Lectures: Martin Jagersand,
Labs: TA TBA
To obtain a working knowledge of how to apply numerical methods
to real-world problems and a thorough understanding of the mathematics
and properties of these methods.
Numerical Computing basics
Numerical representations, Computational accuracy and stability,
forward and backward error analysis, software and hardware for numerical
Linear Equations Some real world applications and motivations,
Gauss and Gauss-Jordan Elimination, LU factorization, Sensitivity and
Conditioning, Overdetermined systems and Linear Least Squares, methods for
QR factorisation, Linear Regression, Data Fitting and Interpolation,
Eigenvalue Problems, methods for Eigen and SVD factorizations.
Nonlinear Equations and Optimization Basic 1D search methods with
and without derivatives. Convergence rates and stopping criteria,
Multidimensional root finding. Conditions for
extrema, local and global min and max. Unconstrained optimization,
Broyden and CG methods. Constrained optimization,
Comparison of root finding and optimization,
Trust region methods, Homotopy methods and Embeddings.
Numerical Integration and Differentiation Numerical Quadrature,
Numerical Differentiation, Richardson Extrapolation.
Mathematical Modeling of Real world problems Throughout the course examples of real world problems will be mathematically modeled and computationally solved.
Lectures: Tuesday, Thursday 11:00-12:20, BS G 110
(Biosciences Bldg, G-wing -- see below for directons)
Labs: Mon 5:00PM - 8PM, CSC2-35
MCM Seminar Wed 5pm CSC 3-49
Written in-class exams:
Exam 1 (24%) Late Oct
24% Written assignments throughout the term (6% each, see detailed web schedule)
Exam 2 (24%) Early Dec
28% Lab assignments throughout the term. (6-8% each, see detailed web schedule)
Project (Optional): An individual project can be defined as a substitute for
one or more labs/assignments.
MCM: Students can take this course and solve training problems
in for the Mathamatical Contest in Modeling for assignment credit, and
do the UofA MCM qualification exam in place of exams.
A grade of "satisfactory" requires a working knowledge of how to apply
numerical methods to solve real world mathematical problems. For
higher grades additionally an understanding of the mathematical derivation
of numerical methods and their properties will be required.
Main source of information: Course web page linked from: http://www.cs.ualberta.ca/~jag
Hours: Meetings directly after class Tue.
Directions to lecture classrom
BS G 110 is in the BioSciences building, the brick building behind the
new glass building CCIS.
The BS building is roughly H-shaped and G110 is in the western end of
the central part, floor 1. Those who are familiar with the old
location of the faculty of science admin and Deans offices can go
there, and BS G110 is nearby in the same hallway.
The closest path from CS involves walking to the loading dock on the
west side of BS, entering up the short stairs to a door next to the
loading dock. Once inside the building go down 1 stair level, and
locate the G hallway to the east. (The different hallways have
different letters preceding the room number.)
A message from the university administration:
The University of Alberta is committed to the highest standards of
academic integrity and honesty. Students are expected to be familiar
with these standards regarding academic honesty and to uphold the
policies of the University in this respect. Students are particularly
urged to familiarize themselves with the provisions of the Code of
Student Behavior (online at www.ualberta.ca/secretariat/appeals.htm)
and avoid any behavior which could potentially result in suspicions of
cheating, plagiarism, misrepresentation of facts and/or participation
in an offence. Academic dishonesty is a serious offence and can result
in suspension or expulsion from the University. Policy can be found in
§23.4(2) of the University Calendar.