def __init__(self, name: str = "",
sense: str = MINIMIZE,
solver_name: str = ''):
# initializing variables with default values
self.name: str = name
self.sense: str = sense
self.solver_name: str = solver_name
self.solver: Solver = None
# list of constraints and variables
self.constrs: List[Constr] = []
self.vars: List[Var] = []
if solver_name.upper() == GUROBI:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
elif solver_name.upper() == CBC:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
else:
# search for the best solver available
if gurobi.has_gurobi:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
elif cbc.has_cbc:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
def __init__(self, name: str = "",
sense: str = MINIMIZE,
solver_name: str = ""):
"""Model constructor
Creates a Mixed-Integer Linear Programming Model. The default model
optimization direction is Minimization. To store and optimize the model
the MIP package automatically searches and connects in runtime to the
dynamic library of some MIP solver installed on your computer, nowadays
gurobi and cbc are supported. This solver is automatically selected,
but you can force the selection of a specific solver with the parameter
solver_name.
Args:
name (str): model name
sense (str): MINIMIZATION ("MIN") or MAXIMIZATION ("MAX")
solver_name: gurobi or cbc, searches for which
solver is available if not informed
"""
# initializing variables with default values
self.name = name
self.solver_name = solver_name
self.solver = None
if "solver_name" in environ:
solver_name = environ["solver_name"]
if "solver_name".upper() in environ:
solver_name = environ["solver_name".upper()]
self.__mipStart = []
# list of constraints and variables
self.constrs = []
self.constrs_by_name = {}
self.vars = []
self.vars_by_name = {}
self.cut_generators = []
if solver_name.upper() == GUROBI:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
elif solver_name.upper() == CBC:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
else:
# checking which solvers are available
from mip import gurobi
if gurobi.has_gurobi:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
self.solver_name = GUROBI
else:
from mip import cbc
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
self.solver_name = CBC
self.sense = sense
def clear(self: Solver):
"""Clears the model
All variables, constraints and parameters will be reset. In addition,
a new solver instance will be instantiated to implement the
formulation.
"""
# creating a new solver instance
sense = self.sense
if self.solver_name.upper() == GUROBI:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
elif self.solver_name.upper() == CBC:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
else:
# checking which solvers are available
from mip import gurobi
if gurobi.found:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
self.solver_name = GUROBI
else:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
self.solver_name = CBC
# list of constraints and variables
self.constrs = ConstrList(self)
self.vars = VarList(self)
# initializing additional control variables
self.__cuts = 1
self.__cuts_generator = None
self.__lazy_constrs_generator = None
self.__start = []
self._status = OptimizationStatus.LOADED
self.__threads = 0
class Model:
def __init__(self, name: str = "",
sense: str = MINIMIZE,
solver_name: str = ''):
# initializing variables with default values
self.name: str = name
self.sense: str = sense
self.solver_name: str = solver_name
self.solver: Solver = None
# list of constraints and variables
self.constrs: List[Constr] = []
self.vars: List[Var] = []
if solver_name.upper() == GUROBI:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
elif solver_name.upper() == CBC:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
else:
# search for the best solver available
if gurobi.has_gurobi:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
elif cbc.has_cbc:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
def __del__(self):
if self.solver:
del self.solver
def __iadd__(self, other) -> 'Model':
if isinstance(other, LinExpr):
if len(other.sense) == 0:
# adding objective function components
self.set_objective(other)
else:
# adding constraint
self.add_constr(other)
elif isinstance(other, tuple):
if isinstance(other[0], LinExpr) and isinstance(other[1], str):
if len(other[0].sense) == 0:
self.set_objective(other[0])
else:
self.add_constr(other[0], other[1])
return self
def add_var(self, name: str = "",
lb: float = 0.0,
ub: float = INF,
obj: float = 0.0,
type: str = CONTINUOUS,
column: "Column" = None) -> "Var":
if len(name.strip()) == 0:
nc = self.solver.num_cols()
name = 'C{:011d}'.format(nc)
idx = self.solver.add_var(obj, lb, ub, type, column, name)
self.vars.append(Var(self, idx, name))
return self.vars[-1]
def add_constr(self, lin_expr: "LinExpr", name: str = "") -> Constr:
if isinstance(lin_expr, bool):
return None # empty constraint
idx = self.solver.add_constr(lin_expr, name)
self.constrs.append(Constr(self, idx, name))
return self.constrs[-1]
def copy(self, solver_name: str = None) -> "Model":
if not solver_name:
solver_name = self.solver_name
copy: Model = Model(self.name, self.sense, solver_name)
# adding variables
for v in self.vars:
copy.add_var(name=v.name, lb=v.lb, ub=v.ub, obj=v.obj, type=v.type)
# adding constraints
for c in self.constrs:
expr = c.expr # todo: make copy of constraint's lin_expr
copy.add_constr(lin_expr=expr, name=c.name)
# setting objective function's constant
copy.set_objective_const(self.get_objective_const())
return copy
def get_objective(self) -> LinExpr:
return self.solver.get_objective()
def get_objective_const(self) -> float:
return self.solver.get_objective_const()
def optimize(self,
maxSeconds=inf,
maxNodes=inf,
maxSolutions=inf
) -> int:
if maxSeconds != inf or maxNodes != inf or maxSolutions != inf:
self.solver.set_processing_limits(maxSeconds, maxNodes, maxSolutions)
self.solver.optimize()
def get_objective_value(self) -> float:
return self.solver.get_objective_value()
def set_start(self, variables: List["Var"], values: List[float]):
self.solver.set_start(variables, values)
def set_objective(self, expr, sense: str = "") -> None:
if isinstance(expr, int) or isinstance(expr, float):
self.solver.set_objective(LinExpr([], [], expr))
elif isinstance(expr, Var):
self.solver.set_objective(LinExpr([expr], [1]))
elif isinstance(expr, LinExpr):
self.solver.set_objective(expr, sense)
def set_objective_const(self, const: float) -> None:
return self.solver.set_objective_const(const)
def write(self, path: str) -> None:
self.solver.write(path)
def read(self, path: str) -> None:
self.solver.read(path)
nCols = self.solver.num_cols()
nRows = self.solver.num_rows()
for i in range(nCols):
self.vars.append(Var(self, i, self.solver.var_get_name(i)))
for i in range(nRows):
self.constrs.append(Constr(self, i, self.solver.constr_get_name(i)))
@property
def num_cols(self) -> int:
return len(self.vars)
@property
def num_rows(self) -> int:
return len(self.constrs)
def __init__(self,
name: str = "",
sense: str = MINIMIZE,
solver_name: str = ""):
"""Model constructor
Creates a Mixed-Integer Linear Programming Model. The default model
optimization direction is Minimization. To store and optimize the model
the MIP package automatically searches and connects in runtime to the
dynamic library of some MIP solver installed on your computer, nowadays
gurobi and cbc are supported. This solver is automatically selected,
but you can force the selection of a specific solver with the parameter
solver_name.
Args:
name (str): model name
sense (str): MINIMIZATION ("MIN") or MAXIMIZATION ("MAX")
solver_name: gurobi or cbc, searches for which
solver is available if not informed
"""
# initializing variables with default values
self.name = name
self.solver_name = solver_name
self.solver = None
# reading solver_name from an environment variable (if applicable)
if not self.solver_name and "solver_name" in environ:
self.solver_name = environ["solver_name"]
if not self.solver_name and "solver_name".upper() in environ:
self.solver_name = environ["solver_name".upper()]
# creating a solver instance
if self.solver_name.upper() == GUROBI:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
elif self.solver_name.upper() == CBC:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
else:
# checking which solvers are available
from mip import gurobi
if gurobi.has_gurobi:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
self.solver_name = GUROBI
else:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
self.solver_name = CBC
# list of constraints and variables
self.constrs = ConstrList(self)
self.vars = VarList(self)
# initializing additional control variables
self.__cuts = 1
self.__cuts_generator = None
self.__lazy_constrs_generator = None
self.__start = None
self.__status = OptimizationStatus.LOADED
self.__threads = 0
self.__n_cols = 0
self.__n_rows = 0
class Model:
""" Mixed Integer Programming Model
This is the main class, providing methods for building, optimizing,
querying optimization results and re-optimizing Mixed-Integer Programming
Models.
To check how models are created please see the examples included.
"""
def __init__(self,
name: str = "",
sense: str = MINIMIZE,
solver_name: str = ""):
"""Model constructor
Creates a Mixed-Integer Linear Programming Model. The default model
optimization direction is Minimization. To store and optimize the model
the MIP package automatically searches and connects in runtime to the
dynamic library of some MIP solver installed on your computer, nowadays
gurobi and cbc are supported. This solver is automatically selected,
but you can force the selection of a specific solver with the parameter
solver_name.
Args:
name (str): model name
sense (str): MINIMIZATION ("MIN") or MAXIMIZATION ("MAX")
solver_name: gurobi or cbc, searches for which
solver is available if not informed
"""
# initializing variables with default values
self.name = name
self.solver_name = solver_name
self.solver = None
# reading solver_name from an environment variable (if applicable)
if not self.solver_name and "solver_name" in environ:
self.solver_name = environ["solver_name"]
if not self.solver_name and "solver_name".upper() in environ:
self.solver_name = environ["solver_name".upper()]
# creating a solver instance
if self.solver_name.upper() == GUROBI:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
elif self.solver_name.upper() == CBC:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
else:
# checking which solvers are available
from mip import gurobi
if gurobi.has_gurobi:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
self.solver_name = GUROBI
else:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
self.solver_name = CBC
# list of constraints and variables
self.constrs = ConstrList(self)
self.vars = VarList(self)
# initializing additional control variables
self.__cuts = 1
self.__cuts_generator = None
self.__lazy_constrs_generator = None
self.__start = None
self.__status = OptimizationStatus.LOADED
self.__threads = 0
self.__n_cols = 0
self.__n_rows = 0
def __del__(self):
if self.solver:
del self.solver
def __iadd__(self, other) -> "Model":
if isinstance(other, LinExpr):
if len(other.sense) == 0:
# adding objective function components
self.objective = other
else:
# adding constraint
self.add_constr(other)
elif isinstance(other, tuple):
if isinstance(other[0], LinExpr) and isinstance(other[1], str):
if len(other[0].sense) == 0:
self.objective = other[0]
else:
self.add_constr(other[0], other[1])
return self
def add_var(self,
name: str = "",
lb: float = 0.0,
ub: float = INF,
obj: float = 0.0,
var_type: str = CONTINUOUS,
column: "Column" = None) -> "Var":
""" Creates a new variable in the model, returning its reference
Args:
name (str): variable name (optional)
lb (float): variable lower bound, default 0.0
ub (float): variable upper bound, default infinity
obj (float): coefficient of this variable in the objective
function, default 0
var_type (str): CONTINUOUS ("C"), BINARY ("B") or INTEGER ("I")
column (Column): constraints where this variable will appear,
necessary only when constraints are already created in
the model and a new variable will be created.
Examples:
To add a variable :code:`x` which is continuous and greater or
equal to zero to model :code:`m`::
x = m.add_var()
The following code creates a vector of binary variables
:code:`x[0], ..., x[n-1]` to model :code:`m`::
x = [m.add_var(var_type=BINARY) for i in range(n)]
"""
if var_type == BINARY:
lb = 0.0
ub = 1.0
if len(name.strip()) == 0:
nc = self.solver.num_cols()
name = "C{:011d}".format(nc)
self.solver.add_var(obj, lb, ub, var_type, column, name)
self.__n_cols += 1
return Var(self, self.__n_cols - 1)
def add_constr(self, lin_expr: "LinExpr", name: str = "") -> "Constr":
""" Creates a new constraint (row)
Adds a new constraint to the model, returning its reference
Args:
lin_expr (LinExpr): linear expression
name (str): optional constraint name, used when saving model to\
lp or mps files
Examples:
The following code adds the constraint :math:`x_1 + x_2 \leq 1`
(x1 and x2 should be created first using
:func:`add_var<mip.model.Model.add_var>`)::
m += x1 + x2 <= 1
Which is equivalent to::
m.add_constr( x1 + x2 <= 1 )
Summation expressions can be used also, to add the constraint \
:math:`\displaystyle \sum_{i=0}^{n-1} x_i = y` and name this \
constraint :code:`cons1`::
m += xsum(x[i] for i in range(n)) == y, "cons1"
Which is equivalent to::
m.add_constr( xsum(x[i] for i in range(n)) == y, "cons1" )
"""
if isinstance(lin_expr, bool):
raise InvalidLinExpr("A boolean (true/false) cannot be \
used as a constraint.")
self.__n_rows += 1
self.solver.add_constr(lin_expr, name)
return Constr(self, self.__n_rows - 1)
def clear(self):
"""Clears the model
All variables, constraints and parameters will be reset. In addition,
a new solver instance will be instantiated to implement the
formulation.
"""
# creating a new solver instance
sense = self.sense
self.__n_cols = 0
self.__n_rows = 0
if self.solver_name.upper() == GUROBI:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
elif self.solver_name.upper() == CBC:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
else:
# checking which solvers are available
from mip import gurobi
if gurobi.has_gurobi:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
self.solver_name = GUROBI
else:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
self.solver_name = CBC
# list of constraints and variables
self.constrs = ConstrList(self)
self.vars = VarList(self)
# initializing additional control variables
self.__cuts = 1
self.__cuts_generator = None
self.__start = []
self.__status = OptimizationStatus.LOADED
self.__threads = 0
def copy(self, solver_name: str = None) -> "Model":
""" Creates a copy of the current model
Args:
solver_name(str): solver name (optional)
Returns:
clone of current model
"""
if not solver_name:
solver_name = self.solver_name
copy = Model(self.name, self.sense, solver_name)
# adding variables
for v in self.vars:
copy.add_var(name=v.name,
lb=v.lb,
ub=v.ub,
obj=v.obj,
var_type=v.var_type)
# adding constraints
for c in self.constrs:
orig_expr = c.expr
expr = LinExpr(const=orig_expr.const, sense=orig_expr.sense)
for (var, value) in orig_expr.expr.items():
expr.add_term(self.vars[var.idx], value)
copy.add_constr(lin_expr=expr, name=c.name)
# setting objective function"s constant
copy.objective_const = self.objective_const
return copy
def constr_by_name(self, name: str) -> "Constr":
""" Queries a constraint by its name
Args:
name(str): constraint name
Returns:
constraint or None if not found
"""
cidx = self.solver.constr_get_index(name)
if cidx < 0 or cidx > len(self.constrs):
return None
return self.constrs[cidx]
def var_by_name(self, name: str) -> "Var":
"""Searchers a variable by its name
Returns:
Variable or None if not found
"""
v = self.solver.var_get_index(name)
if v < 0 or v > len(self.vars):
return None
return self.vars[v]
def optimize(self,
max_seconds: float = inf,
max_nodes: int = inf,
max_solutions: int = inf) -> OptimizationStatus:
""" Optimizes current model
Optimizes current model, optionally specifying processing limits.
To optimize model :code:`m` within a processing time limit of
300 seconds::
m.optimize(max_seconds=300)
Args:
max_seconds (float): Maximum runtime in seconds (default: inf)
max_nodes (float): Maximum number of nodes (default: inf)
max_solutions (float): Maximum number of solutions (default: inf)
Returns:
optimization status, which can be OPTIMAL(0), ERROR(-1),
INFEASIBLE(1), UNBOUNDED(2). When optimizing problems
with integer variables some additional cases may happen,
FEASIBLE(3) for the case when a feasible solution was found
but optimality was not proved, INT_INFEASIBLE(4) for the case
when the lp relaxation is feasible but no feasible integer
solution exists and NO_SOLUTION_FOUND(5) for the case when
an integer solution was not found in the optimization.
"""
if self.__threads != 0:
self.solver.set_num_threads(self.__threads)
# self.solver.set_callbacks(branch_selector,
# incumbent_updater, lazy_constrs_generator)
self.solver.set_processing_limits(max_seconds, max_nodes,
max_solutions)
self.__status = self.solver.optimize()
return self.__status
def read(self, path: str):
"""Reads a MIP model in :code:`.lp` or :code:`.mps` format.
Note: all variables, constraints and parameters from the current model
will be cleared.
Args:
path(str): file name
"""
self.clear()
self.solver.read(path)
self.__n_cols = self.solver.num_cols()
self.__n_rows = self.solver.num_rows()
def relax(self):
""" Relax integrality constraints of variables
Changes the type of all integer and binary variables to
continuous. Bounds are preserved.
"""
self.solver.relax()
for v in self.vars:
if v.type == BINARY or v.type == INTEGER:
v.type = CONTINUOUS
def write(self, path: str):
"""Saves the MIP model, using the extension :code:`.lp` or
:code:`.mps` to specify the file format.
Args:
path(str): file name
"""
self.solver.write(path)
@property
def objective_bound(self) -> float:
return self.solver.get_objective_bound()
@property
def objective(self) -> LinExpr:
"""The objective function of the problem as a linear expression.
Examples:
The following code adds all :code:`x` variables :code:`x[0],
..., x[n-1]`, to the objective function of model :code:`m`
with the same cost :code:`w`::
m.objective = xsum(w*x[i] for i in range(n))
A simpler way to define the objective function is the use of the
model operator += ::
m += xsum(w*x[i] for i in range(n))
Note that the only difference of adding a constraint is the lack of
a sense and a rhs.
"""
return self.solver.get_objective()
@objective.setter
def objective(self, objective):
if isinstance(objective, int) or isinstance(objective, float):
self.solver.set_objective(LinExpr([], [], objective))
elif isinstance(objective, Var):
self.solver.set_objective(LinExpr([objective], [1]))
elif isinstance(objective, LinExpr):
self.solver.set_objective(objective)
@property
def verbose(self) -> int:
"""0 to disable solver messages printed on the screen, 1 to enable
"""
return self.solver.get_verbose()
@verbose.setter
def verbose(self, verbose: int):
self.solver.set_verbose(verbose)
@property
def threads(self) -> int:
"""number of threads to be used when solving the problem.
0 uses solver default configuration, -1 uses the number of available
processing cores and :math:`\geq 1` uses the specified number of
threads. An increased number of threads may improve the solution
time but also increases the memory consumption."""
return self.__threads
@threads.setter
def threads(self, threads: int):
self.__threads = threads
@property
def sense(self) -> str:
""" The optimization sense
Returns:
the objective function sense, MINIMIZE (default) or (MAXIMIZE)
"""
return self.solver.get_objective_sense()
@sense.setter
def sense(self, sense: str):
self.solver.set_objective_sense(sense)
@property
def objective_const(self) -> float:
"""Returns the constant part of the objective function
"""
return self.solver.get_objective_const()
@objective_const.setter
def objective_const(self, objective_const: float):
self.solver.set_objective_const(objective_const)
@property
def objective_value(self) -> float:
"""Objective function value of the solution found
"""
return self.solver.get_objective_value()
@property
def num_solutions(self) -> int:
"""Number of solutions found during the MIP search
Returns:
number of solutions stored in the solution pool
"""
return self.solver.get_num_solutions()
@property
def objective_values(self) -> List[float]:
"""List of costs of all solutions in the solution pool
Returns:
costs of all solutions stored in the solution pool
as an array from 0 (the best solution) to
:attr:`~mip.model.model.num_solutions`-1.
"""
return [
float(self.solver.get_objective_value_i(i))
for i in range(self.num_solutions)
]
@property
def cuts_generator(self) -> "CutsGenerator":
"""Cut generator callback. Cut generators are called whenever a
solution where one or more integer variables appear with
continuous values. A cut generator will try to produce
one or more inequalities to remove this fractional point.
"""
return self.__cuts_generator
@cuts_generator.setter
def cuts_generator(self, cuts_generator: "CutsGenerator"):
self.__cuts_generator = cuts_generator
@property
def lazy_constrs_generator(self) -> "LazyConstrsGenerator":
return self.__lazy_constrs_generator
@lazy_constrs_generator.setter
def lazy_constrs_generator(self,
lazy_constrs_generator: "LazyConstrsGenerator"):
self.__lazy_constrs_generator = lazy_constrs_generator
@property
def emphasis(self) -> SearchEmphasis:
"""defines the main objective of the search, if set to 1 (FEASIBILITY)
then the search process will focus on try to find quickly feasible
solutions and improving them; if set to 2 (OPTIMALITY) then the
search process will try to find a provable optimal solution,
procedures to further improve the lower bounds will be activated in
this setting, this may increase the time to produce the first
feasible solutions but will probably pay off in longer runs;
the default option if 0, where a balance between optimality and
feasibility is sought.
"""
return self.solver.get_emphasis()
@emphasis.setter
def emphasis(self, emphasis: SearchEmphasis):
self.solver.set_emphasis(emphasis)
@property
def cuts(self) -> int:
"""controls the generation of cutting planes, 0 disables completely,
1 (default) generates cutting planes in a moderate way,
2 generates cutting planes aggressively and
3 generates
even more cutting planes. Cutting planes usually improve the LP
relaxation bound but also make the solution time of the LP relaxation
larger, so the overall effect is hard to predict and experimenting
different values for this parameter may be beneficial.
"""
return self.__cuts
@cuts.setter
def cuts(self, cuts: int):
if cuts < 0 or cuts > 3:
print('Warning: invalid value ({}) for parameter cuts, \
keeping old setting.'.format(self.__cuts))
self.__cuts = cuts
@property
def start(self) -> List[Tuple["Var", float]]:
"""Initial feasible solution
Enters an initial feasible solution. Only the main binary/integer
decision variables which appear with non-zero values in the initial
feasible solution need to be informed. Auxiliary or continuous
variables are automatically computed.
"""
return self.__start
@start.setter
def start(self, start: List[Tuple["Var", float]]):
self.__start = start
self.solver.set_start(start)
@property
def num_cols(self) -> int:
"""number of columns (variables) in the model"""
return self.__n_cols
@property
def num_int(self) -> int:
"""number of integer variables in the model"""
return self.solver.num_int()
@property
def num_rows(self) -> int:
"""number of rows (constraints) in the model"""
return self.__n_rows
@property
def num_nz(self) -> int:
"""number of non-zeros in the constraint matrix"""
return self.solver.num_nz()
@property
def cutoff(self) -> float:
"""upper limit for the solution cost, solutions with cost > cutoff
will be removed from the search space, a small cutoff value may
significantly speedup the search, but if cutoff is set to a value too
low the model will become infeasible"""
return self.solver.get_cutoff()
@cutoff.setter
def cutoff(self, cutoff: float):
self.solver.set_cutoff(cutoff)
@property
def max_mip_gap_abs(self) -> float:
"""tolerance for the quality of the optimal solution, if a
solution with cost :math:`c` and a lower bound :math:`l` are available
and :math:`c-l<` :code:`mip_gap_abs`,
the search will be concluded, see mip_gap to determine
a percentage value """
return self.solver.get_mip_gap_abs()
@max_mip_gap_abs.setter
def max_mip_gap_abs(self, max_mip_gap_abs: float):
self.solver.set_mip_gap(max_mip_gap_abs)
@property
def max_mip_gap(self) -> float:
"""value indicating the tolerance for the maximum percentage deviation
from the optimal solution cost, if a solution with cost :math:`c` and
a lower bound :math:`l` are available and
:math:`(c-l)/l <` :code:`max_mip_gap` the search will be concluded."""
return self.solver.get_mip_gap()
@max_mip_gap.setter
def max_mip_gap(self, max_mip_gap: float):
self.solver.set_mip_gap(max_mip_gap)
@property
def max_seconds(self) -> float:
"""time limit in seconds for search"""
return self.solver.get_max_seconds()
@max_seconds.setter
def max_seconds(self, max_seconds: float):
self.solver.set_max_seconds(max_seconds)
@property
def max_nodes(self) -> int:
"""maximum number of nodes to be explored in the search tree"""
return self.solver.get_max_nodes()
@max_nodes.setter
def max_nodes(self, max_nodes: int):
self.solver.set_max_nodes(max_nodes)
@property
def max_solutions(self) -> int:
"""solution limit, search will be stopped when :code:`max_solutions`
were found"""
return self.solver.get_max_solutions()
@max_solutions.setter
def max_solutions(self, max_solutions: int):
self.solver.set_max_solutions(max_solutions)
@property
def status(self) -> OptimizationStatus:
""" optimization status, which can be OPTIMAL(0), ERROR(-1),
INFEASIBLE(1), UNBOUNDED(2). When optimizing problems
with integer variables some additional cases may happen, FEASIBLE(3)
for the case when a feasible solution was found but optimality was
not proved, INT_INFEASIBLE(4) for the case when the lp relaxation is
feasible but no feasible integer solution exists and
NO_SOLUTION_FOUND(5) for the case when an integer solution was not
found in the optimization.
"""
return self.__status
def remove(self, objects):
"""removes variable(s) and/or constraint(s) from the model
Args:
objects: can be a Var, a Constr or a list of these objects
"""
if isinstance(objects, Var):
self.solver.remove_vars([objects.idx])
elif isinstance(objects, Constr):
self.solver.remove_constrs([objects.idx])
elif isinstance(objects, list):
vlist = []
clist = []
for o in objects:
if isinstance(o, Var):
vlist.append(o.idx)
elif isinstance(o, Constr):
clist.append(o.idx)
else:
raise Exception(
"Cannot handle removal of object of type " + type(o) +
" from model.")
if vlist:
vlist.sort()
self.solver.remove_vars(vlist)
self.__n_cols -= len(vlist)
if clist:
clist.sort()
self.solver.remove_constrs(clist)
self.__n_rows -= len(clist)
def __init__(
self: "Model",
name: str = "",
sense: str = MINIMIZE,
solver_name: str = "",
solver: Optional[Solver] = None,
):
"""Model constructor
Creates a Mixed-Integer Linear Programming Model. The default model
optimization direction is Minimization. To store and optimize the model
the MIP package automatically searches and connects in runtime to the
dynamic library of some MIP solver installed on your computer, nowadays
gurobi and cbc are supported. This solver is automatically selected,
but you can force the selection of a specific solver with the parameter
solver_name.
Args:
name (str): model name
sense (str): MINIMIZATION ("MIN") or MAXIMIZATION ("MAX")
solver_name(str): gurobi or cbc, searches for which
solver is available if not informed
solver(Solver): a (:class:`~mip.solver.Solver`) object; note that
if this argument is provided, solver_name will be ignored
"""
self._ownSolver = True
# initializing variables with default values
self.solver_name = solver_name
self.solver = solver # type: Optional[Solver]
# reading solver_name from an environment variable (if applicable)
if not solver:
if not self.solver_name and "solver_name" in environ:
self.solver_name = environ["solver_name"]
if not self.solver_name and "solver_name".upper() in environ:
self.solver_name = environ["solver_name".upper()]
# creating a solver instance
if self.solver_name.upper() in ["GUROBI", "GRB"]:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
elif self.solver_name.upper() == "CBC":
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
else:
# checking which solvers are available
try:
from mip.gurobi import SolverGurobi
has_gurobi = True
except ImportError:
has_gurobi = False
if has_gurobi:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
self.solver_name = GUROBI
else:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
self.solver_name = CBC
# list of constraints and variables
self.constrs = ConstrList(self)
self.vars = VarList(self)
self._status = OptimizationStatus.LOADED
# initializing additional control variables
self.__cuts = -1
self.__cut_passes = -1
self.__clique = -1
self.__preprocess = -1
self.__cuts_generator = None
self.__lazy_constrs_generator = None
self.__start = None
self.__threads = 0
self.__lp_method = LP_Method.AUTO
self.__n_cols = 0
self.__n_rows = 0
self.__gap = INF
self.__store_search_progress_log = False
self.__plog = ProgressLog()
self.__integer_tol = 1e-6
self.__infeas_tol = 1e-6
self.__opt_tol = 1e-6
self.__max_mip_gap = 1e-4
self.__max_mip_gap_abs = 1e-10
class Model:
""" Mixed Integer Programming Model
This is the main class, providing methods for building, optimizing,
querying optimization results and re-optimizing Mixed-Integer Programming
Models.
To check how models are created please see the
:ref:`examples <chapExamples>` included.
Attributes:
vars(VarList): list of problem variables (:class:`~mip.model.Var`)
constrs(ConstrList): list of constraints (:class:`~mip.model.Constr`)
Examples:
>>> from mip import Model, MAXIMIZE, CBC, INTEGER, OptimizationStatus
>>> model = Model(sense=MAXIMIZE, solver_name=CBC)
>>> x = model.add_var(name='x', var_type=INTEGER, lb=0, ub=10)
>>> y = model.add_var(name='y', var_type=INTEGER, lb=0, ub=10)
>>> model += x + y <= 10
>>> model.objective = x + y
>>> status = model.optimize(max_seconds=2)
>>> status == OptimizationStatus.OPTIMAL
True
"""
def __init__(
self: "Model",
name: str = "",
sense: str = MINIMIZE,
solver_name: str = "",
solver: Optional[Solver] = None,
):
"""Model constructor
Creates a Mixed-Integer Linear Programming Model. The default model
optimization direction is Minimization. To store and optimize the model
the MIP package automatically searches and connects in runtime to the
dynamic library of some MIP solver installed on your computer, nowadays
gurobi and cbc are supported. This solver is automatically selected,
but you can force the selection of a specific solver with the parameter
solver_name.
Args:
name (str): model name
sense (str): MINIMIZATION ("MIN") or MAXIMIZATION ("MAX")
solver_name(str): gurobi or cbc, searches for which
solver is available if not informed
solver(Solver): a (:class:`~mip.solver.Solver`) object; note that
if this argument is provided, solver_name will be ignored
"""
self._ownSolver = True
# initializing variables with default values
self.solver_name = solver_name
self.solver = solver # type: Optional[Solver]
# reading solver_name from an environment variable (if applicable)
if not solver:
if not self.solver_name and "solver_name" in environ:
self.solver_name = environ["solver_name"]
if not self.solver_name and "solver_name".upper() in environ:
self.solver_name = environ["solver_name".upper()]
# creating a solver instance
if self.solver_name.upper() in ["GUROBI", "GRB"]:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
elif self.solver_name.upper() == "CBC":
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
else:
# checking which solvers are available
try:
from mip.gurobi import SolverGurobi
has_gurobi = True
except ImportError:
has_gurobi = False
if has_gurobi:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
self.solver_name = GUROBI
else:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
self.solver_name = CBC
# list of constraints and variables
self.constrs = ConstrList(self)
self.vars = VarList(self)
self._status = OptimizationStatus.LOADED
# initializing additional control variables
self.__cuts = -1
self.__cut_passes = -1
self.__clique = -1
self.__preprocess = -1
self.__cuts_generator = None
self.__lazy_constrs_generator = None
self.__start = None
self.__threads = 0
self.__lp_method = LP_Method.AUTO
self.__n_cols = 0
self.__n_rows = 0
self.__gap = INF
self.__store_search_progress_log = False
self.__plog = ProgressLog()
self.__integer_tol = 1e-6
self.__infeas_tol = 1e-6
self.__opt_tol = 1e-6
self.__max_mip_gap = 1e-4
self.__max_mip_gap_abs = 1e-10
def __del__(self: "Model"):
del self.solver
def __iadd__(self: "Model", other) -> "Model":
if isinstance(other, LinExpr):
if len(other.sense) == 0:
# adding objective function components
self.objective = other
else:
# adding constraint
self.add_constr(other)
elif isinstance(other, tuple):
if isinstance(other[0], LinExpr) and isinstance(other[1], str):
if len(other[0].sense) == 0:
self.objective = other[0]
else:
self.add_constr(other[0], other[1])
return self
def add_var(
self: "Model",
name: str = "",
lb: float = 0.0,
ub: float = INF,
obj: float = 0.0,
var_type: str = CONTINUOUS,
column: Column = None,
) -> Var:
""" Creates a new variable in the model, returning its reference
Args:
name (str): variable name (optional)
lb (float): variable lower bound, default 0.0
ub (float): variable upper bound, default infinity
obj (float): coefficient of this variable in the objective
function, default 0
var_type (str): CONTINUOUS ("C"), BINARY ("B") or INTEGER ("I")
column (Column): constraints where this variable will appear,
necessary only when constraints are already created in
the model and a new variable will be created.
Examples:
To add a variable :code:`x` which is continuous and greater or
equal to zero to model :code:`m`::
x = m.add_var()
The following code creates a vector of binary variables
:code:`x[0], ..., x[n-1]` to model :code:`m`::
x = [m.add_var(var_type=BINARY) for i in range(n)]
"""
return self.vars.add(name, lb, ub, obj, var_type, column)
def add_constr(self: "Model", lin_expr: LinExpr, name: str = "") -> Constr:
"""Creates a new constraint (row).
Adds a new constraint to the model, returning its reference.
Args:
lin_expr(LinExpr): linear expression
name(str): optional constraint name, used when saving model to\
lp or mps files
Examples:
The following code adds the constraint :math:`x_1 + x_2 \leq 1`
(x1 and x2 should be created first using
:func:`add_var<mip.model.Model.add_var>`)::
m += x1 + x2 <= 1
Which is equivalent to::
m.add_constr( x1 + x2 <= 1 )
Summation expressions can be used also, to add the constraint \
:math:`\displaystyle \sum_{i=0}^{n-1} x_i = y` and name this \
constraint :code:`cons1`::
m += xsum(x[i] for i in range(n)) == y, "cons1"
Which is equivalent to::
m.add_constr( xsum(x[i] for i in range(n)) == y, "cons1" )
"""
if isinstance(lin_expr, bool):
raise InvalidLinExpr("A boolean (true/false) cannot be "
"used as a constraint.")
return self.constrs.add(lin_expr, name)
def add_lazy_constr(self: "Model", expr: LinExpr):
"""Adds a lazy constraint
A lazy constraint is a constraint that is only inserted
into the model after the first integer solution that violates
it is found. When lazy constraints are used a restricted
pre-processing is executed since the complete model is not
available at the beginning. If the number of lazy constraints
is too large then they can be added during the search process
by implementing a
:class:`~mip.callbacks.ConstrsGenerator` and setting the
property :attr:`~mip.model.Model.lazy_constrs_generator` of
:class:`~mip.model.Model`.
Args:
expr(LinExpr): the linear constraint
"""
self.solver.add_lazy_constr(expr)
def add_sos(self: Solver, sos: List[Tuple[Var, float]], sos_type: int):
"""Adds an Special Ordered Set (SOS) to the model
In models with binary variables it is often the case that from a list
of variables only one can receive value 1 in a feasible solution. When
large constraints of this type exist (packing and partitioning),
branching in one variable at time usually doesn't work well: while
fixing one of these variables to one leaves only one possible feasible
value for the other variables in this set (zero), fixing one variable
to zero keeps all other variables free. This *unbalanced* branching is
highly ineffective. A Special ordered set (SOS) is a set
:math:`\mathcal{S}=\{s_1, s_2, \ldots, s_k\}` with weights
:math:`[w_1, w_2, \ldots, w_k] \in \mathbb{R}^+`. With this structure
available branching on a fractional solution :math:`x^*` for these
variables can be performed computing:
.. math::
\min \{ u_{k'} : u_{k'} = | \sum_{j=1\,\ldots \,k'-1}
w_j \ldotp x^*_j - \sum_{j=k'\,\ldots ,k} w_j \ldotp x^*_j | \}
Then, branching :math:`\mathcal{S}_1` would be
:math:`\displaystyle \sum_{j=1, \ldots, k'-1} x_j = 0`
and
:math:`\displaystyle \mathcal{S}_2 = \sum_{j=k', \ldots, k} x_j = 0`.
Args:
sos(List[Tuple[Var, float]]):
list including variables (not necessarily binary) and
respective weights in the model
sos_type(int):
1 for Type 1 SOS, where at most one of the binary
variables can be set to one and 2 for Type 2 SOS, where at
most two variables from the list may be selected. In type
2 SOS the two selected variables will be consecutive in
the list.
"""
self.solver.add_sos(sos, sos_type)
def clear(self: Solver):
"""Clears the model
All variables, constraints and parameters will be reset. In addition,
a new solver instance will be instantiated to implement the
formulation.
"""
# creating a new solver instance
sense = self.sense
if self.solver_name.upper() == GUROBI:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
elif self.solver_name.upper() == CBC:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
else:
# checking which solvers are available
from mip import gurobi
if gurobi.found:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, self.name, sense)
self.solver_name = GUROBI
else:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, self.name, sense)
self.solver_name = CBC
# list of constraints and variables
self.constrs = ConstrList(self)
self.vars = VarList(self)
# initializing additional control variables
self.__cuts = 1
self.__cuts_generator = None
self.__lazy_constrs_generator = None
self.__start = []
self._status = OptimizationStatus.LOADED
self.__threads = 0
def copy(self: Solver, solver_name: str = "") -> "Model":
""" Creates a copy of the current model
Args:
solver_name(str): solver name (optional)
Returns:
clone of current model
"""
if not solver_name:
solver_name = self.solver_name
copy = Model(self.name, self.sense, solver_name)
# adding variables
for v in self.vars:
copy.add_var(name=v.name,
lb=v.lb,
ub=v.ub,
obj=v.obj,
var_type=v.var_type)
# adding constraints
for c in self.constrs:
orig_expr = c.expr
expr = LinExpr(const=orig_expr.const, sense=orig_expr.sense)
for (var, value) in orig_expr.expr.items():
expr.add_term(self.vars[var.idx], value)
copy.add_constr(lin_expr=expr, name=c.name)
# setting objective function"s constant
copy.objective_const = self.objective_const
return copy
def constr_by_name(self: Solver, name: str) -> Optional[Constr]:
""" Queries a constraint by its name
Args:
name(str): constraint name
Returns:
constraint or None if not found
"""
cidx = self.solver.constr_get_index(name)
if cidx < 0 or cidx > len(self.constrs):
return None
return self.constrs[cidx]
def var_by_name(self: Solver, name: str) -> Optional[Var]:
"""Searchers a variable by its name
Returns:
Variable or None if not found
"""
v = self.solver.var_get_index(name)
if v < 0 or v > len(self.vars):
return None
return self.vars[v]
def optimize(
self: Solver,
max_seconds: float = INF,
max_nodes: int = INF,
max_solutions: int = INF,
) -> OptimizationStatus:
""" Optimizes current model
Optimizes current model, optionally specifying processing limits.
To optimize model :code:`m` within a processing time limit of
300 seconds::
m.optimize(max_seconds=300)
Args:
max_seconds (float): Maximum runtime in seconds (default: inf)
max_nodes (float): Maximum number of nodes (default: inf)
max_solutions (float): Maximum number of solutions (default: inf)
Returns:
optimization status, which can be OPTIMAL(0), ERROR(-1),
INFEASIBLE(1), UNBOUNDED(2). When optimizing problems
with integer variables some additional cases may happen,
FEASIBLE(3) for the case when a feasible solution was found
but optimality was not proved, INT_INFEASIBLE(4) for the case
when the lp relaxation is feasible but no feasible integer
solution exists and NO_SOLUTION_FOUND(5) for the case when
an integer solution was not found in the optimization.
"""
if self.__threads != 0:
self.solver.set_num_threads(self.__threads)
# self.solver.set_callbacks(branch_selector,
# incumbent_updater, lazy_constrs_generator)
self.solver.set_processing_limits(max_seconds, max_nodes,
max_solutions)
self._status = self.solver.optimize()
# has a solution and is a MIP
if self.num_solutions and self.num_int > 0:
best = self.objective_value
lb = self.objective_bound
if abs(best) <= 1e-10:
self.__gap = INF
else:
self.__gap = abs(best - lb) / abs(best)
if self.store_search_progress_log:
self.__plog.log = self.solver.get_log()
self.__plog.instance = self.name
return self._status
def read(self: Solver, path: str):
"""Reads a MIP model or an initial feasible solution.
One of the following file name extensions should be used
to define the contents of what will be loaded:
:code:`.lp`
mip model stored in the
`LP file format <https://www.ibm.com/support/knowledgecenter/SSSA5P_12.9.0/ilog.odms.cplex.help/CPLEX/GettingStarted/topics/tutorials/InteractiveOptimizer/usingLPformat.html>`_
:code:`.mps`
mip model stored in the
`MPS file format <https://en.wikipedia.org/wiki/MPS_(format)>`_
:code:`.sol`
initial feasible solution
Note: if a new problem is readed, all variables, constraints
and parameters from the current model will be cleared.
Args:
path(str): file name
"""
if not isfile(path):
raise OSError(2, "File {} does not exists".format(path))
if path.lower().endswith(".sol") or path.lower().endswith(".mst"):
mip_start = load_mipstart(path)
if not mip_start:
raise Exception("File {} does not contains a valid feasible \
solution.".format(path))
var_list = []
for name, value in mip_start:
var = self.var_by_name(name)
if var is not None:
var_list.append((var, value))
if not var_list:
raise Exception("Invalid variable(s) name(s) in \
mipstart file {}".format(path))
self.start = var_list
return
# reading model
model_ext = [".lp", ".mps"]
fn_low = path.lower()
for ext in model_ext:
if fn_low.endswith(ext):
self.clear()
self.solver.read(path)
self.vars.update_vars(self.solver.num_cols())
self.constrs.update_constrs(self.solver.num_rows())
return
raise Exception("Use .lp, .mps, .sol or .mst as file extension \
to indicate the file format.")
def relax(self: Solver):
""" Relax integrality constraints of variables
Changes the type of all integer and binary variables to
continuous. Bounds are preserved.
"""
self.solver.relax()
def write(self: Solver, file_path: str):
"""Saves a MIP model or an initial feasible solution.
One of the following file name extensions should be used
to define the contents of what will be saved:
:code:`.lp`
mip model stored in the
`LP file format <https://www.ibm.com/support/knowledgecenter/SSSA5P_12.9.0/ilog.odms.cplex.help/CPLEX/GettingStarted/topics/tutorials/InteractiveOptimizer/usingLPformat.html>`_
:code:`.mps`
mip model stored in the
`MPS file format <https://en.wikipedia.org/wiki/MPS_(format)>`_
:code:`.sol`
initial feasible solution
Args:
file_path(str): file name
"""
if file_path.lower().endswith(".sol") or file_path.lower().endswith(
".mst"):
if self.start:
save_mipstart(self.start, file_path)
else:
mip_start = [(var, var.x) for var in self.vars
if abs(var.x) >= 1e-8]
save_mipstart(mip_start, file_path)
elif file_path.lower().endswith(".lp") or file_path.lower().endswith(
".mps"):
self.solver.write(file_path)
else:
raise Exception("Use .lp, .mps, .sol or .mst as file extension \
to indicate the file format.")
@property
def objective_bound(self: Solver) -> Optional[float]:
"""
A valid estimate computed for the optimal solution cost,
lower bound in the case of minimization, equals to
:attr:`~mip.model.Model.objective_value` if the
optimal solution was found.
"""
if self.status not in [
OptimizationStatus.OPTIMAL,
OptimizationStatus.FEASIBLE,
OptimizationStatus.NO_SOLUTION_FOUND,
]:
return None
return self.solver.get_objective_bound()
@property
def name(self: Solver) -> str:
"""The problem (instance) name
This name should be used to identify the instance that this model
refers, e.g.: productionPlanningMay19. This name is stored when
saving (:meth:`~mip.model.Model.write`) the model in :code:`.LP`
or :code:`.MPS` file formats.
"""
return self.solver.get_problem_name()
@name.setter
def name(self: Solver, name: str):
self.solver.set_problem_name(name)
@property
def objective(self: Solver) -> LinExpr:
"""The objective function of the problem as a linear expression.
Examples:
The following code adds all :code:`x` variables :code:`x[0],
..., x[n-1]`, to the objective function of model :code:`m`
with the same cost :code:`w`::
m.objective = xsum(w*x[i] for i in range(n))
A simpler way to define the objective function is the use of the
model operator += ::
m += xsum(w*x[i] for i in range(n))
Note that the only difference of adding a constraint is the lack of
a sense and a rhs.
"""
return self.solver.get_objective()
# TODO how to handle objective += ?
@objective.setter
def objective(self: Solver, objective):
if isinstance(objective, (int, float)):
self.solver.set_objective(LinExpr([], [], objective))
elif isinstance(objective, Var):
self.solver.set_objective(LinExpr([objective], [1]))
elif isinstance(objective, LinExpr):
if objective.sense == MAXIMIZE:
self.solver.set_objective_sense(MAXIMIZE)
elif objective.sense == MINIMIZE:
self.solver.set_objective_sense(MINIMIZE)
self.solver.set_objective(objective)
@property
def verbose(self: Solver) -> int:
"""0 to disable solver messages printed on the screen, 1 to enable
"""
return self.solver.get_verbose()
@verbose.setter
def verbose(self: Solver, verbose: int):
self.solver.set_verbose(verbose)
@property
def lp_method(self: Solver) -> LP_Method:
"""Which method should be used to solve the linear programming
problem. If the problem has integer variables that this affects only
the solution of the first linear programming relaxation."""
return self.__lp_method
@lp_method.setter
def lp_method(self: Solver, lpm: LP_Method):
self.__lp_method = lpm
@property
def threads(self: Solver) -> int:
"""number of threads to be used when solving the problem.
0 uses solver default configuration, -1 uses the number of available
processing cores and :math:`\geq 1` uses the specified number of
threads. An increased number of threads may improve the solution
time but also increases the memory consumption."""
return self.__threads
@threads.setter
def threads(self: Solver, threads: int):
self.__threads = threads
@property
def sense(self: Solver) -> str:
""" The optimization sense
Returns:
the objective function sense, MINIMIZE (default) or (MAXIMIZE)
"""
return self.solver.get_objective_sense()
@sense.setter
def sense(self: Solver, sense: str):
self.solver.set_objective_sense(sense)
@property
def objective_const(self: Solver) -> float:
"""Returns the constant part of the objective function
"""
return self.solver.get_objective_const()
@objective_const.setter
def objective_const(self: Solver, objective_const: float):
self.solver.set_objective_const(objective_const)
@property
def objective_value(self: Solver) -> Optional[float]:
"""Objective function value of the solution found or None
if model was not optimized
"""
if self.status not in [
OptimizationStatus.OPTIMAL,
OptimizationStatus.FEASIBLE,
]:
return None
return self.solver.get_objective_value()
@property
def gap(self: Solver) -> float:
"""
The optimality gap considering the cost of the best solution found
(:attr:`~mip.model.Model.objective_value`)
:math:`b` and the best objective bound :math:`l`
(:attr:`~mip.model.Model.objective_bound`) :math:`g` is
computed as: :math:`g=\\frac{|b-l|}{|b|}`.
If no solution was found or if :math:`b=0` then :math:`g=\infty`.
If the optimal solution was found then :math:`g=0`.
"""
return self.__gap
@property
def search_progress_log(self: Solver) -> ProgressLog:
"""
Log of bound improvements in the search.
The output of MIP solvers is a sequence of improving
incumbent solutions (primal bound) and estimates for the optimal
cost (dual bound). When the costs of these two bounds match the
search is concluded. In truncated searches, the most common
situation for hard problems, at the end of the search there is a
:attr:`~mip.model.Model.gap` between these bounds. This
property stores the detailed events of improving these
bounds during the search process. Analyzing the evolution
of these bounds you can see if you need to improve your
solver w.r.t. the production of feasible solutions, by including an
heuristic to produce a better initial feasible solution, for
example, or improve the formulation with cutting planes, for
example, to produce better dual bounds. To enable storing the
:attr:`~mip.model.Model.search_progress_log` set
:attr:`~mip.model.Model.store_search_progress_log` to True.
"""
return self.__plog
@property
def store_search_progress_log(self: Solver) -> bool:
"""
Wether :attr:`~mip.model.Model.search_progress_log` will be stored
or not when optimizing. Default False. Activate it if you want to
analyze bound improvements over time."""
return self.__store_search_progress_log
@store_search_progress_log.setter
def store_search_progress_log(self: Solver, store: bool):
self.__store_search_progress_log = store
# def plot_bounds_evolution(self):
# import matplotlib.pyplot as plt
# log = self.search_progress_log
#
# # plotting lower bound
# x = [a[0] for a in log]
# y = [a[1][0] for a in log]
# plt.plot(x, y)
# # plotting upper bound
# x = [a[0] for a in log if a[1][1] < 1e+50]
# y = [a[1][1] for a in log if a[1][1] < 1e+50]
# plt.plot(x, y)
# plt.show()
@property
def num_solutions(self: Solver) -> int:
"""Number of solutions found during the MIP search
Returns:
number of solutions stored in the solution pool
"""
return self.solver.get_num_solutions()
@property
def objective_values(self: Solver) -> List[float]:
"""List of costs of all solutions in the solution pool
Returns:
costs of all solutions stored in the solution pool
as an array from 0 (the best solution) to
:attr:`~mip.model.Model.num_solutions`-1.
"""
return [
float(self.solver.get_objective_value_i(i))
for i in range(self.num_solutions)
]
@property
def cuts_generator(self: Solver) -> "ConstrsGenerator":
"""A cuts generator is an :class:`~mip.callbacks.ConstrsGenerator`
object that receives a fractional solution and tries to generate one or
more constraints (cuts) to remove it. The cuts generator is called in
every node of the branch-and-cut tree where a solution that violates
the integrality constraint of one or more variables is found.
"""
return self.__cuts_generator
@cuts_generator.setter
def cuts_generator(self: Solver, cuts_generator: ConstrsGenerator):
self.__cuts_generator = cuts_generator
@property
def lazy_constrs_generator(self: Solver) -> "ConstrsGenerator":
"""A lazy constraints generator is an
:class:`~mip.callbacks.ConstrsGenerator` object that receives
an integer solution and checks its feasibility. If
the solution is not feasible then one or more constraints can be
generated to remove it. When a lazy constraints generator is informed
it is assumed that the initial formulation is incomplete. Thus, a
restricted pre-processing routine may be applied. If the initial
formulation is incomplete, it may be interesting to use the same
:class:`~mip.callbacks.ConstrsGenerator` to generate cuts *and* lazy
constraints. The use of *only* lazy constraints may be useful then
integer solutions rarely violate these constraints.
"""
return self.__lazy_constrs_generator
@lazy_constrs_generator.setter
def lazy_constrs_generator(self: Solver,
lazy_constrs_generator: ConstrsGenerator):
self.__lazy_constrs_generator = lazy_constrs_generator
@property
def emphasis(self: Solver) -> SearchEmphasis:
"""defines the main objective of the search, if set to 1 (FEASIBILITY)
then the search process will focus on try to find quickly feasible
solutions and improving them; if set to 2 (OPTIMALITY) then the
search process will try to find a provable optimal solution,
procedures to further improve the lower bounds will be activated in
this setting, this may increase the time to produce the first
feasible solutions but will probably pay off in longer runs;
the default option if 0, where a balance between optimality and
feasibility is sought.
"""
return self.solver.get_emphasis()
@emphasis.setter
def emphasis(self: Solver, emphasis: SearchEmphasis):
self.solver.set_emphasis(emphasis)
@property
def preprocess(self: Solver) -> int:
"""Enables/disables pre-processing. Pre-processing tries to improve
your MIP formulation. -1 means automatic, 0 means off and 1
means on."""
return self.__preprocess
@preprocess.setter
def preprocess(self: Solver, prep: int):
self.__preprocess = prep
@property
def pump_passes(self: Solver) -> int:
"""Number of passes of the Feasibility Pump :cite:`FGL05` heuristic.
You may increase this value if you are not getting feasible
solutions."""
return self.solver.get_pump_passes()
@pump_passes.setter
def pump_passes(self: Solver, passes: int):
self.solver.set_pump_passes(passes)
@property
def cuts(self: Solver) -> int:
"""Controls the generation of cutting planes, -1 means automatic, 0
disables completely, 1 (default) generates cutting planes in a moderate
way, 2 generates cutting planes aggressively and 3 generates even more
cutting planes. Cutting planes usually improve the LP relaxation bound
but also make the solution time of the LP relaxation larger, so the
overall effect is hard to predict and experimenting different values
for this parameter may be beneficial."""
return self.__cuts
@cuts.setter
def cuts(self: Solver, gencuts: int):
self.__cuts = gencuts
@property
def cut_passes(self: Solver) -> int:
"""Maximum number of rounds of cutting planes. You may set this
parameter to low values if you see that a significant amount of
time is being spent generating cuts without any improvement in
the lower bound. -1 means automatic, values greater than zero
specify the maximum number of rounds."""
return self.__cut_passes
@cut_passes.setter
def cut_passes(self: Solver, cp: int):
self.__cut_passes = cp
@property
def clique(self: Solver) -> int:
"""Controls the generation of clique cuts. -1 means automatic,
0 disables it, 1 enables it and 2 enables more aggressive clique
generation."""
return self.__clique
@clique.setter
def clique(self: Solver, clq: int):
self.__clique = clq
@property
def start(self: Solver) -> List[Tuple[Var, float]]:
"""Initial feasible solution
Enters an initial feasible solution. Only the main binary/integer
decision variables which appear with non-zero values in the initial
feasible solution need to be informed. Auxiliary or continuous
variables are automatically computed.
"""
return self.__start
@start.setter
def start(self: Solver, start: List[Tuple[Var, float]]):
self.__start = start
self.solver.set_start(start)
def validate_mip_start(self: Solver):
"""Validates solution entered in MIPStart
If the solver engine printed messages indicating that the initial
feasible solution that you entered in :attr:`~mip.model.start` is not
valid then you can call this method to help discovering which set of
variables is causing infeasibility. The current version is quite
simple: the model is relaxed and one variable entered in mipstart is
fixed per iteration, indicating if the model still feasible or not.
"""
out.write("Checking feasibility of MIPStart\n")
mc = self.copy()
mc.verbose = 0
mc.relax()
mc.optimize()
if mc.status == OptimizationStatus.INFEASIBLE:
out.write("Model is infeasible.\n")
return
if mc.status == OptimizationStatus.UNBOUNDED:
out.write("Model is unbounded. You probably need to insert "
"additional constraints or bounds in variables.\n")
return
if mc.status != OptimizationStatus.OPTIMAL:
print("Unexpected status while optimizing LP relaxation:"
" {}".format(mc.status))
print("Model LP relaxation bound is {}".format(mc.objective_value))
for (var, value) in self.start:
out.write("\tfixing %s to %g ... " % (var.name, value))
mc += var == value
mc.optimize()
if mc.status == OptimizationStatus.OPTIMAL:
print("ok, obj now: {}".format(mc.objective_value))
else:
print("NOT OK, optimization status: {}".format(mc.status))
return
print("Linear Programming relaxation of model with fixations from "
"MIPStart is feasible.")
print("MIP model may still be infeasible.")
@property
def num_cols(self: Solver) -> int:
"""number of columns (variables) in the model"""
return len(self.vars)
@property
def num_int(self: Solver) -> int:
"""number of integer variables in the model"""
return self.solver.num_int()
@property
def num_rows(self: Solver) -> int:
"""number of rows (constraints) in the model"""
return len(self.constrs)
@property
def num_nz(self: Solver) -> int:
"""number of non-zeros in the constraint matrix"""
return self.solver.num_nz()
@property
def cutoff(self: Solver) -> float:
"""upper limit for the solution cost, solutions with cost > cutoff
will be removed from the search space, a small cutoff value may
significantly speedup the search, but if cutoff is set to a value too
low the model will become infeasible"""
return self.solver.get_cutoff()
@cutoff.setter
def cutoff(self: Solver, cutoff: float):
self.solver.set_cutoff(cutoff)
@property
def integer_tol(self: Solver) -> float:
"""Maximum distance to the nearest integer for a variable to be
considered with an integer value. Default value: 1e-6. Tightening this
value can increase the numerical precision but also probably increase
the running time. As floating point computations always involve some
loss of precision, values too close to zero will likely render some
models impossible to optimize."""
return self.__integer_tol
@integer_tol.setter
def integer_tol(self: Solver, int_tol: float):
self.__integer_tol = int_tol
@property
def infeas_tol(self: Solver) -> float:
"""Maximum allowed violation for constraints. Default value: 1e-6.
Tightening this value can increase the numerical precision but also
probably increase the running time. As floating point computations
always involve some loss of precision, values too close to zero will
likely render some models impossible to optimize."""
return self.__infeas_tol
@infeas_tol.setter
def infeas_tol(self: Solver, inf_tol: float):
self.__infeas_tol = inf_tol
@property
def opt_tol(self: Solver) -> float:
"""Maximum reduced cost value for a solution of the LP relaxation to be
considered optimal. Default value: 1e-6. Tightening this value can
increase the numerical precision but also probably increase the running
time. As floating point computations always involve some loss of
precision, values too close to zero will likely render some models
impossible to optimize."""
return self.__opt_tol
@opt_tol.setter
def opt_tol(self: Solver, tol: float):
self.__opt_tol = tol
@property
def max_mip_gap_abs(self: Solver) -> float:
"""Tolerance for the quality of the optimal solution, if a solution
with cost :math:`c` and a lower bound :math:`l` are available and
:math:`c-l<` :code:`mip_gap_abs`, the search will be concluded, see
:attr:`~mip.model.Model.max_mip_gap` to determine a percentage value.
Default value: 1e-10."""
return self.__max_mip_gap_abs
@max_mip_gap_abs.setter
def max_mip_gap_abs(self: Solver, max_mip_gap_abs: float):
self.__max_mip_gap_abs = max_mip_gap_abs
@property
def max_mip_gap(self: Solver) -> float:
"""value indicating the tolerance for the maximum percentage deviation
from the optimal solution cost, if a solution with cost :math:`c` and
a lower bound :math:`l` are available and
:math:`(c-l)/l <` :code:`max_mip_gap` the search will be concluded.
Default value: 1e-4."""
return self.__max_mip_gap
@max_mip_gap.setter
def max_mip_gap(self: Solver, max_mip_gap: float):
self.__max_mip_gap = max_mip_gap
@property
def max_seconds(self: Solver) -> float:
"""time limit in seconds for search"""
return self.solver.get_max_seconds()
@max_seconds.setter
def max_seconds(self: Solver, max_seconds: float):
self.solver.set_max_seconds(max_seconds)
@property
def max_nodes(self: Solver) -> int:
"""maximum number of nodes to be explored in the search tree"""
return self.solver.get_max_nodes()
@max_nodes.setter
def max_nodes(self: Solver, max_nodes: int):
self.solver.set_max_nodes(max_nodes)
@property
def max_solutions(self: Solver) -> int:
"""solution limit, search will be stopped when :code:`max_solutions`
were found"""
return self.solver.get_max_solutions()
@max_solutions.setter
def max_solutions(self: Solver, max_solutions: int):
self.solver.set_max_solutions(max_solutions)
@property
def status(self: Solver) -> OptimizationStatus:
""" optimization status, which can be OPTIMAL(0), ERROR(-1),
INFEASIBLE(1), UNBOUNDED(2). When optimizing problems
with integer variables some additional cases may happen, FEASIBLE(3)
for the case when a feasible solution was found but optimality was
not proved, INT_INFEASIBLE(4) for the case when the lp relaxation is
feasible but no feasible integer solution exists and
NO_SOLUTION_FOUND(5) for the case when an integer solution was not
found in the optimization.
"""
return self._status
def add_cut(self: Solver, cut: LinExpr):
"""Adds a violated inequality (cutting plane) to the linear programming
model. If called outside the cut callback performs exactly as
:meth:`~mip.model.Model.add_constr`. When called inside the cut
callback the cut is included in the solver's cut pool, which will later
decide if this cut should be added or not to the model. Repeated cuts,
or cuts which will probably be less effective, e.g. with a very small
violation, can be discarded.
Args:
cut(LinExpr): violated inequality
"""
self.solver.add_cut(cut)
def remove(self: Solver, objects):
"""removes variable(s) and/or constraint(s) from the model
Args:
objects: can be a Var, a Constr or a list of these objects
"""
if isinstance(objects, Var) or isinstance(objects, Constr):
objects = [objects]
if isinstance(objects, list):
vlist = []
clist = []
for o in objects:
if isinstance(o, Var):
vlist.append(o)
elif isinstance(o, Constr):
clist.append(o)
else:
raise Exception(
"Cannot handle removal of object of type " + type(o) +
" from model.")
if vlist:
self.vars.remove(vlist)
if clist:
self.constrs.remove(clist)
else:
raise Exception("Cannot handle removal of object of type " +
type(objects) + " from model.")
def translate(self: Solver, ref) -> Union[List[Any], Dict[Any, Any], Var]:
"""Translates references of variables/containers of variables
from another model to this model. Can be used to translate
references of variables in the original model to references
of variables in the pre-processed model."""
res = None # type: Union[List[Any], Dict[Any, Any], Var]
if isinstance(ref, Var):
return self.var_by_name(ref.name)
if isinstance(ref, list):
res = list()
for el in ref:
res.append(self.translate(el))
return res
if isinstance(ref, dict):
res = dict()
for key, value in ref.items():
res[key] = self.translate(value)
return res
return ref
class Model:
""" Mixed Integer Programming Model
This is the main class, providing methods for building, optimizing,
querying optimization results and reoptimizing Mixed-Integer Programming
Models.
To check how models are created please see the examples included.
"""
def __init__(self, name: str = "",
sense: str = MINIMIZE,
solver_name: str = ""):
"""Model constructor
Creates a Mixed-Integer Linear Programming Model. The default model
optimization direction is Minimization. To store and optimize the model
the MIP package automatically searches and connects in runtime to the
dynamic library of some MIP solver installed on your computer, nowadays
gurobi and cbc are supported. This solver is automatically selected,
but you can force the selection of a specific solver with the parameter
solver_name.
Args:
name (str): model name
sense (str): MINIMIZATION ("MIN") or MAXIMIZATION ("MAX")
solver_name: gurobi or cbc, searches for which
solver is available if not informed
"""
# initializing variables with default values
self.name = name
self.solver_name = solver_name
self.solver = None
if "solver_name" in environ:
solver_name = environ["solver_name"]
if "solver_name".upper() in environ:
solver_name = environ["solver_name".upper()]
self.__mipStart = []
# list of constraints and variables
self.constrs = []
self.constrs_by_name = {}
self.vars = []
self.vars_by_name = {}
self.cut_generators = []
if solver_name.upper() == GUROBI:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
elif solver_name.upper() == CBC:
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
else:
# checking which solvers are available
from mip import gurobi
if gurobi.has_gurobi:
from mip.gurobi import SolverGurobi
self.solver = SolverGurobi(self, name, sense)
self.solver_name = GUROBI
else:
from mip import cbc
from mip.cbc import SolverCbc
self.solver = SolverCbc(self, name, sense)
self.solver_name = CBC
self.sense = sense
def __del__(self):
if self.solver:
del self.solver
def __iadd__(self, other) -> "Model":
if isinstance(other, LinExpr):
if len(other.sense) == 0:
# adding objective function components
self.objective = other
else:
# adding constraint
self.add_constr(other)
elif isinstance(other, tuple):
if isinstance(other[0], LinExpr) and isinstance(other[1], str):
if len(other[0].sense) == 0:
self.objective = other[0]
else:
self.add_constr(other[0], other[1])
return self
def add_var(self, name: str = "",
lb: float = 0.0,
ub: float = INF,
obj: float = 0.0,
var_type: str = CONTINUOUS,
column: "Column" = None) -> "Var":
""" Creates a new variable
Adds a new variable to the model.
Args:
name (str): variable name (optional)
lb (float): variable lower bound, default 0.0
ub (float): variable upper bound, default infinity
obj (float): coefficient of this variable in the objective function, default 0
var_type (str): CONTINUOUS ("C"), BINARY ("B") or INTEGER ("I")
column (Column): constraints where this variable will appear, necessary \
only when constraints are already created in the model and a new \
variable will be created.
Examples:
To add a variable x which is continuous and greater or equal to zero to model m::
x = m.add_var()
The following code creates a vector of binary variables x[0], ..., x[n-1] to model m::
x = [m.add_var(type=BINARY) for i in range(n)]
"""
if var_type == BINARY:
lb = 0.0
ub = 1.0
if len(name.strip()) == 0:
nc = self.solver.num_cols()
name = "C{:011d}".format(nc)
idx = self.solver.add_var(obj, lb, ub, var_type, column, name)
self.vars.append(Var(self, idx, name))
self.vars_by_name[name] = self.vars[-1]
return self.vars[-1]
def add_constr(self, lin_expr: "LinExpr", name: str = "") -> Constr:
""" Creates a new constraint (row)
Adds a new constraint to the model
Args:
lin_expr (LinExpr): linear expression
name (str): optional constraint name, used when saving model to\
lp or mps files
Examples:
The following code adds the constraint :math:`x_1 + x_2 \leq 1`
(x1 and x2 should be created first using
:func:`add_var<mip.model.Model.add_var>`)::
m += x1 + x2 <= 1
Which is equivalent to::
m.add_constr( x1 + x2 <= 1 )
Summation expressions can be used also, to add the constraint \
:math:`\displaystyle \sum_{i=0}^{n-1} x_i = y` and name this \
constraint cons1::
m += xsum(x[i] for i in range(n)) == y, "cons1"
"""
if isinstance(lin_expr, bool):
return None # empty constraint
idx = self.solver.add_constr(lin_expr, name)
self.constrs.append(Constr(self, idx, name))
self.constrs_by_name[name] = self.constrs[-1]
return self.constrs[-1]
def copy(self, solver_name: str = None) -> "Model":
""" Creates a copy of the current model
Args:
solver_name(str): solver name (optional)
Returns:
Model: clone of current model
"""
if not solver_name:
solver_name = self.solver_name
copy = Model(self.name, self.sense, solver_name)
# adding variables
for v in self.vars:
copy.add_var(name=v.name, lb=v.lb, ub=v.ub, obj=v.obj, var_type=v.var_type)
# adding constraints
for c in self.constrs:
expr = c.expr # todo: make copy of constraint"s lin_expr
copy.add_constr(lin_expr=expr, name=c.name)
# setting objective function"s constant
copy.objective_const = self.get_objective_const()
return copy
def get_constr_by_name(self, name: str) -> "Constr":
""" Queries a constraint per name
Args:
name(str): constraint name
Returns:
Constr: constraint
"""
return self.constrs_by_name.get(name, None)
@property
def objective_bound(self) -> float:
return self.solver.get_objective_bound()
@property
def objective(self) -> LinExpr:
"""LinExpr: Objective function of the problem
The objective function of the problem as a linear expression.
Examples:
The following code adds all x variables x[0], ..., x[n-1], to
the objective function of model m with weight w::
m.objective = xsum(w*x[i] for i in range(n))
A simpler way to define the objective function is the use of the
model operator += ::
m += xsum(w*x[i] for i in range(n))
Note that the only difference of adding a constraint is the lack of
a sense and a rhs.
"""
return self.solver.get_objective()
@objective.setter
def objective(self, expr):
if isinstance(expr, int) or isinstance(expr, float):
self.solver.set_objective(LinExpr([], [], expr))
elif isinstance(expr, Var):
self.solver.set_objective(LinExpr([expr], [1]))
elif isinstance(expr, LinExpr):
self.solver.set_objective(expr)
@property
def sense(self) -> str:
""" The optimization sense
Returns:
str: the objective function sense, MINIMIZE (default) or (MAXIMIZE)
"""
return self.solver.get_objective_sense()
@sense.setter
def sense(self, sense: str):
self.solver.set_objective_sense(sense)
@property
def objective_const(self) -> float:
""" Returns the current constant part of the objective function
float: the constant part in the objective function
"""
return self.solver.get_objective_const()
@objective_const.setter
def objective_const(self, const: float) -> None:
self.solver.set_objective_const(const)
@property
def objective_value(self) -> float:
""" Objective function value
Returns:
float: returns the objective function value of the solution found.
"""
return self.solver.get_objective_value()
@property
def num_solutions(self) -> int:
""" Number of solutions found during the MIP search
Returns:
int: number of solutions stored in the solution pool
"""
return self.solver.get_num_solutions()
@property
def objective_values(self) -> List[float]:
""" List of costs of all solutions in the solution pool
Returns:
List[float]: costs of all solutions stored in the solution pool
as an array from 0 (the best solution) to num_solutions-1.
"""
return [float(self.solver.get_objective_value_i(i))\
for i in range(self.num_solutions)]
def get_var_by_name(self, name) -> "Var":
""" Searchers a variable by its name
Returns:
Var: a reference to a variable
"""
return self.vars_by_name.get(name, None)
def relax(self):
""" Relax integrality constraints of variables
Changes the type of all integer and binary variables to
continuous. Bounds are preserved.
"""
self.solver.relax()
for v in self.vars:
if v.var_type == BINARY or v.var_type == INTEGER:
v.var_type = CONTINUOUS
def add_cut_generator(self, cuts_generator: "CutsGenerator") -> None:
""" Adds a cut generator
Cut generators are called whenever a solution where one or more
integer variables appear with continuous values. A cut generator will
try to produce one or more inequalities to remove this fractional point.
Args:
cuts_generator : CutsGenerator
"""
self.cut_generators.append(cuts_generator)
@property
def emphasis(self) -> int:
"""int: defines the main objective of the search, if set to 1 (FEASIBILITY) then
the search process will focus on try to find quickly feasible solutions and
improving them; if set to 2 (OPTIMALITY) then the search process will try to
find a provable optimal solution, procedures to further improve the lower bounds will
be activated in this setting, this may increase the time to produce the first
feasible solutions but will probably pay off in longer runs; the default option
if 0, where a balance between optimality and feasibility is sought.
"""
return self.solver.get_emphasis()
@emphasis.setter
def emphasis(self, emph: int):
self.solver.set_emphasis(emph)
def optimize(self,
branch_selector: "BranchSelector" = None,
incumbent_updater: "IncumbentUpdater" = None,
lazy_constrs_generator: "LazyConstrsGenerator" = None,
max_seconds: float = inf,
max_nodes: int = inf,
max_solutions: int = inf) -> int:
""" Optimizes current model
Optimizes current model, optionally specifying processing limits.
To optimize model m within a processing time limit of 300 seconds::
m.optimize(max_seconds=300)
Args:
branch_selector (BranchSelector): Callback to select branch (an object of a class inheriting from BranchSelector must be passed)
cuts_generator (CutsGenerator): Callback to generate cuts (an object of a class inheriting from CutsGenerator must be passed)
incumbent_updater (IncumbentUpdater): Callback to update incumbent solution (an object of a class inheriting from IncumbentUpdater must be passed)
lazy_constrs_generator (LazyConstrsGenerator): Callback to include lazy generated constraints (an object of a class inheriting from LazyConstrsGenerator must be passed)
max_seconds (float): Maximum runtime in seconds (default: inf)
max_nodes (float): Maximum number of nodes (default: inf)
max_solutions (float): Maximum number of solutions (default: inf)
Returns:
int: optimization status, which can be OPTIMAL(0), ERROR(-1), INFEASIBLE(1), UNBOUNDED(2). When optimizing problems
with integer variables some additional cases may happen, FEASIBLE(3) for the case when a feasible solution was found
but optimality was not proved, INT_INFEASIBLE(4) for the case when the lp relaxation is feasible but no feasible integer
solution exists and NO_SOLUTION_FOUND(5) for the case when an integer solution was not found in the optimization.
"""
self.solver.set_callbacks(branch_selector, incumbent_updater, lazy_constrs_generator)
self.solver.set_processing_limits(max_seconds, max_nodes, max_solutions)
return self.solver.optimize()
def read(self, path: str) -> None:
""" Reads a MIP model
Reads a MIP model in .lp or .mps file format.
Args:
path(str): file name
"""
self.solver.read(path)
n_cols = self.solver.num_cols()
n_rows = self.solver.num_rows()
for i in range(n_cols):
self.vars.append(Var(self, i, self.solver.var_get_name(i)))
self.vars_by_name[self.vars[-1].name] = self.vars[-1]
for i in range(n_rows):
self.constrs.append(Constr(self, i, self.solver.constr_get_name(i)))
self.constrs_by_name[self.constrs[-1].name] = self.constrs[-1]
self.sense = self.solver.get_objective_sense()
@property
def start(self) -> List[Tuple["Var", float]]:
""" Enter an initial feasible solution
Enters an initial feasible solution. Only the main binary/integer decision variables.
Auxiliary or continuous variables are automatically computed.
Args:
start_sol: list of tuples Var,float indicating non-zero variables
and their values in the initial feasible solution
"""
return self.__mipStart
@start.setter
def start(self, start_sol: List[Tuple["Var", float]]):
self.__mipStart = start_sol
self.solver.set_start(start_sol)
def write(self, path: str) -> None:
""" Saves the the MIP model
Args:
path(str): file name
Saves the the MIP model, use the extension ".lp" or ".mps" in the file
name to specify the file format.
"""
self.solver.write(path)
@property
def num_cols(self) -> int:
return len(self.vars)
@property
def num_rows(self) -> int:
return len(self.constrs)
@property
def cutoff(self) -> float:
"""float: upper limit for the solution cost, solutions with cost > cutoff
will be removed from the search space, a small cutoff value may significantly
speedup the search, but if cutoff is set to a value too low
the model will become infeasible"""
return self.solver.get_cutoff()
@cutoff.setter
def cutoff(self, value: float):
self.solver.set_cutoff(value)
@property
def mip_gap_abs(self) -> float:
"""float: tolerance for the quality of the optimal solution, if a
solution with cost c and a lower bound l are available and c-l<mip_gap_abs,
the search will be concluded, see mip_gap to determine
a percentage value """
return self.solver.get_mip_gap_abs()
@mip_gap_abs.setter
def mip_gap_abs(self, value):
self.solver.set_mip_gap(value)
@property
def mip_gap(self) -> float:
"""float: percentage indicating the tolerance for the maximum percentage deviation
from the optimal solution cost, if a solution with cost c and a lower bound l
are available and (c-l)/l < mip_gap the search will be concluded."""
return self.solver.get_mip_gap()
@mip_gap.setter
def mip_gap(self, value):
self.solver.set_mip_gap(value)
@property
def max_seconds(self) -> float:
"""float: time limit in seconds for search"""
return self.solver.get_max_seconds()
@max_seconds.setter
def max_seconds(self, max_seconds: float):
self.solver.set_max_seconds(max_seconds)
@property
def max_nodes(self) -> int:
"""int: maximum number of nodes to be explored in the search tree"""
return self.solver.get_max_nodes()
@max_nodes.setter
def max_nodes(self, max_nodes: int):
self.solver.set_max_nodes(max_nodes)
@property
def max_solutions(self) -> int:
"""int: solution limit, search will be stopped when max_solutions were found"""
return self.solver.get_max_solutions()
@max_solutions.setter
def max_solutions(self, max_solutions: int):
self.solver.set_max_solutions(max_solutions)