A research toolkit for particle swarm optimization in Python

🎯 A comprehensive gradient-free optimization framework written in Python

An easy-to-use Python framework to generate adversarial jailbreak prompts.

A curated list of mathematical optimization courses, lectures, books, notes, libraries, frameworks and software.

Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

BCP-MAPF – branch-and-cut-and-price for multi-agent path finding

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A Julia/JuMP Package for Optimal Quantum Circuit Design

DDO a generic and efficient framework for MDD-based optimization.

A Java library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms

My solutions for discrete optimization course on Coursera

Constraint programming solver

Hybrid Models for Learning to Branch (NeurIPS 2020)

Solving and GUI demonstration of traditional N-Queens Problem using Hill Climbing, Simulated Annealing, Local Beam Search, and Genetic Algorithm.

Erlang/Elixir interface to MiniZinc.

Gradient-free optimization method for multivariable functions based on the low rank tensor train (TT) format and maximal-volume principle.

Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)

Discrete optimization in the tensor-network (specifically, MPS-MPO) language.

MHLib.jl - A Toolbox for Metaheuristics and Hybrid Optimization Methods in Julia