CPMpy pysdd interface (cpmpy.solvers.pysdd)

Interface to PySDD’s API

Requires that the ‘PySDD’ python package is installed:

$ pip install PySDD

PySDD is a knowledge compilation package for Sentential Decision Diagrams (SDD) https://pysdd.readthedocs.io/en/latest/

This solver can ONLY be used for solution checking and enumeration over Boolean variables! That is, only logical constraints (and,or,implies,==,!=) and Boolean global constraints.

Documentation of the solver’s own Python API: https://pysdd.readthedocs.io/en/latest/classes/SddManager.html

List of classes

CPM_pysdd

Interface to pysdd's API

Module details

class cpmpy.solvers.pysdd.CPM_pysdd(cpm_model=None, subsolver=None)[source]

Interface to pysdd’s API

Requires that the ‘PySDD’ python package is installed: $ pip install pysdd

See detailed installation instructions at: https://pysdd.readthedocs.io/en/latest/usage/installation.html

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

  • pysdd_vtree: a pysdd.sdd.Vtree

  • pysdd_manager: a pysdd.sdd.SddManager

  • pysdd_root: a pysdd.sdd.SddNode (changes whenever a formula is added)

The DirectConstraint, when used, calls a function on the pysdd_manager object and replaces the root node with a conjunction of the previous root node and the result of this function call.

dot()[source]

Returns a graphviz Dot object

Display (in a notebook) with: import graphviz graphviz.Source(m.dot())

get_core()

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

  • 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, vals)

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=None, assumptions=None)[source]

See if an arbitrary model exists

This is a knowledge compiler:

  • building it is the (computationally) hard part

  • checking for a solution is trivial after that

solveAll(display=None, time_limit=None, solution_limit=None, call_from_model=False, **kwargs)[source]

Compute all solutions and optionally display the solutions.

WARNING: setting ‘display’ will SIGNIFICANTLY slow down solution counting…

Arguments:
  • display: either a list of CPMpy expressions, OR a callback function, called with the variables after value-mapping

    default/None: nothing displayed

  • time_limit, solution_limit, kwargs: not used

  • call_from_model: whether the method is called from a CPMpy Model instance or not

Returns: number of solutions found

solver_var(cpm_var)[source]

Creates solver variable for cpmpy variable

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:

[bool]: Solver support by current system setup.

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.

For PySDD, it can be beneficial to add a big model (collection of constraints) at once…

Parameters:

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

Returns:

list of Expression