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Abstract

本課程是由交通大學 運輸與物流管理學系提供。

本課程為方述誠老師應邀授課，授課內容與課程大綱稍有不同。

課程目標/概述 This course is divided into three major parts dealing with convex analysis, optimality conditions and duality, and computational methods. The ultimate goal in optimization studies is to develop efficient computational schemes for solving the problem at hand.

Convex analysis involves convex sets and convex functions and is central to the study of the field of optimization. Optimality conditions and duality can be used not only to develop termination criteria but also to motivate and design the computational method itself. To describe many computational methods (algorithms) for solving different classes of nonlinear programming problems.

本錄影課程為方述誠老師應邀授課，授課內容與課程大綱稍有不同。

課程章節

課程章節

單元主題

第一週 Mathematical Review

第二週

Convex Sets

第三週 Convex Functions and Generalizations

第四週 Nonlinear Programming

第五週 Motivation, Intuition, Speculation, Theorization

第六週 General Form of NLP

第七週 Conjugate Transformation

第八週 Duality Theory

第九週 Solution Methods of NLP

第十週 One Dimensional Search

第十一週 Newtons Method

第十二週 Conjugate Gradient Method

第十三週 Constrained Optimization

第十四週 Necessary Conditions for Optimality

第十五週 Sensitivity Analysis

第十六週 Lagrangian Dual Problem

第十七週 Constrained Optimization Methods

課程書目 課堂筆記

評分標準

項目

作業

期中考試

期末考試

授課對象：大學生、研究所學生

預備知識：數學相關課程與具研究興趣