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Jupyter notebooks of the algorithm pseudocode from the textbook Scientific Computing by Michael Heath (Heath, 2018).

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numerics-python

Jupyter notebooks with notes and examples implemented in numpy and scipy of the algorithm pseudocode from the textbook Scientific Computing by Michael Heath (Heath, 2018).

Steepest Descent Method, 6.5 Unconstrainted Optimization Taylor Polynomials of Increasing Degree, 7.5 Convergence

Solution to Second Order ODE, 9.7 Single Step Methods Removing High Frequency Noise, 12.3 Applications of the DFT

I compiled these notebooks while taking CS 450 Numerical Analysis at UIUC and they come without any guarantee of accuracy or endorsement by the textbook author. I started a similar repository with end of chapter review questions at marcoemorais/numerics-review.

If you find this repo helpful, please star this repository. Thank you!

@book{heath2018scientific,
  title={Scientific computing: an introductory survey},
  author={Heath, Michael T},
  volume={80},
  year={2018},
  publisher={SIAM}
}

Chapter Notes

01-Scientific-Computing

02-Systems-of-Linear-Equations

03-Linear-Least-Squares

04-Eigenvalue-Problems

05-Nonlinear-Equations

06-Optimization

07-Interpolation

08-Numerical-Integration-and-Differentiation

09-Initial-Value-Problems-for-ODE

10-Boundary-Value-Problems-for-ODE

11-Partial-Differential-Equations

12-Fast-Fourier-Transform

13-Random-Numbers-and-Stochastic-Simulation

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