You are here

Algorithm

Project Leader(s): 

Dr. Michael Monagan, Simon Fraser University & Dr. George Labahn, University of Waterloo

Project team: 
Dr. Jonathan Borwein, Dalhousie University
Dr. Peter Borwein, Simon Fraser University
Dr. Petr Lisonek, Simon Fraser University
Dr. Marni Mishna, Simon Fraser University
Dr. Mark Giesbrecht, University of Waterloo
Dr. Arne Storjohann, University of Waterloo
Dr. Rob Corless, University of Western Ontario
Dr. David Jeffrey, University of Western Ontario
Dr. Marc Moreno Maza, University of Western Ontario
Dr. Greg Reid, University of Western Ontario
Dr. Eric Schost, University of Western Ontario
Dr. Stephen Watt, University of Western Ontario
Dr. Jacques Carette, McMaster University
Dr. Howard Cheng, University of Lethbridge
Dr. Wayne Eberly, University of Calgary
Non-academic participants: 
Funding period: 
February 25, 2022 - March 31, 2021

Computer algebra systems such as Maple compute using mathematical formulae as well as numbers, mechanizing the mathematics used in education and research labs. This project focuses on the design and implementation of algorithms for these systems. Emphasis is placed on efficiency that allows large and complex problems of the type encountered in industrial settings to be solved. In the past year the team has made major advances in the core tools that are needed to solve these complex problems.

Project Leader(s): 

Dr. Holger H. Hoos , University of British Columbia

Project team: 
Dr. Kevin Leyton-Brown, University of British Columbia
Non-academic participants: 
Funding period: 
April 1, 2021 - March 31, 2021

Algorithms for solving difficult computational problems play a key role in many applications, including scheduling, resource allocation, computer-aided design, and software verification. In many cases, heuristic methods are the key to solving these problems effectively. However, the design of effective heuristic algorithms, particularly algorithms for solving computationally hard problems, is a difficult task that requires considerable expertise.

Project Leader(s): 

Dr. Binay Bhattacharya , Simon Fraser University

Funding period: 
February 25, 2022 - March 31, 2021

Efficiency in modern industrial operations requires that available resources are deployed in an optimal manner. The study of facility location is concerned with the placement of one of more facilities in a way that meets a particular objective, such as minimizing transportation costs, providing a high level of service to customer or capturing market share. This project, by exploiting the mathematics of computational geometry and algorithmic graph theory, develops new tools to aid in the location of facilities to optimally serve the demands of customers.

Project Leader(s): 

Dr. Yoshua Bengio, Université de Montréal

Project team: 
Dr. Hugh Chipman, Acadia University
Dr. Dale Schuurmans, University of Alberta
Dr. Pascal Vincent, Université de Montréal
Dr. Shai Ben-David, University of Waterloo
Funding period: 
February 25, 2022 - March 31, 2021

Statistical machine learning is an endeavor in which statisticians and computer scientists use computation to identify useful information from large amounts of data. Telecommunications, insurance and pharmaceutical companies use the team’s machine learning and data mining techniques to determine customer patterns, predict future customer behavior and better understand their needs. The project addresses some of the main practical and theoretical difficulties encountered when dealing with large datasets.