A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

A list of general purpose tricks and techniques for saving tokens in PICO-8.

A course on Optimization Methods

Line search methods for optimization and root-finding

HPC solver for nonlinear optimization problems

A collection of numerical methods written in Nim

Derivative-Free Optimization Method

A summary of code optimization methods

A solver for mixed-integer nonlinear optimization problems

Implementation of various optimization methods

A framework for building fast genetic algorithms in Swift.

GPU Framework for Radio Astronomical Image Synthesis

A metaheuristic optimization framework

💎A site, that contains systematic optimization methods and theory review

Korali is a high-performance framework for uncertainty quantification and optimization of large-scale models.

PSO Application

[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder

Python package with optimization algorithms, wrappers and tools.

library of numerical methods using Armadillo

A Python implementation of the paper "Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel" https://arxiv.org/abs/1512.00708

Python trust-region subproblem solvers for nonlinear optimization

Automated Parameter Estimation for Complex (Ecological) Models

Causal Dynamic Network Modeling

Experiments with F# optimized closures.

Transportation Model for an optimal strategy for distributing a commodity from a group of supply centers to various receiving centers. Solved in C# Windows Form Application

A Python 3 gradient-free optimization library

OR-API is a grpc server that executes operational research algorithms

Bayesian optimization is a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation).

This python code is developed by Sreemannarayana Ikkurthi, as a part of course notes for the course 15AES477: Multidisciplinary Design Optimization (MDO). In support of Dr. Rajesh Senthil Kumar T., Assistant Professor, Department of Aerospace Engneering, Amrita Vishwa Vidyapeetham.