MT624 Optimization

UDOM MT624 Optimization Theory

Course Topics

• Classical Methods
  • Unconstrained and Constrained Optimization
  • Gradient methods, Newton’s/Quasi-Newton Methods, Conjugate Direction Methods
• Linear Programming
  • Simplex / Non-Simplex methods
  • Duality
• Convex Optimization
  • Kuhn-Tucker Conditions
• Non-Linear Programming
• Metaheuristics
  • General Overview
  • Simulated Annealing
  • Tabu Search
  • Genetic Algorithms (overview as slides/handout)
    • Online visualizer
• Dynamic Programming (overview as slides/handout)

References

• Chinneck, John W. Practical Optimization: A Gentle Introduction. Ontario: Carleton University, 2011. Online edition
• Rao, Singiresu S. Engineering Optimization: Theory and Practice, 3rd Ed. New Dehli: New Age International Publishers, 2010.
• Diwekar, Urmila. Introduction to Applied Optimization. The Netherlands: Kluwer Academic Publishers, 2003.

Online resources

• Osborne, Martin J. Mathematical methods for economic theory: a tutorial. University of Toronto: Martin J. Osborne, 2011.
  • Optimization begins in Ch 4 and continues to Ch7 covering Kuhn-Tucker conditions
• Association for the Advancement of Artificial Intelligence (AAAI) (Good bibliographies, one for Genetic Algorithms)
• Introduction to Genetic Algoritms
• Yaghini, Masoud. Genetic Algorithm Lecture Notes. Tehran: Iran University of Science and Technology, 2009.
• Miller, Nolan. “You, the Kuhn-Tucker Conditions and You“. Harvard: Nolan Miller, 2007.
• Shewchuk, Jonathan R. An Introduction to the Conjugate Gradient Method without the Agonizing Pain. Carnegie Mellon University: Jonathan Richard Shewchuk, 1994.