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Introduction to Numerical Analysis

Course Code: MATH 3316
Faculty: Science & Technology
Department: Mathematics
Credits: 3.0
Semester: 15 weeks
Learning Format: Lecture, Tutorial
Typically Offered: TBD. Contact Department Chair for more info.
course overview

This course is a presentation of the problems commonly arising in numerical analysis and scientific computing and the basic methods for their solutions. Topics include number systems and errors, solution of nonlinear equations, systems of linear equations, interpolation and approximation, differentiation and integration, and initial value problems. This course will involve the use of a numerical software package (such as MATLAB) and/or a high-level programming language (such as C/C++).

Course Content

  1. Number systems and errors
  2. Solution of nonlinear equations
  3. Systems of linear equations
  4. Interpolation and approximation
  5. Differentiation and integration
  6. Initial value problems

Methods of Instruction

Lectures, tutorials, problems sessions, assignments (written and/or MATLAB).

Means of Assessment

Evaluation will be carried out in accordance with Douglas College policy.  The instructor will present a written course outline with specific evaluation criteria at the beginning of the semester.  Evaluation will be based on some of the following criteria:

1.    Tutorials    0 – 10% 
2.    Tests   20 – 70%
3.    Assignments/Group work     0 – 20%
4.    Attendance    0 – 5%
5.    Final examination    30 – 40%

Learning Outcomes

Upon completion of MATH 3316 the student should be able to:

  • understand decimal, binary and floating point representation of numbers
  • understand error propagation and how to estimate numerical error
  • implement methods to solve nonlinear equations including, but not limited to, bisection method, secant method, fixed point iteration and Newton’s method
  • understand how to accelerate convergence in different solution methods for nonlinear equations
  • define and determine rate of convergence for solution methods to nonlinear equations
  • solve directly a system of linear equations using Gaussian elimination
  • solve linear systems numerically using matrix factorization, partial pivoting and matrix inverse
  • understand computational complexity of direct methods for Gaussian elimination
  • define the norm, determinant and condition number of a matrix
  • solve linear systems numerically using iterative methods
  • understand how iterative methods for solving linear systems differ from direct methods for solving linear systems
  • recognize eigenvalue problems and understand the issues involved in obtaining eigenvalues numerically
  • understand interpolating polynomials: Lagrange form and error formula
  • implement spline interpolation
  • understand the concept of trigonometric interpolation and Fourier series and Chebyshev polynomials
  • use the method of least squares to deal with inconsistent linear systems
  • use the QR factorization to solve least squares problems
  • understand and implement numerical differentiation techniques including finite differences and Richardson extrapolation
  • understand and implement numerical quadrature, using methods such as Romberg integration and composite rules
  • solve numerically initial value problems using Euler’s method and Runge-Kutta methods
  • use and understand the concepts of convergence, stability and stiffness when solving initial value problems numerically
  • solve numerically systems of ordinary differential equations
  • optional: Discrete Fourier representation and transforms, Singular Value Decomposition

course prerequisites

MATH 1220 and MATH 2232;
Exposure to a high-level programming language or course such as CMPT 1110 is recommended.



curriculum guidelines

Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester/year of the course, consider the previous version as the applicable version.

course schedule and availability
course transferability

Below shows how this course and its credits transfer within the BC transfer system. 

A course is considered university-transferable (UT) if it transfers to at least one of the five research universities in British Columbia: University of British Columbia; University of British Columbia-Okanagan; Simon Fraser University; University of Victoria; and the University of Northern British Columbia.

For more information on transfer visit the BC Transfer Guide and BCCAT websites.


If your course prerequisites indicate that you need an assessment, please see our Assessment page for more information.