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MA40050: Numerical optimisation and large-scale systems

[Page last updated: 22 May 2025]

Academic Year: 2025/26
Owning Department/School: Department of Mathematical Sciences
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 2
Assessment Summary: CWRI 25%, EXCB 75%
Assessment Detail:
  • Coursework (CWRI 25%)
  • Exam (EXCB 75%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: Before taking this module you must take MA20218 AND take MA20222
In taking this module you cannot take MA32057
Learning Outcomes: Students should know a range of modern iterative methods for solving optimisation problems, including those arising in inverse problems. They should be able to analyse the algorithms and have an understanding of their practical performance.


Content: Topics will be chosen from the following: - Motivation for numerical optimisation, especially from the inverse problems and variational regularisation perspective; - First order iterative optimisation methods; - Elements of convex analysis and convex optimisation.

Skills: Problem Solving (T,F&A), Computing (T,F&A), independent study and report writing.

Aims: To teach an understanding of iterative optimisation methods with applications in inverse problems.

Course availability:

MA40050 is Optional on the following courses:

Department of Mathematical Sciences
  • USMA-AFM14 : MMath(Hons) Mathematics (Year 4)
  • USMA-AAM15 : MMath(Hons) Mathematics with Study year abroad (Year 4)
  • USMA-AKM15 : MMath(Hons) Mathematics with Year long work placement (Year 5)

Notes:

  • This unit catalogue is applicable for the 2025/26 academic year only. Students continuing their studies into 2026/27 and beyond should not assume that this unit will be available in future years in the format displayed here for 2025/26.
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