Getting Started

In the following, a few simple examples are shown.

Basic Usage

The following script contains an example in which a QMKP is defined and solved by the implemented constructive procedure.

Defining and Solving a QMKP

import numpy as np
from qmkpy import total_profit_qmkp, QMKProblem
from qmkpy import algorithms

weights = [5, 2, 3, 4]  # four items
capacities = [10, 5, 12, 4, 2]  # five knapsacks
profits = np.array([[3, 1, 0, 2],
                    [1, 1, 1, 4],
                    [0, 1, 2, 2],
                    [2, 4, 2, 3]])  # symmetric profit matrix

qmkp = QMKProblem(profits, weights, capacities)
qmkp.algorithm = algorithms.constructive_procedure
assignments, total_profit = qmkp.solve()

print(assignments)
print(total_profit)

Saving a Problem Instance

It is possible to save a problem instance of a QMKP. This can be useful to share examples as a benchmark dataset to compare different algorithms.

Saving a QMKProblem Instance

import numpy as np
from qmkpy import QMKProblem

weights = [5, 2, 3, 4]  # four items
capacities = [10, 5, 12, 4, 2]  # five knapsacks
profits = np.array([[3, 1, 0, 2],
                    [1, 1, 1, 4],
                    [0, 1, 2, 2],
                    [2, 4, 2, 3]])  # symmetric profit matrix

qmkp = QMKProblem(profits, weights, capacities)

# Save the problem instance using the Numpy npz format
qmkp.save("my_problem.npz", strategy="numpy")

Loading a Saved QMKProblem Instance

You can also load a previously saved QMKProblem instance to get a qmkpy.QMKProblem object with the same profits, weights and weight capacities.

Loading a Saved QMKProblem

from qmkpy import QMKProblem

saved_problem = "my_problem.npz"  # saved model file
qmkp = QMKProblem.load(saved_problem, strategy="numpy")