課程資訊
課程名稱
非線性規劃
NONLINEAR PROGRAMMING 
開課學期
95-2 
授課對象
電機資訊學院  電機工程學研究所  
授課教師
江介宏 
課號
EE5069 
課程識別碼
921EU3040 
班次
 
學分
全/半年
半年 
必/選修
選修 
上課時間
星期二6,7,8(13:20~16:20) 
上課地點
電二225 
備註
英文授課本課程以英語授課。
總人數上限:30人 
 
課程簡介影片
 
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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/spring06-cvx/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; interior-point 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
S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.

D. Luenberger, Linear and Nonlinear Programming, Addison-Wesley.

D. Bertsekas, Nonlinear Programming, Athena Scientific.

 
評量方式
(僅供參考)
   
課程進度
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