J. B. Schroder Home

Math / CS 375, Introduction to Numerical Computing
Spring, 2020

Overview  ::  Dates  ::  Grading  ::  Homework  ::  Lecture Notes  ::  Other Materials

Time and Place: Tuesday / Thursday, SMLC 356, 9:30am-10:45am
Instructor: Jacob B. Schroder, jbschroder -att- unm.edu
Office Hours: SMLC 332
    Wednesday: 12:30pm-2:00pm
    Thursday: 2:00pm-3:30pm

Syllabus:  PDF1
Syllabus Update from Coronavirus:  PDF2


  1. CS 151 or CS 152 or Phys 290 or ECE 131 or comparable programming skills AND
  2. Math 316 or Math 314 or Math 321 or equivalent


  1. Numerical Mathematics and Computing by W. Cheney and D. Kincaid, 7th Edition
    (6th Edition will also work)
  2. Numerical Analysis by T. Sauer, 3rd Edition
    You will need one of these books as a reference, to buttress the in-course slides and derivations. Sauer is likely a better general reference book, but some course material will more closely follow Cheney and Kincaid.

Course Description: This is an introductory numerical analysis course. We study numerical methods to solve linear and nonlinear equations, to interpolate and approximate data, and methods for numerical integration and differentiation. We will implement all algorithms in MATLAB (or Python), and begin the course with a MATLAB (or Python) tutorial. Python is allowed for assignments though we will not spend time learning it in class.

This is a required course for all mathematics majors with concentration in Applied Mathematics or in Computational Mathematics.

Schedule of topics, course goals, and desired learning outcomes: Please see the syllabus.

Important Dates:



The course grade will be determined by

The final grade for the class will be based on the summed weighted percentages above. Letter grades will be assigned as follows:

The instructor reserves the right to curve grades to offset unforeseen circumstances. Such a curve will never decrease a student's letter grade below that from the above scheme.

Exams: There are two exams, a midterm and a final. Cheating on an exam will be handled in accordance with the dishonesty policy in the syllabus. There are no makeup exams; however, I am sympathetic to a student who is unable to take a scheduled test due to a hardship. Please contact the instructor before the exam (if possible), should such a hardship occur.

Exam grading disputes must be submitted in writing within one week after the work is returned.

Absences Policy: It is expected that each student regularly attend class.


Homework: Homework will be posted every 1 to 2 weeks on the course webpage (i.e., here). Each homework will consist of a number of computer and theoretical problems. You need to hand in a written report on the due date at the start of class. All plots/figures in the report must be generated in MATLAB or Python and not hand drawn (unless otherwise specified in the homework question).

You are strongly encouraged to work in pairs (a group of two students) for the homework. Hand in a single report with both collaborators cited at the top. It is expected that both of you can explain the theory and computer codes. Groups of more than two students are not allowed.

Homework may be submitted late up to a week for 50% credit. Homework grading disputes must be submitted in writing within one week after the work is returned.

Software: Use of MATLAB or numerical Python will be required to complete the course homework assignments.

Homework Assignments
  1. Homework 0 (PDF)    Due: Never
  2. Homework 1 (PDF)    Due: Beginning of class, Jan. 30
  3. Homework 2 (PDF)    Due: Beginning of class, Feb. 11
  4. Homework 3 (PDF)    Due: Beginning of class, Feb. 20
  5. Homework 4 (PDF)    Due: Beginning of class, March 3
  6. Homework 5 (PDF)    Due: Beginning of class, March 12
          generate_SPD_mat_and_rhs_vec.m   hw5_q1.m   hw5_q2.m   my_cg.m   my_jacobi.m
  7. NEW HW6 PDF!
    Homework 6 (PDF)    Due: Midnight, Sunday, March 29 on UNM LEARN
    NEW HW6 PDF!
  8. Homework 7 (PDF)    Due: Midnight, Sunday, April 19 on UNM LEARN
  9. Homework 8 (PDF)    Due: Midnight, Sunday, April 26 on UNM LEARN
  10. Homework 9 (PDF)    Due: Midnight, Sunday, May 3 on UNM LEARN
  11. And that's it! No more homework after this.


Lecture Notes:
  1.   Slide Deck 1
  2.   MATLAB tutorial (PDF)
      matlab_tutorial.m   ApproxExp.m   f1.m   df1.m   MyDeriv.m   my_funky_fcn.m
  3.   Slide Deck 2
  4.   Slide Deck 3
  5.   Slide Deck 4      diff_fwd.m      diff_fwd_plot.m      diff_central.m      diff_richard.m
  6.   Slide Deck 5
  7.   Slide Deck 6      test_memory_patterns_matvec.m      test_flops.m      GE_naive.m      GE_naive_test.m
  8.   Slide Deck 7
  9.   Slide Deck 8
  10.   Slide Deck 9      time_LU_vs_Chol.m
  11.   Slide Deck 10      iterative_methods.m
  12.   Slide Deck 11
  13.   Slide Deck 12
  14.   Slide Deck 13

    Begin post Covid-19 online part of the course

  15. By Monday, March 28   |   Read through   slide deck 14 (PDF)
     ⇒  Watch lecture 14 video (streaming)

  16. By Friday, April 3   |   Read through   slide deck 15 (PDF)
     ⇒  Watch lecture 15 video (streaming)   Long lecture, counts as 2 lectures

  17. By Friday, April 10   |   Read through   slide deck 16 (PDF)
     ⇒  Watch lecture 16 video (streaming)   Exam week, only 1 lecture

  18. By Friday, April 17   |   Read through   slide deck 17 (PDF)
     ⇒  Watch lecture 17 video (part 1)
     ⇒  Watch lecture 17 video (part 2)   Long lecture, counts as 2 lectures

    Files for slide deck 17:

  19. By Friday, April 24   |   Read through   slide deck 19 (PDF)
     ⇒  Watch lecture 19 video

    By Friday, April 24   |   Read through   slide deck 20, up to slide 14 (PDF)
     ⇒  Watch lecture 20 video (part 1)

  20. By Friday, May 1   |   Read through   slide deck 20, slide 14 till end (PDF)
     ⇒  Watch lecture 20 video (part 2)
     ⇒  Watch lecture 20 video (part 2 Addendum)

    By Friday, May 1   |   Read through   slide deck 21, up to slide 23 (PDF)
     ⇒  Watch lecture 21 video (part 1)

  21. Review Week (ending Friday, May 7)   |   Mostly online office hours, some discussion of numerical ODEs
Review Questions:
  1.   taylor_and_floating_point.pdf
  2.   differentiation_questions.pdf
  3.   linear_systems.pdf
  4.   iterative_methods.pdf
  5.   root_finding_questions.pdf  
  6.   quadrature_questions.pdf
  7.   poly_int_questions.pdf
  8.   spline_questions.pdf
  9.   svd_least_sq_questions.pdf
  10.   power_method_questions.pdf
  11.   ode_questions.pdf


Other Material

Python / SciPy And more Python / SciPy..