Free generate sudoku puzzles software for linux

Kats Sudoku is a simple helper program for solving Sudoku puzzles. Kats Sudoku starts with an empty board and prints all possible numbers in each field.

After setting a certain field to a number, it deletes all occurrences of this number in the corresponding row/column/block so that scanning of rows/columns/blocks for possible entries is unnecessary.

Otherwise, there is no logic, which means that finding the solution is still the players task.

Java Sudoku is a cross platform version of the popular Sudoku logic game. Java Sudoku features an advanced user interface that is both easy to use and appealing to the eye.
It allows you to generate completely random Sudoku puzzles, enter your own puzzles from newspapers and magazines, or load them from Sudoku XML files. Java Sudoku can also be used as a Sudoku generator and solver.
Main features:
- Random puzzles every time you play
- Helping lines mode in the option menu, so You can see easier, if there is a collision
- 2 different systems of selecting cells and entering numbers
- 3 difficulty levels and an user custom level
- 3 Different Numbers Distributions
- Load/Save Sudoku games without any kind of losses
- Design your own puzzles - Under construction

php Sudoku is a Web-based sudoku game that includes 200,000 sudoku puzzles. It can be easily used on any Web site.

The project does not require MySQL or any other database.

Generate Numly Copyright is a Firefox extension that registers documents and blogs for Numly copyright. Numly Numbers are unique identifiers of electronic media and recognized worldwide by electronic publishing companies and electronic content providers. Numly Numbers are simple and quick to generate and serve as branded identifier for individuals or companies authoring or distributing electronic content and media.

Data::Generate allows you to create various types of synthetic data by parsing "regex-like" data creation rules.

This module generates data by parsing given text statements (data creation rules). These statements are flexible and powerful regex-like way to control the production of synthetic data. Think about a program that instead of selecting data which matches a regex filter expression, produces it. For example, from the rule [a-c], the generator would produce the array a,b,c. The module works as following:

Specify data creation rules.
my $generator= Data::Generate::parse(VC(24) [0-9][2-3]);
At this step first you define one kind of output datatype (for ex. VC(24)= "output is a string with max length 24") and then with the rest of the expression define what it should look like. If parsing is successful a Data Generator object is instantiated.

Get data
my $Data= $generator->get_unique_data(10);
To really get the data, users must call the get_unique_data method by indicating the desired number of output values. The generator returns the values contained in an array reference. Please remark that output format is fixed according to the data type.

sudoku-perl is a Sudoku player that offers a board, assistance, and solving using a (text based) ANSI screen. sudoku-perl offers a simple alphanumeric board display that supports the 9x9 matrix.

It does not generate a Sudoku puzzle; for this, the user must look on the Internet, newspapers, Sudoku booklets, etc. and take over the sudoku initial setting from there.

However, it assists the user in solving the Sudoku by providing some functions, up to and including solving the Sudoku from the current board setting.

A Sudoku Solver in C is a console-based Linux program, written in C language, that solves Su Doku puzzles using deductive logic. It will only resort to trial-and-error and backtracking approaches upon exhausting its deductive moves.
Puzzles must be of the standard 9x9 variety using the (ASCII) characters 1 through 9 for the puzzle symbols. Puzzles should be submitted as 81 character strings which, when read left-to-right will fill a 9x9 Sudoku grid from left-to-right and top-to-bottom. In the puzzle specification, the characters 1 - 9 represent the puzzle givens or clues. Any other non-blank character represents an unsolved cell.
The puzzle solving algorithm is home grown. I did not borrow any of the usual techniques from the literature, e.g. Donald Knuths "Dancing Links." Instead I rolled my own from scratch as a personal challenge. As such, its performance can only be blamed on yours truly. Still, I feel it is quite fast. On a 333 MHz Pentium II Linux box it solves typical medium force puzzles in approximately 800 microseconds or about 1,200 puzzles per second, give or take. On an Athlon XP 3000 it solves about 6,600 puzzles per sec. (Solving time is dependent upon degree of difficulty, so YMMV.)
Description of Algorithm:
The puzzle algorithm initially assumes every unsolved cell can assume every possible value. It then uses the placement of the givens to refine the choices available to each cell. I call this the markup phase.
After markup completes, the algorithm then looks for singleton cells with values that, due to constraints imposed by the row, column, or 3x3 region, may only assume one possible value. Once these cells are assigned values, the algorithm returns to the markup phase to apply these changes to the remaining candidate solutions. The markup/singleton phases alternate until either no more changes occur, or the puzzle is solved. I call the markup/singleton elimination loop the Simple Solver because in a large percentage of cases it solves the puzzle.
If the simple solver portion of the algorithm doesnt produce a solution, then more advanced deductive rules are applied.
Ive implemented two additional rules as part of the deductive puzzle solver. The first is subset elimination wherein a row/column/region is scanned for X number of cells with X number of matching candidate solutions. If such subsets (or tuples) are found in the row, column, or region, then the candidates values from the subset may be eliminated from all other unsolved cells within the row, column, or region, respectively.
The next deductive rule examines each region looking for candidate values that exclusively align themselves along a single row or column, i.e. a vector. If such candidate values are found, then they may be eliminated from the cells outside of the region that are part of the aligned row or column.
Note that each of the advanced deductive rules calls all preceeding rules, in order, if that advanced rule has effected a change in puzzle markup.
Finally, if no solution is found after iteratively applying all deductive rules, then we begin trial-and-error using recursion for backtracking. A working copy is created from our puzzle, and using this copy the first cell with the smallest number of candidate solutions is chosen. One of the solutions values is assigned to that cell, and the solver algorithm is called using this working copy as its starting point. Eventually, either a solution, or an impasse is reached.
If we reach an impasse, the recursion unwinds and the next trial solution is attempted. If a solution is found (at any point) the values for the solution are added to a list. Again, so long as we are examining all possibilities, the recursion unwinds so that the next trial may be attempted. It is in this manner that we enumerate puzzles with multiple solutions.
Note that it is certainly possible to add to the list of applied deductive rules. The techniques known as "X-Wing" and "Swordfish" come to mind. On the other hand, adding these additional rules will, in all likelihood, slow the solver down by adding to the computational burden while producing very few results. Ive seen the law of diminishing returns even in some of the existing rules, e.g. in subset elimination I only look at two and three valued subsets because taking it any further than that degraded performance.
Enhancements:
- Code optimization has resulted in a 30% increase in speed.