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描述
本课程基于计算和实际应用，介绍涵盖决策领域大规模系统的建模以及运用最新最优化软件优化这些系统。应用领域有：交通和物流，模式识别，结构设计，金融工程和电信系统规划。建模工具和方法涵盖线性，网络，离散和非线性规划，启发式方法，灵敏度和事后最优性分析，大规模系统的分解方法和随机规划。本课程以开发和解决大规模最优化模型中出现的计算以及计算相关问题为导向。
课程要求
在选修本课程之前，学生必须修读过MIT课程“最优化方法”和“数学规划导论”二者之一，或者征得任课教授同意。
课程教材
书名：《线性规划导论》, 作者：D.Bertsimas和J.Tsitsiklis,出版社：Athena Scientific,Belmont,MA,1997 (课程提纲里所指的BT）,此课本可以在MIT的Dewey图书馆或新加坡国立大学的中央图书馆预定到。此外，MIT出版社1999年出版的Pascal Van Hentenryck所著的《最优化规划语言》(The OPL Optimization Programming Language)也可以在在MIT的Dewey图书馆预定到。
复习课
MIT的复习课安排在每周的星期五。 复习课不硬性要求，但鼓励学生参加。
评分标准
七次作业： 25%
期中考： 30%
期末考：35%
课堂表现： 10%
Description
A computational and application-oriented introduction to the modeling of largescale systems in a wide variety of decision-making domains and the optimization of such systems using state-of-the-art optimization software. Application domains include transportation and logistics, pattern classification, structural design, financial engineering, and telecommunications system planning. Modeling tools and techniques covered include linear, network, discrete, and nonlinear optimization, heuristic methods, sensitivity and postoptimality analysis, decomposition methods for large-scale systems, and stochastic optimization. This course is oriented around computation and computation-related issues in developing and solving large-scale optimization models.
Prerequisites
MIT subject Optimization Methods or Introduction to Mathematical Programming /Introduction to Mathematical Programming, or permission of instructor
Course Material
Text: Introduction to Linear Optimization, D. Bertsimas and J. Tsitsiklis, Athena Scientific, Belmont, MA, 1997 (BT in syllabus), on reserve at Dewey Library (MIT) and at Central Library (NUS). Also, several copies of the book The OPL Optimization Programming Language, by Pascal Van Hentenryck (MIT Press, 1999), are on reserve in Dewey Library.
Recitations
MIT recitations for the course will be held on Fridays. Voluntary attendance at recitations is encouraged but not required.
Assessment
Seven Problem Sets: 25%
Midterm Exam: 30%
Final Exam: 35%
Class Interaction: 10%
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