It’s over..
Hi all,
Final grades are now in: you can check them in the way you normally do. Here are some stats from HW6 and the final.
HW6:
Mean: 62.56
Median: 79.5
Max: 98
Final:
Mean: 66.8
Median: 68
Max: 100
Notes on the final
* A note on Q3 in the final. You will be able to give your answer using a from-first-principles explanation. you will NOT need to invoke theorems beyond the simple “X is NP-Complete” kind.
* On Q2, you are allowed super-linear preprocessing time (i.e you can sort if you want)
Homework 6, and stats for HW4/5
I’ve emailed the solutions for HW6 to the class mailing list. If you didn’t get it, please email me and I’ll send it to you directly.
HW4 Stats:
Mean: 74.18
Median: 85
Variance: 29.39
Max: 100
HW5 Stats:
Mean: 68.63
Median: 75
Variance: 31.49
Max: 100
Final exam
Posted on December 12th, 2008.Feedback…
Hi all,
Somehow the last class ended without my even realizing that it ended. Ah well. I forgot to discuss possibly the most important issue (from my perspective). I’d like feedback from you all on things that went well and things that didn’t go so well. Suggestions for improvements for the future would be welcome as well.
Just in case you were wondering whether I pay attention to suggestions, I’ll mention that a number of people last time requested programming assignments, which prompted me to introduce them this time. So I do listen :).
It’s been my pleasure to teach you all: you’ve definitely kept me on my toes throughout the semester.
Homework 6: a reprieve
In the spirit of the holiday, and since we’re already in an economic meltdown, I decided to introduce my own stimulus package, in the form of a blanket 2-day amnesty on homework 6. The new deadline is Friday 4:59pm, exactly one minute before the final is released. This applies across the board regardless of whether you’ve cashed your freebie or not.
This stimulus package is being funded via the unclaimed freebies from those of you who didn’t use them, and was passed by a unanimous voice vote of 1-0. Remember, the final starts on friday, so things will get worse before they get better. 
On homework 6
Update: this post is now moot. Please note the new deadline for Hw6.
Another poll for you all. Here’s the problem.
I would like to handout solutions for HW6 on wednesday. However, I can’t do this if people will opt for a freebie to submit late. In any case, the freebie would only extend to Friday when the final is handed out, and if people wished to use that option, I obviously could not give out solutions till all homeworks are in.
Just to be clear: you are only eligible for the freebie if you haven’t already claimed it. So if you’ve used your freebie, and are voting for a friday submission, you are helping your fellow classmates (which is nice) but not yourself 
Make your choice, and I’ll go with the majority vote.
n
Choices:
Lecture 25: Zero Knowledge Proofs
Today’s lecture is drawn from many sources. The basic idea of a zero knowledge proof was developed by Shafi Goldwasser, Silivio Micali (MIck Ali) and Charles Rackoff in a paper first published in 1985. The explanation of ZK proofs in terms of Ali Baba and the 40 thieves is drawn from this reference.
Sudoku (in its n X n variant) was shown to be NP-Complete in 2003. The physical proof I demonstrated is due to Gradwohl et al, and a fun variant of this proof that uses playing cards can be found at Moni Naor’s Sudoku page.
The Millionaire’s problems was first coined by Andy Yao, and the solution comes from his paper. The exact demonstration (and the use of the public-private key pair) comes from this site.
If you want to try this at home, this is a useful RSA encryption applet
Lecture 24: Heuristics
Notes and reading material from today’s lecture are linked here. In addition, here’s some research by Adam Teichert (thanks Adam !) on the origin of the Metropolis-Hastings algorithm:
After looking at Wikipedia for a refresher (and doing a bit of other searching), it looks like some people do refer to the Metropolis-Hastings algorithm as just the Metropolis algorithm, however some people (at least) make a distinction between the two:
Wikipedia says, “The original Metropolis algorithm calls for the proposal density to be symmetric ( Q(x;y) = Q(y;x) ); the generalization by Hastings lifts this restriction.”
This pdf describes the difference between implementing Metropolis and Metropolis-Hastings in R:
http://nitro.biosci.arizona.edu/courses/EEB581-2006/IntroR/Metropolis.pdfFinally, I noticed that the notes in the book say that the simulated annealing was based on the 1953 work of Metropolis et al. Wikipedia tells us that Hastings generalized the Metropolis algorithm
in 1970. (http://probability.ca/hastings/).
Lecture 23: Examples
We went through a series of examples of randomization.
- Randomized median finding: how to apply randomization to divide and conquer
- Randomized MAX 3SAT: The probabilistic method, and designing an approximation algorithm that uses randomness
- The Birthday Paradox: we analyzed the well known phenomenon, and connected it to a problem in hashing (how many items do you need to guarantee a collision in n buckets).
The first two topics were taken from the textbook, and the last topic is covered in the linked notes.
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