A research toolkit for particle swarm optimization in Python
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Updated
Aug 6, 2024 - Python
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.
Derivative-Free Global Optimization Algorithm (C++, Python binding) - Continuous, Discrete, TSP, NLS, MINLP
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
DDO a generic and efficient framework for MDD-based optimization.
A Julia/JuMP Package for Optimal Quantum Circuit Design
A Java library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms
Constraint programming solver
My solutions for discrete optimization course on Coursera
Hybrid Models for Learning to Branch (NeurIPS 2020)
Erlang/Elixir interface to MiniZinc.
Solving and GUI demonstration of traditional N-Queens Problem using Hill Climbing, Simulated Annealing, Local Beam Search, and Genetic Algorithm.
Discrete optimization in the tensor-network (specifically, MPS-MPO) language.
Exact and approximate solvers for minimum-cost-flow problems in bi-directed graphs.
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)
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