COMPSCI 531 Algorithm
Paradigms
Schedule  Resources  Sakai
 Piazza
Description
This course is an introductory graduate course on the design and analysis of algorithms. The course builds on an undergraduatelevel study of the analysis and implementation of data structures and algorithms (COMPSCI 201). The goal is to introduce a number of important algorithm design techniques as well as basic algorithms that are interesting both from a theoretical and also practical point of view. We will cover basic algorithm design techniques such as divideandconquer, dynamic programming, and greedy techniques for optimization. We will cover techniques for proof of the correctness of algorithms and also asymptotic analysis of algorithm time bounds by the solution of recurrence equations. We will apply these design and analysis techniques to derived algorithms for a variety of tasks such as sorting, searching, and graph problems. Some specific algorithm topics include: deterministic and randomized sorting and searching algorithms, depth and breadth first search graph algorithms for finding paths and matchings, and algebraic algorithms for fast multiplication and linear system solving.
Lectures will focus on introducing major algorithmic principles of design and analysis, along with mathematical analysis of algorithmic problems. The weekly lab section will build on that material to explore questions of implementations and applications to real world problems.
Prerequisites
An undergraduatelevel course on the analysis and implementation of data structures and algorithms (COMPSCI 201 or equivalent) and also four semesters of college mathematics. This course requires a certain amount of mathematical sophistication (e.g., as required to solve recurrence equations), and we assume you have prior experience with basic programming at the COMPSCI 201 level. A quiz on recurrence equations early in the course will provide you with some feedback on whether your mathematics training will suffice. If you feel that you may not have sufficient background, please talk with an instructor.
Meetings
All regular meetings will take place in LSRC B101 from 11:45am  1:00pm. Lectures will be held on Tuesdays and Thursdays. Labs will be held on Mondays. You can see the full schedule here. You can view recordings of class meetings here.
Instructors
Professor John Reif (Lectures) 

Office: 

D223 LSRC Building 
Phone: 

9196606568 
Email: 


Web page: 


Office hours: 

TBD 
Brandon Fain (Lab) 

Office: 

D311 LSRC Building 
Email: 


Web page: 


Office hours: 

1:30 – 2:30 pm on Tuesdays and 1:00 – 2:00 pm on Wednesdays in LSRC D309 
Teaching Assistants
Shalin Shah 

Office: 

D104 LSRC Building 
Email: 


Web page: 


Office hours: 

5:00 – 7:00 pm on Thursdays in LSRC D309 
Xinghao Cheng 

Office: 

N021 North Building 
Email: 


Web page: 


Office hours: 

4:30 – 5:30 pm on Tuesdays and 2:00 – 3:00 pm on Wednesdays in LSRC D309. 
Text Books
● [CLRS] Cormen, Leiserson, Rivest, and Stein. Introduction to Algorithms, McGrawHill, third edition, 2009. Be sure to get the third edition (ISBN: 0262033844)
○ Solutions to Selected exercises and problems in CLRS
● [DPV] Dasgupta, Papadimitriou, and Vazirani. Algorithms, McGrawHill, first edition, 2006. (ISBN: 0073523402)
Other References
● G. Brassard and P. Bratley. Algorithmic  Theory and Practice. Prentice Hall, 1988.
● D. Kozen. The Design and Analysis of Algorithms. Springer Verlag, 1991.
● A. Aho, J. Hopcroft, and J. Ullman. Design and Analysis of Algorithms. Addison Wesley 1974.
● R. Tarjan. Data Structures and Network Algorithms. SIAM Publications, 1983.
● C. H. Papadimitriou. Computational Complexity. Addison Wesley, 1994.
Course Topics
● Mathematical Analysis of Algorithms: growth of functions, summations, recurrences, averagecase and randomized analysis
● Worst case versus average case analysis
● Sorting and selection: divideandconquer and randomized techniques
● Search trees
● Hashing in theory and practice
● Amortized analysis
● Priority queues
● Cryptography algorithms
● Greedy algorithms
● Dynamic programming
● Graph algorithms
● Clustering techniques and community detection in social networks
● Matrix and algebraic algorithms
● Approximation algorithms
Homework assignments
There will be two types of homework
assignments: theory homeworks and lab homeworks. All assignments will be released to Students on
the course Sakai webpage, and all solutions should similarly be submitted on
the course Sakai webpage. No credit is given for late solutions. You
should turn in what you have on time for partial credit rather than receive a
0. For exceptional circumstances, see the instructor in advance, rather
than after the due time. Our policy is that one (and only one)
homework during the entire term is allowed not to be handed in, at no loss of
credit. Furthermore, if you hand in all the homework, then we will drop the
lowest graded homework.
In the theory assignments, students will be asked to design algorithms for classic problems and provide mathematical analysis of correctness and asymptotic efficiency. Students will turn in a written set of solutions. It is strongly encouraged that students should type their solutions using LaTeX (we will have a tutorial on LaTeX in the lab section). If handwritten solutions are illegible, they will not be graded. Details about proper style for writing up homework solutions and some guidelines for grading are available.
In the lab mini projects, students will work in teams of between one and three people to implement algorithms in software and provide data driven analysis that gives insight into problems motivated by real world applications. In other words, students will be to go beyond just coding up an algorithm from class. Students will turn in a pdf with data and discussion, along with source code written in java, c++, or python (students may write in any of the three languages they prefer). Each team should give a single submission, and all students on that submission will receive the same grade. Students are not required to work with the same group for each assignment, and may complete the lab homeworks alone if they prefer.
Academic Integrity
For theory homework problems, discussion among students is permitted, but students must write up solutions independently on their own. No materials or sources from prior years' classes or from the Internet can be consulted. For details about what is acceptable, see this honesty matrix.
For the lab mini projects, students may not work together if they are not in the same group, meaning that you should not discuss or share any material with people outside of your group. Furthermore, you may not use code from the internet or other students outside of your group. Source code will be analyzed for plagiarism.
During every exam: all calculators, computers, cell phones, wireless or bluetoothconnected devices, and all other electronic devices must be identified and handed over to the person proctoring the exam. Breaking the rules can result in expulsion. Each student is required to make a copy of this paragraph, sign it indicating that the contents are understood, and turn it in to John Reif.
Grading
● Class interaction (10%)
● Theory homework (15%)
● Lab homework (15%)
● Quiz exam 1 (5%)
● Midterm exam (15%)
● Lateterm exam (15%)
● Final exam (25%)
There will be no makeup exams for missed exams. Missing one of the three midterm exams will result in the remaining midterms and final exam grades being reweighted proportionally. By University Policy, missing the Final exam results in a grade X.
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