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Hands-on: Linear algebra

Explore NumPy's in-built linear algebra routines
In this exercise you can will explore NumPy’s in-built linear algebra routines

Source code for this exercise is located in numpy/linear-algebra/

  1. Construct two symmetric 2×2 matrices A and B.
    Hint: a symmetric matrix can be constructed easily from a square matrix
    as Asym = A + A^T
  2. Calculate the matrix product C = A * B using numpy.dot().
  3. Calculate the eigenvalues of matrix C with numpy.linalg.eigvals().

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Python in High Performance Computing

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