Python pulp.LpProblem方法代码示例

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def update_objective(self, input_data, dmu_code, input_variables,
                         output_variables, lp_model):
        ''' Updates coefficients of the objective function of a given model.

            Args:
                input_data (InputData): object that stores input data.
                dmu_code (str): DMU code.
                input_variables (dict of str to pulp.LpVariable): dictionary
                    that maps variable name to pulp variable corresponding
                    to input categories.
                output_variables (dict of str to pulp.LpVariable): dictionary
                    that maps variable name to pulp variable corresponding
                    to output categories.
                lp_model (pulp.LpProblem): linear programming model.
        '''
        for category in input_data.output_categories:
            lp_model.objective[output_variables[category]] = input_data.coefficients[
                dmu_code, category] 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def update_equality_constraint(self, input_data, dmu_code, input_variables,
                                   output_variables, lp_model):
        ''' Updates coefficients of the equality constraint of a given model.

            Args:
                input_data (InputData): object that stores input data.
                dmu_code (str): DMU code.
                input_variables (dict of str to pulp.LpVariable): dictionary
                    that maps variable name to pulp variable corresponding
                    to input categories.
                output_variables (dict of str to pulp.LpVariable): dictionary
                    that maps variable name to pulp variable corresponding
                    to output categories.
                lp_model (pulp.LpProblem): linear programming model.
        '''
        for category, var in input_variables.items():
            lp_model.constraints['equality_constraint'][var] = input_data.coefficients[
                dmu_code, category] 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def define_lp(fares, product_names):
    """Set up LP.

    Parameters
    ----------
    fares: 2D np array
            contains fares for products, size n_classes*n_products
    products_names: list
            product names (typically relation/class combinations)

    Returns
    -------
    tuple of the form (LP problem, decision variables)
    """
    prob = pulp.LpProblem('network_RM', pulp.LpMaximize)

    # decision variables: available seats per product
    x = pulp.LpVariable.dicts('x', product_names, lowBound=0)

    # objective function
    revenue = pulp.lpSum([x[it]*fares.ravel()[i] for i, it in
                          enumerate(product_names)])
    prob += revenue

    return prob, x 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def label_prop(C, nt, Dct, lp="linear"):
    
#Inputs:
#  C      :    Number of share classes between src and tar
#  nt     :    Number of target domain samples
#  Dct    :    All d_ct in matrix form, nt * C
#  lp     :    Type of linear programming: linear (default) | binary
#Outputs:
#  Mcj    :    all M_ct in matrix form, m * C
    
    Dct = abs(Dct)
    model = pulp.LpProblem("Cost minimising problem", pulp.LpMinimize)
    Mcj = pulp.LpVariable.dicts("Probability",
                                ((i, j) for i in range(C) for j in range(nt)),
                                lowBound=0,
                                upBound=1,
                                cat='Continuous')
    
    # Objective Function
    model += (
    pulp.lpSum([Dct[j, i]*Mcj[(i, j)] for i in range(C) for j in range(nt)])
    )
    
    # Constraints
    for j in range(nt):
        model += pulp.lpSum([Mcj[(i, j)] for i in range(C)]) == 1
    for i in range(C):
        model += pulp.lpSum([Mcj[(i, j)] for j in range(nt)]) >= 1
    
    # Solve our problem
    model.solve()
    pulp.LpStatus[model.status]
    Output = [[Mcj[i, j].varValue for i in range(C)] for j in range(nt)]
    
    return np.array(Output) 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def __init__(self):
        self.prob = LpProblem('Daily Fantasy Sports', LpMaximize) 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def __init__(self, input_data, input_params):
        self.input_data = input_data
        self.input_params = input_params
        self.model = pulp.LpProblem(name='prod_planning', sense=pulp.LpMinimize)
        self._create_decision_variables()
        self._create_main_constraints()
        self._set_objective_function()

    # ================== Decision variables ================== 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def create_model(self):
        def distances(assignment):
            return l2_distance(self.data[assignment[0]], self.centroids[assignment[1]])

        clusters = list(range(self.k))
        assignments = [(i, j)for i in range(self.n) for j in range(self.k)]

        # outflow variables for data nodes
        self.y = pulp.LpVariable.dicts('data-to-cluster assignments',
                                  assignments,
                                  lowBound=0,
                                  upBound=1,
                                  cat=pulp.LpInteger)

        # outflow variables for cluster nodes
        self.b = pulp.LpVariable.dicts('cluster outflows',
                                  clusters,
                                  lowBound=0,
                                  upBound=self.n-self.min_size,
                                  cat=pulp.LpContinuous)

        # create the model
        self.model = pulp.LpProblem("Model for assignment subproblem", pulp.LpMinimize)

        # objective function
        self.model += pulp.lpSum(distances(assignment) * self.y[assignment] for assignment in assignments)

        # flow balance constraints for data nodes
        for i in range(self.n):
            self.model += pulp.lpSum(self.y[(i, j)] for j in range(self.k)) == 1

        # flow balance constraints for cluster nodes
        for j in range(self.k):
            self.model += pulp.lpSum(self.y[(i, j)] for i in range(self.n)) - self.min_size == self.b[j]

        # flow balance constraint for the sink node
        self.model += pulp.lpSum(self.b[j] for j in range(self.k)) == self.n - (self.k * self.min_size) 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def create_model(self):
        def distances(assignment):
            return l2_distance(self.data[assignment[0]], self.centroids[assignment[1]])

        clusters = list(range(self.k))
        assignments = [(i, j)for i in range(self.n) for j in range(self.k)]

        # outflow variables for data nodes
        self.y = pulp.LpVariable.dicts('data-to-cluster assignments',
                                  assignments,
                                  lowBound=0,
                                  upBound=1,
                                  cat=pulp.LpInteger)

        # outflow variables for cluster nodes
        self.b = pulp.LpVariable.dicts('cluster outflows',
                                  clusters,
                                  lowBound=0,
                                  upBound=self.n-self.min_size,
                                  cat=pulp.LpContinuous)

        # create the model
        self.model = pulp.LpProblem("Model for assignment subproblem", pulp.LpMinimize)

        # objective function
        self.model += pulp.lpSum([distances(assignment) * self.y[assignment] for assignment in assignments])

        # flow balance constraints for data nodes
        for i in range(self.n):
            self.model += pulp.lpSum(self.y[(i, j)] for j in range(self.k)) == 1

        # flow balance constraints for cluster nodes
        for j in range(self.k):
            self.model += pulp.lpSum(self.y[(i, j)] for i in range(self.n)) - self.min_size == self.b[j]
            
        # capacity constraint on outflow of cluster nodes
        for j in range(self.k):
            self.model += self.b[j] <= self.max_size - self.min_size 

        # flow balance constraint for the sink node
        self.model += pulp.lpSum(self.b[j] for j in range(self.k)) == self.n - (self.k * self.min_size) 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def create_model(self):
        def distances(assignment):
            return l2_distance(self.data[assignment[0]], self.centroids[assignment[1]])

        assignments = [(i, j) for i in range(self.n) for j in range(self.k)]

        # assignment variables
        self.y = pulp.LpVariable.dicts('data-to-cluster assignments',
                                  assignments,
                                  lowBound=0,
                                  upBound=1,
                                  cat=pulp.LpInteger)

        # create the model
        self.model = pulp.LpProblem("Model for assignment subproblem", pulp.LpMinimize)

        # objective function
        self.model += pulp.lpSum([distances(assignment) * self.weights[assignment[0]] * self.y[assignment] for assignment in assignments]), 'Objective Function - sum weighted squared distances to assigned centroid'
        # this is also weighted, otherwise the weighted centroid computation don't make sense.

        # constraints on the total weights of clusters
        for j in range(self.k):
            self.model += pulp.lpSum([self.weights[i] * self.y[(i, j)] for i in range(self.n)]) >= self.min_weight, "minimum weight for cluster {}".format(j)
            self.model += pulp.lpSum([self.weights[i] * self.y[(i, j)] for i in range(self.n)]) <= self.max_weight, "maximum weight for cluster {}".format(j)

        # make sure each point is assigned at least once, and only once
        for i in range(self.n):
            self.model += pulp.lpSum([self.y[(i, j)] for j in range(self.k)]) == 1, "must assign point {}".format(i) 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def _gclp(self, composition={}, mus={}, phases=[]):
        if not qmpy.FOUND_PULP:
            raise Exception('Cannot do GCLP without installing PuLP and an LP',
                    'solver')
        prob = pulp.LpProblem('GibbsEnergyMin', pulp.LpMinimize)
        phase_vars = pulp.LpVariable.dicts('lib', phases, 0.0)
        prob += pulp.lpSum([ (p.energy -
            sum([ p.unit_comp.get(elt,0)*mu
                for elt, mu in mus.items() ])) * phase_vars[p]
            for p in phases]),\
                    "Free Energy"
        for elt, constraint in composition.items():
            prob += pulp.lpSum([
                p.unit_comp.get(elt,0)*phase_vars[p]
                for p in phases ]) == float(constraint),\
                        'Conservation of '+elt
        ##[vh]
        ##print prob
        if pulp.GUROBI().available():
            prob.solve(pulp.GUROBI(msg=False))
        elif pulp.COIN_CMD().available():
            prob.solve(pulp.COIN_CMD())
        else:
            prob.solve()

        phase_comp = dict([ (p, phase_vars[p].varValue)
            for p in phases if phase_vars[p].varValue > 1e-5])
        
        energy = sum( p.energy*amt for p, amt in phase_comp.items() )
        energy -= sum([ a*composition.get(e, 0) for e,a in mus.items()])
        return energy, phase_comp 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def get_minima(self, phases, bounds):
        """
        Given a set of Phases, get_minima will determine the minimum
        free energy elemental composition as a weighted sum of these
        compounds
        """

        prob = pulp.LpProblem('GibbsEnergyMin', pulp.LpMinimize)
        pvars = pulp.LpVariable.dicts('phase', phases, 0)
        bvars = pulp.LpVariable.dicts('bound', bounds, 0.0, 1.0)
        prob += pulp.lpSum( self.phase_energy(p)*pvars[p] for p in phases ) - \
                pulp.lpSum( self.phase_energy(bound)*bvars[bound] for bound in bounds ), \
                                "Free Energy"
        for elt in self.bound_space:
            prob += sum([ p.unit_comp.get(elt,0)*pvars[p] for p in phases ])\
                        == \
                sum([ b.unit_comp.get(elt, 0)*bvars[b] for b in bounds ]),\
                            'Contraint to the proper range of'+elt
        prob += sum([ bvars[b] for b in bounds ]) == 1, \
                'sum of bounds must be 1'

        if pulp.GUROBI().available():
            prob.solve(pulp.GUROBI(msg=False))
        elif pulp.COIN_CMD().available():
            prob.solve(pulp.COIN_CMD())
        elif pulp.COINMP_DLL().available():
            prob.solve(pulp.COINMP_DLL())
        else:
            prob.solve()

        E = pulp.value(prob.objective)
        xsoln = defaultdict(float,
            [(p, pvars[p].varValue) for p in phases if
                abs(pvars[p].varValue) > 1e-4])
        return xsoln, E 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def _create_lp(self):
        ''' Creates initial linear program.
        '''
        assert len(self.input_data.DMU_codes) != 0
        self._variables.clear()
        self._constraints.clear()
        # create LP for the first DMU - it is the easiest way to
        # adapt current code
        for elem in self.input_data.DMU_codes:
            dmu_code = elem
            break

        orientation = self._concrete_model.get_orientation()
        obj_type = self._concrete_model.get_objective_type()
        self.lp_model = pulp.LpProblem('Envelopment model: {0}-'
                                       'oriented'.format(orientation),
                                       obj_type)
        ub_obj = self._concrete_model.get_upper_bound_for_objective_variable()
        obj_variable = pulp.LpVariable('Variable in objective function',
                                       self._concrete_model.
                                       get_lower_bound_for_objective_variable(),
                                       ub_obj,
                                       pulp.LpContinuous)
        self._variables['obj_var'] = obj_variable

        self.lp_model += (obj_variable,
                          'Efficiency score or inverse of efficiency score')

        lambda_variables = pulp.LpVariable.dicts('lambda',
                                                 self.input_data.DMU_codes,
                                                 0, None, pulp.LpContinuous)
        self._variables.update(lambda_variables)
        self._add_constraints_for_outputs(lambda_variables, dmu_code,
                                          obj_variable)
        self._add_constraints_for_inputs(lambda_variables, dmu_code,
                                         obj_variable) 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def solve(g):
    el = g.get_edge_list()
    nl = g.get_node_list()
    p = LpProblem('min_cost', LpMinimize)
    capacity = {}
    cost = {}
    demand = {}
    x = {}
    for e in el:
        capacity[e] = g.get_edge_attr(e[0], e[1], 'capacity')
        cost[e] = g.get_edge_attr(e[0], e[1], 'cost')
    for i in nl:
        demand[i] = g.get_node_attr(i, 'demand')
    for e in el:
        x[e] = LpVariable("x"+str(e), 0, capacity[e])
    # add obj
    objective = lpSum (cost[e]*x[e] for e in el)
    p += objective
    # add constraints
    for i in nl:
        out_neig = g.get_out_neighbors(i)
        in_neig = g.get_in_neighbors(i)
        p += lpSum(x[(i,j)] for j in out_neig) -\
             lpSum(x[(j,i)] for j in in_neig)==demand[i]
    p.solve()
    return x, value(objective) 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def add_capacity_constraints(prob, x, A, product_names, capacities,
                             leg_names=None):
    """Add capacity contraints as upper bound on the segments/legs.

  Parameters
  ----------
    prob: pulp.LpProblem
        the LP problem
    x: dict
        contains decision variables (each of which has type pulp.pulp.LpVariable)
    A: 2D np array
        incidence matrix, size n_relations*n_legs
    products_names: list
        list of product names (e.g. relation/class combinations)
    capacities: Iterable
        list of capacity on each leg/segment
    leg_names: list
        list of leg names

    Returns
    -------
    list of tuples of the form (constraint, constraint_name)
    where `constraint` is of type pulp.LpConstraint
    and `constraint_name` is of type str
    """
    n_trips, n_legs = A.shape
    n_classes = int(len(product_names) / n_trips)
    A_ = np.tile(A, (n_classes, 1))

    capacity_constraints = []
    for leg, leg_name in enumerate(leg_names):
        leg_load = pulp.lpSum([x[it]*A_[i, leg] for i, it
                               in enumerate(product_names)])
        capacity_constraint = (leg_load <= capacities[leg],
                               "cap_{}".format(leg_name))
        prob += capacity_constraint
        capacity_constraints.append(capacity_constraint)

    return capacity_constraints 

# 需要导入模块: import pulp [as 别名]
# 或者: from pulp import LpProblem [as 别名]
def _solve_balancing_ilp_pulp(A):
    import pulp
    x = [pulp.LpVariable('x%d' % i, lowBound=1, cat='Integer') for i in range(A.shape[1])]
    prob = pulp.LpProblem("chempy balancing problem", pulp.LpMinimize)
    prob += reduce(add, x)
    for expr in [pulp.lpSum([x[i]*e for i, e in enumerate(row)]) for row in A.tolist()]:
        prob += expr == 0
    prob.solve()
    return [pulp.value(_) for _ in x]