CS 4744 Scientific and Engineering Computing II, Fall 2005

Making use of the knowledge acquired in part I of the course, the second semester focuses on well recognized major computational developments that have the greatest influence on the development and practice of science and engineering in the last century, . We draw upon a variety of computational problems from the breadth of science and engineering to interest students and establish the relevance of the computational problem-solving approach. Students will be involved in projects.

Pre-requisites for this course are CS1114, CS3734, MA1122, MA2012, MA2132. Serious numerical calculations will be performed using MATLAB in class and in homework assignments. Students are required to bring their laptop notebook computers to classes. Make sure that MATLAB is properly installed on your laptop. Help with software and hardware issues should be addressed to Information Systems located in RH 305.

Instructor :

K. Ming Leung

E-mail :

mleung@duke.poly.edu
(Please note that because of a PeopleSoft mistake, kleung@poly.edu is not one of my email addresses!)

Office :

LC 127

Office hours :

Monday: 2:00 - 3:00 pm

Tuesday: 10:00 - 11:00 am

Thursday: 1:00 - 2:00 pm

Textbook :

Scientific Computing: An Introductory Survey, 2^nd Edition, (McGraw-Hill, NY, 2002), by Michael T. Heath.

Lectures :

2 lectures a week, totaling to 39 hours per semester.

Recitation :

Roughly one hour per week

Exams :

One midterm and a final examination.

Homework :

Weekly homework assignments

Recitation Infrastructure :

Hands-on programming and exploration using MATLAB and existing public-domain software packages .

Grading System:

Attendance/Participation (10%), HW (30%), Midterm (30%), Final (30%).

Course Objectives :

Knowledge of the fundamental issues concerning scientific and engineering computing.

Learn some of the fundamental algorithms developed in the last century.

Ability to formulate the problem, identify an appropriate computational algorithm, and properly interpret the results.

Ability to apply the right algorithm and programming skills to solve scientific and engineering problems.

Tentative topics:

Weeks 1-2

: Eigenvalue and Eigenvector Problems

Weeks 3-4: Optimization Problems
Weeks 5: Optimization using Genetic Algorithms
Weeks 6-7: Optimization based on Differential Evolution
Weeks 8-9: Vector Space Models for Information Retrieval
Weeks 10-11: Singular Value Analysis of Cryptograms
Weeks 12-13: Discrete Fourier Transform and its Applications

Recitation topics:

Fully coordinated and integrated with lecture material.

ABET Competencies :

This course addresses the following ABET competencies :

a) Students apply knowledge of mathematics (calculus, linear algebra, differential equations) and computational techniques to solve science and engineering problems.

e) Students identify, formulate and solve scientific and engineering problems.

k) Students learn techniques, skills and modern engineering tools necessary for engineering practice by using computational and graphical tools for homework assignments and term projects.

Content by Category :

Engineering Science = 4 credits (100%)

Instructional Materials :

Materials for lectures and recitation classes

Homeworks :

Homework assignments and their solutions