CPMpy Pindakaas interface (cpmpy.solvers.pindakaas)

Interface to the Pindakaas solver’s Python API.

Pindakaas is an open-source Rust library for encoding propositional and pseudo-Boolean constraints to SAT, with support for incremental solving and assumptions.

Always use cp.SolverLookup.get("pindakaas") to instantiate the solver object.

Installation

Requires that the ‘pindakaas’ optional dependency is installed:

$ pip install pindakaas

Detailed installation instructions available at:

The rest of this documentation is for advanced users.

List of classes

CPM_pindakaas

Interface to Pindakaas' Python API.

Module details

class cpmpy.solvers.pindakaas.CPM_pindakaas(cpm_model=None, subsolver=None)[source]

Interface to Pindakaas’ Python API.

Creates the following attributes (see parent constructor for more):

  • pdk_solver: The Pindakaas solver back-end which encodes and solves models through the SAT sub-solver

  • ivarmap: a mapping from integer variables to their encoding for int2bool

  • encoding: the encoding used for int2bool, choose from (“auto”, “direct”, “order”, or “binary”). Set to “auto” but can be changed in the solver object.

  • unsatisfiable: if a constraint is found to be unsatisfiable during the encoding phase, this flag is set to True to prevent further encoding efforts

  • core: if the problem is unsatisfiable, the unsatisfiable core, else None

Documentation of the solver’s own Python API:

add(cpm_expr_orig)[source]

Eagerly add a constraint to the underlying solver.

Any CPMpy expression given is immediately transformed (through transform()) and then posted to the solver in this function.

This can raise ‘NotImplementedError’ for any constraint not supported after transformation

The variables used in expressions given to add are stored as ‘user variables’. Those are the only ones the user knows and cares about (and will be populated with a value after solve). All other variables are auxiliary variables created by transformations.

Parameters:

cpm_expr (Expression or list of Expression) – CPMpy expression, or list thereof

Returns:

self

get_core()[source]

For use with s.solve(assumptions=[...]). Only meaningful if the solver returned UNSAT.

Typically implemented in SAT-based solvers

Returns a small subset of assumption literals that are unsat together. (a literal is either a _BoolVarImpl or a NegBoolView in case of its negation, e.g. x or ~x) Setting these literals to True makes the model UNSAT, setting any to False makes it SAT

has_objective()

Returns whether the solver has an objective function or not.

maximize(expr)

Post the given expression to the solver as objective to maximize

maximize() can be called multiple times, only the last one is stored

minimize(expr)

Post the given expression to the solver as objective to minimize

minimize() can be called multiple times, only the last one is stored

property native_model

Returns the solver’s underlying native model (for direct solver access).

objective(expr, minimize)

Post the given expression to the solver as objective to minimize/maximize

Parameters:

  • expr – Expression, the CPMpy expression that represents the objective function

  • minimize – Bool, whether it is a minimization problem (True) or maximization problem (False)

objective() can be called multiple times, only the last one is stored

objective_value()

Returns the value of the objective function of the latest solver run on this model

Returns:

an integer or ‘None’ if it is not run, or a satisfaction problem

solution_hint(cpm_vars: List[_NumVarImpl], vals: List[int | bool])

For warmstarting the solver with a variable assignment

Typically implemented in SAT-based solvers

Parameters:

  • cpm_vars – list of CPMpy variables

  • vals – list of (corresponding) values for the variables

solve(time_limit: float | None = None, assumptions: List[_BoolVarImpl] | None = None)[source]

Solve the encoded CPMpy model given optional time limit and assumptions, returning whether a solution was found.

Parameters:

  • time_limit – optional, time limit in seconds

  • assumptions – optional, a list of assumptions (Boolean variables which should hold for this solve call)

solveAll(display: Expression | List[Expression] | Callable | None = None, time_limit: float | None = None, solution_limit: int | None = None, call_from_model=False, **kwargs)

Compute all solutions and optionally display the solutions.

This is the generic implementation, solvers can overwrite this with a more efficient native implementation

Parameters:

  • display (-) – either a list of CPMpy expressions, OR a callback function, called with the variables after value-mapping default/None: nothing displayed

  • time_limit (-) – stop after this many seconds (default: None)

  • solution_limit (-) – stop after this many solutions (default: None)

  • call_from_model (-) – whether the method is called from a CPMpy Model instance or not

  • argument (- any other keyword)

Returns:

number of solutions found

solver_var(cpm_var)[source]

Creates solver variable for cpmpy variable or returns from cache if previously created

solver_vars(cpm_vars)

Like solver_var() but for arbitrary shaped lists/tensors

status()
static supported()[source]

Check for support in current system setup. Return True if the system has package installed or supports solver, else returns False.

Returns:

Solver support by current system setup.

Return type:

[bool]

supported_global_constraints: frozenset[str] = frozenset({})
supported_reified_global_constraints: frozenset[str] = frozenset({})
transform(cpm_expr)[source]

Transform arbitrary CPMpy expressions to constraints the solver supports

Implemented through chaining multiple solver-independent transformation functions from the cpmpy/transformations/ directory.

See the ‘Adding a new solver’ docs on readthedocs for more information.

Parameters:

cpm_expr (Expression or list of Expression) – CPMpy expression, or list thereof

Returns:

list of Expression

static version() → str | None[source]

Return the installed version of the solver’s Python API.