Math 351 - Introduction to Numerical Analysis - Winter 2020

Class Information

Instructor: Tuan Pham
Section 1
Class meetings: Bexell Hall 321, MWF 9:00 - 9:50 AM
[Syllabus]   [Tentative calendar]   [Canvas site]

Office Hours

M, W, F 1:00 - 2:00 PM at Kidder Hall 268
Th 12:00 - 2:00 PM at Kidder Hall 268
W 2:00 - 3:00 PM at Kidder Hall 108 J (computer lab)

Assignments

Homework Solution (from grader) Worksheets
Homework 1 Solution Worksheet 1/10
Homework 2 Solution Worksheet 1/13, 1/17
Homework 3 Solution Worksheet 1/20
Homework 4 Solution Worksheet 1/27, 1/29
Worksheet 2/5, 2/7
Homework 5 Solution Worksheet 2/19, 2/21
Homework 6 Solution Worksheet 2/28
Homework 7 Solution Worksheet 3/4, 3/6
Homework 8 Solution Worksheet 3/11

Lecture notes

  • Review (Mar 13): review for Final exam, link to Zoom video
  • Lecture 27 (Mar 11): error estimates of Riemann sums (cont.)
  • Lecture 26 (Mar 9): error estimates of Riemann sums
  • Lecture 25 (Mar 6): numerical integration, Riemann sums
  • Lecture 24 (Mar 4): draw quadratic spline, Matlab example
  • Lecture 23 (Mar 2): compute quadratic spline
  • Lecture 22 (Feb 28): explain Runge phenomenon; spline interpolation
  • Lecture 21 (Feb 26): use polynomial interpolation to approximate a function, Matlab example
  • Lecture 19-20 (Feb 21-24): Newton formula
  • Lecture 18 (Feb 19): programming Lagrange formula on Matlab, Matlab example
  • Lecture 17 (Feb 17): interpolation problems; Lagrange formula
  • Lecture 16 (Feb 14): fixed point method
  • Lecture 15 (Feb 12): an example of Newton's method in higher dimensions
  • Lecture 14 (Feb 7): bisection method and Newton's method in higher dimensions
  • Lecture 13 (Feb 5): stopping condition of Newton's method, Matlab example; practice on order of convergence
  • Lecture 12 (Feb 3): order of convergence
  • Lecture 11 (Jan 31): error analysis of Newton's method
  • Lecture 10 (Jan 29): error analysis of bisection method, Matlab example; Newton's method
  • Lecture 9 (Jan 27): sources of error, root-finding problem, bisection method
  • Lecture 8 (Jan 24): defects of arithmetics in floating-point format and consequences
  • Lecture 7 (Jan 22): addition and multiplication in floating-point format
  • Lecture 6 (Jan 17): floating-point format and fixed-point format
  • Lecture 5 (Jan 15): arithmetic operations in binary system
  • Lecture 4 (Jan 13): estimate an integral using Taylor approximation
  • Lecture 3 (Jan 10): error estimate of Taylor approximation, Matlab example
  • Lecture 2 (Jan 8): Taylor approximation
  • Lecture 1 (Jan 6): introduction
  • Remarks before / after class

  • Final review
  • Matlab practice 3
  • Midterm exam solution
  • Midterm review
  • Matlab practice 2
  • Matlab practice 1
  • Instructions to install Matlab
  • Links

    Department of Mathematics
    Oregon State University

    This page was last modified on Monday, March 16, 2020.