Welcome to the home page for the Computer Science Department's Graduate Algorithms course CLA CS 530. This is the starting point for online course information and documentation.
CS 530 is the central graduate algorithms course in the computer science curriculum. It serves as the a core graduate theory course. It is the successor to the undergraduate algorithms course, CS 330, and has this course as a prerequisite. In CS 530 students will learn fundamental algorithm design and algorithm analysis at the graduate level. The following list of pointers provides access to information concerning the instructor, the TF and the class.
Steven Homer (homer@bu.edu)
Office: MCS 281
Phone: 353-8927
Office hours: Tuesday 11-12 and and Wednesday, 12-1
Teaching Fellow: Nithin Varma (nvarma@bu.edu)
Office: Room MCS 217 (Note: Office hours will be held in room MCS 148)
Office hours are: Monday 2:30-3:30 and Thursday 12-1 in room MCS 148
Most recent CS 530 Course News.
Assignments: All assignments will be turned in using Gradescope software (gradescope.com). Have a look at their system and some of their demos.
You can sign up for gradescope in 530 using our course code which is MWV5YV.
You can use the url piazza.com/bu/fall2018/cs530 to sign up for CS 530's version of Piazza.
Most recent class assignment: HW 5
The previous assignments and quizzes were: quiz 2 - white version, quiz 2 - green version version, quiz 3 - yellow version version. HW 1, HW 2, HW 3, HW 4.
Some answers to homework assignments and the quizes: HW 1, HW 2, Quiz 2, Quiz 3.
Sections:
The 4 section are
Section A2: 9:05am - 9:55am in MCS B23
Section A3: 10:10am - 11:00am in MCS B19
Section A4: 11:15am - 12:05pm in CAS 225
Section A5: 12:20pm - 1:10pm in CAS 324
Please select one of the lab section to attend each week.
Some extra material and notes from class:
A few pages for the Garey and Johnson NP-Completeness book about the bin packing problem which will be a good start in reading about the class discussion of this problem.,
A few comments about HW 3 which may help in solving them,
Some examples from background material needed for the class,
Some material concerning randomized algorithms from the book by Motwani and Raghavan can be found here.