 then it will give a different result. It can also be a
constrained randomization algorithm which can do the same thing but at the
end as long as the answer is correct, as per traditional Sudoku rules, the
algorithm is working perfect.

Future Enhancements

As far as the algorithm is concerned, one point is already mentioned
in the observations section – it cannot handle puzzles with multiple
solutions. It will provide one correct solution and stop. One
enhancement would be to store this result and then check for remaining
numbers (in backtrack) and see if any other solution exists. If it
does, also store that and keep on doing this till no other solution
can be found.

This would take significant amount of time and would considerably slow
down the process so the algorithm needs to be improved to check that
case. One thing that I can think of at this point is, after the logical
solve, try to find the doublets (cells where 2 values are valid) and
while backtracking, assume one value on this cell and then solve the
puzzle. This would reduce the sample space from 9 values to 2 values.
Once this step is done we can find the triplets and so on. This can be
considered to be a random constrained backtrack implementation.

This improvement would not speed up the solving time for most puzzles
but the puzzles with multiple solutions and exotic grids (say 16x16)
would benefit a lot from it.


Here is an overly simplified version of the algorithm.

    Reset all the tags
    Read the puzzle
    Logical Solve Loop
        Check validity in row – set flag if valid
        Check validity in column – set flag if valid
        Check validity in block – set flag if valid
        Set flag if all three flags are set
        Check other numbers to make sure it's a singleton
            If it is not a singleton then skip otherwise assign the value to cell
        Make sure we actually made an assignment if not break out and backtrack
    Backtrack Solve Loop
        Check for values serially column by column and then row
        If plausible number is found, assign it and set proper tags
        Call backtrack again
    If no error is found and this is the last cell – return true and exit
    If error is found (no further values and not the last cell) then go to last cell
    Reset the proper tags
    Continue in the backtrack with next value

The puzzle should be solved at this time if it is not then the
program exits otherwise it copies the result back to the original
array which was passed to the puzzle.


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