MATH6120 Nonlinear Optimisation

Module Overview

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

Learning Outcomes

Having successfully completed this module you will be able to:

demonstrate knowledge and understanding of nonlinear programming modelling techniques
demonstrate knowledge and understanding of nonlinear programming solution algorithms

Syllabus

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

Type	Hours
Lecture	14
Independent Study	57
Practical classes and workshops	4
Total study time	75

Resources & Reading list

Numerical Optimization (1999). Nocedal & Stephen Wright.

MS Bazaraa, CM Shetty & HD Sherali (1994). Non-linear Programming: Theory and Applications.

DP Bertsekas (2004). Non-linear Programming.

Assessment

Summative

Method	Percentage contribution
Closed book Examination	80%
Coursework	20%

Repeat

Method	Percentage contribution
Examination	100%

Referral

Method	Percentage contribution
Examination	100%

Repeat Information

Repeat type: Internal & External