Exactly Solvable Models

Subject MAST90065 (2013)

Note: This is an archived Handbook entry from 2013.

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject is not offered in 2013.

Time Commitment: Contact Hours: 36 hours comprising two 1-hour lectures and one 1-hour practice class per week
Total Time Commitment:

3 contact hours and 7 hours private study per week.


The following subject, or equivalent:

Study Period Commencement:
Credit Points:
Not offered in 2013


Recommended Background Knowledge:

It is recommended that students have completed the following subject, or equivalent:

Study Period Commencement:
Credit Points:
Not offered in 2013

No prior knowledge of physics is assumed.

Non Allowed Subjects:

No disallowed subject combinations among new-generation subjects

Core Participation Requirements:

For the purposes of considering requests for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements for this entry.

The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website: http://www.services.unimelb.edu.au/disability/


Assoc Prof Jan de Gier
Email: jdgier@unimelb.edu.au

Prof Peter Forrester
Email: pjforr@unimelb.edu.au

Subject Overview:

In mathematical physics, a wealth of information comes from the exact, non-perturbative, solution of quantum models in one-dimension and classical models in two-dimensions. This subject is an introduction into Yang-Baxter and Bethe Ansatz integrability, and the orthogonal polynomial method of random matrix theory. Transfer matrices, Yang-Baxter equation and Bethe ansatz are developed in the context of the 6-vertex model, quantum spin chains and other examples. As a solvable model, random matrix theory aims to first identify the explicit eigenvalue distributions for a given matrix distribution. The method of orthogonal polynomials is then used to compute eigenvalue correlation functions that can be compared against (numerical) experiments.


After completing this subject students should:

  • have learned how exactly solvable models apply to a variety of problems in applied mathematics and mathematical physics;
  • appreciate the role of exact solutions and universality in mathematical physics and be able to use concepts of real and complex analysis to determine asymptotic behaviour;
  • be able to compute correlation functions using matrix product techniques or random matrix theory;
  • be familiar with the basic mathematical techniques of exactly solvable models including Yang-Baxter equation, Bethe Ansatz, commuting transfer matrices and matrix product states;
  • understand the basic concepts of random matrix theory and appreciate their applicability;
  • have the ability to pursue further studies in these and related areas.

Up to 40 pages of written assignments (40%: two assignments worth 20% each, due mid and late in semester), a 3-hour written examination (60%, in the examination period).

Prescribed Texts:


Recommended Texts:

R.J. Baxter, Exactly Solved Models in Statistical Mechanics, Dover (2007).

Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Related Course(s): Master of Philosophy - Engineering
Master of Science (Mathematics and Statistics)
Ph.D.- Engineering
Related Majors/Minors/Specialisations: Mathematics and Statistics

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