Module Details

MA305-7-AU-CO: Nonlinear Programming

Year: 2017/18
Department: Mathematical Sciences
Essex credit: 15
ECTS credit: 7.5
Available to Study Abroad / Exchange Students: Yes
Full Year Module Available to Study Abroad / Exchange Students for a Single Term: No
Outside Option: No

Supervisor: Dr Xinan Yang
Teaching Staff: Dr Xinan Yang, email
Contact details: Miss Shauna McNally - Graduate Administrator. email: smcnally (Non essex users should add to create the full email address), Tel 01206 872704

Module is taught during the following terms
Autumn Spring Summer

Module Description

The module provides an understanding at postgraduate level of nonlinear programming. It contains an introduction to the theory, algorithms and applications of nonlinear programming. It teaches principles of good modelling, from formulation of practical problems to computer solution, and how to design a range of algorithms and numerical methods. It acquaints students with general issues concerning computational algorithms, and considers application areas such as mathematical finance.

Nonlinear programming
- Formulation of unconstrained and constrained nonlinear optimisation models.
- One-dimensional search (Newton-Raphson, golden section search)
- Conditions for local optimality (quadratic forms, convex and concave functions, Taylor series for multiple variables).
- Gradient search, Newton's method, Quasi-Newton methods.
- Lagrange multiplier methods.
- Karush-Kuhn-Tucker optimality conditions.
- Penalty function methods.
- Non-derivative methods.

Computer labs
The module has a significant practical component comprising four one-hour computer labs using the Matlab computer package. These will include practicals on Newton-Raphson and golden section search, and on Gradient search, Newton's method and Quasi-Newton methods.

On completing the module, students should be able to:
- carry out a modelling process to convert problems into mathematical form
- apply an appropriate algorithm or numerical method for solving a particular problem;
- discuss the relative advantages and limitations of the various algorithms and numerical methods;
- discuss and analyse the important features and advantages of quasi-Newton methods
- use given implementations of these algorithms in Matlab, and observe and analyse the results;
- understand the derivation and uses of the Karush-Kuhn-Tucker necessary conditions for optimality.

Learning and Teaching Methods

There are 20 lectures, 6 classes and 4 labs in total. There will be regular assessed material at postgraduate level which will be discussed in one of the classes. In the Summer term 3 revision lectures are given.


20 per cent Coursework Mark, 80 per cent Exam Mark


3 problem sheets (4% each) and 1 lab report (8%).

Other details

Information about coursework deadlines can be found in the "Coursework Information" section of the Current Students, Useful Information Maths web pages: Coursework and Test Information

Exam Duration and Period

2:00 during Summer Examination period.


  • Recommended Reading:
  • W. L. Winsten, (2004), Operational Research: Applications and Algorithms (fourth edition), Duxbury Press
  • R.L. Rardin. (1998), Optimization in Operations Research, Prentice Hall

Further information