This course aims to equip students with a thorough grasp of optimization techniques in communication networks. Encompassing concepts, algorithms, and tools. This course also focuses on designing, managing, and optimizing networks. Through theory and practical exercises, the course provides students with the opportunity to enhance their skills to improve network performance, resource utilization, and efficiency.
Python & GUROBI
Upon successful completion of this course, students will be able to:
(1) Recall the fundamental concepts of optimization, including the definitions of objective functions, constraints, and decision variables,
(2) Explain the principles underlying linear programming, including the concepts of feasible regions, linear constraints, and optimal solutions,
(3) Utilize the simplex method to solve various linear programming models for determining optimal solutions,
(4) Analyze integer, binary, and mixed integer programming, while applying these techniques for problem analysis and solution derivation,
(5) Evaluate the functionality of network flow models, such as the shortest path problem, maximum flow problem, and minimum cost flow problem,
(6) Create optimization models tailored to address complex challenges within wireless communication networks.
(1) Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network Flows: Theory, Algorithms and Applications. Prentice Hall.
(2) Bertsekas, D. (1998). Network Optimization: Continuous and Discrete Models (Vol. 8). Athena Scientific.
Test/Exam (60%), Performance Project (Written, Oral) (40%)
| Workload | Hrs |
|---|---|
| Lectures | 42 |
| Course Readings | 28 |
| Exams/Quizzes | 57 |
| Resource Review | 28 |
| Term Project | 70 |