ceres-solver VS autodiff

I can't reply to the guy saying julia is the only one. But there are others.

Ceres uses dual numbers

https://github.com/ceres-solver/ceres-solver/blob/master/inc...

This library from google is used everywhere in robotics, so it's hardly some backwater little side project.

So does c++ autodiff

http://ceres-solver.org (if you want to implement it manually, see tutorials & openCV sfm module)

http://ceres-solver.org/ works well, in my experience.

Hey, people! I was wondering if there is a library for non-linear optimization, equivalent to that for Ceres Solver that you have in C++?

I've not yet seen anything comparable to http://ceres-solver.org/

Disclaimer: I'm one of the authors of Ceres Solver which is widely used for solving computational geometry problems in computer vision. I also wrote TinySolver. And nowadays, I focus on Pigweed; a collection of embedded libraries targeting high-volume consumer electronics products. It's fun to see an overlap of these two areas expertise!

I completely agree. Specifying the metric rather than the Christoffel symbols would make it much easier for the users. Something like https://github.com/autodiff/autodiff might just work as the metric tensor is made up of primitives.

Old username :D So far I have been using Eigen for linear algebra and NLOPT for optimization algorithms. I have found "autodiff" that hopefully looks easy to use: https://github.com/autodiff/autodiff

Forward-mode differentiation is easy to implement in C++ with templates, operator overloading, and dual numbers (https://en.wikipedia.org/wiki/Automatic_differentiation#Auto...). Some libraries such as autodiff (https://github.com/autodiff/autodiff) and CppAD (https://github.com/coin-or/CppAD) use this method.

I was thinking of adding [autodiff](https://github.com/autodiff/autodiff) in the future, mainly because it works seamlessly with *Eigen*. One big advantage would be that I could use it for AD for NonlinearSystems as well.