Talk Sparse matrix computing with SciPy


The scipy.sparse package provides a number of sparse matrix storage schemes like compressed sparse row/column, linked list or coordinate formats. Each of those is suitable for some applications and unsuitable for other ones. I will first introduce every format available and mention its strong points and weaknesses. I will also discuss some common difficulties, originating from the fact, that the sparse matrix objects are not subclasses of numpy.ndarray, namely fancy indexing issues.

Then I will dive into the related scipy.sparse.linalg module, that contains sparse eigenvalue, iterative and direct solvers, and show how to use those solvers.

Rough outline of the tutorial session:

  • Sparse matrix formats in SciPy
    • Introduction of the formats
    • Common issues
  • Sparse matrix solvers in SciPy

If you have an interesting sparse computing-related topic other then those mentioned above, post a comment, please.

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