- CPSC 4110/5110/7110 – Introduction to Algorithms in Facility Location
- CPSC 4210/5210: Wireless Networks
- Optimization Seminar Series – Fri Dec 16, 2016, noon, in B543
- Optimization Seminar Series – Wed. Oct 19, 2016, noon, in C620
- CPSC 3780 for Dr. Gaur (Sept 20-22, 2016)
- CPSC 2620 for Dr. Hossain (Sept 13-15, 2016)
- Optimization Seminar Series – Wed. Jul 13, 2016, noon, in B543
- Optimization Seminar Series – Wed Mar 23, 1Pm in D633
- Optimization Seminar Series – Mon Mar 7 at 1 pm – B756
- Optimization Seminar Series – Fri Feb 12 at 9 am
CPSC 4210/5210: Wireless Networks
The course is available on Moodle.
CPSC 3780 for Dr. Gaur (Sept 20-22, 2016)
Topics: Fourier transforms, the sampling theorem, Shannon formula for the capacity of a communication channel.
- For the Fourier transform, Section 2.6 in Algorithms, by Dasgupta, Papadimitriou, and Vazirani. See an excerpt from from the text here, provided to you under fair dealing.
- For the capacity of a channel, see Information and Measurement, Chapter 8, by Lesurf.
CPSC 2620 for Dr. Hossain (Sept 13-15, 2016)
Lectures for the week: Sep 13-15.
Topic: an introduction to classes.
Text: Chapter 7 in Skansholm.
Notes available here (updated Sept 18).
Source code including homework available at cloud9 or as a zip file (updated Sept 18).
CPSC 4625: Design and analysis of advanced algorithms
CPSC 1820: Discrete Structures
The course materials are available on moodle to registered students.
Discrete Mathematics and Its Applications – 7th Ed, by Rosen (older
Book of Proof – 2nd Ed, by Hammack, available at http://www.
people.vcu.edu/~rhammack/BookOfProof/ (CC Licence).
An older offering of the course is accessible
CPSC 2620: Fundamentals of programming II
All course resources are available on moodle to registered students.
Open Data Structures by Pat Morin.
C++ Primer, 5th Ed, Lippman, Lajoie, Moo.
CPSC 4110/5110 – Advanced Algorithms (Approximations)
This course is about designing approximation algorithms for difficult
optimization problems for which no optimal algorithms with running
time polynomial in the problem size are known. Approximation
algorithms find feasible solutions that may not be optimal but are not
too far from the optimal one.
We overview various interesting
algorithm design techniques that may prove extremely useful to
graduate students tackling research questions in various fields and to
undergraduates who may encounter interesting problems in their future
projects in the industry.