# Basic Usage¶

This page provides examples of the basic of MiniZinc Python. It will show example of the types available in MiniZinc Python and shows how to use features that are often used.

## Using sets¶

There are two types in Python that are associated with MiniZinc’s sets: `set`

and `range`

. Generally a set in MiniZinc will be of the type `set`

. For
example, the minizinc set `{-2, 4, 12}`

will be represented in MiniZinc Python
as `{-2, 4, 12}`

or `set([-2, 4, 12])`

. However, contiguous sets, like index sets in MiniZinc,
can be more efficiently represented in a `range`

object, as this only records
the start and end of the set. For example, the MiniZinc set `-90..310`

is
represented using `range(-90, 311)`

in MiniZinc Python. When creating a set in
Python, either object can be translated to a MiniZinc set.

Note

The end given Python `range`

objects is non-inclusive. This means the
object `range(1, 3)`

only contains 1 and 2. This is unlike the MiniZinc
range syntax, which is inclusive. The MiniZinc set `1..3`

contains 1, 2,
and 3.

The following example shows how to assign set parameters and how to use the solutions for set variables.

```
from minizinc import Instance, Model, Solver
gecode = Solver.lookup("gecode")
model = Model()
model.add_string(
"""
include "all_different.mzn";
set of int: A;
set of int: B;
array[A] of var B: arr;
var set of B: X;
var set of B: Y;
constraint all_different(arr);
constraint forall (i in index_set(arr)) ( arr[i] in X );
constraint forall (i in index_set(arr)) ( (arr[i] mod 2 = 0) <-> arr[i] in Y );
"""
)
instance = Instance(gecode, model)
instance["A"] = range(3, 8) # MiniZinc: 3..8
instance["B"] = {4, 3, 2, 1, 0} # MiniZinc: {4, 3, 2, 1, 0}
result = instance.solve()
print(result["X"]) # range(0, 5)
assert isinstance(result["X"], range)
print(result["Y"]) # {0, 2, 4}
assert isinstance(result["Y"], set)
```

## Using enumerated types¶

The support for enumerated types in MiniZinc Python is still limited. It is, however, already supported to assign enumerated types in MiniZinc using a Python enumeration. When a enumeration is assigned, the values in the solution are ensured to be of the assigned enumerated type. This is demonstrated in the following example:

```
import enum
from minizinc import Instance, Model, Solver
gecode = Solver.lookup("gecode")
model = Model()
model.add_string(
"""
enum DAY;
var DAY: d;
constraint d = min(DAY);
"""
)
instance = Instance(gecode, model)
Day = enum.Enum("Day", ["Mo", "Tu", "We", "Th", "Fr"])
instance["DAY"] = Day
result = instance.solve()
print(result["d"]) # Day.Mo
assert isinstance(result["d"], Day)
```

Enumerations that are defined in MiniZinc are currently not translated into Python enumerations. Their values are currently returned as strings. The following adaptation of the previous example declares an enumerated type in MiniZinc and contains a string in it’s solution.

```
from minizinc import Instance, Model, Solver
gecode = Solver.lookup("gecode")
model = Model()
model.add_string(
"""
enum DAY = {Mo, Tu, We, Th, Fr};
var DAY: d;
constraint d = min(DAY);
"""
)
instance = Instance(gecode, model)
result = instance.solve()
print(result["d"]) # Mo
assert isinstance(result["d"], str)
```

## Finding all optimal solutions¶

MiniZinc does not support finding all *optimal* solutions for a specific
optimisation problem. However, a scheme that is often used to find all
optimal solutions is to first find one optimal solution and then find all
other solutions with the same optimal value. The following example shows this
process for a toy model that maximises the value of an array of unique integers:

```
from minizinc import Instance, Model, Solver
gecode = Solver.lookup("gecode")
model = Model()
model.add_string(
"""
include "all_different.mzn";
array[1..4] of var 1..10: x;
constraint all_different(x);
"""
)
instance = Instance(gecode, model)
with instance.branch() as opt:
opt.add_string("solve maximize sum(x);\n")
res = opt.solve()
obj = res["objective"]
instance.add_string(f"constraint sum(x) = {obj};\n")
result = instance.solve(all_solutions=True)
for sol in result.solution:
print(sol.x)
```

See also

In the example the `Instance.branch()`

method is used to temporarily
add a search goal to the `Instance`

object. More information about
the usage of this method can be found in the advanced examples.