ceres-solver VS Pytorch

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!

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():

import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.