Gurobi

(class from pyomo.contrib.solver.gurobi)

class pyomo.contrib.solver.gurobi.Gurobi(**kwds)

Bases: PersistentSolverUtils, PersistentSolverBase

Interface to Gurobi

__init__(**kwds)

Methods

__init__(**kwds)
add_block(block)	Add a block to the model
add_constraints(cons)	Add constraints to the model
add_parameters(params)	Add parameters to the model
add_sos_constraints(cons)
add_variables(variables)	Add variables to the model
available()	Test if the solver is available on this system.
cbCut(con)	Add a cut within a callback.
cbGet(what)
cbGetNodeRel(vars)
cbGetSolution(vars)
cbLazy(con)
cbSetSolution(vars, solution)
cbUseSolution()
get_gurobi_param_info(param)	Get information about a gurobi parameter.
get_linear_constraint_attr(con, attr)	Get the value of an attribute on a gurobi linear constraint.
get_model_attr(attr)	Get the value of an attribute on the Gurobi model.
get_quadratic_constraint_attr(con, attr)	Get the value of an attribute on a gurobi quadratic constraint.
get_sos_attr(con, attr)	Get the value of an attribute on a gurobi sos constraint.
get_var_attr(var, attr)	Get the value of an attribute on a gurobi var.
is_persistent()
release_license()
remove_block(block)	Remove a block from the model
remove_constraints(cons)	Remove constraints from the model
remove_parameters(params)	Remove parameters from the model
remove_sos_constraints(cons)
remove_variables(variables)	Remove variables from the model
reset()
set_callback([func])	Specify a callback for gurobi to use.
set_gurobi_param(param, val)	Set a gurobi parameter.
set_instance(model)	Set an instance of the model
set_linear_constraint_attr(con, attr, val)	Set the value of an attribute on a gurobi linear constraint.
set_objective(obj)	Set current objective for the model
set_var_attr(var, attr, val)	Set the value of an attribute on a gurobi variable.
solve(model, **kwds)
update([timer])
update_parameters()	Update parameters on the model
update_variables(variables)	Update variables on the model
version()
write(filename)	Write the model to a file (e.g., and lp file).

Attributes

CONFIG
name
symbol_map

Member Documentation

enum Availability(value)

Bases: IntEnum

Class to capture different statuses in which a solver can exist in order to record its availability for use.

as_integer_ratio()

Return integer ratio.

Return a pair of integers, whose ratio is exactly equal to the original int and with a positive denominator.

>>> (10).as_integer_ratio()
(10, 1)
>>> (-10).as_integer_ratio()
(-10, 1)
>>> (0).as_integer_ratio()
(0, 1)

bit_count()

Number of ones in the binary representation of the absolute value of self.

Also known as the population count.

>>> bin(13)
'0b1101'
>>> (13).bit_count()
3

bit_length()

Number of bits necessary to represent self in binary.

>>> bin(37)
'0b100101'
>>> (37).bit_length()
6

conjugate()

Returns self, the complex conjugate of any int.

classmethod from_bytes(bytes, byteorder='big', *, signed=False)

Return the integer represented by the given array of bytes.

bytes

Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol.

byteorder

The byte order used to represent the integer. If byteorder is ‘big’, the most significant byte is at the beginning of the byte array. If byteorder is ‘little’, the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value. Default is to use ‘big’.

signed

Indicates whether two’s complement is used to represent the integer.

to_bytes(length=1, byteorder='big', *, signed=False)

Return an array of bytes representing an integer.

length

Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. Default is length 1.

byteorder

The byte order used to represent the integer. If byteorder is ‘big’, the most significant byte is at the beginning of the byte array. If byteorder is ‘little’, the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value. Default is to use ‘big’.

signed

Determines whether two’s complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.

denominator

the denominator of a rational number in lowest terms

imag

the imaginary part of a complex number

numerator

the numerator of a rational number in lowest terms

real

the real part of a complex number

add_block(block)

Add a block to the model

add_constraints(cons: List[ConstraintData])

Add constraints to the model

add_parameters(params: List[ParamData])

Add parameters to the model

add_variables(variables: List[VarData])

Add variables to the model

available()

Test if the solver is available on this system.

Nominally, this will return True if the solver interface is valid and can be used to solve problems and False if it cannot.

Note that for licensed solvers there are a number of “levels” of available: depending on the license, the solver may be available with limitations on problem size or runtime (e.g., ‘demo’ vs. ‘community’ vs. ‘full’). In these cases, the solver may return a subclass of enum.IntEnum, with members that resolve to True if the solver is available (possibly with limitations). The Enum may also have multiple members that all resolve to False indicating the reason why the interface is not available (not found, bad license, unsupported version, etc).

Returns:

available – An enum that indicates “how available” the solver is. Note that the enum can be cast to bool, which will be True if the solver is runable at all and False otherwise.

Return type:

SolverBase.Availability

cbCut(con)

Add a cut within a callback.

Parameters:

con (pyomo.core.base.constraint.ConstraintData) – The cut to add

cbGetNodeRel(vars)

Parameters:

vars (Var or iterable of Var)

cbGetSolution(vars)

Parameters:

vars (iterable of vars)

cbLazy(con)

Parameters:

con (pyomo.core.base.constraint.ConstraintData) – The lazy constraint to add

get_gurobi_param_info(param)

Get information about a gurobi parameter.

Parameters:

param (str) – The gurobi parameter to get info for. See Gurobi documentation for possible options.

Return type:

six-tuple containing the parameter name, type, value, minimum value, maximum value, and default value.

get_linear_constraint_attr(con, attr)

Get the value of an attribute on a gurobi linear constraint.

Parameters:

• con (pyomo.core.base.constraint.ConstraintData) – The pyomo constraint for which the corresponding gurobi constraint attribute should be retrieved.

• attr (str) – The attribute to get. See the Gurobi documentation

get_model_attr(attr)

Get the value of an attribute on the Gurobi model.

Parameters:

attr (str) – The attribute to get. See Gurobi documentation for descriptions of the attributes.

get_quadratic_constraint_attr(con, attr)

Get the value of an attribute on a gurobi quadratic constraint.

Parameters:

• con (pyomo.core.base.constraint.ConstraintData) – The pyomo constraint for which the corresponding gurobi constraint attribute should be retrieved.

• attr (str) – The attribute to get. See the Gurobi documentation

get_sos_attr(con, attr)

Get the value of an attribute on a gurobi sos constraint.

Parameters:

• con (pyomo.core.base.sos.SOSConstraintData) – The pyomo SOS constraint for which the corresponding gurobi SOS constraint attribute should be retrieved.

• attr (str) – The attribute to get. See the Gurobi documentation

get_var_attr(var, attr)

Get the value of an attribute on a gurobi var.

Parameters:

• var (pyomo.core.base.var.VarData) – The pyomo var for which the corresponding gurobi var attribute should be retrieved.

• attr (str) – The attribute to get. See gurobi documentation

is_persistent()

Returns:

is_persistent – True if the solver is a persistent solver.

Return type:

bool

remove_block(block)

Remove a block from the model

remove_constraints(cons: List[ConstraintData])

Remove constraints from the model

remove_parameters(params: List[ParamData])

Remove parameters from the model

remove_variables(variables: List[VarData])

Remove variables from the model

set_callback(func=None)

Specify a callback for gurobi to use.

Parameters:

func (function) –

The function to call. The function should have three arguments. The first will be the pyomo model being solved. The second will be the GurobiPersistent instance. The third will be an enum member of gurobipy.GRB.Callback. This will indicate where in the branch and bound algorithm gurobi is at. For example, suppose we want to solve

\[ \begin{align}\begin{aligned}min 2*x + y\\s.t.\\ y >= (x-2)**2\\ 0 <= x <= 4\\ y >= 0\\ y integer\end{aligned}\end{align} \]

as an MILP using extended cutting planes in callbacks.

>>> from gurobipy import GRB 
>>> import pyomo.environ as pe
>>> from pyomo.core.expr.taylor_series import taylor_series_expansion
>>> from pyomo.contrib import appsi
>>>
>>> m = pe.ConcreteModel()
>>> m.x = pe.Var(bounds=(0, 4))
>>> m.y = pe.Var(within=pe.Integers, bounds=(0, None))
>>> m.obj = pe.Objective(expr=2*m.x + m.y)
>>> m.cons = pe.ConstraintList()  # for the cutting planes
>>>
>>> def _add_cut(xval):
...     # a function to generate the cut
...     m.x.value = xval
...     return m.cons.add(m.y >= taylor_series_expansion((m.x - 2)**2))
...
>>> _c = _add_cut(0)  # start with 2 cuts at the bounds of x
>>> _c = _add_cut(4)  # this is an arbitrary choice
>>>
>>> opt = appsi.solvers.Gurobi()
>>> opt.config.stream_solver = True
>>> opt.set_instance(m) 
>>> opt.gurobi_options['PreCrush'] = 1
>>> opt.gurobi_options['LazyConstraints'] = 1
>>>
>>> def my_callback(cb_m, cb_opt, cb_where):
...     if cb_where == GRB.Callback.MIPSOL:
...         cb_opt.cbGetSolution(vars=[m.x, m.y])
...         if m.y.value < (m.x.value - 2)**2 - 1e-6:
...             cb_opt.cbLazy(_add_cut(m.x.value))
...
>>> opt.set_callback(my_callback)
>>> res = opt.solve(m) 

set_gurobi_param(param, val)

Set a gurobi parameter.

Parameters:

• param (str) – The gurobi parameter to set. Options include any gurobi parameter. Please see the Gurobi documentation for options.

• val (any) – The value to set the parameter to. See Gurobi documentation for possible values.

set_instance(model)

Set an instance of the model

set_linear_constraint_attr(con, attr, val)

Set the value of an attribute on a gurobi linear constraint.

Parameters:

• con (pyomo.core.base.constraint.ConstraintData) – The pyomo constraint for which the corresponding gurobi constraint attribute should be modified.

• attr (str) –

  The attribute to be modified. Options are:

  CBasis DStart Lazy

• val (any) – See gurobi documentation for acceptable values.

set_objective(obj: ObjectiveData)

Set current objective for the model

set_var_attr(var, attr, val)

Set the value of an attribute on a gurobi variable.

Parameters:

• var (pyomo.core.base.var.VarData) – The pyomo var for which the corresponding gurobi var attribute should be modified.

• attr (str) –

  The attribute to be modified. Options are:

  Start VarHintVal VarHintPri BranchPriority VBasis PStart

• val (any) – See gurobi documentation for acceptable values.

solve(model, **kwds) → Results

Keyword Arguments:

• tee (TextIO_or_Logger, default=False) – tee accepts bool, io.TextIOBase, or logging.Logger (or a list of these types). True is mapped to sys.stdout. The solver log will be printed to each of these streams / destinations.

• working_dir (Path, optional) – The directory in which generated files should be saved. This replaces the keepfiles option.

• load_solutions (Bool, default=True) – If True, the values of the primal variables will be loaded into the model.

• raise_exception_on_nonoptimal_result (Bool, default=True) – If False, the solve method will continue processing even if the returned result is nonoptimal.

• symbolic_solver_labels (Bool, default=False) – If True, the names given to the solver will reflect the names of the Pyomo components. Cannot be changed after set_instance is called.

• timer (optional) – A timer object for recording relevant process timing data.

• threads (NonNegativeInt, optional) – Number of threads to be used by a solver.

• time_limit (NonNegativeFloat, optional) – Time limit applied to the solver (in seconds).

• solver_options (dict, optional) – Options to pass to the solver.

• auto_updates (dict, optional) –

  Configuration options to detect changes in model between solves

  check_for_new_or_removed_constraints: bool, default=True

  If False, new/old constraints will not be automatically detected on subsequent solves. Use False only when manually updating the solver with opt.add_constraints() and opt.remove_constraints() or when you are certain constraints are not being added to/removed from the model.

  check_for_new_or_removed_vars: bool, default=True

  If False, new/old variables will not be automatically detected on subsequent solves. Use False only when manually updating the solver with opt.add_variables() and opt.remove_variables() or when you are certain variables are not being added to / removed from the model.

  check_for_new_or_removed_params: bool, default=True

  If False, new/old parameters will not be automatically detected on subsequent solves. Use False only when manually updating the solver with opt.add_parameters() and opt.remove_parameters() or when you are certain parameters are not being added to / removed from the model.

  check_for_new_objective: bool, default=True

  If False, new/old objectives will not be automatically detected on subsequent solves. Use False only when manually updating the solver with opt.set_objective() or when you are certain objectives are not being added to / removed from the model.

  update_constraints: bool, default=True

  If False, changes to existing constraints will not be automatically detected on subsequent solves. This includes changes to the lower, body, and upper attributes of constraints. Use False only when manually updating the solver with opt.remove_constraints() and opt.add_constraints() or when you are certain constraints are not being modified.

  update_vars: bool, default=True

  If False, changes to existing variables will not be automatically detected on subsequent solves. This includes changes to the lb, ub, domain, and fixed attributes of variables. Use False only when manually updating the solver with opt.update_variables() or when you are certain variables are not being modified.

  update_parameters: bool, default=True

  If False, changes to parameter values will not be automatically detected on subsequent solves. Use False only when manually updating the solver with opt.update_parameters() or when you are certain parameters are not being modified.

  update_named_expressions: bool, default=True

  If False, changes to Expressions will not be automatically detected on subsequent solves. Use False only when manually updating the solver with opt.remove_constraints() and opt.add_constraints() or when you are certain Expressions are not being modified.

  update_objective: bool, default=True

  If False, changes to objectives will not be automatically detected on subsequent solves. This includes the expr and sense attributes of objectives. Use False only when manually updating the solver with opt.set_objective() or when you are certain objectives are not being modified.

  treat_fixed_vars_as_params: bool, default=True

  [ADVANCED option]

  This is an advanced option that should only be used in special circumstances. With the default setting of True, fixed variables will be treated like parameters. This means that z == x*y will be linear if x or y is fixed and the constraint can be written to an LP file. If the value of the fixed variable gets changed, we have to completely reprocess all constraints using that variable. If treat_fixed_vars_as_params is False, then constraints will be processed as if fixed variables are not fixed, and the solver will be told the variable is fixed. This means z == x*y could not be written to an LP file even if x and/or y is fixed. However, updating the values of fixed variables is much faster this way.

update_parameters()

Update parameters on the model

update_variables(variables: List[VarData])

Update variables on the model

version()

Returns:

version – A tuple representing the version

Return type:

tuple

write(filename)

Write the model to a file (e.g., and lp file).

Parameters:

filename (str) – Name of the file to which the model should be written.

CONFIG = <pyomo.contrib.solver.gurobi.GurobiConfig object>