Course Title: Real and Complex Analysis

Part A: Course Overview

Course Title: Real and Complex Analysis

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2150

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 2 2016

MATH2150

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 2 2019,
Sem 2 2020,
Sem 2 2021

Course Coordinator: Prof. Andrew Eberhard

Course Coordinator Phone: +61 3 9925 2616

Course Coordinator Email: andy.eberhard@rmit.edu.au

Course Coordinator Availability: By email and then online appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

You are assumed to have successfully completed the following courses or their equivalent:

MATH1142 Calculus and Analysis 1

MATH1144 Calculus and Analysis 2

MATH2140 Linear Algebra and Vector Calculus


Course Description

This course provides essential mathematical background for many subsequent courses in modern applied mathematics, pure mathematics, numerical analysis, statistics and operations research. Its aim is twofold. Firstly it extends the analysis of real functions of one variable to the analysis of functions of two or more real variables in a systematic manner. Secondly it provides sufficient background in the analysis of functions of a complex variable for you to study advanced engineering mathematics or aspects of probability theory. You will also become more familiar and comfortable with the language, abstract formalisms and proof techniques that are present in all modern texts on analysis and its applications.


Objectives/Learning Outcomes/Capability Development

 This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Science (Applied Mathematics and Statistics):

Knowledge and Technical Competence

  • the ability to use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

Problem-solving

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.


 On completion of this course you should be able to:

  1. Apply the mathematical concept of convergence and its epsilon delta definition to establish the existence of limits and devise proofs of mathematical statements via the definition of convergence
  2. Use fundamental mathematical concepts and theorems, such as mean value and implicit function theorems, to establish inequalities and estimates, to establish if a function of two variables is continuous and\or differentiable at a given point and explain how its partial derivatives relate to this property.
  3. Manipulate calculus of functions of two or more variables and, in particular, make a change of variables using the Jacobian matrix in a multiple integral.
  4. Elaborate the special character of functions of a complex variable and their properties and gain practical skills in analysing and manipulating functions of complex variables (including the evaluation of a line integral of a function of a complex variable using Cauchy’s integral formula, evaluation of real integrals using complex integration, and evaluation of Laurent Series and residues). 
  5. Communicate a mathematical argument and construct some simple mathematical proofs.


Overview of Learning Activities

The basic theory will be delivered in lectorials. Examples provided within lectorials will further be developed in weekly tutorials. Tutorial exercises and assignments will provide timely and continuous feedback so you may gauge your progress during the semester.   

 

Each week we will have two hours of lectorial classes for worked examples, questions and individual work on assignments. Lectures are recorded and place online.   

 

A detailed teaching schedule is available in Canvas. 


Overview of Learning Resources

Lecture notes and recorder online lectures should provide sufficient knowledge in order to perform successfully in this course. Weekly tutorial sheets will provide examples of the type of problems that you are expected to master. Solutions for these will be posted regularly. The RMIT library contains a number of relevant texts that you may use to obtain alternative presentations of the topics given in lectures. 

 

A Library Guide is available at  

http://rmit.libguides.com/mathstats 


Overview of Assessment

Assessment Tasks:

 

Summative Analysis Assessments  

There are 4 take-home summative assessments for real analysis and 4 take-home summative assessments for complex analysis 

These support CLOs 1, 2, 4 and 5. 

Weighting: 40%  

 

Authentic-practical evaluations of professional skill base assessments for both real and complex analysis.  

Weighting: 30% 

These support CLOs 1, 2, 3 and 5. 

 

Case based assessment of summative knowledge in both real and complex analysis.  

Weighting: 30% 

These support CLOs 4 and 5.