| 课程号 |
04833270 |
学分 |
2 |
| 英文名称 |
Applied Algorithms |
| 先修课程 |
程序设计基础，数据结构和算法 |
| 中文简介 |
本课程的目标是帮助你更好地解决算法设计的“现实世界”的问题。这包括：（1）熟悉基本的资源配置问题和解决方法，（2）理解算法技术和如何权衡设计正确、有效的算法和可实现的算法，（3）理解自私用户算法设计的挑战，和（4）知道如何对混乱的现实世界中的问题进行抽象和建模，将其转化为可以使用已有算法解决的干净问题。
希望你能通过这门课更好的欣赏美丽和优雅的算法，以及它们在现实世界中使用。具体来说，我们将研究生产计划，操作系统，媒体点播系统，网络，以及更多的问题。 |
| 英文简介 |
The goal of this course is to help you become better prepared to tackle algorithm design for "real-world" problems. This includes (1) being familiar with fundamental resource-allocation problems and solutions, (2) understanding algorithmic techniques and the tradeoffs involved in designing correct, efficient, and implementable algorithms, (3) understanding challenges in algorithm design for selfish users, and (4) knowing how to model and abstract messy real-world problems into clean problems that can be attacked using known paradigms or specific algorithms.
Hopefully, you will gain a greater appreciation of the beauty and elegance of algorithms as well as where they are used in the real world. Specifically, we will study problems arising in production planning, operating systems, media-on-demand systems, networks, and more. |
| 开课院系 |
信息科学技术学院 |
| 通选课领域 |
|
| 是否属于艺术与美育 |
否 |
| 平台课性质 |
|
| 平台课类型 |
|
| 授课语言 |
英文 |
| 教材 |
Introduction to Algorithms,T. Cormen, C. Leiserson, R. Rivest and C. Stein,MIT Press,Algorithm Design,Jon Kleinberg and éva Tardos,Combinatorial Optimization. Algorithms and Complexity,C. H. Papadimitriou and K. Steiglitz,Scheduling: Theory, Algorithms, and Systems,Michael Pinedo,Scheduling Algorithms,Peter Brucker,Algorithmic Game Theory,N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani, |
| 参考书 |
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| 教学大纲 |
学生课堂掌握所学内容
Introduction, stable matching. (3 hrs).
1. Scheduling Theory. (6 hrs)
2. Facility Location. (3 hrs)
3. Packing Problems (3 hrs)
4. Applications of Flow Networks (3 hrs.)
5. Algorithmic Game Theory. (6 hrs)
6. Genome rearrangements, sorting by transpositions. (3 hrs.)
以课堂授课为主，有少量习题课
平时作业占30%，期末考试占70%
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| 教学评估 |
蒋婷婷：
学年度学期：16-17-3，课程班：应用算法1，课程推荐得分：5.0，教师推荐得分：5.0，课程得分分数段：100-105；
学年度学期：18-19-3，课程班：应用算法1，课程推荐得分：0.0，教师推荐得分：10.0，课程得分分数段：95-100；
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