# Copyright 2010-2018 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Methods for building and solving CP-SAT models. The following two sections describe the main methods for building and solving CP-SAT models. * [`CpModel`](#cp_model.CpModel): Methods for creating models, including variables and constraints. * [`CPSolver`](#cp_model.CpSolver): Methods for solving a model and evaluating solutions. The following methods implement callbacks that the solver calls each time it finds a new solution. * [`CpSolverSolutionCallback`](#cp_model.CpSolverSolutionCallback): A general method for implementing callbacks. * [`ObjectiveSolutionPrinter`](#cp_model.ObjectiveSolutionPrinter): Print objective values and elapsed time for intermediate solutions. * [`VarArraySolutionPrinter`](#cp_model.VarArraySolutionPrinter): Print intermediate solutions (variable values, time). * [`VarArrayAndObjectiveSolutionPrinter`] (#cp_model.VarArrayAndObjectiveSolutionPrinter): Print both intermediate solutions and objective values. Additional methods for solving CP-SAT models: * [`Constraint`](#cp_model.Constraint): A few utility methods for modifying contraints created by `CpModel`. * [`LinearExpr`](#lineacp_model.LinearExpr): Methods for creating constraints and the objective from large arrays of coefficients. Other methods and functions listed are primarily used for developing OR-Tools, rather than for solving specific optimization problems. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import numbers import time from six import iteritems from ortools.sat import cp_model_pb2 from ortools.sat import sat_parameters_pb2 from ortools.sat.python import cp_model_helper from ortools.sat import pywrapsat from ortools.util import sorted_interval_list Domain = sorted_interval_list.Domain # Documentation cleaning. # Remove the documentation of some functions. # See https://pdoc3.github.io/pdoc/doc/pdoc/#overriding-docstrings-with- __pdoc__ = {} __pdoc__['DisplayBounds'] = False __pdoc__['EvaluateLinearExpr'] = False __pdoc__['EvaluateBooleanExpression'] = False __pdoc__['ShortName'] = False # The classes below allow linear expressions to be expressed naturally with the # usual arithmetic operators +-*/ and with constant numbers, which makes the # python API very intuitive. See ../samples/*.py for examples. INT_MIN = -9223372036854775808 # hardcoded to be platform independent. INT_MAX = 9223372036854775807 INT32_MAX = 2147483647 INT32_MIN = -2147483648 # CpSolver status (exported to avoid importing cp_model_cp2). UNKNOWN = cp_model_pb2.UNKNOWN MODEL_INVALID = cp_model_pb2.MODEL_INVALID FEASIBLE = cp_model_pb2.FEASIBLE INFEASIBLE = cp_model_pb2.INFEASIBLE OPTIMAL = cp_model_pb2.OPTIMAL # Variable selection strategy CHOOSE_FIRST = cp_model_pb2.DecisionStrategyProto.CHOOSE_FIRST CHOOSE_LOWEST_MIN = cp_model_pb2.DecisionStrategyProto.CHOOSE_LOWEST_MIN CHOOSE_HIGHEST_MAX = cp_model_pb2.DecisionStrategyProto.CHOOSE_HIGHEST_MAX CHOOSE_MIN_DOMAIN_SIZE = ( cp_model_pb2.DecisionStrategyProto.CHOOSE_MIN_DOMAIN_SIZE) CHOOSE_MAX_DOMAIN_SIZE = ( cp_model_pb2.DecisionStrategyProto.CHOOSE_MAX_DOMAIN_SIZE) # Domain reduction strategy SELECT_MIN_VALUE = cp_model_pb2.DecisionStrategyProto.SELECT_MIN_VALUE SELECT_MAX_VALUE = cp_model_pb2.DecisionStrategyProto.SELECT_MAX_VALUE SELECT_LOWER_HALF = cp_model_pb2.DecisionStrategyProto.SELECT_LOWER_HALF SELECT_UPPER_HALF = cp_model_pb2.DecisionStrategyProto.SELECT_UPPER_HALF # Search branching AUTOMATIC_SEARCH = sat_parameters_pb2.SatParameters.AUTOMATIC_SEARCH FIXED_SEARCH = sat_parameters_pb2.SatParameters.FIXED_SEARCH PORTFOLIO_SEARCH = sat_parameters_pb2.SatParameters.PORTFOLIO_SEARCH LP_SEARCH = sat_parameters_pb2.SatParameters.LP_SEARCH def DisplayBounds(bounds): """Displays a flattened list of intervals.""" out = '' for i in range(0, len(bounds), 2): if i != 0: out += ', ' if bounds[i] == bounds[i + 1]: out += str(bounds[i]) else: out += str(bounds[i]) + '..' + str(bounds[i + 1]) return out def ShortName(model, i): """Returns a short name of an integer variable, or its negation.""" if i < 0: return 'Not(%s)' % ShortName(model, -i - 1) v = model.variables[i] if v.name: return v.name elif len(v.domain) == 2 and v.domain[0] == v.domain[1]: return str(v.domain[0]) else: return '[%s]' % DisplayBounds(v.domain) class LinearExpr(object): """Holds an integer linear expression. A linear expression is built from integer constants and variables. For example, x + 2 * (y - z + 1). Linear expressions are used in CP-SAT models in two ways: * To define constraints. For example model.Add(x + 2 * y <= 5) model.Add(sum(array_of_vars) == 5) * To define the objective function. For example model.Minimize(x + 2 * y + z) For large arrays, you can create constraints and the objective from lists of linear expressions or coefficients as follows: model.Minimize(cp_model.LinearExpr.Sum(expressions)) model.Add(cp_model.LinearExpr.ScalProd(expressions, coefficients) >= 0) """ @classmethod def Sum(cls, expressions): """Creates the expression sum(expressions).""" return _SumArray(expressions) @classmethod def ScalProd(cls, expressions, coefficients): """Creates the expression sum(expressions[i] * coefficients[i]).""" return _ScalProd(expressions, coefficients) @classmethod def Term(cls, expression, coefficient): """Creates `expression * coefficient`.""" return expression * coefficient def GetVarValueMap(self): """Scans the expression, and return a list of (var_coef_map, constant).""" coeffs = collections.defaultdict(int) constant = 0 to_process = [(self, 1)] while to_process: # Flatten to avoid recursion. expr, coef = to_process.pop() if isinstance(expr, _ProductCst): to_process.append( (expr.Expression(), coef * expr.Coefficient())) elif isinstance(expr, _SumArray): for e in expr.Expressions(): to_process.append((e, coef)) constant += expr.Constant() * coef elif isinstance(expr, _ScalProd): for e, c in zip(expr.Expressions(), expr.Coefficients()): to_process.append((e, coef * c)) constant += expr.Constant() * coef elif isinstance(expr, IntVar): coeffs[expr] += coef elif isinstance(expr, _NotBooleanVariable): constant += coef coeffs[expr.Not()] -= coef else: raise TypeError('Unrecognized linear expression: ' + str(expr)) return coeffs, constant def __hash__(self): return object.__hash__(self) def __abs__(self): raise NotImplementedError( 'calling abs() on a linear expression is not supported, ' 'please use CpModel.AddAbsEquality') def __add__(self, expr): return _SumArray([self, expr]) def __radd__(self, arg): return _SumArray([self, arg]) def __sub__(self, expr): return _SumArray([self, -expr]) def __rsub__(self, arg): return _SumArray([-self, arg]) def __mul__(self, arg): if isinstance(arg, numbers.Integral): if arg == 1: return self elif arg == 0: return 0 cp_model_helper.AssertIsInt64(arg) return _ProductCst(self, arg) else: raise TypeError('Not an integer linear expression: ' + str(arg)) def __rmul__(self, arg): cp_model_helper.AssertIsInt64(arg) if arg == 1: return self return _ProductCst(self, arg) def __div__(self, _): raise NotImplementedError( 'calling / on a linear expression is not supported, ' 'please use CpModel.AddDivisionEquality') def __truediv__(self, _): raise NotImplementedError( 'calling // on a linear expression is not supported, ' 'please use CpModel.AddDivisionEquality') def __mod__(self, _): raise NotImplementedError( 'calling %% on a linear expression is not supported, ' 'please use CpModel.AddModuloEquality') def __neg__(self): return _ProductCst(self, -1) def __eq__(self, arg): if arg is None: return False if isinstance(arg, numbers.Integral): cp_model_helper.AssertIsInt64(arg) return BoundedLinearExpression(self, [arg, arg]) else: return BoundedLinearExpression(self - arg, [0, 0]) def __ge__(self, arg): if isinstance(arg, numbers.Integral): cp_model_helper.AssertIsInt64(arg) return BoundedLinearExpression(self, [arg, INT_MAX]) else: return BoundedLinearExpression(self - arg, [0, INT_MAX]) def __le__(self, arg): if isinstance(arg, numbers.Integral): cp_model_helper.AssertIsInt64(arg) return BoundedLinearExpression(self, [INT_MIN, arg]) else: return BoundedLinearExpression(self - arg, [INT_MIN, 0]) def __lt__(self, arg): if isinstance(arg, numbers.Integral): cp_model_helper.AssertIsInt64(arg) if arg == INT_MIN: raise ArithmeticError('< INT_MIN is not supported') return BoundedLinearExpression( self, [INT_MIN, cp_model_helper.CapInt64(arg - 1)]) else: return BoundedLinearExpression(self - arg, [INT_MIN, -1]) def __gt__(self, arg): if isinstance(arg, numbers.Integral): cp_model_helper.AssertIsInt64(arg) if arg == INT_MAX: raise ArithmeticError('> INT_MAX is not supported') return BoundedLinearExpression( self, [cp_model_helper.CapInt64(arg + 1), INT_MAX]) else: return BoundedLinearExpression(self - arg, [1, INT_MAX]) def __ne__(self, arg): if arg is None: return True if isinstance(arg, numbers.Integral): cp_model_helper.AssertIsInt64(arg) if arg == INT_MAX: return BoundedLinearExpression(self, [INT_MIN, INT_MAX - 1]) elif arg == INT_MIN: return BoundedLinearExpression(self, [INT_MIN + 1, INT_MAX]) else: return BoundedLinearExpression(self, [ INT_MIN, cp_model_helper.CapInt64(arg - 1), cp_model_helper.CapInt64(arg + 1), INT_MAX ]) else: return BoundedLinearExpression(self - arg, [INT_MIN, -1, 1, INT_MAX]) class _ProductCst(LinearExpr): """Represents the product of a LinearExpr by a constant.""" def __init__(self, expr, coef): cp_model_helper.AssertIsInt64(coef) if isinstance(expr, _ProductCst): self.__expr = expr.Expression() self.__coef = expr.Coefficient() * coef else: self.__expr = expr self.__coef = coef def __str__(self): if self.__coef == -1: return '-' + str(self.__expr) else: return '(' + str(self.__coef) + ' * ' + str(self.__expr) + ')' def __repr__(self): return 'ProductCst(' + repr(self.__expr) + ', ' + repr( self.__coef) + ')' def Coefficient(self): return self.__coef def Expression(self): return self.__expr class _SumArray(LinearExpr): """Represents the sum of a list of LinearExpr and a constant.""" def __init__(self, expressions): self.__expressions = [] self.__constant = 0 for x in expressions: if isinstance(x, numbers.Integral): cp_model_helper.AssertIsInt64(x) self.__constant += x elif isinstance(x, LinearExpr): self.__expressions.append(x) else: raise TypeError('Not an linear expression: ' + str(x)) def __str__(self): if self.__constant == 0: return '({})'.format(' + '.join(map(str, self.__expressions))) else: return '({} + {})'.format(' + '.join(map(str, self.__expressions)), self.__constant) def __repr__(self): return 'SumArray({}, {})'.format( ', '.join(map(repr, self.__expressions)), self.__constant) def Expressions(self): return self.__expressions def Constant(self): return self.__constant class _ScalProd(LinearExpr): """Represents the scalar product of expressions with constants and a constant.""" def __init__(self, expressions, coefficients): self.__expressions = [] self.__coefficients = [] self.__constant = 0 if len(expressions) != len(coefficients): raise TypeError( 'In the LinearExpr.ScalProd method, the expression array and the ' ' coefficient array must have the same length.') for e, c in zip(expressions, coefficients): cp_model_helper.AssertIsInt64(c) if c == 0: continue if isinstance(e, numbers.Integral): cp_model_helper.AssertIsInt64(e) self.__constant += e * c elif isinstance(e, LinearExpr): self.__expressions.append(e) self.__coefficients.append(c) else: raise TypeError('Not an linear expression: ' + str(e)) def __str__(self): output = None for expr, coeff in zip(self.__expressions, self.__coefficients): if not output and coeff == 1: output = str(expr) elif not output and coeff == -1: output = '-' + str(expr) elif not output: output = '{} * {}'.format(coeff, str(expr)) elif coeff == 1: output += ' + {}'.format(str(expr)) elif coeff == -1: output += ' - {}'.format(str(expr)) elif coeff > 1: output += ' + {} * {}'.format(coeff, str(expr)) elif coeff < -1: output += ' - {} * {}'.format(-coeff, str(expr)) if self.__constant > 0: output += ' + {}'.format(self.__constant) elif self.__constant < 0: output += ' - {}'.format(-self.__constant) return output def __repr__(self): return 'ScalProd([{}], [{}], {})'.format( ', '.join(map(repr, self.__expressions)), ', '.join(map(repr, self.__coefficients)), self.__constant) def Expressions(self): return self.__expressions def Coefficients(self): return self.__coefficients def Constant(self): return self.__constant class IntVar(LinearExpr): """An integer variable. An IntVar is an object that can take on any integer value within defined ranges. Variables appear 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. """ def __init__(self, model, domain, name): """See CpModel.NewIntVar below.""" self.__model = model self.__index = len(model.variables) self.__var = model.variables.add() self.__var.domain.extend(domain.FlattenedIntervals()) self.__var.name = name self.__negation = None def Index(self): """Returns the index of the variable in the model.""" return self.__index def Proto(self): """Returns the variable protobuf.""" return self.__var def __str__(self): if not self.__var.name: if len(self.__var.domain ) == 2 and self.__var.domain[0] == self.__var.domain[1]: # Special case for constants. return str(self.__var.domain[0]) else: return 'unnamed_var_%i' % self.__index return self.__var.name def __repr__(self): return '%s(%s)' % (self.__var.name, DisplayBounds(self.__var.domain)) def Name(self): return self.__var.name def Not(self): """Returns the negation of a Boolean variable. This method implements the logical negation of a Boolean variable. It is only valid if the variable has a Boolean domain (0 or 1). Note that this method is nilpotent: `x.Not().Not() == x`. """ for bound in self.__var.domain: if bound < 0 or bound > 1: raise TypeError( 'Cannot call Not on a non boolean variable: %s' % self) if not self.__negation: self.__negation = _NotBooleanVariable(self) return self.__negation class _NotBooleanVariable(LinearExpr): """Negation of a boolean variable.""" def __init__(self, boolvar): self.__boolvar = boolvar def Index(self): return -self.__boolvar.Index() - 1 def Not(self): return self.__boolvar def __str__(self): return 'not(%s)' % str(self.__boolvar) class BoundedLinearExpression(object): """Represents a linear constraint: `lb <= linear 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) """ def __init__(self, expr, bounds): self.__expr = expr self.__bounds = bounds def __str__(self): if len(self.__bounds) == 2: lb = self.__bounds[0] ub = self.__bounds[1] if lb > INT_MIN and ub < INT_MAX: if lb == ub: return str(self.__expr) + ' == ' + str(lb) else: return str(lb) + ' <= ' + str( self.__expr) + ' <= ' + str(ub) elif lb > INT_MIN: return str(self.__expr) + ' >= ' + str(lb) elif ub < INT_MAX: return str(self.__expr) + ' <= ' + str(ub) else: return 'True (unbounded expr ' + str(self.__expr) + ')' else: return str(self.__expr) + ' in [' + DisplayBounds( self.__bounds) + ']' def Expression(self): return self.__expr def Bounds(self): return self.__bounds class Constraint(object): """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()) """ def __init__(self, constraints): self.__index = len(constraints) self.__constraint = constraints.add() def OnlyEnforceIf(self, boolvar): """Adds an enforcement literal to the constraint. This method adds one or more literals (that is, a boolean variable or its negation) as enforcement literals. The conjunction of all these literals determines whether the constraint is active or not. It acts as an implication, so if the conjunction is true, it implies that the constraint must be enforced. If it is false, then the constraint is ignored. BoolOr, BoolAnd, and linear constraints all support enforcement literals. Args: boolvar: A boolean literal or a list of boolean literals. Returns: self. """ if isinstance(boolvar, numbers.Integral) and boolvar == 1: # Always true. Do nothing. pass elif isinstance(boolvar, list): for b in boolvar: if isinstance(b, numbers.Integral) and b == 1: pass else: self.__constraint.enforcement_literal.append(b.Index()) else: self.__constraint.enforcement_literal.append(boolvar.Index()) return self def Index(self): """Returns the index of the constraint in the model.""" return self.__index def Proto(self): """Returns the constraint protobuf.""" return self.__constraint class IntervalVar(object): """Represents an 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, in which case these scheduling constraints will 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. """ def __init__(self, model, start_index, size_index, end_index, is_present_index, name): self.__model = model self.__index = len(model.constraints) self.__ct = self.__model.constraints.add() self.__ct.interval.start = start_index self.__ct.interval.size = size_index self.__ct.interval.end = end_index if is_present_index is not None: self.__ct.enforcement_literal.append(is_present_index) if name: self.__ct.name = name def Index(self): """Returns the index of the interval constraint in the model.""" return self.__index def Proto(self): """Returns the interval protobuf.""" return self.__ct.interval def __str__(self): return self.__ct.name def __repr__(self): interval = self.__ct.interval if self.__ct.enforcement_literal: return '%s(start = %s, size = %s, end = %s, is_present = %s)' % ( self.__ct.name, ShortName(self.__model, interval.start), ShortName(self.__model, interval.size), ShortName(self.__model, interval.end), ShortName(self.__model, self.__ct.enforcement_literal[0])) else: return '%s(start = %s, size = %s, end = %s)' % ( self.__ct.name, ShortName(self.__model, interval.start), ShortName(self.__model, interval.size), ShortName(self.__model, interval.end)) def Name(self): return self.__ct.name class CpModel(object): """Methods for building a CP model. Methods beginning with: * ```New``` create integer, boolean, or interval variables. * ```Add``` create new constraints and add them to the model. """ def __init__(self): self.__model = cp_model_pb2.CpModelProto() self.__constant_map = {} self.__optional_constant_map = {} # Integer variable. def NewIntVar(self, lb, ub, name): """Create an integer variable with domain [lb, ub]. The CP-SAT solver is limited to integer variables. If you have fractional values, scale them up so that they become integers; if you have strings, encode them as integers. Args: lb: Lower bound for the variable. ub: Upper bound for the variable. name: The name of the variable. Returns: a variable whose domain is [lb, ub]. """ return IntVar(self.__model, Domain(lb, ub), name) def NewIntVarFromDomain(self, domain, name): """Create an integer variable from a domain. A domain is a set of integers specified by a collection of intervals. For example, `model.NewIntVarFromDomain(cp_model. Domain.FromIntervals([[1, 2], [4, 6]]), 'x')` Args: domain: An instance of the Domain class. name: The name of the variable. Returns: a variable whose domain is the given domain. """ return IntVar(self.__model, domain, name) def NewBoolVar(self, name): """Creates a 0-1 variable with the given name.""" return IntVar(self.__model, Domain(0, 1), name) def NewConstant(self, value): """Declares a constant integer.""" return IntVar(self.__model, Domain(value, value), '') # Linear constraints. def AddLinearConstraint(self, linear_expr, lb, ub): """Adds the constraint: `lb <= linear_expr <= ub`.""" return self.AddLinearExpressionInDomain(linear_expr, Domain(lb, ub)) def AddLinearExpressionInDomain(self, linear_expr, domain): """Adds the constraint: `linear_expr` in `domain`.""" if isinstance(linear_expr, LinearExpr): ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] coeffs_map, constant = linear_expr.GetVarValueMap() for t in iteritems(coeffs_map): if not isinstance(t[0], IntVar): raise TypeError('Wrong argument' + str(t)) cp_model_helper.AssertIsInt64(t[1]) model_ct.linear.vars.append(t[0].Index()) model_ct.linear.coeffs.append(t[1]) model_ct.linear.domain.extend([ cp_model_helper.CapSub(x, constant) for x in domain.FlattenedIntervals() ]) return ct elif isinstance(linear_expr, numbers.Integral): if not domain.Contains(linear_expr): return self.AddBoolOr([]) # Evaluate to false. # Nothing to do otherwise. else: raise TypeError( 'Not supported: CpModel.AddLinearExpressionInDomain(' + str(linear_expr) + ' ' + str(domain) + ')') def Add(self, ct): """Adds a `BoundedLinearExpression` to the model. Args: ct: A [`BoundedLinearExpression`](#boundedlinearexpression). Returns: An instance of the `Constraint` class. """ if isinstance(ct, BoundedLinearExpression): return self.AddLinearExpressionInDomain( ct.Expression(), Domain.FromFlatIntervals(ct.Bounds())) elif ct and isinstance(ct, bool): return self.AddBoolOr([True]) elif not ct and isinstance(ct, bool): return self.AddBoolOr([]) # Evaluate to false. else: raise TypeError('Not supported: CpModel.Add(' + str(ct) + ')') # General Integer Constraints. def 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. """ ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.all_diff.vars.extend( [self.GetOrMakeIndex(x) for x in variables]) return ct def AddElement(self, index, variables, target): """Adds the element constraint: `variables[index] == target`.""" if not variables: raise ValueError('AddElement expects a non-empty variables array') ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.element.index = self.GetOrMakeIndex(index) model_ct.element.vars.extend( [self.GetOrMakeIndex(x) for x in variables]) model_ct.element.target = self.GetOrMakeIndex(target) return ct def 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 integers between 0 and the number of nodes - 1. Returns: An instance of the `Constraint` class. Raises: ValueError: If the list of arcs is empty. """ if not arcs: raise ValueError('AddCircuit expects a non-empty array of arcs') ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] for arc in arcs: cp_model_helper.AssertIsInt32(arc[0]) cp_model_helper.AssertIsInt32(arc[1]) lit = self.GetOrMakeBooleanIndex(arc[2]) model_ct.circuit.tails.append(arc[0]) model_ct.circuit.heads.append(arc[1]) model_ct.circuit.literals.append(lit) return ct def AddAllowedAssignments(self, variables, tuples_list): """Adds AllowedAssignments(variables, tuples_list). An AllowedAssignments constraint is a constraint on an array of variables, which requires that when all variables are assigned values, the resulting array equals one of the tuples in `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. """ if not variables: raise ValueError( 'AddAllowedAssignments expects a non-empty variables ' 'array') ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.table.vars.extend([self.GetOrMakeIndex(x) for x in variables]) arity = len(variables) for t in tuples_list: if len(t) != arity: raise TypeError('Tuple ' + str(t) + ' has the wrong arity') for v in t: cp_model_helper.AssertIsInt64(v) model_ct.table.values.extend(t) return ct def 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 *i*th value of a tuple corresponds to the *i*th 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. """ if not variables: raise ValueError( 'AddForbiddenAssignments expects a non-empty variables ' 'array') index = len(self.__model.constraints) ct = self.AddAllowedAssignments(variables, tuples_list) self.__model.constraints[index].table.negated = True return ct def 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 automaton 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 is 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 automaton. starting_state: The initial state of the automaton. final_states: A non-empty list of admissible final states. transition_triples: A list of transitions for the automaton, 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. """ if not transition_variables: raise ValueError( 'AddAutomaton expects a non-empty transition_variables ' 'array') if not final_states: raise ValueError('AddAutomaton expects some final states') if not transition_triples: raise ValueError('AddAutomaton expects some transtion triples') ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.automaton.vars.extend( [self.GetOrMakeIndex(x) for x in transition_variables]) cp_model_helper.AssertIsInt64(starting_state) model_ct.automaton.starting_state = starting_state for v in final_states: cp_model_helper.AssertIsInt64(v) model_ct.automaton.final_states.append(v) for t in transition_triples: if len(t) != 3: raise TypeError('Tuple ' + str(t) + ' has the wrong arity (!= 3)') cp_model_helper.AssertIsInt64(t[0]) cp_model_helper.AssertIsInt64(t[1]) cp_model_helper.AssertIsInt64(t[2]) model_ct.automaton.transition_tail.append(t[0]) model_ct.automaton.transition_label.append(t[1]) model_ct.automaton.transition_head.append(t[2]) return ct def 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 lengths, or if they are empty. """ if not variables or not inverse_variables: raise TypeError( 'The Inverse constraint does not accept empty arrays') if len(variables) != len(inverse_variables): raise TypeError( 'In the inverse constraint, the two array variables and' ' inverse_variables must have the same length.') ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.inverse.f_direct.extend( [self.GetOrMakeIndex(x) for x in variables]) model_ct.inverse.f_inverse.extend( [self.GetOrMakeIndex(x) for x in inverse_variables]) return ct def 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 constraint expects 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 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 filling. 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. """ if max_level < min_level: return ValueError( 'Reservoir constraint must have a max_level >= min_level') ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.reservoir.times.extend([self.GetOrMakeIndex(x) for x in times]) model_ct.reservoir.demands.extend(demands) model_ct.reservoir.min_level = min_level model_ct.reservoir.max_level = max_level return ct def 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 `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 filling. 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. """ if max_level < min_level: return ValueError( 'Reservoir constraint must have a max_level >= min_level') ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.reservoir.times.extend([self.GetOrMakeIndex(x) for x in times]) model_ct.reservoir.demands.extend(demands) model_ct.reservoir.actives.extend( [self.GetOrMakeIndex(x) for x in actives]) model_ct.reservoir.min_level = min_level model_ct.reservoir.max_level = max_level return ct def AddMapDomain(self, var, bool_var_array, offset=0): """Adds `var == i + offset <=> bool_var_array[i] == true for all i`.""" for i, bool_var in enumerate(bool_var_array): b_index = bool_var.Index() var_index = var.Index() model_ct = self.__model.constraints.add() model_ct.linear.vars.append(var_index) model_ct.linear.coeffs.append(1) model_ct.linear.domain.extend([offset + i, offset + i]) model_ct.enforcement_literal.append(b_index) model_ct = self.__model.constraints.add() model_ct.linear.vars.append(var_index) model_ct.linear.coeffs.append(1) model_ct.enforcement_literal.append(-b_index - 1) if offset + i - 1 >= INT_MIN: model_ct.linear.domain.extend([INT_MIN, offset + i - 1]) if offset + i + 1 <= INT_MAX: model_ct.linear.domain.extend([offset + i + 1, INT_MAX]) def AddImplication(self, a, b): """Adds `a => b` (`a` implies `b`).""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.bool_or.literals.append(self.GetOrMakeBooleanIndex(b)) model_ct.enforcement_literal.append(self.GetOrMakeBooleanIndex(a)) return ct def AddBoolOr(self, literals): """Adds `Or(literals) == true`.""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.bool_or.literals.extend( [self.GetOrMakeBooleanIndex(x) for x in literals]) return ct def AddBoolAnd(self, literals): """Adds `And(literals) == true`.""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.bool_and.literals.extend( [self.GetOrMakeBooleanIndex(x) for x in literals]) return ct def AddBoolXOr(self, literals): """Adds `XOr(literals) == true`.""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.bool_xor.literals.extend( [self.GetOrMakeBooleanIndex(x) for x in literals]) return ct def AddMinEquality(self, target, variables): """Adds `target == Min(variables)`.""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.int_min.vars.extend( [self.GetOrMakeIndex(x) for x in variables]) model_ct.int_min.target = self.GetOrMakeIndex(target) return ct def AddMaxEquality(self, target, variables): """Adds `target == Max(variables)`.""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.int_max.vars.extend( [self.GetOrMakeIndex(x) for x in variables]) model_ct.int_max.target = self.GetOrMakeIndex(target) return ct def AddDivisionEquality(self, target, num, denom): """Adds `target == num // denom` (integer division rounded towards 0).""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.int_div.vars.extend( [self.GetOrMakeIndex(num), self.GetOrMakeIndex(denom)]) model_ct.int_div.target = self.GetOrMakeIndex(target) return ct def AddAbsEquality(self, target, var): """Adds `target == Abs(var)`.""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] index = self.GetOrMakeIndex(var) model_ct.int_max.vars.extend([index, -index - 1]) model_ct.int_max.target = self.GetOrMakeIndex(target) return ct def AddModuloEquality(self, target, var, mod): """Adds `target = var % mod`.""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.int_mod.vars.extend( [self.GetOrMakeIndex(var), self.GetOrMakeIndex(mod)]) model_ct.int_mod.target = self.GetOrMakeIndex(target) return ct def AddMultiplicationEquality(self, target, variables): """Adds `target == variables[0] * .. * variables[n]`.""" ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.int_prod.vars.extend( [self.GetOrMakeIndex(x) for x in variables]) model_ct.int_prod.target = self.GetOrMakeIndex(target) return ct def AddProdEquality(self, target, variables): """Deprecated, use AddMultiplicationEquality.""" return self.AddMultiplicationEquality(target, variables) # Scheduling support def 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. """ start_index = self.GetOrMakeIndex(start) size_index = self.GetOrMakeIndex(size) end_index = self.GetOrMakeIndex(end) return IntervalVar(self.__model, start_index, size_index, end_index, None, name) def 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. """ is_present_index = self.GetOrMakeBooleanIndex(is_present) start_index = self.GetOrMakeIndex(start) size_index = self.GetOrMakeIndex(size) end_index = self.GetOrMakeIndex(end) return IntervalVar(self.__model, start_index, size_index, end_index, is_present_index, name) def 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. """ ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.no_overlap.intervals.extend( [self.GetIntervalIndex(x) for x in interval_vars]) return ct def 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 plane. 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. """ ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.no_overlap_2d.x_intervals.extend( [self.GetIntervalIndex(x) for x in x_intervals]) model_ct.no_overlap_2d.y_intervals.extend( [self.GetIntervalIndex(x) for x in y_intervals]) return ct def 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. """ ct = Constraint(self.__model.constraints) model_ct = self.__model.constraints[ct.Index()] model_ct.cumulative.intervals.extend( [self.GetIntervalIndex(x) for x in intervals]) model_ct.cumulative.demands.extend( [self.GetOrMakeIndex(x) for x in demands]) model_ct.cumulative.capacity = self.GetOrMakeIndex(capacity) return ct # Helpers. def __str__(self): return str(self.__model) def Proto(self): """Returns the underlying CpModelProto.""" return self.__model def Negated(self, index): return -index - 1 def GetOrMakeIndex(self, arg): """Returns the index of a variable, its negation, or a number.""" if isinstance(arg, IntVar): return arg.Index() elif (isinstance(arg, _ProductCst) and isinstance(arg.Expression(), IntVar) and arg.Coefficient() == -1): return -arg.Expression().Index() - 1 elif isinstance(arg, numbers.Integral): cp_model_helper.AssertIsInt64(arg) return self.GetOrMakeIndexFromConstant(arg) else: raise TypeError('NotSupported: model.GetOrMakeIndex(' + str(arg) + ')') def GetOrMakeBooleanIndex(self, arg): """Returns an index from a boolean expression.""" if isinstance(arg, IntVar): self.AssertIsBooleanVariable(arg) return arg.Index() elif isinstance(arg, _NotBooleanVariable): self.AssertIsBooleanVariable(arg.Not()) return arg.Index() elif isinstance(arg, numbers.Integral): cp_model_helper.AssertIsBoolean(arg) return self.GetOrMakeIndexFromConstant(arg) else: raise TypeError('NotSupported: model.GetOrMakeBooleanIndex(' + str(arg) + ')') def GetIntervalIndex(self, arg): if not isinstance(arg, IntervalVar): raise TypeError('NotSupported: model.GetIntervalIndex(%s)' % arg) return arg.Index() def GetOrMakeIndexFromConstant(self, value): if value in self.__constant_map: return self.__constant_map[value] index = len(self.__model.variables) var = self.__model.variables.add() var.domain.extend([value, value]) self.__constant_map[value] = index return index def VarIndexToVarProto(self, var_index): if var_index > 0: return self.__model.variables[var_index] else: return self.__model.variables[-var_index - 1] def _SetObjective(self, obj, minimize): """Sets the objective of the model.""" if isinstance(obj, IntVar): self.__model.ClearField('objective') self.__model.objective.coeffs.append(1) self.__model.objective.offset = 0 if minimize: self.__model.objective.vars.append(obj.Index()) self.__model.objective.scaling_factor = 1 else: self.__model.objective.vars.append(self.Negated(obj.Index())) self.__model.objective.scaling_factor = -1 elif isinstance(obj, LinearExpr): coeffs_map, constant = obj.GetVarValueMap() self.__model.ClearField('objective') if minimize: self.__model.objective.scaling_factor = 1 self.__model.objective.offset = constant else: self.__model.objective.scaling_factor = -1 self.__model.objective.offset = -constant for v, c, in iteritems(coeffs_map): self.__model.objective.coeffs.append(c) if minimize: self.__model.objective.vars.append(v.Index()) else: self.__model.objective.vars.append(self.Negated(v.Index())) elif isinstance(obj, numbers.Integral): self.__model.objective.offset = obj self.__model.objective.scaling_factor = 1 else: raise TypeError('TypeError: ' + str(obj) + ' is not a valid objective') def Minimize(self, obj): """Sets the objective of the model to minimize(obj).""" self._SetObjective(obj, minimize=True) def Maximize(self, obj): """Sets the objective of the model to maximize(obj).""" self._SetObjective(obj, minimize=False) def HasObjective(self): return self.__model.HasField('objective') def 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. """ strategy = self.__model.search_strategy.add() for v in variables: strategy.variables.append(v.Index()) strategy.variable_selection_strategy = var_strategy strategy.domain_reduction_strategy = domain_strategy def ModelStats(self): """Returns a string containing some model statistics.""" return pywrapsat.SatHelper.ModelStats(self.__model) def Validate(self): """Returns a string indicating that the model is invalid.""" return pywrapsat.SatHelper.ValidateModel(self.__model) def AssertIsBooleanVariable(self, x): if isinstance(x, IntVar): var = self.__model.variables[x.Index()] if len(var.domain) != 2 or var.domain[0] < 0 or var.domain[1] > 1: raise TypeError('TypeError: ' + str(x) + ' is not a boolean variable') elif not isinstance(x, _NotBooleanVariable): raise TypeError('TypeError: ' + str(x) + ' is not a boolean variable') def AddHint(self, var, value): self.__model.solution_hint.vars.append(self.GetOrMakeIndex(var)) self.__model.solution_hint.values.append(value) def EvaluateLinearExpr(expression, solution): """Evaluate a linear expression against a solution.""" if isinstance(expression, numbers.Integral): return expression value = 0 to_process = [(expression, 1)] while to_process: expr, coef = to_process.pop() if isinstance(expr, _ProductCst): to_process.append((expr.Expression(), coef * expr.Coefficient())) elif isinstance(expr, _SumArray): for e in expr.Expressions(): to_process.append((e, coef)) value += expr.Constant() * coef elif isinstance(expr, _ScalProd): for e, c in zip(expr.Expressions(), expr.Coefficients()): to_process.append((e, coef * c)) value += expr.Constant() * coef elif isinstance(expr, IntVar): value += coef * solution.solution[expr.Index()] elif isinstance(expr, _NotBooleanVariable): value += coef * (1 - solution.solution[expr.Not().Index()]) return value def EvaluateBooleanExpression(literal, solution): """Evaluate a boolean expression against a solution.""" if isinstance(literal, numbers.Integral): return bool(literal) elif isinstance(literal, IntVar) or isinstance(literal, _NotBooleanVariable): index = literal.Index() if index >= 0: return bool(solution.solution[index]) else: return not solution.solution[-index - 1] else: raise TypeError('Cannot interpret %s as a boolean expression.' % literal) class CpSolver(object): """Main solver class. The purpose of this class is to search for a solution to the model provided 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. """ def __init__(self): self.__model = None self.__solution = None self.parameters = sat_parameters_pb2.SatParameters() def Solve(self, model): """Solves the given model and returns the solve status.""" self.__solution = pywrapsat.SatHelper.SolveWithParameters( model.Proto(), self.parameters) return self.__solution.status def SolveWithSolutionCallback(self, model, callback): """Solves a problem and passes each solution found to the callback.""" self.__solution = ( pywrapsat.SatHelper.SolveWithParametersAndSolutionCallback( model.Proto(), self.parameters, callback)) return self.__solution.status def SearchForAllSolutions(self, model, callback): """Search for all solutions of a satisfiability problem. This method searches for all feasible solutions of a given model. Then it feeds the solution to the callback. Note that the model cannot contain an objective. Args: model: The model to solve. callback: The callback that will be called at each solution. Returns: The status of the solve: * *FEASIBLE* if some solutions have been found * *INFEASIBLE* if the solver has proved there are no solution * *OPTIMAL* if all solutions have been found """ if model.HasObjective(): raise TypeError('Search for all solutions is only defined on ' 'satisfiability problems') # Store old values. enumerate_all = self.parameters.enumerate_all_solutions self.parameters.enumerate_all_solutions = True self.__solution = ( pywrapsat.SatHelper.SolveWithParametersAndSolutionCallback( model.Proto(), self.parameters, callback)) # Restore parameters. self.parameters.enumerate_all_solutions = enumerate_all return self.__solution.status def Value(self, expression): """Returns the value of a linear expression after solve.""" if not self.__solution: raise RuntimeError('Solve() has not be called.') return EvaluateLinearExpr(expression, self.__solution) def BooleanValue(self, literal): """Returns the boolean value of a literal after solve.""" if not self.__solution: raise RuntimeError('Solve() has not be called.') return EvaluateBooleanExpression(literal, self.__solution) def ObjectiveValue(self): """Returns the value of the objective after solve.""" return self.__solution.objective_value def BestObjectiveBound(self): """Returns the best lower (upper) bound found when min(max)imizing.""" return self.__solution.best_objective_bound def StatusName(self, status): """Returns the name of the status returned by Solve().""" return cp_model_pb2.CpSolverStatus.Name(status) def NumBooleans(self): """Returns the number of boolean variables managed by the SAT solver.""" return self.__solution.num_booleans def NumConflicts(self): """Returns the number of conflicts since the creation of the solver.""" return self.__solution.num_conflicts def NumBranches(self): """Returns the number of search branches explored by the solver.""" return self.__solution.num_branches def WallTime(self): """Returns the wall time in seconds since the creation of the solver.""" return self.__solution.wall_time def UserTime(self): """Returns the user time in seconds since the creation of the solver.""" return self.__solution.user_time def ResponseStats(self): """Returns some statistics on the solution found as a string.""" return pywrapsat.SatHelper.SolverResponseStats(self.__solution) def ResponseProto(self): """Returns the response object.""" return self.__solution class CpSolverSolutionCallback(pywrapsat.SolutionCallback): """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. It inherits the following methods from its base class: * `ObjectiveValue(self)` * `BestObjectiveBound(self)` * `NumBooleans(self)` * `NumConflicts(self)` * `NumBranches(self)` * `WallTime(self)` * `UserTime(self)` These methods returns the same information as their counterpart in the `CpSolver` class. """ def __init__(self): pywrapsat.SolutionCallback.__init__(self) def OnSolutionCallback(self): """Proxy for the same method in snake case.""" self.on_solution_callback() def 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. """ if not self.HasResponse(): raise RuntimeError('Solve() has not be called.') if isinstance(lit, numbers.Integral): return bool(lit) elif isinstance(lit, IntVar) or isinstance(lit, _NotBooleanVariable): index = lit.Index() return self.SolutionBooleanValue(index) else: raise TypeError('Cannot interpret %s as a boolean expression.' % lit) def 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 LinearExpr. """ if not self.HasResponse(): raise RuntimeError('Solve() has not be called.') if isinstance(expression, numbers.Integral): return expression value = 0 to_process = [(expression, 1)] while to_process: expr, coef = to_process.pop() if isinstance(expr, _ProductCst): to_process.append( (expr.Expression(), coef * expr.Coefficient())) elif isinstance(expr, _SumArray): for e in expr.Expressions(): to_process.append((e, coef)) value += expr.Constant() * coef elif isinstance(expr, _ScalProd): for e, c in zip(expr.Expressions(), expr.Coefficients()): to_process.append((e, coef * c)) value += expr.Constant() * coef elif isinstance(expr, IntVar): value += coef * self.SolutionIntegerValue(expr.Index()) elif isinstance(expr, _NotBooleanVariable): value += coef * (1 - self.SolutionIntegerValue(expr.Not().Index())) return value class ObjectiveSolutionPrinter(CpSolverSolutionCallback): """Display the objective value and time of intermediate solutions.""" def __init__(self): CpSolverSolutionCallback.__init__(self) self.__solution_count = 0 self.__start_time = time.time() def on_solution_callback(self): """Called on each new solution.""" current_time = time.time() obj = self.ObjectiveValue() print('Solution %i, time = %0.2f s, objective = %i' % (self.__solution_count, current_time - self.__start_time, obj)) self.__solution_count += 1 def solution_count(self): """Returns the number of solutions found.""" return self.__solution_count class VarArrayAndObjectiveSolutionPrinter(CpSolverSolutionCallback): """Print intermediate solutions (objective, variable values, time).""" def __init__(self, variables): CpSolverSolutionCallback.__init__(self) self.__variables = variables self.__solution_count = 0 self.__start_time = time.time() def on_solution_callback(self): """Called on each new solution.""" current_time = time.time() obj = self.ObjectiveValue() print('Solution %i, time = %0.2f s, objective = %i' % (self.__solution_count, current_time - self.__start_time, obj)) for v in self.__variables: print(' %s = %i' % (v, self.Value(v)), end=' ') print() self.__solution_count += 1 def solution_count(self): """Returns the number of solutions found.""" return self.__solution_count class VarArraySolutionPrinter(CpSolverSolutionCallback): """Print intermediate solutions (variable values, time).""" def __init__(self, variables): CpSolverSolutionCallback.__init__(self) self.__variables = variables self.__solution_count = 0 self.__start_time = time.time() def on_solution_callback(self): """Called on each new solution.""" current_time = time.time() print('Solution %i, time = %0.2f s' % (self.__solution_count, current_time - self.__start_time)) for v in self.__variables: print(' %s = %i' % (v, self.Value(v)), end=' ') print() self.__solution_count += 1 def solution_count(self): """Returns the number of solutions found.""" return self.__solution_count