Optimization

Syllabus

Optimization

Prerequisite

• Either MATH 3030 or both MATH 2641 (Formerly MATH 3435) and MATH 2215 with grades of C or higher.

• The ability to program in a high-level language such as MATLAB or Python.

Textbook

• Introduction to Optimization, 4th edition, Edwin K. P. Chong and Stanislaw H. Zak, Wiley.

Course Description

• Basics of numerical optimization.

• Gradient method, Newton’s and quasi-Newton methods, Conjugate Gradient.

• Linear programming, duality, simplex method.

• Constrained optimization.

• Convex optimization problems.

• Algorithms for constrained optimization.

Lecture Notes

• Mathematical review

• Optimization basics

• Gradient method

• Newton's method

• Conjugate gradient method

• Quasi-Newton method

• Linear programming

• Duality

• Non-simplex method

• Constrained optimization

• Convex optimization problems

• Algorithms for constrained optimization

Homework Assignments

Students must follow homework policy and instruction to complete and submit homework assignments. Homework assignments and due dates are posted here.

Computer Programming

Computer coding in a high-level programming language is a necessary component of this course. For more information, see the course coding guide.

Conduct Policy

Any type of inappropriate conduct may result in your being administratively dropped from the course. See the University's Policy on Disruptive Behavior in Georgia State University Code of Conduct.