Stars
Implementation of a language-level autograd compiler
Meta-Algorithms for OptiGraphs with Plasmo.jl
Embed trained machine learning predictors into JuMP and ExaModels
Transforms your CasADi functions into batchable JAX-compatible functions. By combining the power of CasADi with the flexibility of JAX, JAXADi enables the creation of efficient code that runs smoot…
An open source model predictive control package for Julia.
A Platform for Scalable Modeling and Optimization
Experimental design and (multi-objective) bayesian optimization.
A differentiable PDE solving framework for machine learning
Fatrop is a nonlinear optimal control problem solver that aims to be fast, support a broad class of optimal control problems and achieve a high numerical robustness.
Aircraft design optimization made fast through computational graph transformations (e.g., automatic differentiation). Composable analysis tools for aerodynamics, propulsion, structures, trajectory …
A Julia package for solving multi-objective optimization problems
A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python 🚀
Julia package for automated Bayesian inference on a factor graph with reactive message passing
Automatic differentiation of implicit functions
A scikit-learn compatible neural network library that wraps PyTorch
GPU-based first-order solver for linear programming.
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Code for solving LP on GPU using first-order methods
Differentiating optimization programs w.r.t. program parameters
KaHyPar.jl is a Julia interface to the KaHyPar multilevel hypergraph partitioning package.
Use PyTorch Models with CasADi for data-driven optimization or learning-based optimal control. Supports Acados.
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.