Math/CS 466/666
Fall 2025 University of Nevada Reno
Math 466/666 NUMERICAL METHODS I (3+0) 3 credits
Numerical solution of linear systems, including linear programming;
iterative solutions of non-linear equations; computation of eigenvalues
and eigenvectors, matrix diagonalization. Prerequisite(s): MATH 330.
Instructor Course Section Time Room
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Eric Olson Math 466/666 Numerical Methods I MWF noon-12:50pm DMSC106
Course Information
- Instructor:
- Eric Olson
- email:
- Please contact me through WebCampus
- Office:
- MWF 11:00-11:50PM in DMS 238 and through Zoom by appointment
- Homepage:
- http://fractal.math.unr.edu/~ejolson/466/
- Course Textbook:
- T.A. Driscoll and R.J. Braun, Fundamentals of Numerical
Computation, Julia Edition, SIAM, 2022.
- Supplemental References:
- Hosking,
Joe, Joyce and Turner, First Steps in Numerical Analysis,
2nd Edition, Arnold, 1996.
- Germund
Dahlquist and Ake Bjorck, Numerical Methods, Dover, 2003.
- Clemens Heitzinger, Algorithms with JULIA, Springer, 2022.
- Other Books:
- Lloyd Trefethen, Numerical Linear Algebra, Siam 1997.
- Justin Solomon,
Numerical Algorithms: Methods for Computer
Vision, Machine Learning and Graphics, CRC Press, 2015.
- Anthony Ralston and Philip Rabinowitz, A First Course in Numerical
Analysis, Second Edition, Dover, 1978.
- Richard Hamming, Numerical Methods for Scientists and Engineers,
Second Edition, Dover, 1986.
- Endre Suli, David F. Mayers,
An Introduction to Numerical Analysis, 1st Edition,
Cambridge University Press, 2003.
- Jeffery Leader, Numerical Analysis and Scientific
Computation, Pearson, 2004.
Class Handouts
Course materials specific for this section of Math 466 are available
by clicking on this link. Details for how to
access these files may be found on our course page in WebCampus.
Information about Software
Student Learning Outcomes
Upon completion of this course, students will be able to
- The effects of rouding error in numerical computation.
- Newton's method, interpolation and numerical Linear Algebra.
- Practical use of the computer to solve numerical problems.
In-Class Computer Labs
Homework
- Exercise 1.1.1(a,b,c), 1.1.3(a,b)
Lecture Notes
Announcements
[23-Aug-2025] Setting up Julia
We will be using the Julia edition of Fundamentals of Numerical
Computation by Driscoll and Braun.
I will set up all necessary software including the Julia programming
language and support files for the text in the computing lab
reserved for the course.
Since the software is free and open source you may wish to set it
up at home or on a laptop computer. Instructions how to set up
your computing environment are
here.
Note that the textbook in available online in a
Julia
only version as well
as a combined Julia, Matlab
and Python version.
I will focus on Julia in the course. If you are an expert with
Matlab or Python you may choose to use those instead; however, I
will be unable to provide as much help with those languages.
[25-Aug-2025] Welcome Fall 2025
I am looking forward to seeing you August 25 starting the first
week of class.
There will be a number of in-class computing labs and quizzes,
a theoretical exam, a practical exam and a final exam.
In person
attendance is mandatory for all
computing labs, quizzes, exams and the final.
Grading
Theoretical Midterm 50 points
Practical exam 50 points
5 Computing Labs 10 points each
Final 100 points
------------------------------------------
250 points total
Exams and quizzes will be interpreted according to the following
grading scale:
Grade Minimum Percentage
A 90 %
B 80 %
C 70 %
D 60 %
The instructor reserves the right to give plus or minus grades and
higher grades
than shown on the scale if it is believed they are warranted.
Calendar
Aug 25 -- Chapter 1.1: Floating-Point Numbers
Aug 27 -- Chapter 1.2: Problems and Conditioning
Aug 29 -- Chapter 1.3: Algorithms
Sep 01 ***Labor Day***
Sep 03 -- Chapter 1.4: Stability
Sep 05 -- Chapter 2.1: Polynomial Interpolation
Sep 08 -- Chapter 2.2: Computing with Matrices
Sep 10 -- Chapter 2.3: Linear Systems
Sep 12 -- Computing Lab 1
Sep 15 -- Chapter 2.4: LU factorization
Sep 17 -- Chapter 2.5: Efficiency of Matrix Computations
Sep 19 -- Chapter 2.6: Row Pivoting
Sep 22 -- Chapter 2.7: Vector and Matrix Norms
Sep 24 -- Chapter 2.8: Conditioning of Linear Systems
Sep 26 -- Computing Lab 2
Sep 29 -- Chapter 2.9: Exploiting Matrix Structure
Oct 01 -- Chapter 3.1: Fitting Functions to Data
Oct 03 -- Chapter 3.2: The Normal Equations
Oct 06 -- Chapter 3.3: The QR Factorization
Oct 08 -- Chapter 3.4: Computing QR Factorizations (optional)
Oct 10 -- Computing Lab 3
Oct 13 -- Chapter 4.1: The Rootfinding Problem
Oct 15 -- Chapter 4.2: Fixed-point Iteration
Oct 17 -- Chapter 4.3: Newton's Method
Oct 20 -- Chapter 4.4: Interpolation-based Methods
Oct 22 -- Chapter 4.5: Newton for Nonlinear Systems
Oct 24 -- Chapter 4.6: Quasi-Newton Methods
Oct 27 -- Chapter 4.7: Nonlinear Least Squares (optional)
Oct 29 -- Theoretical Midterm
Oct 31 ***Nevada Day***
Nov 03 -- Chapter 5.1: The Interpolation Problem
Nov 05 -- Chapter 5.2: Piecewise Linear Interpolation
Nov 07 -- Computing Lab 4
Nov 10 -- Chapter 5.4: Finite Differences
Nov 12 -- Chapter 5.5: Convergence of Finite Differences
Nov 14 -- Chapter 5.6: Numerical Integration
Nov 17 -- Chapter 6.1: Basics of Initial-Value Problems
Nov 19 -- Chapter 6.2: Euler's Method
Nov 21 -- Computing Lab 5
Nov 24 -- Chapter 6.3: IVP Systems
Nov 26 -- Chapter 6.4: Runge-Kutta Methods
Nov 28 ***Family Day***
Dec 01 -- Chapter 6.5: Adaptive Runge-Kutta
Dec 03 -- Chapter 6.6: Multistep Methods
Dec 05 -- Practical Exam
Dec 08 -- Chapter 6.7: Implementation of Multistep Methods
Dec 10 ***Prep Day***
Dec 17 ***Final Exam at 12:45-2:45pm***
Course Policies
Communications Policy
Lectures and classroom activities will held in person.
If you wish to set up an appointment for office hours
please send me a message through
WebCampus.
Late Policy
Students must have an approved university excuse to be eligible for a
make-up exam. If you know that you will miss a scheduled exam please
let me know as soon as possible.
AI Policy
In this course you are welcome to use generative artificial
intelligence/large language model tools (such as ChatGPT, Claude, Gemini,
Grok, Perplexity, etc.). Using these tools aligns with the course learning
outcomes/student goals for an in-depth understanding of calculus.
Please be aware that many AI companies collect and store personal
information. Please do not enter your confidential information as part
of a prompt.
Also, please note that some of these large language models may make up
or hallucinate information. These tools may reflect misconceptions and
biases of specific data. Students are responsible for checking facts,
finding reliable sources for, and making a critical examination of any
work that is submitted.
Plagiarism
Students are encouraged to work in groups and consult resources outside
of the required textbook when doing the homework for this class. Please
cite any sources you used to complete your work including Wikipedia, other
books, online discussion groups, generative AI such as ChatGPT as well
as personal communications. Note that answers obtained from any source
should be verified and fully understood for homework to have a positive
learning outcome. In all cases your sources need to be cited.
Exams and quizzes, unless otherwise noted, will be closed book, closed
notes and must reflect your own independent work.
Academic Conduct
Bring your student identification to all exams.
Work independently on all exams and quizzes.
Behaviors inappropriate to test taking may disturb other
students and will be considered cheating.
Don't send electronic messages, talk or pass notes with other
students during a quiz or exam.
Homework may be discussed freely.
When taking a quiz or exam
don't read notes or books unless explicitly permitted.
Sanctions for violations are specified in the
University Academic Standards Policy.
If you are unclear as to what constitutes cheating,
please consult with me.
Statement on Academic Success Services
Your student fees cover usage of the University Math Center, University
Tutoring Center, and University Writing and Speaking Center. These
centers support your classroom learning; it is your responsibility to
take advantage of their services. Keep in mind that seeking help outside
of class is the sign of a responsible and successful student.
Equal Opportunity Statement
The University of Nevada Department of Mathematics and Statistics
is committed to equal opportunity in education
for all students, including those with documented physical disabilities
or documented learning disabilities. University policy states that it is
the responsibility of students with documented disabilities to speak
with the Disability Resource
Center during the first week of each semester to discuss appropriate
accommodations to ensure equity in grading, classroom experiences and
outside assignments.
For assistance with accessibility, or to report an issue,
please use the
Accessibility
Help Form. The form is set up to automatically route your request
to the appropriate office that can best assist you.
Diversity
This course is designed to comply with UNR Core Objective 10.
More information about the core curriculum may be found in the
UNR Catalog
here.
Statement on Audio and Video Recording
Surreptitious or covert video-taping of class or unauthorized audio
recording of class is prohibited by law and by Board of Regents
policy. This class may be videotaped or audio recorded only with the
written permission of the instructor. In order to accommodate students
with disabilities, some students may be given permission to record class
lectures and discussions. Therefore, students should understand that
their comments during class may be recorded.
Final Exam
The final exams will be held in person at the time listed in
the standard schedule of final exams for this section. Namely,
the final exam is Wednesday, December 17, 2025
from 12:45-2:45pm in DMS106.
Last Updated:
Mon Aug 18 10:26:44 AM PDT 2025