Fall Quarter, 2009

Intermediate Data Structures and Algorithms

Lecture Schedule Lab Session Topics Grade mapping Previous offerings Tutorials Animations

Overview

CS 141 introduces what many say is the core of Computer Science: data structures like graphs, and problem solving techniques. This material is essential in almost all of our upper-division courses.

Catalog description: CS 141 Intermediate Data Structures and Algorithms 4 Lecture, 3 hours; laboratory, 3 hours. Prerequisite(s): CS 014 with a grade of "C-" or better; CS 111/MATH 111; MATH 009C or MATH 09HC; proficiency in C++. Explores basic algorithm analysis using asymptotic notations, summation and recurrence relations, and algorithms and data structures for discrete structures including trees, strings, and graphs. Also covers general algorithm design techniques including "divide-and-conquer," the greedy method, and dynamic programming. Homework and programming assignments integrate knowledge of data structures, algorithms, and programming.

UCR course schedule, UCR course catalog. Note: Students receiving less than a C- in the CS 14 prerequisite will be dropped automatically a few weeks into the quarter, as the course relies heavily on basic knowledge of and skills in data structures, discrete mathematics, and programming.

Basic information

Syllabus: Download the PDF file.

Instructor: Stefano Lonardi (stelo@cs.ucr.edu)
Office hours: Tuesday 2-3pm. Office: Engineering 2, 317.
Teaching Assistant:

Neeti Pokhriyal (neeti@cs.ucr.edu), Office hours: Tuesdays 1-2pm

Office hours are held in Engineering 2, 110.
Facebook:

If you are a CS 141 F09 student, please join the CS 141 Facebook. We will use the facebook page to get in touch with students.

Lectures:

MWF 3:10-4:00, SPTH 1222

Labs:

(021) Mondays 08:10am-11:00 am ENGR2 135, Neeti

Text Book:

Algorithms, by Sanjoy Dasgupta, Christos Papadimitriou, Umesh Vazirani. McGrawHill 2007.

References:

Introduction to the Design and Analysis of Algorithms, 2nd edition, by A. Levitin.

Introduction to Algorithms (2nd Edition) by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Cliff Stein, MIT Press.

Algorithm Design (Foundations, Analysis, and Internet Examples) by Michael T. Goodrich and Roberto Tamassia, Wiley.

Approximate Time Requirements: This is a four-unit CS course. As such, you should expect to spend the following approximate amount of time: 3 hours/week in lecture
3 hours/week in lab
6 to 10 hours/week doing individual study (readings, homeworks, programming assignments, preparation for lectures, etc).
Please don't underestimate the time you will need to spend on this course. These are real time amounts spent by average successful past students. Computer Science and Engineering are challenging disciplines requiring extensive time to master.

Lectures

See the syllabus. Read the book before lecture! Reading ahead is one of the most effective ways of doing better in class -- you'll be amazed how much more comprehensible and useful the lectures will be. The schedule is subject to change as the quarter progresses.

Lecture Notes:

Course Overview [PDF]

Algorithm Analysis [PDF]

DivideConquer [PDF]

Greedy [PDF]

Dynamic Programming [PDF]

Graphs [PDF]

Weighted Graphs [PDF]

Tentative list of topics

week 1: Course overview, analysis of algorithms

week 2: Discrete math for algorithm analysis

week 3: Greedy approach for algorithm design

week 4: Divide and conquer, dynamic programming

week 5: Graphs, and their representation

week 6: Graph traversal, connectivity

week 7: Minimum cost spanning tree, single-source shortest path

week 8: All-pais shortest path

week 9: String matching, KMP

week 10: NP completeness, review

Actual list of topics

Sep 25th: Overview of the course, Algorithm Analysis [slides 1-18]
Sep 28th: Algorithm Analysis [slides 19-40]
Sep 30th: Algorithm Analysis [slides 40-52][HW1 posted]
Oct 2nd: Algorithm Analysis [slides 52-72]
Ocr 5th: Algorithm Analysis [slides 72-83]
Oct 7th: Algorithm Analysis [slides 84-91] [PRJ1 posted]
Oct 9th: Algorithm Analysis [slides 91-end], DivideConquer [1-9]
Oct 12: DivideConquer [10-27]
Oct 14: DivideConquer [27-end], Greedy [1-8] [HW1 due]
Oct 16: Greedy [9-29]
Oct 19: Greedy [30-42]
Oct 21: Greedy [43-end] [HW2 posted] [PRJ1 due]
Oct 23: Dynamic programming [1-18]
Oct 26: Prep for Quiz 1
Oct 28: QUIZ 1 (in class, closed book, closed notes) [PRJ2 posted]
Oct 30: Dynamic programming [18-35]
Nov 2: Quiz 1 review, Dynamic programming [36-40]
Nov 4: Dynamic programming [40-50] [HW2 due]
Nov 6: Dynamic programming [50-end], Graphs [1-19]
Nov 10: Graphs [20-45]
Nov 12: [HW3 posted] [PRJ2 due]
Nov 13: Graphs [46-84]
Nov 16: Prep for Quiz 2
Nov 18: QUIZ 2 (in class, closed book, closed notes) [PRJ3 posted]
Nov 20: Quiz 2 review, Graphs [85-end], overview of project 3
Nov 23: Weighted graphs [1-22]
Nov 25: Weighted graphs [23-48, skipped page 34] [HW3 due]
Nov 30: Weighted graphs [49-]
Dec 2: Weighted graphs [-] [PRJ3 due]
Dec 4: Weighted graphs [-], Prep for final
Dec 10th: Final 7-10 pm, Spieth Hall 1222

Do not put off reading the assignments or you will risk falling behind. Reading ahead is one of the most effective ways of doing better in class -- you'll be amazed how much more comprehensible and useful the lectures will be.

Homework

Homework 1 [PDF] (posted Sept 30th, due Oct 14th)
Homework 1 solution [PDF] (posted Oct 16th)
Homework 2 [PDF] (posted Oct 21st, due November 4th)
Homework 2 solution [PDF] (posted Nov 7th)
Homework 3 [PDF] (posted Nov 11th, due Nov 25th)
Homework 3 solution [PDF] (posted Nov 25)

Projects

Project 1: BigInt (posted Oct 7, due Oct 21)
Project 2 (posted May 2nd, due May 16th)
Test input for Project 2
Project 3 (posted Nov 19th, due Dec 2nd)

Quizzes

Labs

Week 1:
Introduction of TA; class accounts, Facebook
Debugging and testing in C++; working on Linux
Counting execution time of a program
Review of linear, tree and graphical data structures
Top-down design of algorithms; pseudo-code
Asymptotic (big-O, Theta) analysis of running time
More details

Week 2:
Preview the first homework.
More asymptotic analysis of algorithms
Treatment of for-loops using summation
Pointers and recursive algorithms/programs
More details

Week 3:
Preview the first programming assignment
Recurrence relations and analysis of recursive algorithms
Recursive programming - computing the Fibonacci function
More details

Week 4:
Review the first homework solution
Review the first programming assignment
Examples of the greedy method
Implementation of the task scheduling algorithm
More details
Source code

Week 5:
Preview the second homework
Go over some of the questions in the warm-up and in the mock exam
Start implementing 0-1 knapsack
More details

Week 6:
Address questions regarding the second homework
Address questions regarding Quiz 1
Preview the second programming assignment
Continue/finish implementation of 0-1 knapsack (dynamic programming) algorithm
More details

Week 7:
Go over the solution of the second homework
Address questions regarding the second programming assignment
Discuss data structures for graphs and digraphs
Implement DFS for undirected graphs
More details

Week 8: Preview the third homework
Implement topological sorting
More details

Week 9: Answer questions about the third homework
Answer questions about project 3
Discuss Dijkstra and Bellman-Ford algorithms
Implement transitive closure
More details

Week 10:
Answer questions about the third homework
Review the third homework solutions
Review for the final exam
More details

General course features and policies

Material covered: Lectures don't cover all required material, which also appears in the text and reference books and may be covered only in the lab sessions.
Academic dishonesty: Cheating will be strongly punished (typically with an F in the course). You can report cheating anonymously at: https://www.cs.ucr.edu/cheating/. Assignment submissions must represent your original work. Copying from any sources (web, other books, past or current students, etc.) is strictly prohibited -- learning to program requires you to learn to solve programming problems by yourself; using existing code and other resources becomes important later on in your studies. While discussing assignments together is encouraged, team coding (even of pseudo-code) or letting others see your code are specifically not allowed. Be aware that programs are automatically compared to the current and prior quarter's programs for plagiarism, using a sophisticated code-comparison tool that ignores insignificant differences (like whitespace and variable names). Be aware that a subset of exams may be photocopied, for comparison with exams submitted for regrades. Also, be aware that lying to an instructor in order to be able to makeup a missed exam or in other ways to obtain a better grade can be treated as academic dishonesty.
Regrade policy: Regrade requests must be submitted in writing and within two weeks of the distribution of the graded material. The entire homework/test/assignment may be regraded, not just the problem in question, so the grade may go up or down. Thus, think your regrade requests through carefully. To have a homework/test regraded, communicate with the TA(s) who graded the involved questions. For assignments, send email requests to the CS141 grader account (cs141-g) with detailed explanations. Recording errors should also be pointed out to TAs or the instructor before the last class.
Final grades: Per university policy, changes to your final grade will be made only in the event of a clerical error. Asking your instructor how far you were from a cutoff and what extra work you can do to improve the grade is not appropriate.
Communicating with the instructors and TAs: When sending electronic mail to the instructors or TAs or graders, please include your full name, student ID number, and UCR email address, so that we may properly identify you (remember, many students have similar names). Also, please try to be polite and use reasonable grammar and formatting.
Cell phones: During lectures and lab sessions, please turn off your cell phone. During exams, cell phones must not be visible (e.g., store them inside a backpack).
Written Assignments: All assignments and solutions will be posted on the class homepage. Write your full name with upper-case LAST name, assignment number, student ID, login, class section, lab section. Write legibly: what cannot be read will not be graded. Consider typing the assignment if you handwriting is hard to read. Written assignments have to be submitted before the beginning of the class on the due date on the instructor's desk. Include the following on first page ``I certify that this submission represent my own original work'' (date, signature). No late assignment will be accepted. Any problem with grading of a written assignment should be addressed at the latest two weeks after the assignment is returned to you.
Programming assignments: All programming assignments will be turned in electronically. Each file should begin with our report template header. Source code should comply with professionally accepted coding standards. In particular, all code must be compiled with the -Wall -W -Werror command line options, or else it will not be graded. Also, you must include a section explaining the design, structure, etc of your program and how it works. Comments in source code are important, but they are not a replacement for an explanatory section. Please remember that successfully writing a computer program that compiles and runs correctly is not something anyone can do in just a few minutes, even when it looks easy. Debugging typically takes three times longer than figuring out how to write your program. So don't leave your programming assignments to the last minute!
Reading: Read the current book sections before coming to lecture, and attend all lectures in their entirety. Feel free to ask questions.

Lab guidelines

Students are expected to attend the lab sessions in their entirety. The lab sessions typically consist of tutorials, discussions, reviews, practical exercises/tests, and time for students to work on homes and programming assignments.
During lab tutorial/discussion/review time, students should not use the computers. Ideally, students would move their chairs to the whiteboard area.
To reduce disruptions and provide for the best educational environment, all persons in lab during scheduled lab time should be formally registered in that section. In general, no swapping sections and no unregistered people in the lab are allowed, even if there are extra computers.

Grade mapping

The following mapping shows how your overall numerical scores will be translated into letter grades at the end of the quarter:

90+ -> A+
85+ -> A
80+ -> A-
77+ -> B+
73+ -> B
70+ -> B-
67+ -> C+
63+ -> C
60+ -> C-
57+ -> D+
53+ -> D
50+ -> D-
49- -> F