CS 312 Spring 2006

Final Grades

Everything has been graded, so the total percentage on Blackboard is your final grade, minus the .5 percent per early project. So you can add .5 * the number of early projects (listed on Blackboard as well) to your total percentage to get the percentage of your final grade. You can then match up the percentage to the values listed on the syllabus to get what should be your final letter grade, unless Tonga decides to slide the scale any (I think it only goes lower, so you should at least get the grade on the syllabus, if not better).

Thanks for being such great students, it has been fun being your TA. Good luck in the future!

project 7 FYI

I have recieved project 7 from most of you (including those sent to the cs312ta@cs.byu.edu email account). However, I'm waiting for a clarification about the answer to the problem, so it probably won't be graded until I get that clarification (hopefully tomorrow).

Homework Keys 13-15

Here are the keys for homeworks 13 - 15
Homework 13 RTF
Homework 14 RTF
Homework 15 RTF

Final Exam Study Guide

Here is a list of things you might want to study in order to be well prepared for the final exam:

Make sure you understand the concepts from the homeworks
Understand the minimax algorithm.
How to prove that a problem is NP complete.
Floyd's algorithm / shortest paths
Principle of Optimality
Deriving the big-Theta bounds for certain kinds of divide-and-conquer algorithms using the methods for solving recurrence relations covered in class. In particular, be sure to be able to do the first problem in the last homework assignment on recurrences. This will involve knowing some logarithm and exponent identities.
How to turn a problem description into a linear programming problem using standard form, slack form, or simplex form (know all three ways).
Understand the various knapsack problems
Understand how depth-first and breadth-first search work.
Design decisions in creating and implementing branch and bound algorithms.
Basics of solving the TSP with a branch and bound algorithm.
How to compute amplification of stochastic advantage for repeated Monte Carlo algorithms.
How to compute speedup and efficiency for parallel algorithms and how to tell if a parallel algorithm is good or not.

If you study these topics, and understand them, you should be ready for the final.

Lecture 21

Homework 15

The very last homework! Congratulations on enduring to the end.

1. Problem 11.4
2. Suppose you have a parallel algorithm that runs in O(T(n,p)) time for T(n,p) = log n for a problem of size n using p processors. Suppose that the best known sequential algorithm solves problems of size n in O(S(n)) time for S(n) = n log n. What is the speedup for this algorithm and what is the efficiency? Did this algorithm achieve linear speedup?

Due Monday, June 19, 2006

Lecture 20

Lecture 20
Lecture 20 part 2

Partial Grades

I calculated grades up until project 6, so they don't include the last 2 homeworks, project 7, or the final exam. The total out of the 100 grade percentage points was 72.9. The items not included complete the remaining 27.1 percent of the final grade. So if you want to see how you are doing, take your Current Grade score from blackboard and divide it by 72.9 to get your score (minus the last 2 homeworks, project 7, and the final which haven't been scored yet). I'll try to update the Current Grade score as things get turned in and graded.

Reminder

Everything needs to be turned in by Monday, June 19, Midnight. If you do not have enough time to complete lab 5, then talk to Ryan so that you can get a working copy to complete lab6. You can also talk to Ryan about possible partial credit for your lab5.

project 7

Please make sure to read the linear programming handout. There is some helpful information on it about implementation of the project 7. Also the class link from the previous semester

http://vv.cs.byu.edu/cs312/archives/old_project_info/project_7/index.html

might be helpful too.

Good Luck.

tonga

lecture 19

Lecture 19
Lecture 19 part 2

Homework 14

Homework in Section 3.3 of the Linear Programming Handout

due June 16, 2006

Keys for homeworks 8 - 12

Here are the keys for homeworks 8 - 12
Homework 8 RTF
Homework 9 RTF
Homework 10 RTF
Homework 11 RTF
Homework 12 RTF

Lecture 18

Lecture 18
Lecture 18 part 2
Lecture 18 part 3

Homework 13

Homework in Section 1.1 of the Linear Programming Handout
Homework in Section 2.3 of the Linear Programming Handout

Due June 14, 2006