Set Packing

Warning

This tutorial uses the old version of JijZept.

Set Packing#

Here we show how to solve set packing problem using JijZept and JijModeling. This problem is also mentioned in 4.2. Set Packing on Lucas, 2014, “Ising formulations of many NP problems”.

What is Set Packing?#

Let us consider the same setup as the Exact Cover problem, but now ask a different question: what is the largest number of subsets \(W_i\) which are all disjoint?

Mathematical Model#

Let \(x_i\) be a binary variable that takes on the value 1 if subset \(W_i\) is selected, and 0 otherwise.

Constraint: each element in \(U\) appears in exactly one selected subset#

This can be expressed as following using \(V_{i, j}\) where it represents a mapping from a subset \(i\) to a set of elements \(j\) that it contains. Here we set \(V_{i, j}\) to be a matrix that is 1 when \(W_i\) contains \(j\) and 0 otherwise. $\( \sum_{i=1}^N x_i \cdot V_{i, j} = 1 \text{ for } j = 1, \ldots, M \tag{1} \)$

Objective function : maximize the number of sets#

We simply counts the number of sets we include as the following.

\[ \max \sum_i x_i \tag{2} \]

Modeling by JijModeling#

Next, we show an implementation using JijModeling. We first define variables for the mathematical model described above.

import jijmodeling as jm

# define variables
U = jm.Placeholder('U')
N = jm.Placeholder('N')
M = jm.Placeholder('M')
V = jm.Placeholder('V', ndim=2)
x = jm.BinaryVar('x', shape=(N,))
i = jm.Element('i', N)
j = jm.Element('j', M)

We use the same variables in the exact cover problem.

Constraint#

We implement a constraint Equation (1).

# set problem
problem = jm.Problem('Set Packing',sense = jm.ProblemSense.MAXIMIZE)
# set constraint: each element j must be in exactly one subset i
problem += jm.Constraint('onehot', jm.sum(i, x[i]*V[i, j]) == 1, forall=j)

Objective function#

Next, we implement an objective function Equation (2).

# set objective function: maximize the number of sets
problem += x[:].sum()

Let’s display the implemented mathematical model in Jupyter Notebook.

problem
\[\begin{split}\begin{array}{cccc}\text{Problem:} & \text{Set Packing} & & \\& & \max \quad \displaystyle \sum_{\ast_{0} = 0}^{N - 1} x_{\ast_{0}} & \\\text{{s.t.}} & & & \\ & \text{onehot} & \displaystyle \sum_{i = 0}^{N - 1} x_{i} \cdot V_{i, j} = 1 & \forall j \in \left\{0,\ldots,M - 1\right\} \\\text{{where}} & & & \\& x & 1\text{-dim binary variable}\\\end{array}\end{split}\]

Prepare an instance#

We prepare as below.

import numpy as np

# set a list of W
W_1 = [1, 2, 3]
W_2 = [4, 5]
W_3 = [6]
W_4 = [7]
W_5 = [2, 5, 7]
W_6 = [6, 7]

# set the number of Nodes
inst_N = 6
inst_M = 7

# Convert the list of lists into a NumPy array
inst_V = np.zeros((inst_N, inst_M))
for i, subset in enumerate([W_1, W_2, W_3, W_4, W_5, W_6]):
    for j in subset:
        inst_V[i, j-1] = 1  # -1 since element indices start from 1 in the input data

instance_data = {'V': inst_V, 'M': inst_M, 'N': inst_N}

Solve by JijZept’s SA#

We solve this problem using JijZept JijSASampler. We also use the parameter search function by setting search=True.

import jijzept as jz

# set sampler
config_path = "../../../config.toml"
sampler = jz.JijSASampler(config=config_path)
# solve problem
response = sampler.sample_model(problem, instance_data, multipliers={'onehot': 0.5}, num_reads=100, search=True)

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[6], line 1
----> 1 import jijzept as jz
      3 # set sampler
      4 config_path = "../../../config.toml"

ModuleNotFoundError: No module named 'jijzept'

Check the solution#

In the end, we extract the solution from the feasible solutions.

# get sampleset
sampleset = response.get_sampleset()
# extract feasible samples
feasible_samples = sampleset.feasibles()
# get the values of feasible objectives
feasible_objectives = [sample.eval.objective for sample in feasible_samples]
if len(feasible_objectives) == 0:
    print("No feasible sample found ...")
else:
    # get the index of the highest objective value
    highest_index = np.argmax(feasible_objectives)
    # get the highest solution
    highest_solution = feasible_samples[highest_index].var_values["x"].values
    # get indices of x == 1
    x_indices = [key[0] for key in highest_solution.keys()]
    # show the result
    for i in x_indices:
        print(f"W_{i+1} = {inst_V[i, :].nonzero()[0]+1}")

W_2 = [4 5]
W_1 = [1 2 3]
W_4 = [7]
W_3 = [6]

As we expected, JijZept successfully returns the result.

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