CENG 504 Optimization Methods

B.1. Objective of the course:  

Optimization is of paramount importance to solve the problems involving decision making in engineering. This course aims to give the students the knowledge common optimization methods together with their numerical implementations.

B.2. Prerequisites: 

Knowledge of calculus and elementary programming.

C.1 Course contents:

Mathematical review:  vector spaces and matrices, elements of calculus
Unconstrained optimization: one-dimensional search method: golden section, Fibonacci, Newton's method.
  gradient search methods: Steepest-descent, Newton's method, conjugate-gradient, Least squares analysis
Constrained optimization: Linear programming, integer programming

Non-derivative methods: Neural networks (NN), simulated annealing (SA)

C.2. Course Schedule:

Week 1: Mathematical Review I: Vector spaces and matrices
Week 2: Mathematical Review I: Calculus
Week 3: One dimensional search
Week 4: Gradient search
Week 5: Least squares analysis
Week 6: Midterm Exam
Week 7: Constrained optimization
Week 8: Linear Programming
Week 9: Integer Programming
Week 10: Non-derivative methods
Week 11: Heuristics for optimization
Week 12: Simulated Annealing, Genetic algorithm
Week 13: Particle Swarm, Bee Colony, Ant colony
Week 14: Term project presentations

D. Lecture Notes:

E. Grading:

Written midterm exam  - %25
Written final exam - %35
Assignments - %10
Term Project - %30

F. Books:

- E. K. P. Chong and S. H. Zak, An Introduction to Optimization, Third Edition,
  New York, NY: John Wiley & Sons, Inc. (Wiley-Interscience Series), 2008

G. Assignments:

Given during the lectures.

H. Term Projects:

Term Project 2018 Spring

Test data: http://www.math.uwaterloo.ca/tsp/world/countries.html

I. Materials and Useful Links:

There might be some other materials in password protected area. Please ask the instructor for the username and password of this course.