PersistentSolverBase

(class from pyomo.contrib.solver.base)

class pyomo.contrib.solver.base.PersistentSolverBase(**kwds)[source]

Bases: SolverBase

Base class upon which persistent solvers can be built. This inherits the methods from the solver base class and adds those methods that are necessary for persistent solvers.

Example usage can be seen in the Gurobi interface.

__init__(**kwds) → None

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_variables`(variables)	Add variables to the model
`available`()	Test if the solver is available on this system.
`is_persistent`()
`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_variables`(variables)	Remove variables from the model
`set_instance`(model)	Set an instance of the model
`set_objective`(obj)	Set current objective for the model
`solve`(model, **kwargs)
`update_parameters`()	Update parameters on the model
`update_variables`(variables)	Update variables on the model
`version`()

Attributes

CONFIG

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

abstract add_block(block: BlockData)[source]: Add a block to the model

abstract add_constraints(cons: List[ConstraintData])[source]: Add constraints to the model

abstract add_parameters(params: List[ParamData])[source]: Add parameters to the model

abstract add_variables(variables: List[VarData])[source]: Add variables to the model

abstract available() → bool

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

is_persistent()[source]

Returns:: is_persistent – True if the solver is a persistent solver.
Return type:: bool

abstract remove_block(block: BlockData)[source]: Remove a block from the model

abstract remove_constraints(cons: List[ConstraintData])[source]: Remove constraints from the model

abstract remove_parameters(params: List[ParamData])[source]: Remove parameters from the model

abstract remove_variables(variables: List[VarData])[source]: Remove variables from the model

abstract set_instance(model)[source]: Set an instance of the model

abstract set_objective(obj: ObjectiveData)[source]: Set current objective for the model

abstract solve(model: BlockData, **kwargs) → Results[source]

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.

abstract update_parameters()[source]: Update parameters on the model

abstract update_variables(variables: List[VarData])[source]: Update variables on the model

abstract version() → Tuple

Returns:: version – A tuple representing the version
Return type:: tuple