# Number Partitioning¶

In the Number Partitioning problem, the goal is to find, given a set of \(k\) positive numbers \(S=\{n_1, \dots , n_k\}\), whether there exists a partition of this set of numbers into two disjoint sets \(R\) and \(S \setminus R\) such that the sum of the elements in both sets is as close as possible.

For example, given the set of numbers \(S=\{1, 2, 3, 6, 10\}\), one can find that there is a perfect number partition since we can take \(R=\{2, 3, 6\}\) and \(S\setminus R =\{1, 10\}\), so that \(|R|=|S\setminus R |=11\).

A Kind of Knapsack Problem

The Number Partitioning problem can be seen as a special case of the Knapsack problem, where there are \(n\) items with same value, and the weight capacity is \(W=\frac12 \sum_{i}^k n_k\).

## The Cost Function¶

The function to minimize can be defined as

where \(\sigma \in \{-1, 1\}^k\). That is, a variable \(\sigma_i\) is attached to each number \(n_i\), and the variable's value determines on which side of the partition the number is assigned to. The smallest value \(C(\cdot)\) can take is 0, which happens when \(\sigma\) is a perfect partition.

Since this formulation is already in terms of Ising variables, it can be used directly in QAOA.

## Number Partitioning in OpenQAOA¶

The Number Partitioning problem is defined by a set of integers, you can thus define the problem as follows:

```
from openqaoa.problems import NumberPartition
int_set = [1,2,3,4,10]
np_prob = NumberPartition(numbers=int_set)
np_qubo = np_prob.qubo
```

We can then access the underlying cost hamiltonian

You may also check all details of the problem instance in the form of a dictionary:

```
> np_qubo.asdict()
{'constant': 130,
'metadata': {},
'n': 5,
'problem_instance': {'n_numbers': 5,
'numbers': [1, 2, 3, 4, 10],
'problem_type': 'number_partition'},
'terms': [[0, 1],
[0, 2],
[0, 3],
[0, 4],
[1, 2],
[1, 3],
[1, 4],
[2, 3],
[2, 4],
[3, 4]],
'weights': [4.0, 6.0, 8.0, 20.0, 12.0, 16.0, 40.0, 24.0, 60.0, 80.0]}
```