Navigation menu menu

Learning outcomes

  1. Be able to apply Linear Programming and Mixed-Integer Programming model to solve real-world problems.
  2. Be able to recognize and formulate convex optimization problems arising in practice.
  3. Demonstrate an understanding of theoretical foundations of convex optimization and be able to use it to characterize optimal solutions to general problems.
  4. Be able to define an appropriate local search neighbourhood for a given problem.
  5. Be able to use a variety of meta-heuristics to escape local minima in a neighbourhood
  6. Demonstrate an understanding of the propagation of a global constraint in a Constraint programming system.

Semester 2 2023 details

See prerequisites and requirements under programs and courses:

  • COMP4691

  • COMP8691

  • Course Convener:
    • Felipe Trevizan
  • Lecturers:
    • Felipe Trevizan
  • Tutors:
    • Jerry Zhang

Assessments

Assessment COMP4691 COMP8691
Final Exam 50% (40% hurdle) 50% (40% hurdle)
Assignment 1 10% 10%
Assignment 2 10% 10%
5 Quizzes 30% 20%
Seminar – 10%

Schedule

The lecture and lab session times and locations can be found by searching for the course under timetabling. See activities and deliverables for more details.

Updated:    23 Jul 2024 / Responsible Officer:    Director, School of Computing / Page Contact:    Felipe Trevizan