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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
    • Ahmad Attarha
  • Tutors:
    • Dillon Chen
    • Oliver XI
    • Robert McArthur

Assessments

Assessment COMP4691 COMP8691
Final Exam 50% (40% hurdle) 50% (40% hurdle)
4 Assignments 44% 34%
Seminar – 10%
Lab and Forum Participation 6% 6%

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:    11 Oct 2023 / Responsible Officer:    Director, School of Computing / Page Contact:    Felipe Trevizan