feloopy 0.2.3

FelooPy: An Integrated Optimization Environment (IOE) for AutoOR in Python.

FelooPy: An integrated optimization environment for AutoOR in Python

Version 0.2.3 is out! More stable than ever!

FelooPy (/fɛlupaɪ/, an acronym for feasible, logical, optimal, and Python), is both a hyper-optimization interface and an integrated optimization environment for automated operations research in Python.

Using FelooPy, operations research scientists can: provide their target, representor, or learner model to get results; move focus from coding to modeling, and from modeling to analytics; automate time-consuming, iterative tasks of optimization model development, debugging, and implementation; access to 259 single-objective heuristic and exact optimization algorithms; switch between optimization interfaces and algorithms with no need of code changes; and use tools such as sensitivity analysis, automated encoding and decoding for heuristic optimization, timers, etc.

Specific features

• Free and Open-Source integrated optimization environment developed under MIT license.
• Straightforward mathematical programming workflow.
• Using single optimization programming syntax for 15 exact and heuristic optimization interfaces in Python.
• Accessing 82 exact and 177 heuristic optimization algorithms (total: 259).
• Supporting scalable optimization for large-scale real-world problems.
• Supporting benchmarking with various optimization solvers.
• Supporting multi-parameter sensitivity analysis on a single objective.
• Supporting specific solver options such as logging, number of threads, absolute gap or releative gap.

Supported optimization interfaces

Exact optimization:

• cplex
• cvxpy
• cylp
• gekko
• gurobi
• linopy
• mip
• ortools
• picos
• pulp
• pymprog
• pyomo
• xpress

Heuristic optimization:

• feloopy
• mealpy

Installation

Optional downloads: Python 3.10, (Visual Studio Code or Anaconda)

Note 1: Installation process requires python==3.10.x, pip>=22.3.1 and a stable internet connection.

Note 2: Ensure to add Python to PATH during the installation process (usually the first menu).

Note 3: To use FelooPy inside Google Colab environment, please first run the following code to configure Python version. Note that this code requires you to choose the desired version during implementation.

!sudo apt-get update -y
!sudo apt-get install python3.10
!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1
!sudo update-alternatives --config python3
!sudo apt install python3-pip

Method 1: Terminal command (e.g., CMD or GC):

pip install feloopy==0.2.3

Method 2: IDE command (e.g., Spyder):

Note: After installation, this line of code should be deleted.

!pip install feloopy==0.2.3

Method 3: Inside your Python code

Note: After installation, this piece of code should be deleted.

import pip

def install(package):
    if hasattr(pip, 'main'):
        pip.main(['install','-U', package])
    else:
        pip._internal.main(['install','-U', package])

install('feloopy')

Method 4: From GitHub Releases section

1. Download the feloopy-0.2.3.zip file.
2. Extract it into a specific directory.
3. Open a terminal in that directory.
4. Type: pip install .

Method 5: From GitHub repository (insiders version)

1. Download and install git.

2. Run this command inside a terminal:

pip install -U git+https://github.com/ktafakkori/feloopy

Documentation

• Tutorial
• Examples
• Exact Solvers
• Heuristic Solvers
• Changelog

Citation

• APA 7:

Tafakkori, K. (2023). Feloopy: An integrated optimization environment for AutoOR in Python (0.2.3) [Python]. https://github.com/ktafakkori/feloopy (Original work published 2023)

• LaTeX:

@software{ktafakkori2023Feb,
  author       = {Keivan Tafakkori},
  title        = {{FelooPy: An integrated optimization environment for AutoOR in Python}},
  year         = {2023},
  month        = feb,
  publisher    = {GitHub},
  version      = {v0.2.3},
  url          = {https://github.com/ktafakkori/feloopy/}
}