pykp 1.1.0

Tooling for sampling and solving instances of the 0-1 Knapsack Problem.

PyKP

PyKP is a free and open-source library for sampling and solving instances of the knapsack problem. It provides tools to define knapsack instances, solve them eficiently, and analyse computational complexity metrics. You can also use pykp to randomly sample knapsack problem instances based on specified distributions.

Features

• Define knapsack problem instances with custom items, weights, and values.
• Solve knapsack instances using branch-and-bound and other methods.
• Compute optimal and feasible solutions for different knapsack configurations.
• Analyse computational complexity metrics.
• Generate synthetic knapsack instances with custom weight, density, and solution value ranges.

Installation

PyKP support Python version 3.12 and higher. To install PyKP, run

pip install pykp

Usage

Defining and Solving a Knapsack Problem

To start, define a knapsack problem with a set of items and solve it using the Knapsack class.

import numpy as np
from pykp import Knapsack
from pykp import Item

# Define items for the knapsack
items = np.array([
    Item(value=10, weight=5),
    Item(value=15, weight=10),
    Item(value=7, weight=3)
])

# Initialise a Knapsack instance
capacity = 15
knapsack = Knapsack(items=items, capacity=capacity)
knapsack.solve()

# Display the optimal solution
print("Optimal Solution Value:", knapsack.optimal_nodes[0].value)

Generating Knapsack Instances with Sampler

The Sampler class allows you to generate knapsack instances based on specific ranges for item densities (value/weight ratio) and optimal solution values.

from pykp import Sampler

# Initialise a Sampler instance with desired ranges
sampler = Sampler(
    num_items=5,
    normalised_capacity=0.6,
    density_range=(0.5, 1.5),
    solution_value_range=(100, 200)
)

# Generate a sampled knapsack instance
sampled_knapsack = sampler.sample()
print("Sampled Knapsack Capacity:", sampled_knapsack.capacity)

Analysing Knapsack Solutions

The package provides methods to analyse the optimal solutions and other feasible arrangements.

# Display a summary of the knapsack solutions
print(sampled_knapsack.summary())

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome. Please fork the repository and submit a pull request.

Contact

For questions or feedback, please reach out at [e-mail].