Artificial Bee Colony Algorithm in Python.
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Updated
Apr 19, 2021 - Python
Artificial Bee Colony Algorithm in Python.
Python library for stochastic numerical optimization
Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
Create Your Own Metropolis-Hastings Markov Chain Monte Carlo Algorithm for Bayesian Inference (With Python)
[ICML 2021] The official PyTorch Implementations of Positive-Negative Momentum Optimizers.
[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder
Utilities for parsing MPS and SMPS file formats.
StochOPy WebApp is hosted online at
[NeurIPS 2023] The PyTorch Implementation of Scheduled (Stable) Weight Decay.
A software package for flexible HPC GPs
Hessian-based stochastic optimization in TensorFlow and keras
StochANNPy (STOCHAstic Artificial Neural Network for PYthon) provides user-friendly routines compatible with Scikit-Learn for stochastic learning.
Framework to model two stage stochastic unit commitment optimization problems.
Particle Swarm Optimization Python implementation
parameter optimization of a reinforcement learning deep Q network with memory replay buffer using genetic algorithm in the snake game. base code for snake env from codecamp
Implementation of beehive method (particle swarm optimization) for global optimization of multidimentional functions
One-week side project to play around stochastic optimization (how to take *good* decisions under uncertainty)
Originally a visualisation tool for solving sudokus using stochastic optimisation that has been optimised utilising a genetic algorithm for research. This project was pushed for a GitHub Developer Member invitation.
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