Free solver software for linux

Automagical Sudoku Solver project is an attempt to solve Sudoku puzzles using a computer. It can solve rather tough puzzles, but not all of them.

Installation:

The source code should compile flawlessly using the make utility. Then, simply run "ass < foo", where foo is a sudoku puzzle in text format like the ones in the puzzles/ directory.

Arguments

-s show progress using an ncurses interface
-v show version number
-d [2-4] show debug info (4 is highest level)

Rush Hour Puzzle Solver project is a Rush Hour puzzle solver that illustrates the solution with PostScript.

Rush Hour Puzzle Solver is a small C++ program that reads a Rush Hour board from a text file and produces a nice PostScript file that shows the shortest solution.

Gerris project is an Open Source Free Software library for the solution of the partial differential equations describing fluid flow.
Gerris is supported by NIWA (National Institute of Water and Atmospheric research) and by the Marsden Fund of the Royal Society of New Zealand.
The code is written entirely in C and uses both the GLib Library and the GTS Library for geometrical functions and object-oriented programming.
Main features:
- The same code base is compiled with 2D and 3D support.
- Quadtree-based (Octree in 3D) spatial discretisation with automatic and dynamic local refinement.
- Multigrid Poisson solver.
- Second-order Godunov type advection scheme.
- Solves the time-dependent incompressible variable-density Euler, Stokes or Navier-Stokes equations or the 2D shallow-water and 3D hydrostatic oceanic equations.
- Support for complex solid boundaries (automatic locally-refined mesh generation).
- Semi-implicit multigrid diffusion solver with support for complex boundaries and associated boundary conditions in 2D and 3D.
- Semi-implicit multigrid barotropic solver for the oceanic equations.
- Adaptive mesh refinement: the resolution is adapted dynamically to the features of the flow.
- Flexible and powerful specifications of parameters.
- Flexible object-oriented custom specification of initial and boundary conditions, source terms, outputs etc...
- Portable parallel support using the MPI library.
- Volume of Fluid advection scheme for interfacial flows.
Enhancements:
- Bugfixes and significant speedups in the multilevel Poisson solver.
- Support for variable mesh resolution along solid boundaries.
- Improvements have been made to the robustness of very complex solid boundaries.
- Adaptive refinement of VOF-advected tracers has been added, as well as a preliminary implementation of CSF surface tension using Renardy El Ab.
- "proper discretisation".
- Solid boundaries can be refined according to the local curvature.
- Implicit Coriolis terms work with the Navier-Stokes solver.
- There is support for "thin" 3D domains.

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.