CSCI 2300 Spring 2008

Rensselaer Polytechnic Institute
Department of Computer Science

CSCI 2300: Data Structures and Algorithms

Spring 2008

ANNOUNCEMENTS

Final exam and course grades are now posted on WebCT.
Solutions to HW8, as well as graded HWs 7 and 8 are now available in the box outside Sanmay's office. Sanmay will have office hours as usual on Tuesday at 1:30.
The final exam is on Thur 5/8 from 3-6PM in DCC 318 (not 308!).
All grades are now posted on WebCT. If you believe any of these grades are in error, please get in touch with us immediately. Send an e-mail to all three of us if you see any problems with any of your grades.
There's a Google group for class discussions here set up by a student. We'll try to help out there. Please remember and respect the collaboration policy.
ACM Tutoring sessions will be held Tuesdays from 6:00 PM to 8:00 PM in Lally 102.

OVERVIEW

This is a foundational course in the design and analysis of algorithms and the data structures they are coupled with. While we will concentrate on the theoretical design and analysis of algorithms and data structures, we will reinforce the theory with examples, laboratories, and applied homework questions.

The goal of this course is to provide a strong foundation in algorithms and data structures in preparation for jobs in industry or for more advanced courses. Algorithms are the basic language of computer science. After taking this course, you should be able to prove the correctness of and analyze the running times of different algorithms. You should also be able to use different algorithm-design techniques to solve particular problems. Data structures and algorithms also form a major component of any software system. After taking this course you should be able to make intelligent decisions about alternative techniques in the context of practical problems, choosing from existing data structures and algorithms or designing your own when necessary.

STAFF

Instructor: Sanmay Das
Office: Lally 302
Phone: x2782
Office hours: Tuesdays from 1:30-2:30 PM, or by appointment.

TA: Suzanne Matthews (email: matths3 at rpi)
Labs: W 10-11:50 and 12-1:50

TA: Ameya Hate (email: hatea at rpi)
Labs: W 2-3:50 and 4-5:50

POLICIES

TEXTBOOKS

There are two textbooks for this class

DPV: Algorithms by Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani, McGraw-Hill, ISBN 0073523408.
Weiss: Data Structures and Algorithm Analysis in C++ (3rd Ed) by Mark Allen Weiss, Addison-Wesley, ISBN 032144146X.

LECTURES

Lectures will be on Tuesdays and Fridays from 12pm to approximately 1:25pm in Darrin 308. Chapter 1 of Weiss is background reading for the whole class.

Date	Topics	Readings	Extras
Jan 15	Introduction. Course policies. Course overview. Fibonacci numbers. Max subsequence sum.	DPV Chapter 0, Weiss Chapter 2
Jan 18	Max subsequence sum contd. Generating random permutations. Basic arithmetic.	DPV Section 1.1, plus readings from previous lecture	Notes on generating random permutations
Jan 22	Modular arithmetic. Euclid's algorithm.	DPV Section 1.2
Jan 25	Divide and conquer methods: integer multiplication, recurrence relations, mergesort, sorting lower bound.	DPV Sections 2.1, 2.2, 2.3
Jan 29	Divide-and-conquer continued: Median-finding, Quicksort, Matrix multiplication.	DPV Sections 2.4, 2.5. Weiss Section 7.7.
Feb 1	Graphs: Introduction, reachability, depth-first search.	DPV Sections 3.1, 3.2, 3.3.1.
Feb 5	DFS in directed graphs. Linearization. Strongly connected components.	DPV Sections 3.3, 3.4.
Feb 8	Eulerian tours. Breadth-first search. Dijkstra's algorithm.	DPV Sections 4.1-4.4.
Feb 12	Dijkstra's algorithm contd. Heaps.	DPV Sections 4.4-4.5.
Feb 15	Negative edge lengths. The Bellman-Ford Algorithm. Shortest paths in DAGs. More on heaps and heapsort.	DPV Sections 4.6-4.7. Weiss Sections 6.1-6.3 and 7.5.	Notes on binary heap creation
Feb 22	Binomial queues. Amortized analysis.	Weiss Sections 6.8, 11.1-11.2
Feb 26	More amortized analysis. Lazy merging for binomial queues. Intro to AVL trees.	Weiss Sections 11.4.2, 4.1-4.4
Feb 29	AVL Trees. Splay trees.	Weiss Sections 4.4, 4.5, 11.5
Mar 7	Midterm solutions. Universal hashing.	DPV Section 1.5
Mar 18	Minimum Spanning Trees. Kruskal's algorithm. Disjoint sets.	DPV Section 5.1
Mar 21	Disjoint sets contd. Prim's algorithm.	DPV Section 5.1
Mar 25	Huffman encoding	DPV Section 5.2
Mar 28	Horn formulas. Set cover.	DPV Sections 5.3-5.4
Apr 1	Dynamic programming: longest increasing subsequence, edit distance.	DPV Sections 6.1-6.3
Apr 4	Dynamic programming contd.: knapsack	DPV Sections 6.4
Apr 8	Dynamic programming contd.: chain matrix multiplication, shortest paths	DPV Sections 6.5-6.6
Apr 11	Dynamic programming contd.: TSP, independent sets in trees. Flow networks.	DPV Sections 6.6, 6.7, 7.2
Apr 15	Maximum flows and minimum cuts. Bipartite matching.	DPV Sections 7.2-7.3
Apr 18	Search problems. P and NP. NP-completeness.	DPV Chapter 8.
Apr 22	Reductions between NP-complete problems.	DPV Chapter 8.
Apr 25	Reductions, contd.	DPV Chapter 8.
Apr 29	Loose ends.

LABS

Jan 23: Lab 1
Jan 30: Lab 2 (Files necessary for Lab 2 are here (gzipped tarball))
Feb 6: Lab 3 (Use the test files posted above for lab 2)
Feb 20: Lab 4 (Necessary files are here)
Mar 26: Lab 5 (Necessary file is here)
Apr 3: No lab (GM week).
Apr 9: Lab 6 (Necessary files here)
Apr 16: Lab 7 (Files: data1 and data2)
Apr 23: No lab. TAs will hold office hours/recitation.
Apr 30: Lab 8. Attend lab. Receive 3 points for doing so, nothing if you don't show up. This gives you more time to work on HW8!

HOMEWORKS

HW 1: Due Jan 29
HW 2: Due Feb 5
HW 3: Due Feb 12
HW 4: Due Feb 26
HW 5: Due Mar 25
HW 6: Due Apr 1
HW 7: Due Apr 15
HW 8: Due Apr 29