Propose a natural language on top of cp_model_pb2 python proto.
This file implements a easy-to-use API on top of the cp_model_pb2 protobuf defined in ../ .
DisplayBounds(bounds)Displays a flattened list of intervals.
ShortName(model, i)Returns a short name of an integer variable, or its negation.
LinearExpression(self, /, *args, **kwargs)Holds an integer linear expression.
An linear expression is built from integer constants and variables.
x + 2 * (y - z + 1) is one such linear expression, and can be written that way directly in Python, provided x, y, and z are integer variables.
Linear expressions are used in two places in the cp_model. When used with equality and inequality operators, they create linear inequalities that can be added to the model as in:
model.Add(x + 2 * y <= 5)
model.Add(sum(array_of_vars) == 5)
Linear expressions can also be used to specify the objective of the model.
model.Minimize(x + 2 * y + z)
LinearExpression.GetVarValueMap(self)Scan the expression, and return a list of (var_coef_map, constant).
IntVar(self, model, bounds, name)An integer variable.
An IntVar is an object that can take on any integer value within defined ranges. Variables appears in constraint like:
x + y >= 5
AllDifferent([x, y, z])
Solving a model is equivalent to finding, for each variable, a single value from the set of initial values (called the initial domain), such that the model is feasible, or optimal if you provided an objective function.
IntVar.Not(self)Returns the negation of a Boolean variable.
This method implements the logical negation of a Boolean variable. It is only valid of the variable has a Boolean domain (0 or 1).
Note that this method is nilpotent: x.Not().Not() == x.
LinearInequality(self, expr, bounds)Represents a linear constraint: lb <= expression <= ub.
The only use of this class is to be added to the CpModel through CpModel.Add(expression), as in:
model.Add(x + 2 * y -1 >= z)
Constraint(self, constraints)Base class for constraints.
Constraints are built by the CpModel through the Add methods. Once created by the CpModel class, they are automatically added to the model. The purpose of this class is to allow specification of enforcement literals for this constraint.
b = model.BoolVar('b')
x = model.IntVar(0, 10, 'x')
y = model.IntVar(0, 10, 'y')
model.Add(x + 2 * y == 5).OnlyEnforceIf(b.Not())
Constraint.OnlyEnforceIf(self, boolvar)Adds an enforcement literal to the constraint.
Args: boolvar: A boolean literal, that is a boolean variable or its negation. An enforcement literal (boolean variable or its negation) decides whether the constraint is active or not. It acts as an implication, so if the literal is true, that implies the constraint must be enforced.
IntervalVar(self, model, start_index, size_index, end_index, is_present_index, name)Represents a Interval variable.
An interval variable is both a constraint and a variable. It is defined by three integer variables: start, size, and end.
It is a constraint because, internally, it enforces that start + size == end.
It is also a variable as it can appear in specific scheduling constraints: NoOverlap, NoOverlap2D, Cumulative.
Optionally, an enforcement literal can be added to this constraint. This enforcement literal is understood by the same constraints. These constraints ignore interval variables with enforcement literals assigned to false. Conversely, these constraints will also set these enforcement literals to false if they cannot fit these intervals into the schedule.
CpModel(self)Wrapper class around the cp_model proto.
This class provides two types of methods:
- NewXXX to create integer, boolean, or interval variables.
- AddXXX to create new constraints and add them to the model.
CpModel.NewIntVar(self, lb, ub, name)Creates an integer variable with domain [lb, ub].
CpModel.NewEnumeratedIntVar(self, bounds, name)Creates an integer variable with an enumerated domain.
Args: bounds: A flattened list of disjoint intervals. name: The name of the variable.
Returns: a variable whose domain is union[bounds[2i]..bounds[2i + 1]].
To create a variable with domain [1, 2, 3, 5, 7, 8], pass in the array [1, 3, 5, 5, 7, 8].
CpModel.NewBoolVar(self, name)Creates a 0-1 variable with the given name.
CpModel.AddLinearConstraint(self, terms, lb, ub)Adds the constraints lb <= sum(terms) <= ub, where term = (var, coef).
CpModel.AddSumConstraint(self, variables, lb, ub)Adds the constraints lb <= sum(variables) <= ub.
CpModel.AddLinearConstraintWithBounds(self, terms, bounds)Adds the constraints sum(terms) in bounds, where term = (var, coef).
CpModel.Add(self, ct)Adds a LinearInequality to the model.
CpModel.AddAllDifferent(self, variables)Adds AllDifferent(variables).
This constraint forces all variables to have different values.
Args: variables: a list of integer variables.
Returns: An instance of the Constraint class.
CpModel.AddElement(self, index, variables, target)Adds the element constraint: variables[index] == target.
CpModel.AddCircuit(self, arcs)Adds Circuit(arcs).
Adds a circuit constraint from a sparse list of arcs that encode the graph.
A circuit is a unique Hamiltonian path in a subgraph of the total graph. In case a node 'i' is not in the path, then there must be a loop arc 'i -> i' associated with a true literal. Otherwise this constraint will fail.
Args: arcs: a list of arcs. An arc is a tuple (source_node, destination_node, literal). The arc is selected in the circuit if the literal is true. Both source_node and destination_node must be integer value between 0 and the number of nodes - 1.
Returns: An instance of the Constraint class.
Raises: ValueError: If the list of arc is empty.
CpModel.AddAllowedAssignments(self, variables, tuples_list)Adds AllowedAssignments(variables, tuples_list).
An AllowedAssignments constraint is a constraint on an array of variables that forces, when all variables are fixed to a single value, that the corresponding list of values is equal to one of the tuple of the tuple_list.
Args: variables: A list of variables. tuples_list: A list of admissible tuples. Each tuple must have the same length as the variables, and the ith value of a tuple corresponds to the ith variable.
Returns: An instance of the Constraint class.
Raises: TypeError: If a tuple does not have the same size as the list of variables. ValueError: If the array of variables is empty.
CpModel.AddForbiddenAssignments(self, variables, tuples_list)Adds AddForbiddenAssignments(variables, [tuples_list]).
A ForbiddenAssignments constraint is a constraint on an array of variables where the list of impossible combinations is provided in the tuples list.
Args: variables: A list of variables. tuples_list: A list of forbidden tuples. Each tuple must have the same length as the variables, and the ith value of a tuple corresponds to the ith variable.
Returns: An instance of the Constraint class.
Raises: TypeError: If a tuple does not have the same size as the list of variables. ValueError: If the array of variables is empty.
CpModel.AddAutomaton(self, transition_variables, starting_state, final_states, transition_triples)Adds an automaton constraint.
An automaton constraint takes a list of variables (of size n), an initial state, a set of final states, and a set of transitions. A transition is a triplet ('tail', 'transition', 'head'), where 'tail' and 'head' are states, and 'transition' is the label of an arc from 'head' to 'tail', corresponding to the value of one variable in the list of variables.
This automata will be unrolled into a flow with n + 1 phases. Each phase contains the possible states of the automaton. The first state contains the initial state. The last phase contains the final states.
Between two consecutive phases i and i + 1, the automaton creates a set of arcs. For each transition (tail, transition, head), it will add an arc from the state 'tail' of phase i and the state 'head' of phase i + 1. This arc labeled by the value 'transition' of the variables 'variables[i]'. That is, this arc can only be selected if 'variables[i]' is assigned the value 'transition'.
A feasible solution of this constraint is an assignment of variables such that, starting from the initial state in phase 0, there is a path labeled by the values of the variables that ends in one of the final states in the final phase.
Args: transition_variables: A non empty list of variables whose values correspond to the labels of the arcs traversed by the automata. starting_state: The initial state of the automata. final_states: A non empty list of admissible final states. transition_triples: A list of transition for the automata, in the following format (current_state, variable_value, next_state).
Returns: An instance of the Constraint class.
Raises: ValueError: if transition_variables, final_states, or transition_triples are empty.
CpModel.AddInverse(self, variables, inverse_variables)Adds Inverse(variables, inverse_variables).
An inverse constraint enforces that if 'variables[i]' is assigned a value 'j', then inverse_variables[j] is assigned a value 'i'. And vice versa.
Args: variables: An array of integer variables. inverse_variables: An array of integer variables.
Returns: An instance of the Constraint class.
Raises: TypeError: if variables and inverse_variables have different length, or if they are empty.
CpModel.AddReservoirConstraint(self, times, demands, min_level, max_level)Adds Reservoir(times, demands, min_level, max_level).
Maintains a reservoir level within bounds. The water level starts at 0, and at any time >= 0, it must be between min_level and max_level. Furthermore, this constraints expect all times variables to be >= 0. If the variable times[i] is assigned a value t, then the current level changes by demands[i] (which is constant) at the time t.
Note that level min can be > 0, or level max can be < 0. It just forces some demands to be executed at time 0 to make sure that we are within those bounds with the executed demands. Therefore, at any time t >= 0: sum(demands[i] if times[i] <= t) in [min_level, max_level]
Args: times: A list of positive integer variables which specify the time of the filling or emptying the reservoir. demands: A list of integer values that specifies the amount of the emptying or feeling. min_level: At any time >= 0, the level of the reservoir must be greater of equal than the min level. max_level: At any time >= 0, the level of the reservoir must be less or equal than the max level.
Returns: An instance of the Constraint class.
Raises: ValueError: if max_level < min_level.
CpModel.AddReservoirConstraintWithActive(self, times, demands, actives, min_level, max_level)Adds Reservoir(times, demands, actives, min_level, max_level).
Maintain a reservoir level within bounds. The water level starts at 0, and at any time >= 0, it must be within min_level, and max_level. Furthermore, this constraints expect all times variables to be >= 0. If the actives[i] is true, and if times[i] is assigned a value t, then the level of the reservoir changes by demands[i] (which is constant) at time t.
Note that level_min can be > 0, or level_max can be < 0. It just forces some demands to be executed at time 0 to make sure that we are within those bounds with the executed demands. Therefore, at any time t >= 0: sum(demands[i] * actives[i] if times[i] <= t) in [min_level, max_level] The array of boolean variables 'actives', if defined, indicates which actions are actually performed.
Args: times: A list of positive integer variables which specify the time of the filling or emptying the reservoir. demands: A list of integer values that specifies the amount of the emptying or feeling. actives: a list of boolean variables. They indicates if the emptying/refilling events actually take place. min_level: At any time >= 0, the level of the reservoir must be greater of equal than the min level. max_level: At any time >= 0, the level of the reservoir must be less or equal than the max level.
Returns: An instance of the Constraint class.
Raises: ValueError: if max_level < min_level.
CpModel.AddMapDomain(self, var, bool_var_array, offset=0)Adds var == i + offset <=> bool_var_array[i] == true for all i.
CpModel.AddImplication(self, a, b)Adds a => b.
CpModel.AddBoolOr(self, literals)Adds Or(literals) == true.
CpModel.AddBoolAnd(self, literals)Adds And(literals) == true.
CpModel.AddBoolXOr(self, literals)Adds XOr(literals) == true.
CpModel.AddMinEquality(self, target, variables)Adds target == Min(variables).
CpModel.AddMaxEquality(self, target, args)Adds target == Max(variables).
CpModel.AddDivisionEquality(self, target, num, denom)Adds target == num // denom.
CpModel.AddModuloEquality(self, target, var, mod)Adds target = var % mod.
CpModel.AddProdEquality(self, target, args)Adds target == PROD(args).
CpModel.NewIntervalVar(self, start, size, end, name)Creates an interval variable from start, size, and end.
An interval variable is a constraint, that is itself used in other constraints like NoOverlap.
Internally, it ensures that start + size == end.
Args: start: The start of the interval. It can be an integer value, or an integer variable. size: The size of the interval. It can be an integer value, or an integer variable. end: The end of the interval. It can be an integer value, or an integer variable. name: The name of the interval variable.
Returns: An IntervalVar object.
CpModel.NewOptionalIntervalVar(self, start, size, end, is_present, name)Creates an optional interval var from start, size, end and is_present.
An optional interval variable is a constraint, that is itself used in other constraints like NoOverlap. This constraint is protected by an is_present literal that indicates if it is active or not.
Internally, it ensures that is_present implies start + size == end.
Args: start: The start of the interval. It can be an integer value, or an integer variable. size: The size of the interval. It can be an integer value, or an integer variable. end: The end of the interval. It can be an integer value, or an integer variable. is_present: A literal that indicates if the interval is active or not. A inactive interval is simply ignored by all constraints. name: The name of the interval variable.
Returns: An IntervalVar object.
CpModel.AddNoOverlap(self, interval_vars)Adds NoOverlap(interval_vars).
A NoOverlap constraint ensures that all present intervals do not overlap in time.
Args: interval_vars: The list of interval variables to constrain.
Returns: An instance of the Constraint class.
CpModel.AddNoOverlap2D(self, x_intervals, y_intervals)Adds NoOverlap2D(x_intervals, y_intervals).
A NoOverlap2D constraint ensures that all present rectangles do not overlap on a plan. Each rectangle is aligned with the X and Y axis, and is defined by two intervals which represent its projection onto the X and Y axis.
Args: x_intervals: The X coordinates of the rectangles. y_intervals: The Y coordinates of the rectangles.
Returns: An instance of the Constraint class.
CpModel.AddCumulative(self, intervals, demands, capacity)Adds Cumulative(intervals, demands, capacity).
This constraint enforces that: for all t: sum(demands[i] if (start(intervals[t]) <= t < end(intervals[t])) and (t is present)) <= capacity
Args: intervals: The list of intervals. demands: The list of demands for each interval. Each demand must be >= 0. Each demand can be an integer value, or an integer variable. capacity: The maximum capacity of the cumulative constraint. It must be a positive integer value or variable.
Returns: An instance of the Constraint class.
CpModel.GetOrMakeIndex(self, arg)Returns the index of a variables, its negation, or a number.
CpModel.GetOrMakeBooleanIndex(self, arg)Returns an index from a boolean expression.
CpModel.Minimize(self, obj)Sets the objective of the model to minimize(obj).
CpModel.Maximize(self, obj)Sets the objective of the model to maximize(obj).
CpModel.AddDecisionStrategy(self, variables, var_strategy, domain_strategy)Adds a search strategy to the model.
Args: variables: a list of variables this strategy will assign. var_strategy: heuristic to choose the next variable to assign. domain_strategy: heuristic to reduce the domain of the selected variable. Currently, this is advanced code, the union of all strategies added to the model must be complete, i.e. instantiates all variables. Otherwise, Solve() will fail.
EvaluateLinearExpression(expression, solution)Evaluate an linear expression against a solution.
EvaluateBooleanExpression(literal, solution)Evaluate an boolean expression against a solution.
CpSolverSolutionCallback(self)Solution callback.
This class implements a callback that will be called at each new solution found during search.
The method OnSolutionCallback() will be called by the solver, and must be implemented. The current solution can be queried using the BooleanValue() and Value() methods.
CpSolverSolutionCallback.BooleanValue(self, lit)Returns the boolean value of a boolean literal.
Args: lit: A boolean variable or its negation.
Returns: The boolean value of the literal in the solution.
Raises: RuntimeError: if 'lit' is not a boolean variable or its negation.
CpSolverSolutionCallback.Value(self, expression)Evaluates an linear expression in the current solution.
Args: expression: a linear expression of the model.
Returns: An integer value equal to the evaluation of the linear expression against the current solution.
Raises: RuntimeError: if 'expression' is not a LinearExpression.
CpSolver(self)Main solver class.
The purpose of this class is to search for a solution of a model given to the Solve() method.
Once Solve() is called, this class allows inspecting the solution found with the Value() and BooleanValue() methods, as well as general statistics about the solve procedure.
CpSolver.Solve(self, model)Solves the given model and returns the solve status.
CpSolver.SolveWithSolutionCallback(self, model, callback)Solves a problem and pass each solution found to the callback.
CpSolver.SearchForAllSolutions(self, model, callback)Search for all solutions of a satisfiability problem.
This method searches for all feasible solution of a given model. Then it feeds the solution to the callback.
Args: model: The model to solve. callback: The callback that will be called at each solution.
Returns: The status of the solve (FEASIBLE, INFEASIBLE...).
CpSolver.Value(self, expression)Returns the value of an linear expression after solve.
CpSolver.BooleanValue(self, literal)Returns the boolean value of a literal after solve.
CpSolver.ObjectiveValue(self)Returns the value of objective after solve.
CpSolver.StatusName(self, status)Returns the name of the status returned by Solve().
CpSolver.NumBooleans(self)Returns the number of boolean variables managed by the SAT solver.
CpSolver.NumConflicts(self)Returns the number of conflicts since the creation of the solver.
CpSolver.NumBranches(self)Returns the number of search branches explored by the solver.
CpSolver.WallTime(self)Return the wall time in seconds since the creation of the solver.
CpSolver.UserTime(self)Return the user time in seconds since the creation of the solver.