ArnoldiMethod.jl
Linear-Algebra-With-Python
| ArnoldiMethod.jl | Linear-Algebra-With-Python | |
|---|---|---|
| 1 | 1 | |
| 93 | 2,160 | |
| - | - | |
| 8.3 | 0.0 | |
| 9 days ago | over 1 year ago | |
| Julia | Jupyter Notebook | |
| MIT License | MIT License |
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ArnoldiMethod.jl
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What Is a Schur Decomposition?
For large and matrices you can use the (restarted) Arnoldi method to compute a partial Schur decomposition AQ=QR where Q is tall and skinny and R has a few dominant eigenvalues on the diagonal (i.e. eigenvalues on the boundary of the convex hull).
MATLAB uses ARPACK's implementation of this when you call `eigs`
I wrote my own implementation ArnoldiMethod.jl in julia, which unlike MATLAB/ARPACK supports arbitrary number types, and also should be more stable in general, and equally fast.
[1] https://github.com/JuliaLinearAlgebra/ArnoldiMethod.jl
Linear-Algebra-With-Python
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Python for Econometrics for Practitioners [Free Online Courses]
Linear Algebra with Python: This training will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skill sets. Suitable for statisticians, econometricians, quantitative analysts, data scientists, etc. to quickly refresh linear algebra with the assistance of Python computation and visualization. Core concepts covered are: linear combination, vector space, linear transformation, eigenvalues and -vector, diagnolization, singular value decomposition, etc.
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