Where and When:
Mathematics background: Introduction to basics of functional spaces as
a framework to motivate and unify optimization methods.
Basic techniques in optimization: Basics of linear programming, basics
of nonlinear programming, calculus of variation, optimal control, dynamic
programming, neuro-dynamic programming, random search, simulated annealing,
genetic algorithms.
Textbook: Lokenath
Debnath and Piotr Mikusinski,
ISBN: 0122084365, Academic Press, 1999. Available in the Textbook Annex.
Course Prerequisite: Ordinary Differential Equations, Basic Probability Theory, Linear algebra.
Exam: There will be an in-class midterm exam to check the basic mathematical concepts.
Assignments: There will be some written problem sets and survey papers and presentations to illustrate the concepts.
Grading : Grades will be apportioned roughly as follows:
FAQ:
- Midterm 30%
- Problem Sets 10%
- Presentation 20%
- Final Exam/term paper 40%