Electrical and Computer Engineering 710
Engineering Optimization

Objective:

To develop a comprehensive understanding of formal optimization methods and their application to engineering design problems.

Instructor:

Dr. Tim Davidson,
ITB-A111A, Ext. 27352.
davidson@mcmaster.ca

Course web page:

http://www.ece.mcmaster.ca/~davidson/ECE710

Recommended Text:

Boyd and Vandenberghe, Convex Optimization, Cambridge University Press, Cambridge, 2004. (Book web page.)

Recommended Reading:

Bertsekas, Convex optimization theory, Athena Scientific, Belmont, MA, 2009.

Bertsekas, with Nedic and Ozdaglar, Convex analysis and optimization, Athena Scienific, Belmont, MA, 2003.

Nocedal and Wright, Numerical Optimization, Second Edition, Springer, New York, 2006.

Bertsekas, Nonlinear Programming, Second Edition, Athena Scientific, Belmont, MA, 1999.

Gill, Murry and Wright, Practical Optimization, Academic Press, London, 1986.

Antoniou and Lu, Practical Optimization: Algorithms and Engineering Applications, Springer, New York, 2007.

Chong and Zak, An Introduction to Optimization, second edition, Wiley, 2001.

Prerequisite:

A solid background in linear algebra. Exposure to numerical computing, programming, optimization and engineering design will be helpful, but is not required.

Course Outline:

• Principles of engineering optimization: modelling, formulation, solution and verification
• A taxonomy of optimization problems and solution methods
• Convex sets, convex functions and convex optimization
• Linear and convex quadratic optimization problems
• Geometric and semidefinite optimization
• Duality
• Computational complexity and NP-completeness
• Algorithms for smooth unconstrained optimization
• Algorithms for constrained convex optimization, including interior point methods
• Algorithms for smooth constrained non-convex optimization, including sequential quadratic/convex programming
• Block/coordinate techniques
• Derivative-free optimization
• Space-mapping and surrogate optimization methods
• Outline of techniques for discrete optimization, including relaxation
• Introduction to robust optimization
• Applications to engineering design

Assessment:

• Midterm test: 10%
• Final Exam: 40%
• Design Project and Presentation: 50%

Term:

II.

Lectures:

There will be two lectures a week, each of 1 1/2 to 2 hours in duration.

Policy reminder:

Academic dishonesty consists of misrepresentation by deception or by other fraudulent means and can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: "Grade of F assigned for academic dishonesty"), and/or suspension or expulsion from the university.

It is your responsibility to understand what constitutes academic dishonesty. For information on the various kinds of academic dishonesty please refer to the Academic Integrity Policy, specifically Appendix 3, located at http://www.mcmaster.ca/senate/academic/ac_integrity.htm.

The following illustrates only three forms of academic dishonesty:

• Plagiarism, e.g. the submission of work that is not one's own or for which other credit has been obtained.
• Improper collaboration in group work.
• Copying or using unauthorized aids in tests and examinations.

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