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Algorithm for a posteriori error estimation of eigenvectors
We consdier four eigenvalue problems with the aim to provide explicit estimation of the eigenvectors for each eigenvalue problem.
Directory description:
- Matrix_eigenprob: The eigenvalue problem of matrices.
- Square_domain: The Laplacian eigenvalue problem over a square domain.
- L_shaped_domain: The Laplacian eigenvalue problem over an L-shaped domain.
- Dumbbell_domain: The Laplacian eigenvalue problem over a dumbbell-shaped domain.
How to run the code?
Start the demo at this page and the system will start a virtual machine to load Jupyter notebook, where the .ipynb file can be opened and executed.
Reference
The theoretical description of the algorithm can be found in the paper below:
- Xuefeng Liu, Tomáš Vejchodský, Fully computable a posteriori error bounds for eigenfunctions, https://arxiv.org/abs/1904.07903
Contact for demo codes: Xuefeng LIU (xfliu.math@gmail.com)
About the directory
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Virtual Machine Setting
Warning!
You are starting the virtual machine as a visitor to current project. As a visitor, you can change files in the booted virtual machine, but the changed files will be aborted when the server is shut down.