課程名稱 
非線性規劃 NONLINEAR PROGRAMMING 
開課學期 
942 
授課對象 
電機資訊學院 電子工程學研究所 
授課教師 
江介宏 
課號 
EE5069 
課程識別碼 
921EU3040 
班次 

學分 
3 
全/半年 
半年 
必/選修 
選修 
上課時間 
星期一2,3,4(9:10~12:10) 
上課地點 
博理114 
備註 
本課程以英語授課。 
Ceiba 課程網頁 
http://ceiba.ntu.edu.tw/942921U3040 
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課程概述 
Mathematical programming has pervasive applications in scientific and engineering optimization problems, e.g., in electrical engineering (especially in fields like control, communication, signal processing, electronics design automation, etc.), computer science (especially in machine learning, approximation algorithms, etc.), economics, and operations research, just to name a few. Among optimization problems in mathematical programming, convex optimization problems are special in that they can be solved efficiently, and thus gain much attention. Moreover, many optimization problems fall into this category of convex optimization. (For instance, linear programming is a special case of convex optimization.) In fact, the importance of convex optimization becomes more and more apparent in recent years as there are many more emerging scientific and engineering problems being solved efficiently in this manner.
Course webpage:
http://cc.ee.ntu.edu.tw/~jhjiang/instruction/courses/spring06cvx/cvx.html

課程目標 
This course aims to provide students the capability of recognizing and formulating convex optimization problems raising from their own research fields, and to let students understand how such problems are solved and have some experience in solving them. The intended audience includes, but not limited to, students from EDA, control, image processing, communication, computer science, and other related fields dealing with optimization problems.
Course outline.
1. Introduction to convex optimization
2. Basics in linear programming
3. Convex sets and functions
4. Convexity and optimization
5. Duality theory
Optimality conditions; sensitivity analysis; applications of duality theory.
6. Algorithms
Descent methods; Newton’s method; interiorpoint algorithms.
7. Applications
Approximation and fitting, statistical estimation, physical design optimization, etc.

課程要求 
Prerequisites.
Linear algebra
Grading policy.
To be determined.

預期每週課後學習時數 

Office Hours 

指定閱讀 

參考書目 

評量方式 (僅供參考) 

