# PLPGEN

PLPGEN is a utility for generating propositional problems in the format proposed by DIMACS and used by SATLib.

## Installation

`$ npm install plpgen`

## Usage

`var plpgen = ;`

## Create Clause Sets

Module `plpgen`

exports the following functions, all of which return a set of clauses as a
string that conforms to the DIMACS conventions:

### pigeonHoles (holes)

Creates an instance of the *Pigeon Hole Problem* (i.e., the impossibility to fit *n+1* pigeons into *n* holes
when each hole can hold at most one pigeon). The number of holes is specified through parameter `holes`

,
which must be an integer greater than or equal to 1.
The resulting set of clauses is guaranteed to be unsatisfiable.

### nQueens (n)

Creates an instance of the *n-Queens Problem*, a theoretical chess problem that
poses the question whether *n* queens can be
placed on an *n* by *n* chess board so that no queen threatens any other queen. The resulting set of clauses
is satisfiable for all *n* except *n = 2* and *n = 3*. The number of propositional variables is the square
of *n*, and the truth value of variable *i* represents the presence (true) or absence (false) of a queen in
row *1 + (i - 1) div n* and column *1 + (i - 1) mod n*.

*See also*: **nQueensFromAssignment**

### rndClauseSet (size, maxVar, minLits, maxLits)

Creates a set of random clauses. The number of clauses is specified through `size`

, the number of
propositional variables through `maxVar`

. The optional parameters `minLits`

and `maxLits`

specify the minimal and maximal number of literals per clause, respectively. If omitted or invalid
`minLits`

will be 3. `maxLits`

will be equal to `minLits`

if omitted or invalid or
less than `minLits`

.

Note that the set of random clauses created with this function may contain duplicates, but guarantees to have exactly the given size.

*See also*: **rndClauseSetNoDuplicates**

### rndClauseSetNoDuplicates (size, maxVar, minLits, maxLits)

Creates a set of random clauses like **rndClauseSet**, but guarantees that no clause occurs
more than once. However, the final set of clauses may contain less clauses than desired, in particular
if the number of distinct clauses is less than the given `size`

.

Note that neither function for creating random sets of clauses guarantees that the set of clauses
is free of redundancies in terms of one clause subsuming another. (This function does guarantee that
only if `minLits`

is equal to `maxLits`

.) Furthermore, both functions may produce satisfiable
or unsatisfiable sets of clauses.

### sudokuToPL (Sudoku)

Transforms a classic Sudoku problem
into a set of propositional clauses with the property that the
set of clauses is satisfiable *if and only if* the given Sudoku problem has a solution
that complies with the Sudoku rules.
From an assignment satisfying the set of clauses the solved or completed Sudoku can be created with
the help of **sudokuFromAssignment**.

The Sudoku problem is specified through parameter `sudoku`

. Its value can be either a string
or an array. If it is an array, the elements of the array are the rows. These can again be either
strings or arrays of characters. If `sudoku`

is a string, the rows may be separated via newline characters,
but do not have to be. Without newline characters the first nine characters represent the first row and
so on. The characters (or numbers) `'1'`

through `'9'`

obviously represent
cells occupied with the respective number. All other characters represent an empty cell and are
replaced with `'*'`

.

A Sudoku problem is transformed into a set of 10287 + *n* clauses, where *n*
is the number of non-empty cells, each clause drawing its literals from a pool of 729 propositional variables.
Not surprisingly (given the backtracking nature of Sudoku problems)
Tableaux-based methods are the
weapon of choice for tackling such a set of clauses.

## Convenience Functions

Module `plpgen`

also exports the following convenience functions to interpret or
visualize results (assignments) obtained for certain clause sets.

### nQueensFromAssignment (assignment)

Transforms the given assignment (i.e., associations of propositional variables with the value `true`

or `false`

) into a chess board represented by an array of strings. Each string is a row and
consists of characters '+' (empty square) and 'X' (square occupied by a queen).

*Example*:

Assuming that module `propsat`

has been installed, the following code can be used to solve the n-Queens
problem (in this case for *n=4*):

`var assignment = assignment;if assignment console;else console;`

The output of this particular run would be:

`-SOLUTION-++X+X++++++X+X++`

### sudokuFromAssignment (assignment)

Transforms the given assignment (i.e., associations of propositional variables with the value `true`

or `false`

) into a completed Sudoku. The return value is an array of strings.
Each string represents a row and hence has length 9. (The array naturally also has length 9.)
The assignment must be an object with properties named 1 through 729 (i.e., the propositional variables
used when transforming a Sudoku into a set of clauses in DIMACS format; see **sudokuToPL**).
The values should be either `true`

or `false`

, although anything that evaluates to the desired
Boolean value is accepted.

Note that this method does not check compliance with Sudoku
rules other than that each cell is assigned
one number and one number only. However, if the given assignment is legit in that it satisfies the
clauses produced by **sudokuToPL** the completed Sudoku will comply with all Sudoku rules.

Typically you would present a system capable of finding an assignment for a given set of propositional
clauses with the clauses produced by **sudokuToPL**, and then use **sudokuFromAssignment** to create
the completed or solved Sudoku.

*Example*:

Assuming that module `propsat`

has been installed and that variable `sudoku`

holds a Sudoku
problem, the following code can be used to solve Sudoku problems:

`var assignment = assignment;if assignment console; console;else console;`