Free gerris flow solver software for linux

Sudoku solver application was created for solving a Sudoku with a backtracking algorithm. Instead of using a 9 x 9 matrix, it extends the matrix to 10 x 36 (10 rows, 36 columns), storing information in the extra cells.

The last row is used for keeping track of how many cells, for the current column, are still available.

The columns 9-17 (0-based) are used for storing the numbers which are still available in rows 0-8.

The columns 18-26 are used for storing the numbers which are still available in columns 0-8.

The columns 27-35 are used for storing the numbers which are still available in each square (counting from left to right and from top to bottom).

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.

Akarmi - Flow is a template-based C++ framework. Akarmi - Flow simplifies creating programs from independent processing elements that are connected by event channels.

It is currently being rewritten to use terms similar to CORBAs event service.

Installation:

cmake .
make all
make install

[testing: optional]
cd test
make test

OpenTaxSolver (OTS) project is a free program for calculating Tax Form entries and tax-owed or refund-due, such as Federal or State personal income taxes.
An optional graphical front-end, OTS_GUI, has been added. Currently, TaxSolver has been updated for the 2005 tax-year for the following forms: US 1040 and Schedules A, B, C, & D.
As well as for California, Massachusetts, New Jersey, and Pennsylvania State Taxes for 2005 tax-year, thanks to contributors. Updates for the following additional states are expected to be posted soon: North Carolina, New York, Ohio, and Virginia. Preliminary versions for Canada and the United Kingdom were posted in previous years and may be updated with help from volunteers.
Motivations:
- To make tax preparation software available for all platforms.
- To provide insight into how our taxes are calculated in clear unambiguous equations/code.
- To avoid invasive, bloated commercial software packages.
- To avoid rewriting our own individual programs each year by combining efforts.
- To provide a simple reliable tax-package requiring only rudimentary knowledge to maintain.
Enhancements:
- Automatic phone credit was added to US1040.
- It will automatically calculate standard one-time phone credit, if not otherwise specified on US1040 line 71.
- The NJ State form F line 5 was fixed.

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.

Games::LMSolve::Base is a base class for puzzle solvers.

SYNOPSIS

package MyPuzzle::Solver;

use Games::LMSolve::Base;

@ISA = qw(Games::LMSolve::Base);

# Override these methods:

sub input_board { ... }
sub pack_state { ... }
sub unpack_state { ... }
sub display_state { ... }
sub check_if_final_state { ... }
sub enumerate_moves { ... }
sub perform_move { ... }

# Optionally:
sub render_move { ... }
sub check_if_unsolvable { ... }

package main;

my $self = MyPuzzle::Solver->new();

$self->solve_board($filename);

This class implements a generic solver for single player games. In order to use it, one must inherit from it and implement some abstract methods. Afterwards, its interface functions can be invoked to actually solve the game.