Simulated Annealing Python Packages

openjij

OpenJij : Framework for the Ising model and QUBO.

jij-cimod

C++ library for a binary quadratic model

gradient-free-optimizers

Lightweight optimization with local, global, population-based and sequential techniques across mixed search spaces

moptipy

A package for metaheuristic optimization in Python.

accelbrainbeat

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

qqa

Quasi-Quantum Annealing (QQA): a GPU solver for combinatorial & spin-glass optimisation — PyTorch library + live Streamlit dashboard. ICLR 2025.

pysummarization

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

pyqlearning

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

sko

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

pydbm

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

optapy

An AI constraint solver that optimizes planning and scheduling problems

tsp-framework

Framework de algoritmos para resolver el Problema del Vendedor Viajero

accel-brain-base

accel-brain-base is a basic library of the Deep Learning for rapid development at low cost. This library makes it possible to design and implement deep learning, which must be configured as a complex system, by combining a plurality of functionally differentiated modules such as a Deep Boltzmann Machines(DBMs), an Auto-Encoder, an Encoder/Decoder based on Long Short-Term Memory(LSTM), a Convolutional Auto-Encoder(CAE), and Transformer.

rig-c-sa

Python C module for the Rig simulated annealing placer C kernel

pygan

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

invest-ucm-calibration

Automated calibration of the InVEST urban cooling model with simulated annealing

pycomposer

pycomposer is Python library for Algorithmic Composition or Automatic Composition based on the stochastic music theory and the Statistical machine learning problems. Especialy, this library provides apprication of the Algorithmic Composer based on Generative Adversarial Networks(GANs) and Conditional Generative Adversarial Networks(Conditional GANs).

pyamosa

Python implementation of the Archived Multi-Objective Simulated Annealing (AMOSA) optimization heuristic

data-morph-ai

Morph an input dataset of 2D points into select shapes, while preserving the summary statistics to a given number of decimal points through simulated annealing. It is intended to be used as a teaching tool to illustrate the importance of data visualization.

simplestsimulatedannealing

Simplest fast implementation of simulated annlealing method

rectangle-packing-solver

A solver to find a solution of the 2D rectangle packing problem by simulated annealing (SA) optimization.

turingbot

Python interface for TuringBot, a symbolic regression software that discovers mathematical formulas from data

phase-slip-sampler

Phase-Slip is a stochastic intervention architecture that operates on the Key-Value Cache of the model. Phase-Slip gently rotates the semantic vectors of the context window, asking the model: "How would you finish this sentence if you looked at it from a slightly different perspective?"

optimizationalgo

Метод имитации отжига и муравьиный алгоритм