Course

Numerical methods

Course under construction · 16 of 35 concepts published; more are added as they are reviewed.

You learn algorithms to approximate roots, linear systems, eigenvalues, interpolants, integrals, derivatives and differential equations, along with the error and stability analysis that determines their reliability. It serves to solve numerically engineering and science problems that have no closed-form solution; it is advisable to have taken Calculus, Differential Equations, Linear Algebra, Real Analysis.

Recommended before this course: Calculus · Differential equations · Linear algebra · Real analysis (7 concepts come from those courses and are tagged)

23concepts
5topics
250exercises
≈ 12 hof study approx.

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Course path

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Sources

  • James Stewart, Calculus: Early Transcendentals, 8.ª edición, Cengage Learning, 2015.
  • Michael Spivak, Calculus, 4.ª edición, Publish or Perish, 2008.
  • Stephen Abbott, Understanding Analysis, 2nd edition, Springer, 2015.
  • Walter Rudin, Principles of Mathematical Analysis, 3rd edition, McGraw-Hill, 1976.
  • Robert G. Bartle, Donald R. Sherbert, Introduction to Real Analysis, 4th edition, Wiley, 2011.
  • Richard L. Burden, J. Douglas Faires, Annette M. Burden, Numerical Analysis, 10th edition, Cengage Learning, 2016.
  • Lloyd N. Trefethen, David Bau III, Numerical Linear Algebra, 1st edition, SIAM, 1997.
  • Gilbert Strang, Introduction to Linear Algebra, 6th edition, Wellesley-Cambridge Press, 2023.
  • Sheldon Axler, Linear Algebra Done Right, 4th edition, Springer, 2024.
  • William E. Boyce, Richard C. DiPrima, Douglas B. Meade, Elementary Differential Equations and Boundary Value Problems, 12th edition, Wiley, 2021.
  • Dennis G. Zill, A First Course in Differential Equations with Modeling Applications, 11th edition, Cengage, 2018.