Inverses and Newton's Method in Computational Thinking - Lecture 5

Inverses and Newton's Method in Computational Thinking - Lecture 5

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- Newton's method is not good if your starting value is too far from the root

29 of 44

29 of 44

- Newton's method is not good if your starting value is too far from the root

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Classroom Contents

Inverses and Newton's Method in Computational Thinking - Lecture 5

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  1. 1 - Introduction/Announcements
  2. 2 - Non-linear maps continued
  3. 3 - Invite to interact on Discord
  4. 4 - Another example of perspective projection
  5. 5 - Definition of a linear transformation
  6. 6 - Linearity relation is simply matrix times vector
  7. 7 - What is a matrix multiplication?
  8. 8 - Visual representation of how it works
  9. 9 - Coordinate transformations vs object transformations
  10. 10 - Julia: Images are arrays
  11. 11 - Julia: Probing a pixel
  12. 12 - Linking image coordinates (i,j) to spatial coordinates (x,y)
  13. 13 - Transforming image of a Corgi
  14. 14 - Function to transform xy to ij
  15. 15 - Creating reusable functions
  16. 16 - Inverse functions
  17. 17 - Trying different scaling
  18. 18 - What does inverse really do?
  19. 19 - Inverting non-linear transformations
  20. 20 - Bringing it all together
  21. 21 - Collisions
  22. 22 - Why are we doing this backwards?
  23. 23 - David takes over for the next part - Motivation
  24. 24 - What does it mean by calculating an inverse?
  25. 25 - Newton's method in 1D
  26. 26 - How does it work? - Interactive visualization using Plots.jl
  27. 27 - Trying the same with a new function
  28. 28 - What happens if we start from a different initial value?
  29. 29 - Newton's method is not good if your starting value is too far from the root
  30. 30 - Julia: Using Symbolics.jl
  31. 31 - Julia: Using ForwardDiff
  32. 32 - Perturbation to a non-linear function
  33. 33 - Looking back at the interactive visualization for Newton's method
  34. 34 - Writing the mathematical expressions behind the visualization
  35. 35 - Expanding via the definition of a derivative
  36. 36 - Obtaining a generic expression for the solution
  37. 37 - Julia: Implementing Newton's method in 1D
  38. 38 - Julia: For loop, x -= a is the same as x = x - a
  39. 39 - Derivatives in 2D
  40. 40 - Newton's method in 2D
  41. 41 - Changing to non-linear transformation
  42. 42 - Julia: Implementing Newton's method in 2D
  43. 43 - Julia: Use of the "\" operator
  44. 44 - Concluding remarks

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