GitHub / aneeshdurai / sparse_data_final_project
We explore improving sparse matrix operation efficiency using the Compressed Sparse Row (CSR) format. In the process, we explore an application of the Lanczos algorithm in efficiently computing a low–rank approximation of SVD for sparse matrices.
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Language: Jupyter Notebook
Size: 2.51 MB
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Created at: over 1 year ago
Updated at: over 1 year ago
Pushed at: over 1 year ago
Last synced at: about 1 year ago
Topics: lanczos-algorithm, linear-regression, singular-value-decomposition, sparse-matrices