Singapore University of Social Sciences

Advanced Mathematical Optimization

Advanced Mathematical Optimization (MTH356)

Applications Open: 01 October 2021

Applications Close: 15 December 2021

Next Available Intake: January 2022

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Science & Technology

Schemes: Lifelong Learning Credit (L2C)

Funding: To be confirmed

School/Department: School of Science & Technology


MTH356 will provide undergraduates with an understanding of the common algorithms used in nonlinear optimization. The topics covered are of central importance for many applications in data science and data analytics. The course gives a comprehensive introduction to the gradient method and that of constrained nonlinear programming. Additionally, the course covers how such algorithms are implemented using the software Baron. MTH356e will be paired with MTH355e so that students upon the completion of both courses receive a better comprehension of the contents in the specialist field of Optimization.

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


  • Basic Iterative Schemes to Solve Systems of Nonlinear Equations
  • Convergence of Simple Iterative Methods of Nonlinear Equations
  • Grid Search Method
  • Golden Section Search
  • Alternating Variable Search Method for a Minimum
  • Steepest Descent Search Method for a Minimum
  • Conjugate Direction Methods
  • Stopping Criteria for the Iteration Methods
  • Equality Constrained Models
  • Newton-Lagrange Method
  • Conversion to Equality Constraints
  • Conditions for a Local Minimiser

Learning Outcome

  • Solve single nonlinear equations and systems of nonlinear equations.
  • Interpret the convexity of sets and functions.
  • Determine the existence and uniqueness of solutions to a given nonlinear programming problem.
  • Show the necessary and sufficient optimality conditions for a given nonlinear programming problem.
  • Apply various numerical algorithms to solve nonlinear programming problems.
  • Analyze the convergence of various gradient-based iterative methods.
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