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MATH6120 Nonlinear Optimisation

Module Overview

This course provides an introduction to nonlinear programming and covers modelling techniques as well as solution algorithms.

Aims and Objectives

Module Aims

This module provides an introduction to nonlinear programming and covers modelling techniques as well as solution algorithms.

Learning Outcomes

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

TypeHours
Practical classes and workshops4
Lecture14
Independent Study57
Total study time75

Resources & Reading list

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

Numerical Optimization (1999). Nocedal & Stephen Wright. 

DP Bertsekas (2004). Non-linear Programming. 

Assessment

Summative

MethodPercentage contribution
Closed book Examination 80%
Coursework 20%

Repeat

MethodPercentage contribution
Examination 100%

Referral

MethodPercentage contribution
Examination 100%

Repeat Information

Repeat type: Internal & External

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