Free solving software for linux

Solving Constraint Integer Programs is a framework for constraint integer programming. For solving Integer Programs and Constraint Programs, a very similar technique is used: the problem is successively divided into smaller subproblems (branching) that are solved recursively.
On the other hand, Integer Programming and Constraint Programming have different strengths: Integer Programming uses LP relaxations and cutting planes to provide strong dual bounds, while Constraint Programming can handle arbitrary (non-linear) constraints and uses propagation to tighten the variables domains.
SCIP is a framework for Constraint Integer Programming oriented towards the needs of Mathematical Programming experts who want to have total control of the solution process and access detailed information down to the guts of the solver. SCIP can also be used as pure MIP solver or as framework for branch-cut-and-price.
Main features:
- It is a framework for branching, cutting, pricing, and propagation.
- It is highly flexible through many possible user plugins:
- constraint handlers to implement arbitrary constraints,
- variable pricers to dynamically create problem variables,
- domain propagators to apply constraint independent propagations on the variables domains,
- cut separators to apply cutting planes on the LP relaxation,
- relaxators to provide relaxations and dual bounds in addition to the LP relaxation,
- primal heuristics to search for feasible solutions with specific support for probing and diving,
- node selectors to guide the search,
- branching rules to split the problem into subproblems,
- presolvers to simplify the solved problem,
- file readers to parse different input file formats,
- event handlers to be informed on specific events, e.g., when a node was solved, a specific variable changed its bounds, or a new primal solution was found,
- display handlers to create additional columns in the solvers output.
- dialog handlers to extend the included command shell.
- Every existing unit is implemented as a plugin, leading to an interface flexible enough to meet the needs of most additional user extensions.
- A dynamic cut pool management is included.
- The user may mix preprocessed and active problem variables in expressions: they are automatically transformed to corresponding active problem variables.
- Arbitrarily many children per node can be created, and the different children can be arbitrarily defined.
- It has an open LP solver support (currently supporting ILOG CPLEX, Dash XPress-MP, SoPlex, and CLP.
- The LP relaxation need not to be solved at every single node (it can even be turned off completely, mimicing a pure constraint solver).
- Additional relaxations (e.g., semidefinite relaxations or Lagrangian relaxations) can be included, working in parallel or interleaved.
- Conflict analysis can be applied to learn from infeasible subproblems.
- Dynamic memory management reduces the number of operation system calls with automatic memory leakage detection in debug mode.
Enhancements:
- The new heuristics "RENS", "mutation", "veclendiving", and "intshifting", were included.
- The existing ones were improved.
- Presolving and c-MIR cut generation were improved.
- A few bugs were fixed.

Spin is a transparent threading solution for non-freezing Swing applications.
Every non trivial GUI sooner or later encounters the problem of "freeze".
This annoying behaviour is experienced by users every time the application performs extensive calculations or blocks for network or disk I/O.
Spin offers a new approach for solving this problem.
It offers transparent thread handling with minimal impact on your application code.
Enhancements:
- Now built with Maven.

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.

Sudoku Savant is a simple GUI-driven application to solve and generate Sudoku puzzles through logical means.

Sudoku Savant generates and solves standard Sudoku puzzles with anything from 3x3 to 5x6 or 6x5 grids, using the following strategies:

- Singletons
- Locked candidates
- Number subsets
- X-Wings, Swordfish and Jellyfish
- Both Simple and Multi-Colouring.

These strategies should be enough to provide a step by step solution for any Sudoku puzzle that you are likely to find in a newspaper or magazine, although Savant can also resort to "trial and error" if presented with something really pathologically nasty. (This will at least confirm that the puzzles solution is unique.)

You may also parameterise or turn off the more advanced strategies, to see whether they were really needed.

Finally, Sudoku Savant lets you solve the puzzle by hand, providing hints, cell colouring, up to 4 pencil marks per cell, and the ability to high-light any incorrect moves. A partially completed puzzle can be saved in a simple text format so that you can continue solving it later.