J. B. Schroder Home

Math / CS 375, Introduction to Numerical Computing
Spring, 2019

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

Time and Place: Tuesday / Thursday, SMLC 356, 11am-12:15pm
Instructor: Jacob B. Schroder, jbschroder -att- unm.edu
Office Hours: SMLC 332
    Wednesday: 9:30am-11am
    Thursday: 1:00pm-2:30pm

Syllabus:  PDF1


  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

Text: Numerical Mathematics and Computing by W. Cheney and D. Kincaid, 7th Edition
(6th Edition will also work)

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 in 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. MATLAB tutorial (PDF) matlab_tutorial.m ApproxExp.m   f1.m   df1.m   MyDeriv.m   my_funky_fcn.m
    Due: Never
  2. Homework 0 (PDF)    Due: Never
  3. Homework 1 (PDF)    Due: Beginning of class, Jan. 31, 2019
  4. Homework 2 (PDF)    Due: Beginning of class, Feb. 12, 2019
  5. Homework 3 (PDF)    Due: Beginning of class, Feb. 19, 2019 Feb. 21, 2019
  6. Homework 4 (PDF)    Due: Beginning of class, March 7, 2019
          (can turn in early, for feedback before mid-term)
          generate_SPD_mat_and_rhs_vec.m   hw4_q1.m   hw4_q2.m   my_cg.m   my_jacobi.m
  7. Homework 5 (PDF)    Due: Beginning of class, March 21, 2019
  8. Homework 6 (PDF)    Due: Beginning of class, April 2, 2019
  9. Homework 7 (PDF)    Due: Beginning of class, April 11, 2019
  10. Homework 8 (PDF)    Homework 8 with clarifications in red (PDF)
          Due: Beginning of class, April 25, 2019
  11. Homework 9 (PDF)    Due: Beginning of class, May 2, 2019 (It's short, don't worry!)


Lecture Notes:
  1.   Slide Deck 1
  2.   Slide Deck 2
  3.   Slide Deck 3
  4.   Slide Deck 4
  5.   Slide Deck 5      test_memory_patterns_matvec.m      test_flops.m      GE_naive.m      GE_naive_test.m
  6.   Slide Deck 6
  7.   Slide Deck 7
  8.   Slide Deck 8      time_LU_vs_Chol.m
  9.   Slide Deck 9      iterative_methods.m
  10.   Slide Deck 10
  11.   Slide Deck 11
  12.   Slide Deck 12
  13.   Slide Deck 13
  14.   Slide Deck 14
  15.   Slide Deck 15
  16.   Slide Deck 16      diff_fwd.m      diff_fwd_plot.m      diff_central.m      diff_richard.m
  17.   Slide Deck 17      trap_int_test.m      simp_int_test.m
  18.   Slide Deck 18      lgwt_table.m      int_gauss.m      int_gauss_test.m      int_compare_gauss_trapezoid_simpson.m
  19.   Slide Deck 19
  20.   Slide Deck 20
  21.   Slide Deck 21
  22.   Slide Deck 22    (fun topic, not on final)      monte_pi.c      monte_pi.m
Review Questions:
  1.   taylor_and_floating_point.pdf
  2.   linear_systems.pdf
  3.   iterative_methods.pdf
  4.   root_finding_questions.pdf   Midterm will only cover root finding up to Newton's method.
  5.   differentiation_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..


Last updated Spring, 2019