Chris Pollett > Old Classses >
CS255

( Print View )

Student Corner:
  [Grades Sec1]

  [Submit Sec1]

  [
Lecture Notes]

  [Discussion Board]

Course Info:
  [Texts & Links]
  [Topics/Outcomes]
  [Outcomes Matrix]
  [Grading]
  [HW/Quiz Info]
  [Exam Info]
  [Regrades]
  [Honesty]
  [Additional Policies]
  [Announcements]

HWs and Quizzes:
  [Hw1]  [Hw2]  [Hw3]
  [Hw4]  [Hw5]  [Quizzes]

Practice Exams:
  [Mid]  [Final]

                           












CS255 Spring 2015 Sec1 Home Page/Syllabus

Design and Analysis of Algorithms

Instructor: Chris Pollett
Office: MH 214
Phone Number: (408) 924 5145
Email: chris@pollett.org
Office Hours: MW 4:15pm-5:30pm
Class Meets:
Sec1 MW 3:00pm-4:15pm in MH223

Prerequisites

To take this class you must have taken: CS155 with a grade of C- or better.

Texts and Links

Required Texts: Introduction to Algorithms, 3rd Ed.. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein
Randomized Algorithms. Rajeev Motwani and Prabhakar Raghavan
Online References and Other Links: Java Website.
Java APIs.
MathJax.
AsciiMathML Syntax.

Topics and Outcomes

This course covers the basics of randomized algorithms, parallel algorithms, distributed algorithms, algorithms related to the theory of NP-completeness, and approximation algorithms. Randomized algorithms are algorithms which make use of a random number generator. For instance, one fast way to check if a number is prime makes of such a generator. Parallel algorithms are algorithms which are designed to be partitionable with minimal overhead among many processors who share a global clock. Distributed algorithms are algorithms designed to work on multiple processors which don't share a global clock. An example situation might be to get an algorithm to get a bunch of computers on a network to agree on a common value. NP problems are languages with polynomial time proofs of membership. For instance, given a potential factorization of a number we can in polynomial time check whether it is correct. NP-complete problems are problems in NP to which any other problem in NP can polynomially time reduced. We will consider different algorithms for during this kind of reduction. Approximations algorithms are usually efficient algorithms which approximately solve some optimization problem which is not known to have a efficient solutions. For instance, one might have an algorithm which approximately finds a traveling salesman tour. In addition to these algorithms, we will also go over the computer algebra algorithms neccessary to do basic cryptographic protocols such as RSA. By the end of this course, you should be able to code one example of a randomized algorithm, parallel algorithm, distributed algorithm, a polynomial time reduction, an approximation algorithm, and a computer algebra algorithm. For each of these types of algorithms, by the end of this course you should be able: LO1 -- Analyze or code a randomized algorithm, LO2 -- Analyze or a code a parallel algorithm using Java Threads, LO3 -- Analyze or code a parallel algorithm using a library such as OpenCL, LO4 -- Analyze the correctness and run time of a distributed algorithm, LO5 -- Ananlyze or code a number theoretic algorithm, LO6 -- Given a problem determine within NP that is promised to be either in P or NP-complete prove which, LO7 -- Analyze or code an approximation algorithm for a optimization problem whose decision problem is NP-complete.

Below is a tentative time table for when we'll do things this quarter:

Week 1: Jan 26 Jan 28 Appendix C, Start Chapter 5 Probabilistic and Randomized Algorithms (CLRS)
Week 2: Feb 2 , Feb 4 Finish Ch5, Start Ch 27 Multithreaded Algorithms (CLRS)
Week 3: Feb 9 , Feb 11 (HW1 due) Finish Ch27
Week 4: Feb 16 , Feb 18 Read Ch 12 Parallel and Distributed Algorithms (MR)
Week 5: Feb 23 , Feb 25 Map Reduce Paper, Read Ch 13 Online Algorithms (MR)
Week 6: Mar 2 , Mar 4 Start Ch 31 (CLRS) and Ch 14 (MR) Number Theoretic Algorithms
Week 7: Mar 9 (HW2 due) , Mar 11 Finish Ch 31
Week 8: Mar 16 , Mar 18 (Midterm) Review, Midterm
Week 9: Mar 23 , Mar 25 Spring Recess
Week 10: Mar 30 , Apr 1 Ch 34 NP, and NP-completeness (CLRS)
Week 11: Apr 6 (HW3 due) , Apr 8 Ch 34 NP-Completeness (CLRS)
Week 12: Apr 13 , Apr 15 Finish Ch 34
Week 13: Apr 20 (HW4 due) , Apr 22 Start Ch 5 The Probabilistic Method (MR)
Week 14: Apr 27 , Apr 29 Finish Ch 5 MR
Week 15: May 4 , May 6 Start Ch 35 Approximation Algorithms
Week 16: May 11 (Hw5 due) , May 13 Finish Ch 35, Review
The final will be 12:15pm-2:30pm, Friday, May 15

Grading

Homeworks and Quizzes 50%
Midterm 20%
Final 30%
Total100%

Grades will be calculated in the following manner: The person or persons with the highest aggregate score will receive an A+. A score of 55 will be the cut-off for a B-. The region between this high and low score will be divided into eight equal-sized regions. From the top region to the low region, a score falling within a region receives the grade: A, A-, B+, B, B-. If the boundary between an A and an A- is 85, then the score 85 counts as an A-. Scores below 55 but above 50 receive the grade D. Those below 50 receive the grade F.

If you do better than an A- in this class and want me to write you a letter of recommendation, I will generally be willing provided you ask me within two years of taking my course. Be advised that I write better letters if I know you to some degree.

Homework and Project Info

This semester we will have five homeworks and weekly quizzes. Every Monday that we meet this semester, except the first day of class; there will be a quiz on the previous week's material. The answer to the quiz will either be multiple choice or true-false, and will basically make sure you reviewed the previous weeks notes. Each quiz is worth a maximum of 1pt. Out of the total of thirteen quizzes this semester, I will keep your ten best scores.

Links to the current list of homeworks, quizzes, and projects can be found on the left hand frame of the class homepage.

After an homework has been returned a link to its solution (based on the best student solutions) will be placed off the homework page. Material from assignments may appear on midterms and finals. For homeworks you are encouraged to work in groups of up to three people. Only one person out of this group needs to submit the homework assignment; however, the members of the group need to be clearly identified in all submitted files. Homeworks for this class will be submitted and returned completely electronically. To submit an assignment click on the submit homework link for your section on the left hand side of the homepage and filling out the on-line form. Hardcopies or e-mail versions of your assignments will be rejected and not receive credit. Homeworks will always be due by the start of class on the day their due. Late homeworks will not be accepted and missed quizzes cannot be made up; however, your lowest score amongst the five homeworks and your quiz total will be dropped.

When doing the programming part of an assignment please make sure to adhere to the specification given as closely as possible. Names of files should be as given, etc. Failure to follow the specification may result in your homework not being graded and you receiving a zero for your work.

Exams

The midterms will be during class time on: Mar 18.

The final will be: 12:15pm-2:30pm, Friday, May 15.

All exams are closed book, closed notes and in this classroom. You will be allowed only the test and your pen or pencil on your desk during these exams. Beeper or cell-phone interruptions will result in immediate excusal from the test. The final will cover material from the whole quarter although there will be an emphasis on material after the last midterm. No make ups will be given. The final exam may be scaled to replace a midterm grade if it was missed under provably legitimate circumstances. These exams will test whether or not you have mastered the material both presented in class or assigned as homework during the quarter. My exams usually consist of a series of essay style questions. I try to avoid making tricky problems. The week before each exam I will give out a list of problems representative of the level of difficulty of problems the student will be expected to answer on the exam. Any disputes concerning grades on exams should be directed to me, Professor Pollett.

Regrades

If you believe an error was made in the grading of your program or exam, you may request in person a regrade from me, Professor Pollett, during my office hours. I do not accept e-mail requests for regrades. A request for a regrade must be made no more than a week after the homework or a midterm is returned. If you cannot find me before the end of the semester and you would like to request a regrade of your final, you may see me in person at the start of the immediately following semester.

Academic Honesty

Your own commitment to learning, as evidenced by your enrollment at San Jose State University, and the University's Academic Integrity Policy requires you to be honest in all your academic course work. Faculty members are required to report all infractions to the Office of Student Conduct and Ethical Development. The policy on academic integrity can be found at http://www.sjsu.edu/studentconduct/.

Specifically, for this class, you should obviously not cheat on tests. For homeworks, you should not discuss or share code or problem solutions between groups! At a minimum a 0 on the assignment or test will be given. A student caught using resources like Rent-a-coder will receive an F for the course and be referred to University for disciplinary action.

Additional Policies and Procedures

I have created a discussion group for this class which I encourage students to use for asking questions that others might benefit from knowing the answer to. If you know the answer to a question, and the answer does not involve sharing a program solution feel free to answer it on the board. If possible I will answer questions through the board as well. Let's keep the tone on the board positive and encouraging. I will monitor posts and reserve the right to deny posting privileges if message tones become uncivil.

The campus policy to ensure compliance with the Americans with Disabilities Act is:
"If you need course adaptations or accommodations because of a disability, or if you need special arrangements in case the building must be evacuated, please make an appointment with me as soon as possible, or see me during office hours. Presidential Directive 97-03 requires that students with disabilities register with
Accessible Education Center to establish a record of their disability."

More information about SJSU policies and procedures can be found at the following links: