Non – Linear Programming

Course Description

Give the essential foundations of nonlinear optimization, which are necessary for graduate students.

Learning Objectives

At the end of this course, the student will be able to:

• Know numerical solution of unconstrained optimization problems,
• Know nonlinear least squares and nonlinear systems of algebraic equations,
• Understand constrained optimization,
• Understand quadratic programming,
• Discuss large scale nonlinear optimization.

Skills to be developed in this course

• Discuss the formulation, the solution and the analysis of optimization problems.
• Apply modern algorithms for the solution of nonlinear optimization problems.
• Discuss how analytic tools are necessary for the validity of a model.

Methods of Assessment
Homework & research project 35%
Mid-term exam 25%
Final exam 40%

References

• Nocedal, J. and Wright, S. (1999) Numerical optimization. New York: Springer.
• Fletcher, R. (2000) Practical methods of optimization, John Wiley & Sons.
• Ruszczyński, A. (2006) Nonlinear optimization, Princeton, N.J.: Princeton University Press.
• Bazaraa, M., Sherali, H. and Shetty, C. (2006) Nonlinear programming, New York: Wiley.
• Luenberger, D.G. (2003) Linear and nonlinear programming, 2nd edition, Kluwer academic publishers.

Course ID: 8117523