The module has two parts. The first part provides an introduction to the topic of operational research (OR). The key role of using models in OR to obtain solutions of practical problems arising in a variety of contexts is emphasised. Some classical problems are analysed and standard techniques for solving them are investigated.
The second part of the module covers computer programming and its use in solving certain types of mathematical problems. The computer programming language used is Python.
One of the pre-requisites for MATH2013
Pre-requisites: MATH1048 AND MATH1059
Aims and Objectives
Having successfully completed this module you will be able to:
- Formulate a mathematical model for certain types of practical problem
- Implement simple mathematical modeling and algorithms for to computational solutions of decision making problems.
- Code simple mathematical algorithm in a programming language (Python), to analyze the computational behavior of such algorithms, and to describe them in writing.
- Appreciate the capabilities and limitations of OR techniques.
- Demonstrate knowledge and understanding of selected OR techniques
- Linear programming (LP): assumptions, applications, geometry of LPs, graphical solution method, simplex method (single-phase and two-phase).
- Elements of integer programming modeling.
- Introduction to algorithms: definition and specification of algorithms; asymptotic estimates of their running times.
- Sorting algorithms.
- Shortest path algorithms for graphs with nonegative arc lengths.
- Python: introduction, basic usage of the software used (either Jupyter notebooks or Spyder)
- Variables: Definition, naming conventions and using sensible names. Integer, float, strings, printing.
- Loops: Concept of iteration, using for and while loops, range function. Semantic whitespace in Python.
- Control flow: Logical statements and boolean variables. if/elif/else.
- Functions: Concept and procedural programming. Definition in Python: def and return keywords. Docstrings and help. Script files, import, packages.
- Data structures: Lists, tuples, dictionaries and sets. Vectors and arrays through numpy.
- LaTeX: Basic environments and sections. Packages such as amsmath. BibTeX and reference managers. Creating long documents.
- Excel: advanced data analysis and presentation. Linking to other packages (eg Python via xlrd and xlwt).
Learning and Teaching
Teaching and learning methods
Lectures, problem classes, computer workshops, private study.
|Preparation for scheduled sessions||12|
|Supervised time in studio/workshop||20|
|Completion of assessment task||60|
|Total study time||150|
Resources & Reading list
A.Saha (2015). Doing Math with Python. No Starch Press.
S.Dasgupta, C.H. Papadimirriou, U. Vazirani (2006). Algorithms. McGraw-Hill.
H.P. Langtangen (2016). A Primer on Scientific Programming with Python. Springer.
W.L. Winston (2004). Operations Research: Applications and Algorithms. Thomson Brooks/Cole.
Summative assessment description
|Closed book Examination||40%|
Referral assessment description
|Closed book Examination||50%|
Repeat type: Internal & External