CS 330 - Spring '17 - Introduction to Algorithms - Syllabus

Official Course Description:

This course examines the basic principles of algorithm analysis; techniques of efficient programming; analysis of sorting and searching; graph algorithms; string-matching algorithms; matrix algorithms; integer and polynomial arithmetic; the fast Fourier transform; and NP-hard and NP-complete problems.

Course Handouts Page

Our Piazza page (includes lab handouts)

Prerequisites: The class assumes working knowledge of CS 112 and CS 131 (or MA 293) as a prerequisite. CS majors typically complete their Group B coursework (any two of CS 132, CS 235 and CS 237) before taking CS 330, and that is recommended. If you don't have the prerequisites, please talk to an instructor before deciding to continue with this class.

Instructors and Teaching Fellows

Prof. John W. Byers
Homepage: http:// www.cs.bu.edu/fac/byers
Email:    byers @ cs . bu . edu [preferred]
Office Hours:  Tues 1:30-2:30 PM (by appointment) and Wed 2-4 in MCS 295B
During office hours, if it's not too busy, I'll answer my phone at 617-353-8925. Other times, I generally let phone calls go to voicemail. Please send email instead.

Prof. Dora Erdos
Email:    edori @ bu . edu
Office Hours Tues 3-5 and Thurs 2-3 in MCS 288.

TF Maryam Ghasemi
Email:    ghasemi @ cs . bu . edu
Office Hours Wed 4-5 and Thurs 4-6 in EMA 302 (undergrad lab).

TF Mark Lemay
Email:    lemay @ bu . edu
Office Hours Tues 5-6 and Wed 5-7 in EMA 302 (undergrad lab). Bonus tutoring hour: Tues 4-5.

The class will be co-taught by Professors Byers and Erdos. On any given lecture date, one of the two instructors will deliver the lecture for both the A1 and B1 sections. The TFs will lead the discussion sessions. The objective is to reinforce the concepts covered in the lectures through problem-solving, and to provide clarifications and guidance on the homework assignments. The purpose of the office hours of the Instructors and Teaching Fellows is to answer specific questions or clarify specific issues. Your fastest route to get an answer to most questions is via Piazza. Office hours are not to be used to fill you in on a class you skipped or to re-explain entire topics. Office hours are scheduled at times to provide the most help to students who start the homework before the last minute.


Lecture A1: Tues/Thurs 11 - 12:15 AM, KCB 101.
Lecture B1: Tues/Thurs 9:30 - 10:45 AM, KCB 101.

We expect students to come to class, and to come on time. While the class is large, class participation and questions will be encouraged. Also, while our textbook will be very helpful, it is an imperfect substitute for in-class learning, which is the fastest (and easiest) way to learn the material. If you miss a class, please get the notes and work through the material with a fellow student.

Discussion Labs

Lab A2: Mon 9:05 - 9:55AM (Mark)
Lab A3: Mon 10:10 - 11:00AM (Mark)
Lab A4: Mon 12:20 - 1:10PM (Mark)
Lab B2: Mon 1:25 - 2:15PM (Maryam)
Lab B3: Mon 4:40 - 5:30PM (Maryam)
Lab B4: Mon 2:30 - 3:20PM (Maryam)

Labs will be an invaluable part of the course involving interactive problem-solving sessions, tips on homework questions, and supplemental material not covered in lecture. We will post labs on Piazza in advance -- please read before coming to lab. Attendance is mandatory and will be taken. Lab solutions will be posted on Monday evening after all labs conclude.


Algorithm Design, by Kleinberg and Tardos. ISBN 0-321-29535-8.


We will be using Piazza for all discussions outside of class. The system is highly catered to getting you answers to your questions fast and efficiently from classmates, the TFs, and instructors. Please do not email questions to the teaching staff -- post your questions on Piazza instead. We also encourage you to post answers to other students' questions there (but obviously, not answers to problems on the problem sets!). Our class page is located at: https://piazza.com/bu/spring2017/cs330/home. Please go there to sign up today. We will also use Piazza to post announcements, homework assignments, labs and lab solutions, etc.

Grading and Attendance

The course grade will break down as follows: Last day to drop without a W: Feb 23. With a W: March 31. Incompletes for this class will not be granted.

Workload:    Be forewarned -- the workload in this course will be moderately heavy. We will have seven assignments, plus or minus one, due roughly every other week. The majority of the assignments will consist of problem sets, but some of the problems will contain small-scale implementations and simulations, especially later in the semester. These can readily be done in a language of your choice -- for example, either Java or Python is fine. As you likely already know, assignments requiring substantial creativity can take more time than you expect, so plan to finish a day early.

Exams:    There will be one eighty minute in-class midterm held during the middle of the semester before spring break, on Thursday, March 2. The cumulative final will be held during the normal two-hour final exam slot: Tuesday May 9, 9-11AM for students in the 9:30AM lecture and Thursday May 11, 12:30 - 2:30PM for students in the 11AM lecture. Both in KCB 101. Please make your spring break and end-of-semester travel plans accordingly.

Homework Submission:     Assignments will typically be due Thursdays at 7PM. You must submit a hardcopy no later than 7PM in the drop box on the first floor of the MCS building, near the CS department office. From the CS office, walk toward the shorter end of the hallway, and turn right. Drop box is immediately on your right. Assignments must go *in the box*, not on the shelves above, which is where we will *return* assignments. LET ME REPEAT: Assignments must go *in the box*, not on the shelves above, which is where we will *return* assignments.

Late Policy:    During the course, you will have 2 chances to turn in assignments up to 22 hours late with no penalty, with Friday 5PM *in the drop box* as the cutoff (hard deadline). Any assignment arriving between Thursday 7PM and Friday 5PM is considered late.

Regrading Procedure:     If, after reviewing your solution, you still believe a portion of your homework was graded in error, you may request a regrade. Please write, on a PostIt, the problem number and a brief description of the incorrect deduction, stick it on your homework, and give it to one of the instructors for a regrade. Note that when we regrade a problem, your score may go up or down.

Attendance: It is expected that you will attend lecture and the laboratory section for this course. Attendance will be taken in labs. Some material covered in lecture and lab will not be covered by our textbooks. We ask that you please arrive in class on time, since it is disruptive to have students flowing in throughout the class period. Moreover, when students are at a borderline between grades, we will factor in attendance before making a final determination.


We will no doubt drift from any formal plan, but a rough schedule of where we are headed is provided in the roadmap below. Each of the topics will take roughly one week, except as noted. A more detailed and continually updated schedule will be maintained on the main course homepage.

  1. Course overview. Stable matching, implementation, running times.
  2. Graphs and basic graph algorithms (2 weeks)
  3. Greedy algorithms for optimization problems (2 weeks)
  4. Divide-and-conquer; fast multiplication of integers, matrices, and polynomials (2 weeks)
  5. Dynamic programming for optimization problems (2 weeks)
  6. Max-flow/min-cut
  7. Optimization problems for which no polynomial time algorithms are known; introduction to NP (2 weeks)
  8. Lower bounds and approximation algorithms
  9. Local search and heuristic approaches
  10. Randomized algorithms, including median-finding and order statistics

Academic Conduct

Academic standards and the code of academic conduct are taken very seriously by our university, by the College of Arts and Sciences, and by the Department of Computer Science. Course participants must adhere to the CAS Academic Conduct Code -- please take the time to review this document if you are unfamiliar with its contents.

Collaboration Policy

The collaboration policy for this class is as follows. The last point is particularly important: if you don't make an honest effort on the homework but always get ideas from others, your exam scores (accounting for the majority of your grade) will reflect it.