Today marks the beginning of Spring Break for us and the midway point of the semester as well. This is most definitely the hardest semester I’ve ever had so far and although it’s very stressful at times, I’m really enjoying it a lot. I guess in some ways this semester was a way for me to determine how much I actually loved computer science at the its core – to see how much I enjoyed it if I fully immersed myself in it. The results so far are better than what I had expected: Even though the great majority of the my homework and assignments are about computer science and even though I am studying or attending class on some CS subject nearly every single day, my interest in it has only increased. I feel very at home when learning about the theoretical aspects of computation and complexity, or knee-deep in the analysis of some algorithm. Even though the workload is just so very barely manageable, I’m glad I am taking what I decided to take and I’d like, to the extent possible, to complete all six courses I am enrolled in this semester. Six courses is a bit on the high side for an engineer here, but I guess it’s more about how interesting you find them. As I’ve said before, it only feels like work if you can actually distinguish it from play. In any case, most of the work that I have fits comfortably in the category of play, so it’s basically an excuse to do something I’d probably be doing – or wishing I could be doing – during that time anyway.

I had a few goals for the year. One to take at least one graduate class before the end of the year; to study lesser known data structures and algorithms; to learn at least one more popular language and one more obscure language; and to get an introduction on quantum computation. Of those, I am in the process of accomplishing the first few of them. Thanks to my class on approximation algorithms, which is both tough and very enjoyable, I am satisfying the first one. In my Theory of Algorithms course, we are covering quite a few different interesting data structures as well as algorithms. Although these are not exactly “lesser known” in the greater sense of computer science, they’re also not the ones you usually learn in an introductory course. Specifically, I was very happy to see that we covered Fibonacci heaps, rank-pairing heaps, AVL trees, and RAVL trees. These, especially the first, are among the more advanced data structures used for very large data sets in industry. If I recall correctly, I believe Google Maps uses some implementation of F-heaps in storing data. In general, self-adjusting data structures are extremely interesting and it’s so neat to get a pretty bound on their performance regardless of how convoluted the implementation and procedures may be. As far as the other goals, I’m making (slow) progress on picking up Python and we’re using C++ for our computer graphics course. And as far as lesser known languages, our compilers course is taught strictly in ML – a functional language. I am yet to fully wrap my head around those exotic things. More thought is necessary on that front.

Although I’m satisfied with the coverage of algorithms for trees and graphs so far, I’m now also interested in learning about probabilistic algorithms. I was turned in this direction after seeing the power of probabilistic rounding techniques in solving LP-relaxation problems for approximation algorithms for very common problems such as vertex cover. Although they may not always provide an optimal bound, they do sometimes make the code and analysis much simpler and offer better worst-case performance. And they are everywhere too. From the example I just mentioned to an implementation of MST in linear time (!) in the number of edges, randomized and probabilistic algorithms play a huge role. And looking at it from a more pure sense, it’s also particularly interesting to see how the nature of random numbers can reveal insight on the structure of computation. I’m starting to develop some ideas of my own; perhaps the better word for them would be “questions” or maybe just situations and combinations of known elements in a way I haven’t seen anywhere. Hopefully I will soon be able to gain the mathematical and analytical machinery to actually see if these ideas make any sense at all.

In other news, my copy of Knuth’s classic Art of Computer Programming Vol. 1-4A finally arrived this week! As you may know, the fourth volume was just released after something like 38 years. I’m very excited to check it out. It’s currently sitting, still shrink-wrapped, on the table beside me. During the next week I’ll see if I can make sense of it. On another note, I am very pleasantly surprised by the quality of CLRS, another very excellent algorithms book. The analysis is surprisingly clean and the writing is precise, which makes it a pleasure to read. And they tackle complicated concepts with ease, making it look like it takes hardly any effort, which is impressive. Over winter break, I had started on Michael Sipser’s Introduction to the Theory of Computation, another classic text. It turns out this gave me a very nice edge when we were covering DFA/NFAs, regular expressions, and context-free grammars in compilers. Although I had had an introduction to these concepts in a previous course, I accredit Sipser’s book for teaching them to me in a rigorous sense. Once again, I see the same ease with with Sipser explains these concepts and proofs and it’s quite impressive. It makes it as painless as possible to cover these proofs with sufficient depth. So I will have to continue on with that text, seeing as it coincides very closely with what I am interested in. And as for the last goal, it is yet unfulfilled. I need a whole lot more knowledge before I go near that subject. Perhaps next semester or next spring. There’s just so much learn.