Sample Solution to Test 1 (Fall 2014) and Test 2 (Fall 2014) are place in rpi lms website.
Office Hours for the Week of 8th December - Tuesday 9am - Noon and Wednesday 1-3:00 pm (because of the weather conditions Wednesday Office hours are
changed to the afternoon).
Final Exam is on Thursday 12/11/2014 from 3 to 6:00 pm in West Hall Auditorium
Sample Final exam and Sample Solution
LMS has a link to submit your programming project (submit a zip file consissting of Code, sample input, sample output, Readme (how to run your program and how to compile your code).
Lab11 optional - will not be graded - do it for your own enjoyment
Home work 7 is posted (Due Dec 1) Here.
Lab 10 (11/19/2014) is on line here
Lab 9 (11/12/2014) is on line here
No Lab on Wednesday November 5th (due to Test 2)
Home work 6 is posted (Due October Nov 13th) Here.
Extra Office Hours on Sunday (11/2/2014) at 10:00 am in my office (lally 305)
Sample Exam 2 is here
Exam 2 (open book, open notes) covers Chapters 4,5, 6 and later parts of Chapter 3 (not covered in Exam 1)
Lab 8 (10/29/2014) is on line here
Lab 7 (10/22/2014) is on line here
Home work 5 is posted (Due October Oct 30) Here.
Lab 6 (10/15/2014) is on line here
Exam 1 statistics Average 78.70, Median 81.50, Standard Deviation 13.47
Watch this you tube video starting from 2:08 minute to hear about pancake flipping problem from Futurama writer talking to Simon Singh
when you are at it please watch this too Simpson's epsiode Bill Gates paper Here
For this Week only (Week of 6th October 2014) Congrui Lis office hour
will be at 2-3pm on Friday (10/10/2014) instead of Wednesday(10/8/2014).
Lab 5 (10/08/2014) is on line here
Question 3 is from Find a Peak element . Here is a c peak programin C
Sample Solution to Test 1 is here
Home work 4 is posted (Due October Oct 16) Here.
Bill Babbitt's office hours changed to AE 119
Lab and home work solutions are posted in RPI lms site
Topics of Test 1 is posted.
Sample Test 1 (with answers) is posted
Lab 4 is posted here
Home Work 3 is posted (Due October 2nd) Here.
Lab 3 is posted here
Home Work 2 is posted (Due September 18th) Here.
Lab 2 is posted here
Lab 0 is posted Here.
Home Work 1 is posted (Due September 4th) Here.
Lab 0 Solution is posted Here.
Lab 1 is posted Here.
Instructor: Moorthy - Home Page
Office Hours: Tuesday, Friday 2:00 -3:30pm
Office: Lally 305
Teaching Asst: William Babbitt
Office Hours: Thursday 9-10:00 am
Labs: Sections 5 (JROWL 2C06) and 6 (sage 3101)
Location: AE 119 (Lounge)
Office Hours: wednesday 4-5:00 pm
Location: AE 217
Labs: Sections 3 (Sage 2715) and 4 (Sage 2715)
Teaching Asst:Hongzhao Huang
Office Hours: Friday 12:30-1:30 pm
Location: AE 217
Labs: Sections 1 (AE 216) and 2 (AE 216)
Undergraduate Lab TAs:
Lab 1 W 6-7:50 pm Eaton 216 Xi (UTA), Michael(UTA)
Lab 2 W 2-3:50 pm Eaton 216 Abramson(UTA) Wyler(UTA)
Lab 3 W 2-3:50 pm Sage 2715 Radocy(UTA) Manzini(UTA)
Lab 4 W Noon-1:50 am Sage 2715 Radocy(UTA) Manzini(UTA)
Lab 5 W 10-11:50 am J-ROWL 2C06 Xi(UTA) Eric(UTA)
Lab 6 Noon-1:50 pm Sage 3101 Abramson(UTA), Wyler(UTA)
Students at the end of the course will be able to design, implement and analyze algorithms
for problems in Science and Engineering. Students will learn different Algorithmic Paradigms
and learn techniques for analyzing the algorithms. Students will also learn efficiency both in
design and implementation. Students will learn to compare different algorithms for solving
the same problem. Students will be exposed to an elementary treatment of NP-complete problems.
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, the student, should be able to:
Understand the correctness of, and analyze the running times of, different algorithms.
Use different algorithm-design techniques, including, but not limited to, greedy, divide-and-conquer, and dynamic programming techniques, to solve particular problems.
Model real problems abstractly using the language of graphs and flows.
Solve problems by reducing to other problems whose solution is known, and show that
problems are hard by reducing from other problems.
Make intelligent decisions about alternative data structures and algorithmic techniques in
the context of practical software problems, choosing from existing data structures and algorithms or designing your own when necessary
Class Time: Monday and Thursday,
12:00 - 1:20pm
Classroom: DCC 318
Prerequisites: CS2 (CSCI-1200) and
Foundations of Mathematics (CSCI-2200).
This course discusses
algorithms, and the mathematical techniques necessary to design
and analyze them.
Chapters are from Dasgupta's book
Chapter 0 Introduction
Chapter 1 Algorithm
with Numbers and Randomized Algorithms
Chapter 2 Divide and, Conquer Algorithms
Chapter 3 Decomposition of Graphs
Chapter 4 Paths in Graphs
Chapter 5 Greey Algorithms
Chapter 6 Dynamic Programming
Chapter 8 NP-complete Problems
Chapter 9 Coping with NP-complete Problema
Chapter 7 Linear Programming
Class 1 (8/25): Chapter 0
Class 2 (8/28): Chapter 0 and 1
Class 3 (9/4): Chapter 1
Class 4 (9/8): Chapters 1 and 2
Class 5 (9/11): Chapter 2
Class 6 (9/15): Chapters 2 and 3
Class 7 (9/18): Chapter 3
Class 8 (9/22): Chapter 3 and 4
Class 9 (9/25): Chapter 4
Class 10 (9/29): Exam 1
Class 11 (10/2): Chapter 4
Class 12 (10/6): Chapter 5
Class 13 (10/9): Chapter 5
Class 14 (10/14): Chapter 6
Class 15 (10/16): Chapter 6
Class 16 (10/20): Chapter 6
Class 17 (10/23): Chapters 6 and 8
Class 18 (10/27): Chapter 8
Class 19 (10/30): Chapter 8 and Review
Class 20 (11/3): Exam 2
Class 21 (11/6): Chapter 8
Class 22 (11/10): Chapter 8
Class 23 (11/13): Chapter 9
Class 24 (11/17): Chapter 9
Class 25 (11/19): Chapter 9
Class 26 (11/24): Chapter 7
Class 27 (12/1): Chapter 7
Class 28 (12/4): Reciew
Labs Wed 6pm - 7:50pm Sec 1 Eaton 216 HongZhao(GTA)Xi (UTA), Michael(UTA)
Wed 2pm - 3:50pm Sec
2 Eaton 216 HongZhao(GTA) Abramson(UTA) Wyler(UTA)
Sec 3 Sage 2715 Congrui(GTA) Radocy(UTA) Manzini(UTA) Wed 2 - 3:50pm
Sec 4 Sage 2715 Congrui(GTA) Radocy(UTA) Manzini(UTA) Wed Noon- 1:50pm
Sec 5 J-ROWL 2C06 William(GTA) Xi(UTA) Eric(UTA) Wed 10 - 11:50am
Sec 6 Sage 3101 William(GTA) Abramson(UTA), Wyler(UTA) Wed Noon - 1:50pm
8/27 - No Lab - You dont have to go to Lab on August 27th
Lab 0 (optional)
Lab 1 (sep 3) Lab1.
Lab 2 (Sep 10) Lab 2
Lab 3 (Sep 17) Lab 3
Lab 4 (Sep 24) Lab 4
Lab 5 (Oct 8) Lab 5
Lab 6 (Oct 15) Lab 6
Lab 7 (Oct 22) Lab 7
Lab 8 (Oct 29) Lab8
Lab 9 (Nov 12) Lab 9
Lab 10 (Nov 19) Lab 10
Lab 11 (Dec 3) (optional) - you do not have to go the lab. Lab11 optional - will not be graded - do it for your own enjoyment
Collaboration is not allowed. Homeworks and exams should be
solved and written by individuals alone. If anyone is caught cheating then
severe measures will be taken such as lowering the final grade, and the event will
be reported to the appropriate authorities in the campus.
No Late Submissions (unless there is a medical Excuse) Homework
will not be accepted.
Homework 1: Due in class on Thursday, Sep 4. HW1 Home Work 1
Homework 2: Due
in class on Thursday, Sep 18.
Homework 3: Due
in class on Thursday, Oct 2.
Home Work HW3
Homework 4: Due
in class on Thursday, Oct 16.
Homework 5: Due
in class on Thursday, Oct 30.
Homework 6: Due
in class on Thursday, Nov 13.
Homework 7 Due
in class on Monday, Dec 1.
Exams (in class exams ) All Exams are Open Book Open Notes Exams
Exam 1 on Monday Sept 29, from 12-1:20 pm - covers Chapters 0 to 3
Exam 2 on Monday Nov 3, from 12-1:20 pm Covers Chapters 4,5,6 and 8
Final Exam is on Thursday 12/11/2014 from 3 to 6:00 pm in West Hall Auditorium Covers all the Chapters covered in class (Chapters 0-9)
15% Labs, 21% Homework (3 points for each homework); 20% Exam 1; 20% Exam 2; 25% Final
Algorithms, Dasgupta, Papadamitriou and Vazirani
McGraw Hill, 2008
Introduction to Algorithms, Cormen, Leiserson, Rivest and Stein
Most Recent Edition McGraw Hill
Department of Computer Science
Rensselaer Polytechnic Institute
Troy , NY 12180