CPMpy SCIP interface (cpmpy.solvers.scip)

Interface to the SCIP’s python “PySCIPOpt” package

Installation

Requires that the ‘PySCIPOpt’ Python package is installed:

$ pip install pyscipopt

(more information on https://github.com/scipopt/PySCIPOpt)

List of classes

CPM_scip

Interface to SCIP's API

Module details

Supports FloatSum objectives.

class cpmpy.solvers.scip.CPM_scip(cpm_model=None, subsolver=None)

Interface to SCIP’s API

Requires that the SCIPOptSuite and ‘pyscipopt’ python package is installed See detailed installation instructions at the top of this file.

Creates the following attributes (see parent constructor for more): - scip_model: object, SCIP’s Model object

Detailed documentation on the Model(): https://scipopt.github.io/PySCIPOpt/docs/html/classpyscipopt_1_1scip_1_1Model.html

The DirectConstraint, when used, calls a function on the scip_model object.

add(cpm_expr: Expression | bool | bool | Sequence[Expression | bool | bool | Sequence[NestedBoolExprLike] | ndarray] | ndarray) → CPM_scip

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 (NestedBoolExprLike) – CPMpy expression, or list thereof

Returns:

self

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: Expression | FloatSum) → None

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: Expression | FloatSum) → None

Post the given expression to the solver as objective to minimize

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

classmethod mus_native(soft, hard=[])

For using the solver’s internal MUS extractor

Parameters:

• soft – List of soft constraints over which a MUS needs to be found

• hard – List of hard constraints that always need to be satisfied

Returns a MUS.

property native_model

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

objective(expr: Expression | FloatSum, minimize: bool = True) → None

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

Parameters:

• expr – a CPMpy Expression

• 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() → int | None

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

objective_value_: int | None

print_display(display: Expression | Sequence[Expression] | ndarray | Callable[[], None] | None) → None

Helper function for printing the display argument used in solveAll().

Parameters:

display – either a CPMpy Expression, OR a list of expressions, OR a callback function (no-arg) to call.

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=None, solution_callback=None, **kwargs)

Call the SCIP solver

Arguments: - time_limit: maximum solve time in seconds (float, optional). Persists across solve() calls until overridden. - kwargs: any keyword argument, sets parameters of solver object.

Arguments correspond to solver parameters (passed via setParams). Due to naming /, you can pass these options as a dict with e.g. solve(**{“limits/nodes”: 1000, “limits/solutions”: 1, “parallel/maxnthreads”: 4, “display/verblevel”: 4, “separating/maxrounds”: 0}). For a full list see https://www.scipopt.org/doc/html/PARAMETERS.php. Note, passing limits/time overrides time_limit.

solveAll(display=None, time_limit=None, solution_limit=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)

Creates solver variable for cpmpy variable or returns from cache if previously created or returns a constant if the variable is a constant

solver_vars(cpm_vars: Iterable[Expression | int | integer | bool]) → list[Any]

Like solver_var() but for arbitrary shaped lists/tensors

status()

static supported()

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({'abs', 'mul', 'xor'})

supported_reified_global_constraints: frozenset[str] = frozenset({})

transform(cpm_expr: Expression | bool | bool | Sequence[Expression | bool | bool | Sequence[NestedBoolExprLike] | ndarray] | ndarray) → list[Expression]

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 (NestedBoolExprLike) – CPMpy expression, or list thereof

Returns:

transformed constraints

Return type:

list[Expression]

classmethod version() → str | None

Returns the installed version of the solver’s Python API (pyscipopt).