ForwardDiff.jl
ceres-solver
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ForwardDiff.jl | ceres-solver | |
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4 | 8 | |
854 | 3,601 | |
1.4% | 2.6% | |
5.7 | 8.1 | |
22 days ago | 8 days ago | |
Julia | C++ | |
GNU General Public License v3.0 or later | 3-Clause BSD License |
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ForwardDiff.jl
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The Elements of Differentiable Programming
You seem somewhat obsessed with the idea that reverse-mode autodiff is not the same technique as forward-mode autodiff. It makes you,,, angry? Seems like such a trivial thing to act a complete fool over.
What's up with that?
Anyway, here's a forward differentiation package with a file that might interest you
https://github.com/JuliaDiff/ForwardDiff.jl/blob/master/src/...
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Excited for Julia v1.9
Just so you know, v1.9 doesn't solve the load problems. What it does it gives package authors the tools to solve the problems, specifically precompilation as binaries and package extensions. It won't actually solve the load problems until the packages are updated to effectively make use of these features. This is already underway, https://sciml.ai/news/2022/09/21/compile_time/ with things like and https://github.com/JuliaDiff/ForwardDiff.jl/pull/625, but it is a fairly heavy lift to ensure things aren't invalidating and that everything that's necessary is precompiling.
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Looking for numerical/iterative approach for determining a value
As a quick way to do it, you can use ForwardDiff.jl to determine the partial with respect to h. Then use a Newton-Raphson algorithm to solve for the value of h. I'm not familiar with the actual problem you're solving so there may be more appropriate ways to solve this based on the shape of your function, but this is my knee-jerk reaction to a problem like this. You could also calculate the partial derivative analytically if that is something that you want.
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Question About Numerical Derivatives/Gradients: Why has no one yet implemented a gradient function in Julia that is similar to the gradient function in MATLAB and NumPy?
In these discussions, which are the only ones I could find that are the most pertinent and similar to what I'm talking about, https://github.com/JuliaDiff/ForwardDiff.jl/issues/390 and https://discourse.julialang.org/t/differentiation-without-explicit-function-np-gradient/57784 , nobody suggested or answered FiniteDiff.jl's finite differencing gradient for getting the numerical derivatives/gradients of an array of values. The answer is either the diff() function or Interpolations.jl, which I already explained in the post why I would want an alternative to those two options to exist, without having to call NumPy's gradient function.
ceres-solver
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The Elements of Differentiable Programming
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
- A large scale non-linear optimization library
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Photometric Bundle Adjustment library?
http://ceres-solver.org (if you want to implement it manually, see tutorials & openCV sfm module)
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Gradients Without Backpropagation
http://ceres-solver.org/ works well, in my experience.
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Is there a library for non-linear optimization in Rust?
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++?
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What libraries do you miss from other languages?
I've not yet seen anything comparable to http://ceres-solver.org/
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Non-linear equation solver for microcontrollers
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!
What are some alternatives?
Zygote.jl - 21st century AD
Eigen
FiniteDiff.jl - Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support
casadi - CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
GLM - OpenGL Mathematics (GLM)
ChainRules.jl - forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
OpenBLAS - OpenBLAS is an optimized BLAS library based on GotoBLAS2 1.13 BSD version.
NBodySimulator.jl - A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
QuantLib - The QuantLib C++ library
Tullio.jl - ⅀
CGal - The public CGAL repository, see the README below