Course Syllabus

MA2600–Scientific Computing

College of Science and Arts

Fall 2014

Instructor Information

Instructor:Jiguang Sun, PhD, Associate Professor

Office Location:313 Fisher Hall

Telephone:Office – (906)487-3172

E-mail:

Office Hours:MWF 11:00am – 12:00pm or by appointment

Course Identification

Course Number:MA2600-R01

Course Name:Scientific Computing

Course Location:MF 132 Fisher Hall, W: 330 Fisher Hall.

Class Times:MWF 10:05am – 10:55am

Prerequisites:MA2160and one of MA2320, MA2321, MA2330

Course Description/Overview

This course is an introduction to numerical methods; software used in the course is MATLAB. Topics include systems of linear algebraic equations, interpolation, numerical differentiation and integration, initial value problems, optimization, and other related topics.

Course Learning Objectives

The objective of this course will be to ensure that students:

  1. Obtain a clear understanding of the importance of scientific computing.
  2. Understand methods to solve linear algebraic equations and be able to use softwareto solve large sparse linear equation systems.
  3. Understand the fundamental concepts of numerical methods for differential equations and be able to write programs to compute solutions of differential equations.
  4. Understand interpolation.
  5. Be able to implement numerical differential and integration.
  6. Be able to implement numerical methods to solve initial value problems.
  7. Understand fundament methods in optimization and be able to numerically solve optimization problems.

Course Resources

Course Website(s)
  • Canvas
  • Personal Website <
Required Course Text
  • Numerical Methods in Engineering with Matlab, 2nd Ed., bt J. Kiusalaas, ,©2010, Cambridge University Press, ISBN 978-0-521-19133-3 (required)
  • Elements of Scientific, byA. Tveito, H. P. Langtangen, B. F. Nielsen and X. Cai, ©2010 Springer, ISBN 978-3-642-11299-7 (optional)

Grading Scheme

Grading System
Letter Grade / Percentage / Grade points/credit / Rating
A / 90% & above / 4.00 / Excellent
AB / 85% – 89% / 3.50 / Very good
B / 80% – 84% / 3.00 / Good
BC / 75% – 79% / 2.50 / Above average
C / 70% – 74% / 2.00 / Average
CD / 65% – 69% / 1.50 / Below average
D / 60% - 64% / 1.00 / Inferior
F / 59% and below / 0.00 / Failure
I / Incomplete; given only when a student is unable to complete a segment of the course because of circumstances beyond the student’s control.
X / Conditional, with no grade points per credit; given only when the student is at fault in failing to complete a minor segment of a course, but in the judgment of the instructor does not need to repeat the course. It must be made up by the close of the next semester or the grade becomes a failure (F). A (X) grade is computed into the grade point average as a (F) grade.
Grading Policy

Grades will be based on the following:

Homework/Projects / 50%
MidtermExam / 20%
Final Exam/Project / 30%
Total / 100%
Late Assignments

Late assignments will be returned without grading.

Collaboration/Plagiarism Rules

Collaboration on homework is encouraged. However, the final work needs to be done independently.

Cell phones, Blackberries, iPods, PDAs, or any other electronic devices are not to be used in the classroom. Please make sure to bring a calculator with you to class. Calculators on other devices are strictly prohibited. Information exchanges on these devices during class are also prohibited and violate the Academic Integrity Code of Michigan Tech.

University Policies

Michigan Tech has standard policies on academic misconduct and complies
with all federal and state laws and regulations regardingdiscrimination, including the Americans with Disabilities Act of 1990. For more information aboutreasonable accommodation for or equal access to education or services at MichiganTech, please call the Dean of Students Office, at(906) 487- 2212or go to

Further Information:

Academic Integrity:

Academic regulations and procedures are governed by University policy. Academic misconduct cases will be handled in accordance the University's policies.

Disability Services:

If you have a disability that could affect your performance in any class or that requires an accommodation under the Americans with Disabilities Act, please contact your instructor as soon as possible so that appropriate arrangements can be made.

Affirmative Programs:

The Affirmative Programs Office has asked that you be made aware of the following:Michigan Technological University complies with all federal and state laws and regulations regarding discrimination, including the Americans with Disabilities Act of 1990. If you have a disability and need a reasonable accommodation for equal access to education or services at Michigan Tech, please call the Dean of Students Office at 487-2212.

Equal Opportunity, Discrimination, or Harassment Statement:

For other concerns about discrimination, you may contact your advisor, Chair/Dean of your academic unit, or the Affirmative Programs Office at 487-3310.

Course Schedule (Tentative)

Week 1

W 9/3Course Overview and Introduction of Matlab

W 9/51.2-1.4 Data Types and Variables, Operators, and Flow Controls

Week 2

M 9/81.5-1.6 Functions, Input/Output

W 9/101.7 Array Manipulation

F 9/12 1.8-1.9 Writing and Running Programs, Plotting

Week 3

M 9/152.1 Introduction

W 9/172.2 Gauss Elimination Method

F 9/192.3 LU Decomposition Method

Week 4

M 9/222.4-2.5 Symmetric and Banded Coefficient Matrices, Pivoting

W 9/242.6 Matrix Inversion

F 9/262.7 Iterative Methods

Week 5

M 9/292.7 Iterative Methods (continued)

W 10/13.1-3.2 Introduction, Polynomial Interpolation

F 10/33.3 Interpolation with Cubic Spline

Week 6

M 10/63.4 Least-Square Fit

W 10/84.1-4.2 Introduction, Incremental Search Method

F 10/104.3-4.4 Method of Bisection, Methods Based on Linear Interpolation

Week 7

M 10/134.5 Newton-Raphson Method

W 10/15Review

F 10/17Midterm

Week 8

M 10/205.1-5.2 Introduction, Finite difference Approximations

W 10/225.3 Richardson Extrapolation

F 10/245.4 Derivative by Interpolation

Week 9

M 10/276.1-6.2Introduction, Newton-Cotes Formulas

W 10/296.4 Gaussian Integration

F 10/316.5 Multiples Integrals

Week 10

M 11/37.1 Introduction

W 11/57.2 Taylor Series Method

F 11/7 7.3 Runge-Kutta Methods

Week 11

M 11/107.4 Stability and Stiffness

W 11/128.1 Introduction

F 11/148.2 Shooting Method

Week 12

M 11/178.3 Finite Difference Method

W 11/199.1-9.2 Introduction, Jacobi Method

F 11/219.3 Inverse Power and Power Method

Week 13

M 12/110.1-10.2 Introduction, Minimization Along a Line

W 12/310.3 Powell’s method

F 12/510.4 Downhill Simplex Method

Week 14

M 12/8Topics 1

W 12/10Topics 2

F 12/12Topics 3

Finals Week

TDB