The current implementation of `LinearAlgebra.eigen` does not support sensitivities. DifferentiableEigen.jl offers an `eigen` function that is differentiable by every AD-framework with support for ChainRulesCore.jl or ForwardDiff.jl.
Author ThummeTo
18 Stars
Updated Last
1 Year Ago
Started In
February 2023


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What is DifferentiableEigen.jl?

The current implementation of LinearAlgebra.eigen does not support sensitivities. This package adds a new function eigen, that wraps the original function, but returns an array of reals instead of complex numbers (this is necessary, because some AD-frameworks do not support complex numbers). This eigen function is differentiable by every AD-framework with support for ChainRulesCore.jl and ForwardDiff.jl.

How can I use DifferentiableEigen.jl?

1. Open a Julia-REPL, switch to package mode using ], activate your preferred environment.

2. Install DifferentiableEigen.jl:

(@v1.6) pkg> add DifferentiableEigen

3. If you want to check that everything works correctly, you can run the tests bundled with DifferentiableEigen.jl:

(@v1.6) pkg> test DifferentiableEigen

How does it work?

import DifferentiableEigen
import LinearAlgebra
import ForwardDiff

A = rand(3,3)   # Random matrix 3x3 

eigvals, eigvecs = LinearAlgebra.eigen(A)   # This is the default eigen-function in Julia. Note, that eigenvalues and -vectors are complex numbers.
jac = ForwardDiff.jacobian((A) -> LinearAlgebra.eigen(A)[1], A)   # That doesn't work!

eigvals, eigvecs = DifferentiableEigen.eigen(A)   # This is the differentiable eigen-function. Note, that eigenvalues and -vectors are not complex numbers, but real arrays!  
jac = ForwardDiff.jacobian((A) -> DifferentiableEigen.eigen(A)[1], A)   # That does work! eigenvalue- and eigenvector-sensitvities


This package was motivated by this discourse thread. For now, there is no other (known) ready to use solution for differentiable eigenvalues and -vectors. If this changes, please feel free to open a PR or discussion.

The sensitivity formulas are picked from:

Michael B. Giles. 2008. An extended collection of matrix derivative results for forward and reverse mode algorithmic differentiation. PDF

How to cite? Related publications?

Tobias Thummerer and Lars Mikelsons. 2023. Eigen-informed NeuralODEs: Dealing with stability and convergence issues of NeuralODEs. ArXiv. PDF

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