Singapore University of Social Sciences

Basic Mathematical Optimisation

Basic Mathematical Optimisation (MTH355)

Synopsis

MTH355 Basic Mathematical Optimisation will provide undergraduates with an understanding of the common algorithms used in linear optimisation. The topics covered are of central importance for many applications in data science and data analytics. The course gives a comprehensive introduction to the simplex method and integer programming whilst only assuming a knowledge of basic linear algebra. Additionally, the course will teach students how such algorithms are implemented using the software Gurobi.

Level: 3
Credit Units: 5
Presentation Pattern: Every January

Topics

  • LU Decomposition
  • Matrix Iterative Methods
  • Formulation of a Linear Programming Model
  • The Simplex Method
  • Dual Form of a Linear Programming Problem
  • Sensitivity Analysis
  • Parametric Linear Programming
  • Branch and Bound Method
  • Either/or and 0-1 Variable Models
  • The Barrier Method
  • Goal Programming Problems involving Multiple Goals
  • Maximising Minima and Minimising Maxima

Learning Outcome

  • Formulate linear optimisation problems into mathematical and graphical linear models
  • Solve linear optimisation modelling problems using the simplex method
  • Analyse linear optimization problems with the two-phase simplex solution technique
  • Apply the LU decomposition technique and the conditions of convergence for linear sets of equations
  • Employ the branch and bound method to solve integer programming and 0-1 variable models
  • Compute the optimum solution of a large linear programming model
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