Introduction

Description

quickopt is a Python optimization toolkit developed in C++ geared towards optimization problems of multimodal functions, such as those encountered in hyperparameter tuning.

It contains a variety of optimization algorithms, which are listed below.

The package is designed to be easy to use and flexible, allowing users to use popular optimization methods out of the box in as little as one line of code, while also allowing users to customize the optimization process to suit their needs.

A list of the optimization algorithms included in the package is provided below:

• Simulated Annealing: Compatible with string, float, and integer inputs

• Bayesian Optimization with Tree-Structrued Parzen Estimators: Compatible with float inputs

• Genetic Algorithm: Compatible with string, float, and integer inputs

• Particle Swarm Optimization: Compatible with float inputs

with more to come soon!

What to Expect

This documentation will provide a guide on how to use the algorithms provided in quickopt, as well as example usages, descriptions of the algorithms, and references to learn more.

Installation

To install the package using PyPi, run the following command:

pip install quickopt

Other

Contact: varunpiram@gmail.com

This project is licensed under the MIT License. See the LICENSE file for details.