### Please note:

To view the Summer 2020 Academic Calendar go to www.sfu.ca/students/calendar/2020/summer.html

# Applied and Computational Mathematics

The Master of Science (MSc) in Applied and Computational Mathematics offers advanced education and research training in modern applied mathematics. Students admitted to the program will complete one of two program options.

## Admission Requirements

Applicants must satisfy the University admission requirements as stated in Graduate General Regulations 1.3 in the SFU Calendar. Applicants with backgrounds in areas other than mathematics (for example, a bachelor's degree or its equivalent in a related discipline such as statistics, engineering or physics) may be considered suitably prepared for these programs. Direct admission is only permitted into the thesis option.

## Program Requirements

This program consists of course work and requirements from either a thesis option for a minimum of 34 units or course option for a minimum of 32 units.

All course work is subject to approval by the supervisory committee and the departmental graduate studies committee.

Students must complete

a breadth requirement consisting of four courses from at least three different groups listed below

and an additional six units of graduate course work*

and the requirements from one of the two options below

*students who only complete 15 units of course work in the breadth requirement, must complete an additional unit of graduate course work

### Thesis Option

and a thesis

Graded on a satisfactory/unsatisfactory basis.

### Course Option

and an additional 10 units of graduate course work

### Groups

#### Group 1: Analysis and Differential Equations

Analysis and computation of classical problems from applied mathematics such as eigenfunction expansions, integral transforms, and stability and bifurcation analyses. Methods include perturbation, boundary layer and multiple-scale analyses, averaging and homogenization, integral asymptotics and complex variable methods as applied to differential equations.

First order non-linear partial differential equations (PDEs) and the method of characteristics. Hamilton-Jacobi equation and hyperbolic conservation laws; weak solutions. Second-order linear PDEs (Laplace, heat and wave equations); Green's functions. Sobolev spaces. Second-order elliptic PDEs; Lax-Milgram theorem.

Section | Instructor | Day/Time | Location |
---|---|---|---|

G100 |
Nilima Nigam |
We, Fr 10:30 AM – 12:20 PM |
REMOTE LEARNING, Burnaby |

Infinite dimensional vector spaces, convergence, generalized Fourier series. Operator Theory; the Fredholm alternative. Application to integral equations and Sturm-Liouville systems. Spectral theory.

An intensive study of Lebesque measure, integration and the Lebesque convergence theorems together with the treatment of such topics as absolute continuity, the fundamental theorem of calculus, the Lp-spaces, comparison of types of convergence in function spaces, the Baire category theorem.

Section | Instructor | Day/Time | Location |
---|---|---|---|

G100 |
Weiran Sun |
We, Fr 12:30 PM – 2:20 PM |
REMOTE LEARNING, Burnaby |

#### Group 2: Computational Methods

Conditioning and stability of numerical methods for the solution of linear systems, direct factorization and iterative methods, least squares, and eigenvalue problems. Applications and mathematical software.

Section | Instructor | Day/Time | Location |
---|---|---|---|

G100 |
Manfred Trummer |
Tu, Th 2:30 PM – 4:20 PM |
REMOTE LEARNING, Burnaby |

Analysis and application of numerical methods for solving partial differential equations. Potential topics include finite difference methods, spectral methods, finite element methods, and multi-level/multi-grid methods.

Theory and algorithms of non-linear programming with an emphasis on modern computational considerations. Topics may include: optimality conditions for unconstrained and constrained optimization, gradient methods, conjugate direction methods, Newton method, quasi-Newton methods, penalty and barrier methods, augmented Langrangian methods and interior point methods.

#### Group 3: Mathematical Modelling and Applications

Basic equations governing compressible and incompressible fluid mechanics. Finite difference and finite volume schemes for hyperbolic, elliptic, and parabolic partial differential equations. Practical applications in low Reynolds number flow, high-speed gas dynamics, and porous media flow. Software design and use of public-domain codes. Students with credit for MATH 930 may not complete this course for further credit.

Analysis of models from the natural and applied sciences via analytical, asymptotic and numerical studies of ordinary and partial differential equations.

Topics vary depending on faculty availability and student interest. Recent offerings include: geophysical fluid dynamics, adaptive numerical methods for differential equations, learning theory, and stability, pattern formation and chaos.

Fundamental algorithmic techniques used to solve computational problems encountered in molecular biology. This area is usually referred to as Bioinformatics or Computational Biology. Students who have taken CMPT 881 (Bioinformatics) in 2007 or earlier may not take CMPT 711 for further credit.

Section | Instructor | Day/Time | Location |
---|---|---|---|

G100 |
Kay C Wiese |
Mo 4:30 PM – 5:20 PM We, Fr 4:30 PM – 5:20 PM |
REMOTE LEARNING, Burnaby REMOTE LEARNING, Burnaby |

#### Group 4: Discrete Mathematics

Convex geometry, the simplex method and duality, pivot rules, degeneracy, decomposition and column generation methods, the complexity of linear programming and the ellipsoid algorithm, interior point methods for linear programming.

Algebraic graph theory, extremal graph theory, coloring problems, path and cycle structure of graphs, application of graphs, hypergraphs, and current research topics.

An introduction to the theory of incidence structures (finite geometries, block designs) and their relation to linear codes. Algebraic techniques - finite group actions, orbit enumeration, generation of orbit representatives. Exact and asymptotic enumeration of labelled and unlabelled structures.

Section | Instructor | Day/Time | Location |
---|---|---|---|

G100 |
Amarpreet Rattan |
Tu, Th 10:30 AM – 12:20 PM |
REMOTE LEARNING, Burnaby |

#### Group 5: Mathematics of Data

Theory and algorithms for problems in data science with an emphasis on mathematical aspects. Topics may include dimension reduction, supervised learning, including regression and classification, unsupervised learning, including clustering and latent variable modeling, deep learning, algorithms for big data, and foundations of learning.

The statistical theory that supports modern statistical methodologies. Distribution theory, methods for construction of tests, estimators, and confidence intervals with special attention to likelihood and Bayesian methods. Properties of the procedures including large sample theory will be considered. Consistency and asymptotic normality for maximum likelihood and related methods (e.g., estimating equations, quasi-likelihood), as well as hypothesis testing and p-values. Additional topics may include: nonparametric models, the bootstrap, causal inference, and simulation. Prerequisite: STAT 450 or permission of the instructor. Students with credit for STAT 801 may not take this course for further credit.

Section | Instructor | Day/Time | Location |
---|---|---|---|

G100 |
Richard Lockhart |
Tu 2:30 PM – 4:20 PM Th 2:30 PM – 4:20 PM |
REMOTE LEARNING, Burnaby REMOTE LEARNING, Burnaby |

Advanced mathematical statistics for PhD students. Topics in probability theory including densities, expectation and random vectors and matrices are covered. The theory of point estimation including unbiased and Bayesian estimation, conditional distributions, variance bounds and information. The theoretical framework of hypothesis testing is covered. Additional topics that may be covered include modes of convergence, central limit theorems for averages and medians, large sample theory and empirical processes. Prerequisite: STAT 830 or permission from the instructor.

NOTE: SFU students enrolled in the Accelerated master's degree program within the Department of Mathematics may apply a maximum of 10 graduate course units, taken while completing the bachelor’s degree, towards the upper division undergraduate electives of the bachelor’s program and the requirements of the master’s degree. For more information go to: http://www.sfu.ca/dean-gradstudies/future/academicprograms/AcceleratedMasters.html.

## Program Length

Students are expected to complete the program requirements within six terms in the thesis option and five terms in the course option.

## Other Information

### Course Work

As per Graduate General Regulation 1.4.2, enrollment in courses from outside the Department of Mathematics requires approval of the course instructor.

### Satisfactory Progress

A cumulative grade point average (CGPA) of at least 3.5 is required to maintain good standing in the thesis stream. Any student unable to maintain the CGPA of at least 3.5 after their first two terms will be required to transfer at that time into the course option.

### Thesis

The thesis is submitted and assessed by the student’s examining committee as per GGR 1.10.

## Academic Requirements within the Graduate General Regulations

All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.