# Constraints

Most constraints are specified using equality or inequality expressions
that are created using a rule, which is a Python function. For example,
if the variable `model.x`

has the indexes ‘butter’ and ‘scones’, then
this constraint limits the sum over these indexes to be exactly three:

```
def teaOKrule(model):
return(model.x['butter'] + model.x['scones'] == 3)
model.TeaConst = Constraint(rule=teaOKrule)
```

Instead of expressions involving equality (==) or inequalities (<= or
>=), constraints can also be expressed using a 3-tuple if the form
(lb, expr, ub) where lb and ub can be `None`

, which is interpreted as
lb <= expr <= ub. Variables can appear only in the middle expr. For
example, the following two constraint declarations have the same
meaning:

```
model.x = Var()
def aRule(model):
return model.x >= 2
model.Boundx = Constraint(rule=aRule)
def bRule(model):
return (2, model.x, None)
model.boundx = Constraint(rule=bRule)
```

For this simple example, it would also be possible to declare
`model.x`

with a `bounds`

option to accomplish the same thing.

Constraints (and objectives) can be indexed by lists or sets. When the
declaration contains lists or sets as arguments, the elements are
iteratively passed to the rule function. If there is more than one, then
the cross product is sent. For example the following constraint could be
interpreted as placing a budget of \(i\) on the
\(i^{\mbox{th}}\) item to buy where the cost per item is given by
the parameter `model.a`

:

```
model.A = RangeSet(1,10)
model.a = Param(model.A, within=PositiveReals)
model.ToBuy = Var(model.A)
def bud_rule(model, i):
return model.a[i]*model.ToBuy[i] <= i
aBudget = Constraint(model.A, rule=bud_rule)
```

Note

Python and Pyomo are case sensitive so `model.a`

is not the same as
`model.A`

.