課程資訊
 課程名稱 非線性規劃NONLINEAR PROGRAMMING 開課學期 94-2 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 課程簡介影片 核心能力關聯 核心能力與課程規劃關聯圖 課程大綱 為確保您我的權利,請尊重智慧財產權及不得非法影印 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 參考書目 指定閱讀 評量方式(僅供參考)
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