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lect07

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lect07, Tue 01/28

Fitting data by least squares

• Lecture Files

Reading assignment

For next Tuesday, read Section 2.9 (norms and condition numbers) of the NCM book.

References for today’s lecture

Sections 5.1 through 5.5 of the NCM book.

Outline

• More on the Jacobi and conjugate gradient methods
• Sparse matrices in numpy/scipy
• The factorization A = QR for non-square matrices A
• numpy/scipy routines:
  • scipy.sparse.csr_matrix()
  • scipy.sparse.linalg.spsolve()
  • scipy.sparse.linalg.cg()
  • spla.qr() [note: this is slightly different from npla.qr]
• Lecture codes:
  • Jsolve()
  • CGsolve()