Serial Number
16186
Course Number
CommE5050
Course Identifier
942 U0640
- Class 01
- 3 Credits
Elective
GRADUATE INSTITUTE OF ELECTRICAL ENGINEERING / GRADUATE INSTITUTE OF ELECTRONICS ENGINEERING
GRADUATE INSTITUTE OF ELECTRICAL ENGINEERING
GRADUATE INSTITUTE OF ELECTRONICS ENGINEERING
Elective- KUEN-YU TSAI
- View Courses Offered by Instructor
COLLEGE OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE DEPARTMENT OF ELECTRICAL ENGINEERING
ktsai@ntu.edu.tw
- R724, Minda bldg. (明達館724室)
02-33663689
- Admin. Assistance: Ms. Yi-Ru Chen (陳儀儒) 02-33669638 diochen@ntu.edu.tw
- Thu 2, 3, 4
電二101
Type 2
25 Student Quota
NTU 25
Specialization Program
Signal Processing
- English
- NTU COOL
- Core Capabilities and Curriculum Planning
- NotesThe course is conducted in English。
NTU Enrollment Status
Enrolled0/25Other Depts0/0Remaining0Registered0- Course Description[Course description] Mathematical optimization has pervasive applications in not only almost all natural science and engineering disciplines such as electrical engineering (especially in control, communication, signal processing, and electronic design automation), computer science, and operations research, just to name a few, but also social sciences such as economics. Among various types of optimization problems, convex optimization problems are special in that they can be solved very efficiently, and thus they have gained much attention. Moreover, many optimization problems fall into this broad category. For instances, linear least squares, linear programming, quadratic programming, geometric programming, and semidefinite programming are special cases of convex optimization. The importance of convex optimization is becoming more and more apparent as there are many more emerging scientific and engineering problems being solved efficiently in this manner. This course primarily focuses on developing students’ capability of recognizing and formulating convex optimization problems arising from their own research fields, and when time permits, it also introduces how such problems are solved and applied. Students are also encouraged to develop heuristic approaches based on convex optimization theory and techniques to tackle general nonlinear, nonconvex optimization problems encountered in their fields of interest. [Course topics] 1. Introduction to convex optimization 2. Convex sets 3. Convex functions 3. Convex optimization problems 4. Duality theory (optimality conditions; sensitivity analysis) 5. Algorithms* (descent methods; Newton's method; interior-point methods) 6. Applications* (approximation and fitting, statistical estimation, etc.)
- Course Objective[Course goals] Basic: - Recognize/formulate some engineering problems as convex optimization problems - Utilize or develop code to solve CVX optimization problems - Characterize optimal solution (e.g. limits of performance) Bonus: - Advance your own research work at NTU - Develop good presentation and technical writing skills in English - Develop heuristic approaches based on convex optimization to tackle general nonlinear, nonconvex optimization problems
- Course Requirement[Prerequisites] Calculus (esp. Lagrange multiplier), linear algebra (esp. linear least squares and singular value decomposition)
- Expected weekly study hours after class
- Office Hour
(appointments by email)
- Designated Reading[BV04] S. Boyd and L. Vandenberghe, Convex Optimization
- References[Ber99] D. P. Bertsekas, Nonlinear Programming, 2nd ed. [Ven01] P. Venkataraman, Applied Optimization with Matlab Programming [BSS06] M. S. Bazaraa, M. D. Sherali, and C. M. Sherali, Nonlinear Programming, 3rd ed. [LY08] D. G. Luenberger and Y. Ye, Linear and Nonlinear Programming
- Grading
0% Attendance
0% Bonus Projects
e.g., topical report/presentation, clarification of course material 20% max bonus.
50% Final Project
Proposal:Presentation:Report = 2:2:6; Format:Content = 1:1
50% Final Exam
Open books and notes. Maybe substituted with Course Study Report per instructor's decision.
0% Homework
- Adjustment methods for students
Adjustment Method Description Teaching methods Assisted by video
Exam methods Written (oral) reports replace exams
- Course Schedule
Week 1 Course info. and Introduction Week 2 Week 3 Convex sets Week 4 Convex functions Week 5 Convex optimization problems Week 6 Duality (I) Week 7 Week 8 Duality (II) Week 9 Week 10 Unconstrained minimization*; Equality-constrained minimization* Week 11 Final project proposal presentation (Final project proposal due) Week 12 Interior-point methods* Week 13 Approximation and fitting* Week 14 Statistical estimation* Week 15 Geometric problems* Week 16 Summary and Review; Techniques for Non-convex problems* Week 17 Final exam Week 18 Final project report/presentation 1st version due (present in class) (Final-exam week) 10% penalty/work day; last grade submission: 6/17