# Suffixes

Suffixes provide a mechanism for declaring extraneous model data, which can be used in a number of contexts. Most commonly, suffixes are used by solver plugins to store extra information about the solution of a model. This and other suffix functionality is made available to the modeler through the use of the Suffix component class. Uses of Suffix include:

- Importing extra information from a solver about the solution of a mathematical program (e.g., constraint duals, variable reduced costs, basis information).
- Exporting information to a solver or algorithm to aid in solving a mathematical program (e.g., warm-starting information, variable branching priorities).
- Tagging modeling components with local data for later use in advanced scripting algorithms.

## Suffix Notation and the Pyomo NL File Interface

The Suffix component used in Pyomo has been adapted from the suffix notation used in the modeling language AMPL [AMPL]. Therefore, it follows naturally that AMPL style suffix functionality is fully available using Pyomo’s NL file interface. For information on AMPL style suffixes the reader is referred to the AMPL website:

A number of scripting examples that highlight the use AMPL style suffix
functionality are available in the `examples/pyomo/suffixes`

directory
distributed with Pyomo.

## Declaration

The effects of declaring a Suffix component on a Pyomo model are determined by the following traits:

- direction: This trait defines the direction of information flow for
the suffix. A suffix direction can be assigned one of four possible
values:
`LOCAL`

- suffix data stays local to the modeling framework and will not be imported or exported by a solver plugin (default)`IMPORT`

- suffix data will be imported from the solver by its respective solver plugin`EXPORT`

- suffix data will be exported to a solver by its respective solver plugin`IMPORT_EXPORT`

- suffix data flows in both directions between the model and the solver or algorithm

- datatype: This trait advertises the type of data held on the suffix
for those interfaces where it matters (e.g., the NL file interface). A
suffix datatype can be assigned one of three possible values:
`FLOAT`

- the suffix stores floating point data (default)`INT`

- the suffix stores integer data`None`

- the suffix stores any type of data

Note

Exporting suffix data through Pyomo’s NL file interface requires all
active export suffixes have a strict datatype (i.e.,
`datatype=None`

is not allowed).

The following code snippet shows examples of declaring a Suffix component on a Pyomo model:

```
import pyomo.environ as pyo
model = pyo.ConcreteModel()
# Export integer data
model.priority = pyo.Suffix(
direction=pyo.Suffix.EXPORT, datatype=pyo.Suffix.INT)
# Export and import floating point data
model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT_EXPORT)
# Store floating point data
model.junk = pyo.Suffix()
```

Declaring a Suffix with a non-local direction on a model is not guaranteed to be compatible with all solver plugins in Pyomo. Whether a given Suffix is acceptable or not depends on both the solver and solver interface being used. In some cases, a solver plugin will raise an exception if it encounters a Suffix type that it does not handle, but this is not true in every situation. For instance, the NL file interface is generic to all AMPL-compatible solvers, so there is no way to validate that a Suffix of a given name, direction, and datatype is appropriate for a solver. One should be careful in verifying that Suffix declarations are being handled as expected when switching to a different solver or solver interface.

## Operations

The Suffix component class provides a dictionary interface for mapping Pyomo modeling components to arbitrary data. This mapping functionality is captured within the ComponentMap base class, which is also available within Pyomo’s modeling environment. The ComponentMap can be used as a more lightweight replacement for Suffix in cases where a simple mapping from Pyomo modeling components to arbitrary data values is required.

Note

ComponentMap and Suffix use the built-in `id()`

function for
hashing entry keys. This design decision arises from the fact that
most of the modeling components found in Pyomo are either not
hashable or use a hash based on a mutable numeric value, making them
unacceptable for use as keys with the built-in `dict`

class.

Warning

The use of the built-in `id()`

function for hashing entry keys in
ComponentMap and Suffix makes them inappropriate for use in
situations where built-in object types must be used as keys. It is
strongly recommended that only Pyomo modeling components be used as
keys in these mapping containers (`Var`

, `Constraint`

, etc.).

Warning

Do not attempt to pickle or deepcopy instances of ComponentMap or Suffix unless doing so along with the components for which they hold mapping entries. As an example, placing one of these objects on a model and then cloning or pickling that model is an acceptable scenario.

In addition to the dictionary interface provided through the ComponentMap base class, the Suffix component class also provides a number of methods whose default semantics are more convenient for working with indexed modeling components. The easiest way to highlight this functionality is through the use of an example.

```
model = pyo.ConcreteModel()
model.x = pyo.Var()
model.y = pyo.Var([1,2,3])
model.foo = pyo.Suffix()
```

In this example we have a concrete Pyomo model with two different types of variable components (indexed and non-indexed) as well as a Suffix declaration (foo). The next code snippet shows examples of adding entries to the suffix foo.

```
# Assign a suffix value of 1.0 to model.x
model.foo.set_value(model.x, 1.0)
# Same as above with dict interface
model.foo[model.x] = 1.0
# Assign a suffix value of 0.0 to all indices of model.y
# By default this expands so that entries are created for
# every index (y[1], y[2], y[3]) and not model.y itself
model.foo.set_value(model.y, 0.0)
# The same operation using the dict interface results in an entry only
# for the parent component model.y
model.foo[model.y] = 50.0
# Assign a suffix value of -1.0 to model.y[1]
model.foo.set_value(model.y[1], -1.0)
# Same as above with the dict interface
model.foo[model.y[1]] = -1.0
```

In this example we highlight the fact that the `__setitem__`

and
`setValue`

entry methods can be used interchangeably except in the
case where indexed components are used (model.y). In the indexed case,
the `__setitem__`

approach creates a single entry for the parent
indexed component itself, whereas the `setValue`

approach by default
creates an entry for each index of the component. This behavior can be
controlled using the optional keyword ‘expand’, where assigning it a
value of `False`

results in the same behavior as `__setitem__`

.

Other operations like accessing or removing entries in our mapping can
performed as if the built-in `dict`

class is in use.

```
>>> print(model.foo.get(model.x))
1.0
>>> print(model.foo[model.x])
1.0
>>> print(model.foo.get(model.y[1]))
-1.0
>>> print(model.foo[model.y[1]])
-1.0
>>> print(model.foo.get(model.y[2]))
0.0
>>> print(model.foo[model.y[2]])
0.0
>>> print(model.foo.get(model.y))
50.0
>>> print(model.foo[model.y])
50.0
>>> del model.foo[model.y]
>>> print(model.foo.get(model.y))
None
>>> print(model.foo[model.y])
Traceback (most recent call last):
...
KeyError: "Component with id '...': y"
```

The non-dict method `clear_value`

can be used in place of
`__delitem__`

to remove entries, where it inherits the same default
behavior as `setValue`

for indexed components and does not raise a
KeyError when the argument does not exist as a key in the mapping.

```
>>> model.foo.clear_value(model.y)
>>> print(model.foo[model.y[1]])
Traceback (most recent call last):
...
KeyError: "Component with id '...': y[1]"
>>> del model.foo[model.y[1]]
Traceback (most recent call last):
...
KeyError: "Component with id '...': y[1]"
>>> model.foo.clear_value(model.y[1])
```

A summary non-dict Suffix methods is provided here:

clearAllValues()Clears all suffix data.clear_value(component, expand=True)Clears suffix information for a component.setAllValues(value)Sets the value of this suffix on all components.setValue(component, value, expand=True)Sets the value of this suffix on the specified component.updateValues(data_buffer, expand=True)Updates the suffix data given a list of component,value tuples. Providesan improvement in efficiency over calling setValue on every component.getDatatype()Return the suffix datatype.setDatatype(datatype)Set the suffix datatype.getDirection()Return the suffix direction.setDirection(direction)Set the suffix direction.importEnabled()Returns True when this suffix is enabled for import from solutions.exportEnabled()Returns True when this suffix is enabled for export to solvers.

## Importing Suffix Data

Importing suffix information from a solver solution is achieved by declaring a Suffix component with the appropriate name and direction. Suffix names available for import may be specific to third-party solvers as well as individual solver interfaces within Pyomo. The most common of these, available with most solvers and solver interfaces, is constraint dual multipliers. Requesting that duals be imported into suffix data can be accomplished by declaring a Suffix component on the model.

```
model = pyo.ConcreteModel()
model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)
model.x = pyo.Var()
model.obj = pyo.Objective(expr=model.x)
model.con = pyo.Constraint(expr=model.x >= 1.0)
```

The existence of an active suffix with the name dual that has an import
style suffix direction will cause constraint dual information to be
collected into the solver results (assuming the solver supplies dual
information). In addition to this, after loading solver results into a
problem instance (using a python script or Pyomo callback functions in
conjunction with the `pyomo`

command), one can access the dual values
associated with constraints using the dual Suffix component.

```
>>> results = pyo.SolverFactory('glpk').solve(model)
>>> pyo.assert_optimal_termination(results)
>>> print(model.dual[model.con])
1.0
```

Alternatively, the `pyomo`

option `--solver-suffixes`

can be used to
request suffix information from a solver. In the event that suffix names
are provided via this command-line option, the `pyomo`

script will
automatically declare these Suffix components on the constructed
instance making these suffixes available for import.

## Exporting Suffix Data

Exporting suffix data is accomplished in a similar manner as to that of importing suffix data. One simply needs to declare a Suffix component on the model with an export style suffix direction and associate modeling component values with it. The following example shows how one can declare a special ordered set of type 1 using AMPL-style suffix notation in conjunction with Pyomo’s NL file interface.

```
model = pyo.ConcreteModel()
model.y = pyo.Var([1,2,3], within=pyo.NonNegativeReals)
model.sosno = pyo.Suffix(direction=pyo.Suffix.EXPORT)
model.ref = pyo.Suffix(direction=pyo.Suffix.EXPORT)
# Add entry for each index of model.y
model.sosno.set_value(model.y, 1)
model.ref[model.y[1]] = 0
model.ref[model.y[2]] = 1
model.ref[model.y[3]] = 2
```

Most AMPL-compatible solvers will recognize the suffix names `sosno`

and `ref`

as declaring a special ordered set, where a positive value
for `sosno`

indicates a special ordered set of type 1 and a negative
value indicates a special ordered set of type 2.

Note

Pyomo provides the `SOSConstraint`

component for declaring special
ordered sets, which is recognized by all solver interfaces, including
the NL file interface.

Pyomo’s NL file interface will recognize an EXPORT style Suffix component with the name ‘dual’ as supplying initializations for constraint multipliers. As such it will be treated separately than all other EXPORT style suffixes encountered in the NL writer, which are treated as AMPL-style suffixes. The following example script shows how one can warmstart the interior-point solver Ipopt by supplying both primal (variable values) and dual (suffixes) solution information. This dual suffix information can be both imported and exported using a single Suffix component with an IMPORT_EXPORT direction.

```
model = pyo.ConcreteModel()
model.x1 = pyo.Var(bounds=(1,5),initialize=1.0)
model.x2 = pyo.Var(bounds=(1,5),initialize=5.0)
model.x3 = pyo.Var(bounds=(1,5),initialize=5.0)
model.x4 = pyo.Var(bounds=(1,5),initialize=1.0)
model.obj = pyo.Objective(
expr=model.x1*model.x4*(model.x1 + model.x2 + model.x3) + model.x3)
model.inequality = pyo.Constraint(
expr=model.x1*model.x2*model.x3*model.x4 >= 25.0)
model.equality = pyo.Constraint(
expr=model.x1**2 + model.x2**2 + model.x3**2 + model.x4**2 == 40.0)
### Declare all suffixes
# Ipopt bound multipliers (obtained from solution)
model.ipopt_zL_out = pyo.Suffix(direction=pyo.Suffix.IMPORT)
model.ipopt_zU_out = pyo.Suffix(direction=pyo.Suffix.IMPORT)
# Ipopt bound multipliers (sent to solver)
model.ipopt_zL_in = pyo.Suffix(direction=pyo.Suffix.EXPORT)
model.ipopt_zU_in = pyo.Suffix(direction=pyo.Suffix.EXPORT)
# Obtain dual solutions from first solve and send to warm start
model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT_EXPORT)
ipopt = pyo.SolverFactory('ipopt')
```

The difference in performance can be seen by examining Ipopt’s iteration log with and without warm starting:

Without Warmstart:

ipopt.solve(model, tee=True)

... iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 1.6109693e+01 1.12e+01 5.28e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 1.6982239e+01 7.30e-01 1.02e+01 -1.0 6.11e-01 - 7.19e-02 1.00e+00f 1 2 1.7318411e+01 3.60e-02 5.05e-01 -1.0 1.61e-01 - 1.00e+00 1.00e+00h 1 3 1.6849424e+01 2.78e-01 6.68e-02 -1.7 2.85e-01 - 7.94e-01 1.00e+00h 1 4 1.7051199e+01 4.71e-03 2.78e-03 -1.7 6.06e-02 - 1.00e+00 1.00e+00h 1 5 1.7011979e+01 7.19e-03 8.50e-03 -3.8 3.66e-02 - 9.45e-01 9.98e-01h 1 6 1.7014271e+01 1.74e-05 9.78e-06 -3.8 3.33e-03 - 1.00e+00 1.00e+00h 1 7 1.7014021e+01 1.23e-07 1.82e-07 -5.7 2.69e-04 - 1.00e+00 1.00e+00h 1 8 1.7014017e+01 1.77e-11 2.52e-11 -8.6 3.32e-06 - 1.00e+00 1.00e+00h 1 Number of Iterations....: 8 ...

With Warmstart:

### Set Ipopt options for warm-start # The current values on the ipopt_zU_out and ipopt_zL_out suffixes will # be used as initial conditions for the bound multipliers to solve the # new problem model.ipopt_zL_in.update(model.ipopt_zL_out) model.ipopt_zU_in.update(model.ipopt_zU_out) ipopt.options['warm_start_init_point'] = 'yes' ipopt.options['warm_start_bound_push'] = 1e-6 ipopt.options['warm_start_mult_bound_push'] = 1e-6 ipopt.options['mu_init'] = 1e-6 ipopt.solve(model, tee=True)

... iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 1.7014032e+01 2.00e-06 4.07e-06 -6.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 1.7014019e+01 3.65e-12 1.00e-11 -6.0 2.50e-01 - 1.00e+00 1.00e+00h 1 2 1.7014017e+01 4.48e-12 6.42e-12 -9.0 1.92e-06 - 1.00e+00 1.00e+00h 1 Number of Iterations....: 2 ...

## Using Suffixes With an AbstractModel

In order to allow the declaration of suffix data within the framework of
an AbstractModel, the Suffix component can be initialized with an
optional construction rule. As with constraint rules, this function will
be executed at the time of model construction. The following simple
example highlights the use of the `rule`

keyword in suffix
initialization. Suffix rules are expected to return an iterable of
(component, value) tuples, where the `expand=True`

semantics are
applied for indexed components.

```
model = pyo.AbstractModel()
model.x = pyo.Var()
model.c = pyo.Constraint(expr=model.x >= 1)
def foo_rule(m):
return ((m.x, 2.0), (m.c, 3.0))
model.foo = pyo.Suffix(rule=foo_rule)
```

```
>>> # Instantiate the model
>>> inst = model.create_instance()
>>> print(inst.foo[inst.x])
2.0
>>> print(inst.foo[inst.c])
3.0
>>> # Note that model.x and inst.x are not the same object
>>> print(inst.foo[model.x])
Traceback (most recent call last):
...
KeyError: "Component with id '...': x"
```

The next example shows an abstract model where suffixes are attached only to the variables:

```
model = pyo.AbstractModel()
model.I = pyo.RangeSet(1,4)
model.x = pyo.Var(model.I)
def c_rule(m, i):
return m.x[i] >= i
model.c = pyo.Constraint(model.I, rule=c_rule)
def foo_rule(m):
return ((m.x[i], 3.0*i) for i in m.I)
model.foo = pyo.Suffix(rule=foo_rule)
```

```
>>> # instantiate the model
>>> inst = model.create_instance()
>>> for i in inst.I:
... print((i, inst.foo[inst.x[i]]))
(1, 3.0)
(2, 6.0)
(3, 9.0)
(4, 12.0)
```