corneto.backend package#

Module contents#

class corneto.backend.Backend(default_solver=None, sparse_class=<class 'scipy.sparse._csr.csr_array'>)#

Bases: ABC

Parameters:
is_available()#
Return type:

bool

version()#
Return type:

str

static get_symbols(expressions)#
Return type:

Set[CSymbol]

Parameters:

expressions (Iterable[CExpression])

abstract available_solvers()#
Return type:

List[str]

abstract Variable(name=None, shape=None, lb=None, ub=None, vartype=VarType.CONTINUOUS)#

Create a variable for optimization.

This method defines an optimization variable with optional bounds, type, and additional graph-related properties.

Parameters:

  • name (Optional[str]) – The name of the variable. Defaults to None.

  • shape (Optional[Tuple[int, ...]]) – The shape of the variable as a tuple. Defaults to None.

  • lb (Optional[Union[float, np.ndarray]]) – The lower bound of the variable. Can be a scalar or an array. Defaults to None.

  • ub (Optional[Union[float, np.ndarray]]) – The upper bound of the variable. Can be a scalar or an array. Defaults to None.

  • vartype (VarType) – The type of the variable (e.g., continuous, integer). Defaults to VarType.CONTINUOUS.

Returns:

The created variable symbol, to be used in further expressions or constraints.

Return type:

CSymbol

abstract Parameter(name=None, shape=None, value=None)#
Return type:

CSymbol

Parameters:
Problem(constraints=None, objectives=None, expressions=None, weights=None, direction=Direction.MIN)#
Return type:

ProblemDef

Parameters:

  • constraints (CExpression | List[CExpression] | None)

  • objectives (CExpression | List[CExpression] | None)

  • expressions (Dict[str, CExpression] | None)

  • weights (float | List[float] | None)

  • direction (Direction)

abstract build(p)#
Return type:

Any

Parameters:

p (ProblemDef)

solve(p, solver=None, max_seconds=None, warm_start=False, verbosity=0, **options)#
Parameters:

  • p (ProblemDef)

  • solver (str | Solver | None)

  • max_seconds (int | None)

  • warm_start (bool)

  • verbosity (int)

Flow(g, lb=0, ub=10, n_flows=1, values=False, shared_bounds=False, varname='_flow', create_nonzero_indicators=False, alias_flow='flow', alias_flow_ipos='positive_flow', alias_flow_ineg='negative_flow', alias_nonzero_flow='with_flow', indicator_tolerance=0.0001)#
Return type:

ProblemDef

Parameters:
AcyclicFlow(g, lb=0, ub=10, values=False, max_parents=None, vertex_lb_dist=None, varname='_flow', alias_flow='flow', alias_flow_ipos='positive_flow', alias_flow_ineg='negative_flow', alias_nonzero_flow='with_flow', indicator_tolerance=0.0001)#
Return type:

ProblemDef

Parameters:
Indicator(V, indexes=None, suffix='_i', name=None)#
Return type:

ProblemDef

Parameters:
NonZeroIndicator(V, indexes=None, suffix_pos='_ipos', suffix_neg='_ineg', tolerance=0.001)#
Return type:

ProblemDef

Parameters:
Indicators(V, tolerance=0.001, positive=True, negative=True, suffix_pos='_ipos', suffix_neg='_ineg')#
Return type:

ProblemDef

Parameters:

V (CSymbol)

Xor(x, y, varname='_xor')#
Parameters:

  • x (CExpression)

  • y (CExpression)

linear_or(x, axis=None, varname='or')#
Parameters:

  • x (CExpression)

  • axis (int | None)

linear_and(x, axis=None, varname='and')#
Parameters:

  • x (CExpression)

  • axis (int | None)

vstack(arg_list)#
Parameters:

arg_list (Iterable[CSymbol])

hstack(arg_list)#
Parameters:

arg_list (Iterable[CSymbol])

class corneto.backend.VarType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)#

Bases: str, Enum

INTEGER = 'integer'#
CONTINUOUS = 'continuous'#
BINARY = 'binary'#
class corneto.backend.CvxpyBackend(default_solver=None, sparse_class=<class 'scipy.sparse._csr.csr_array'>)#

Bases: Backend

Parameters:
available_solvers()#
Return type:

List[str]

Variable(name=None, shape=None, lb=None, ub=None, vartype=VarType.CONTINUOUS)#

Create a variable for optimization.

This method defines an optimization variable with optional bounds, type, and additional graph-related properties.

Parameters:

  • name (Optional[str]) – The name of the variable. Defaults to None.

  • shape (Optional[Tuple[int, ...]]) – The shape of the variable as a tuple. Defaults to None.

  • lb (Optional[Union[float, np.ndarray]]) – The lower bound of the variable. Can be a scalar or an array. Defaults to None.

  • ub (Optional[Union[float, np.ndarray]]) – The upper bound of the variable. Can be a scalar or an array. Defaults to None.

  • vartype (VarType) – The type of the variable (e.g., continuous, integer). Defaults to VarType.CONTINUOUS.

Returns:

The created variable symbol, to be used in further expressions or constraints.

Return type:

CSymbol

Parameter(name=None, shape=None, value=None)#
Return type:

CSymbol

Parameters:
build(p)#
Return type:

Any

Parameters:

p (ProblemDef)

class corneto.backend.PicosBackend(default_solver=None)#

Bases: Backend

Parameters:

default_solver (str | None)

available_solvers()#
Return type:

List[str]

build(p)#
Return type:

Any

Parameters:

p (ProblemDef)

Variable(name=None, shape=None, lb=None, ub=None, vartype=VarType.CONTINUOUS, variable=True)#

Create a variable for optimization.

This method defines an optimization variable with optional bounds, type, and additional graph-related properties.

Parameters:

  • name (Optional[str]) – The name of the variable. Defaults to None.

  • shape (Optional[Tuple[int, ...]]) – The shape of the variable as a tuple. Defaults to None.

  • lb (Optional[Union[float, np.ndarray]]) – The lower bound of the variable. Can be a scalar or an array. Defaults to None.

  • ub (Optional[Union[float, np.ndarray]]) – The upper bound of the variable. Can be a scalar or an array. Defaults to None.

  • vartype (VarType) – The type of the variable (e.g., continuous, integer). Defaults to VarType.CONTINUOUS.

  • variable (bool)

Returns:

The created variable symbol, to be used in further expressions or constraints.

Return type:

CSymbol

Parameter(name=None, shape=None, value=None)#
Return type:

CSymbol

Parameters:
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