CSCI-UA.0310-003 Basic Algorithms , Spring 2013
Lecturer: Esther Ezra ,
my first name at cs.nyu.edu, (212) 998-4859, room 416, WWH. Office hour: Wednesday 3:15--4:15PM
Meeting Time/Place: M/W 2:00--3:15PM, WWH 202
Recitation Time/Place: Thur 3:30--4:45PM, WWH 202
Graders: Wen Xing email@example.com .
Mailing list: To subscribe to the class list follow instructions at
To post a message to all the list members, send email to
firstname.lastname@example.org. Please, post only messages
interesting to everybody taking the class. Specific class-related
questions and most of your other correspondence should be directed to
Course Homepage: http://cs.nyu.edu/courses/spring13/CSCI-UA.0310-003/index.html
Recitation 3 is based on the material from recitation 2 (which I did not present
earlier in class).
No reciation 4: We had a lecture instead.
Brief Course Description:
This is an introductory course in algorithms.
This course covers the design and analysis of combinatorial algorithms.
The curriculum is concept-based and emphasizes the art of problem-solving. The course features
weekly exercises designed to strengthen conceptual understanding and problem
solving skills. Students are presumed to have adequate programming skills and
to have a solid understanding of basic data structures and their implementation
in the programming languages of their choice. Although some mathematical
sophistication would be helpful for this course, the necessary mathematics is
contained within the curriculum.
Because of the emphasis on problem solving, students are
expected to attend the Thursday recitation sessions.
Be sure that you register for the recitation (section 003) as well as the class (section 001/002).
- Algorithmic analysis:
Correctness. Worst-case running time asymptotic
analysis. Big Oh notation. CLRS, chaps. 1-3. Siegel, chap. 1,
chaps 2 through section 2.3.
- Divide-and-conquer Recurrence equations.
CLRS chap. 4. Siegel, section 2.4 pp. 52-66.
- Sorting algorithms:
N2 sorts, Heapsort, Mergesort, Quicksort, CountingSort,
Radix sort. Lower bound. CLRS chaps. 6-8. Siegel, chap. 7.
- Sets: Lists. Hash tables. Binary search trees. 2-3-trees/B-trees.
chap. 10, 11, 12, 18. Siegel Chapter 6 through 6.4.7.
- Directed graphs: Implementation. Depth-first search. DAGS, Topological sort.
Shortest paths. All pairs shortest paths CLRS chaps. 22,24,25. Siegel, chaps. 7, 8
8.4. 8.3.6 is optional.
- Undirected graphs: Minimum spanning tree. Max flow, Union-find algorithm.
CLRS chaps. 21, 23. Siegel, sections 8.6 and 9.4
- Algorithmic techniques: Dynamic programming.
1. An Introduction to Algorithms: their methods and madness,
by A.R. Siegel, available at NYU Copy Central 547 LaGuardia Place.
2. Introduction to Algorithms by
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Cliff Stein,
published by MIT Press.
You can get either the THIRD EDITION
or the SECOND EDITION.
There will be one in-class midterm and a final exam, in addition to
approximately weekly homework assignments. Tentative grade split is
30% homework, 30% midterm and 40% final exam.
Students can increase their total grade by up to 5%
by doing well in self-grading their homework, as explained in
class. Please make sure you make a copy of your homework before
handing it in.
There will be approximately 12 written homework assignments.
Some of the homework exercises will be routine, but others will be
more challenging. I do not expect you to solve all of the homework
problems, but I hope that you will benefit from working on the more
difficult ones. Homework will be assigned on Monday, and will be due
the following Monday. No late homework will be accepted.
Self-graded homework will be due one week from the assignment
It will not be returned to the students.
An advice about the homework assignments:
- Start early. Most problems will not be hard,
but others will be. Such more difficult problems are not
typically solved in one sitting. Start early and let the ideas
come to you over the course of a few days.
- Be rigorous. Each problem has a (sometimes
unwritten) requirement that you prove your algorithm
correct and analyze its running time. To obtain full
credit for a problem, it is necessary to fulfill these
requirements. We expect real proofs and real analysis, not
"proof by hand waving."
- Be concise. Express your algorithms at the
proper level of detail. Give enough details to clearly present your
solution, but not so many that the main ideas are
obscured. English is often a good way to express an
algorithm; pseudocode is good for communicating complex control
- Collaboration? You are encouraged to
solve all the homework questions on your own, but are
permitted to brainstorm difficult problems in small
groups, as long as each of you writes the solutions
individually and honestly acknowledges the cooperation.
Needless to say, if you work with others but never come up with
the solution on your own, you may do OK in the homework
component of your grade, but you will suffer on exams, so be