Simulated Annealing Python Packages

jij-cimod

C++ library for a binary quadratic model

openjij

OpenJij : Framework for the Ising model and QUBO.

gradient-free-optimizers

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

moptipy

Implementation of metaheuristic optimization methods in Python for scientific, industrial, and educational scenarios. Experiments can be executed in parallel or in a distributed fashion. Experimental results can be evaluated in various ways, including diagrams, tables, and export to Excel.

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.

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.

accel-brain-base

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

OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

pydbm

`pydbm` is Python library for building Restricted Boltzmann Machine(RBM), Deep Boltzmann Machine(DBM), Long Short-Term Memory Recurrent Temporal Restricted Boltzmann Machine(LSTM-RTRBM), and Shape Boltzmann Machine(Shape-BM). From the view points of functionally equivalents and structural expansions, this library also prototypes many variants such as Encoder/Decoder based on LSTM, Convolutional Auto-Encoder, and Spatio-temporal Auto-Encoder.

rig-c-sa

Python C module for the Rig simulated annealing placer C kernel

tsp-framework

Framework de algoritmos para resolver el Problema del Vendedor Viajero

sko

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

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

qqa

Quasi-Quantum Annealing (QQA): a general-purpose GPU solver for combinatorial and spin-glass optimization, with PI-GNN/CPRA and a parallel SA baseline.

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.

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).

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?"

simplestsimulatedannealing

Simplest fast implementation of simulated annlealing method

pyamosa

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

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

optimizationalgo

Optimization algorithms for the traveling salesman problem