A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
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Updated
Aug 10, 2024 - C++
A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
A JuMP extension for Stochastic Dual Dynamic Programming
A curated list of mathematical optimization courses, lectures, books, notes, libraries, frameworks and software.
An intuitive modeling interface for infinite-dimensional optimization problems.
Artificial Bee Colony Algorithm in Python.
Python library for stochastic numerical optimization
Templated C++/CUDA implementation of Model Predictive Path Integral Control (MPPI)
An open-source parallel optimization solver for structured mixed-integer programming
Riemannian stochastic optimization algorithms: Version 1.0.3
[NeurIPS 2023] The PyTorch Implementation of Scheduled (Stable) Weight Decay.
Create Your Own Metropolis-Hastings Markov Chain Monte Carlo Algorithm for Bayesian Inference (With Python)
A simple implementation of SPSA with automatic learning rate tuning
Data-driven decision making under uncertainty using matrices
A collection of papers and readings for non-convex optimization
A julia implementation of the CMA Evolution Strategy for derivative-free optimization of potentially non-linear, non-convex or noisy functions over continuous domains.
Hessian-based stochastic optimization in TensorFlow and keras
Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
[ICML 2021] The official PyTorch Implementations of Positive-Negative Momentum Optimizers.
An interactive visual simulator for distance-based protein folding
This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.
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