Google OR-Tools v9.14
a fast and portable software suite for combinatorial optimization
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cp_model.py File Reference

Go to the source code of this file.

Classes

class  ortools.sat.python.cp_model.IntVar
class  ortools.sat.python.cp_model.Constraint
class  ortools.sat.python.cp_model.VariableList
class  ortools.sat.python.cp_model.IntervalVar
class  ortools.sat.python.cp_model.CpModel
class  ortools.sat.python.cp_model.CpSolver
class  ortools.sat.python.cp_model.CpSolverSolutionCallback
class  ortools.sat.python.cp_model.ObjectiveSolutionPrinter
class  ortools.sat.python.cp_model.VarArrayAndObjectiveSolutionPrinter
class  ortools.sat.python.cp_model.VarArraySolutionPrinter

Namespaces

namespace  ortools
namespace  ortools.sat
namespace  ortools.sat.python
namespace  ortools.sat.python.cp_model

Functions

str ortools.sat.python.cp_model.display_bounds (Sequence[int] bounds)
str ortools.sat.python.cp_model.short_name (cp_model_pb2.CpModelProto model, int i)
str ortools.sat.python.cp_model.short_expr_name (cp_model_pb2.CpModelProto model, cp_model_pb2.LinearExpressionProto e)
bool ortools.sat.python.cp_model.object_is_a_true_literal (LiteralT literal)
bool ortools.sat.python.cp_model.object_is_a_false_literal (LiteralT literal)
Union[Iterable[LiteralT], LiteralTortools.sat.python.cp_model.expand_generator_or_tuple (Union[Tuple[LiteralT,...], Iterable[LiteralT]] args)
Union[Iterable[LinearExprT], LinearExprTortools.sat.python.cp_model.expand_generator_or_tuple (Union[Tuple[LinearExprT,...], Iterable[LinearExprT]] args)
 ortools.sat.python.cp_model.expand_generator_or_tuple (args)
pd.Index ortools.sat.python.cp_model._get_index (_IndexOrSeries obj)
pd.Series ortools.sat.python.cp_model._convert_to_integral_series_and_validate_index (Union[IntegralT, pd.Series] value_or_series, pd.Index index)
pd.Series ortools.sat.python.cp_model._convert_to_linear_expr_series_and_validate_index (Union[LinearExprT, pd.Series] value_or_series, pd.Index index)
pd.Series ortools.sat.python.cp_model._convert_to_literal_series_and_validate_index (Union[LiteralT, pd.Series] value_or_series, pd.Index index)

Variables

 ortools.sat.python.cp_model.Domain = sorted_interval_list.Domain
 ortools.sat.python.cp_model.BoundedLinearExpression = cmh.BoundedLinearExpression
 ortools.sat.python.cp_model.FlatFloatExpr = cmh.FlatFloatExpr
 ortools.sat.python.cp_model.FlatIntExpr = cmh.FlatIntExpr
 ortools.sat.python.cp_model.LinearExpr = cmh.LinearExpr
 ortools.sat.python.cp_model.NotBooleanVariable = cmh.NotBooleanVariable
 ortools.sat.python.cp_model.INT_MIN = -(2**63)
int ortools.sat.python.cp_model.INT_MAX = 2**63 - 1
 ortools.sat.python.cp_model.INT32_MIN = -(2**31)
int ortools.sat.python.cp_model.INT32_MAX = 2**31 - 1
 ortools.sat.python.cp_model.UNKNOWN = cp_model_pb2.UNKNOWN
 ortools.sat.python.cp_model.MODEL_INVALID = cp_model_pb2.MODEL_INVALID
 ortools.sat.python.cp_model.FEASIBLE = cp_model_pb2.FEASIBLE
 ortools.sat.python.cp_model.INFEASIBLE = cp_model_pb2.INFEASIBLE
 ortools.sat.python.cp_model.OPTIMAL = cp_model_pb2.OPTIMAL
 ortools.sat.python.cp_model.CHOOSE_FIRST = cp_model_pb2.DecisionStrategyProto.CHOOSE_FIRST
 ortools.sat.python.cp_model.CHOOSE_LOWEST_MIN = cp_model_pb2.DecisionStrategyProto.CHOOSE_LOWEST_MIN
 ortools.sat.python.cp_model.CHOOSE_HIGHEST_MAX = cp_model_pb2.DecisionStrategyProto.CHOOSE_HIGHEST_MAX
 ortools.sat.python.cp_model.CHOOSE_MIN_DOMAIN_SIZE = cp_model_pb2.DecisionStrategyProto.CHOOSE_MIN_DOMAIN_SIZE
 ortools.sat.python.cp_model.CHOOSE_MAX_DOMAIN_SIZE = cp_model_pb2.DecisionStrategyProto.CHOOSE_MAX_DOMAIN_SIZE
 ortools.sat.python.cp_model.SELECT_MIN_VALUE = cp_model_pb2.DecisionStrategyProto.SELECT_MIN_VALUE
 ortools.sat.python.cp_model.SELECT_MAX_VALUE = cp_model_pb2.DecisionStrategyProto.SELECT_MAX_VALUE
 ortools.sat.python.cp_model.SELECT_LOWER_HALF = cp_model_pb2.DecisionStrategyProto.SELECT_LOWER_HALF
 ortools.sat.python.cp_model.SELECT_UPPER_HALF = cp_model_pb2.DecisionStrategyProto.SELECT_UPPER_HALF
 ortools.sat.python.cp_model.SELECT_MEDIAN_VALUE = cp_model_pb2.DecisionStrategyProto.SELECT_MEDIAN_VALUE
 ortools.sat.python.cp_model.SELECT_RANDOM_HALF = cp_model_pb2.DecisionStrategyProto.SELECT_RANDOM_HALF
 ortools.sat.python.cp_model.AUTOMATIC_SEARCH = sat_parameters_pb2.SatParameters.AUTOMATIC_SEARCH
 ortools.sat.python.cp_model.FIXED_SEARCH = sat_parameters_pb2.SatParameters.FIXED_SEARCH
 ortools.sat.python.cp_model.PORTFOLIO_SEARCH = sat_parameters_pb2.SatParameters.PORTFOLIO_SEARCH
 ortools.sat.python.cp_model.LP_SEARCH = sat_parameters_pb2.SatParameters.LP_SEARCH
 ortools.sat.python.cp_model.PSEUDO_COST_SEARCH = sat_parameters_pb2.SatParameters.PSEUDO_COST_SEARCH
tuple ortools.sat.python.cp_model.PORTFOLIO_WITH_QUICK_RESTART_SEARCH
 ortools.sat.python.cp_model.HINT_SEARCH = sat_parameters_pb2.SatParameters.HINT_SEARCH
 ortools.sat.python.cp_model.PARTIAL_FIXED_SEARCH = sat_parameters_pb2.SatParameters.PARTIAL_FIXED_SEARCH
 ortools.sat.python.cp_model.RANDOMIZED_SEARCH = sat_parameters_pb2.SatParameters.RANDOMIZED_SEARCH
 ortools.sat.python.cp_model.IntegralT = Union[int, np.int8, np.uint8, np.int32, np.uint32, np.int64, np.uint64]
tuple ortools.sat.python.cp_model.IntegralTypes
 ortools.sat.python.cp_model.NumberT
tuple ortools.sat.python.cp_model.NumberTypes
 ortools.sat.python.cp_model.LiteralT = Union[cmh.Literal, IntegralT, bool]
 ortools.sat.python.cp_model.BoolVarT = cmh.Literal
 ortools.sat.python.cp_model.VariableT = Union["IntVar", IntegralT]
 ortools.sat.python.cp_model.LinearExprT = Union[LinearExpr, "IntVar", IntegralT]
 ortools.sat.python.cp_model.ObjLinearExprT = Union[LinearExpr, NumberT]
 ortools.sat.python.cp_model.ArcT = Tuple[IntegralT, IntegralT, LiteralT]
 ortools.sat.python.cp_model._IndexOrSeries = Union[pd.Index, pd.Series]