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
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 __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