Nonlinear programming is used in a variety of applications, ranging from machine learning and data science to finance and engineering. This course provides an introduction to nonlinear programming and covers modelling techniques as well as solution algorithms.
Aims and Objectives
Having successfully completed this module you will be able to:
- demonstrate knowledge and understanding of nonlinear programming solution algorithms
- demonstrate knowledge and understanding of nonlinear programming modelling techniques
The basics of nonlinear optimization: constrained and unconstrained optimization problems. Optimality criteria. Modelling. Applications of nonlinear optimization in finance, business and engineering. Algorithms for solving constrained and unconstrained problems, e.g. direct search methods, steepest
descent, Newton's method, penalty and barrier methods.
Learning and Teaching
Teaching and learning methods
Fourteen 1-hour lectures
Four 1-hour computer sessions
|Practical classes and workshops||4|
|Total study time||75|
Resources & Reading list
Numerical Optimization (1999). Nocedal & Stephen Wright. Springer Verlag.
MS Bazaraa, CM Shetty & HD Sherali (1994). Non-linear Programming: Theory and Applications. Wiley.
DP Bertsekas (2004). Non-linear Programming. Athena Scientific.
This is how we’ll formally assess what you have learned in this module.
|Closed book Examination||80%|
This is how we’ll assess you if you don’t meet the criteria to pass this module.
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Repeat type: Internal & External