Ryan's Repository of Random Reflections

Dynamic Programming is a programming technique used to solve large, complex problems that have overlapping sub-problems. It does this by computing each sub-problem only once, ensuring non-exponential behavior. It differs from memoization (no, that's not a typo) in that instead of computing the result of a function then storing it in a cache, it instead generates a certain result before calculating any other result that relies on it. Dynamic Programming problems often require a different way of thinking than typical problems. Most often, dynamic programming becomes useful when you want to reverse engineer a tree recursive function. By that I mean you want to start of with the base case(s), then use those to generate the next few cases, and repeat this process until all points of interest have been calculated. Most often, at least in imperative languages, these dynamic programming algorithms are iterative, meaning they don't have recursive calls. That being said, it's still...

In this blog post, I will give some general background info about Markov Chains, a mathematical structure that arises in Probability theory. I was first introduced to Markov Chains last year and I found them extremely interesting. I found them particularly interesting because they combine ideas from linear algebra, graph theory, and probability theory. Markov chains have applications in many real world situations, including sabermetrics and stock market analysis. A Markov Chain can be thought of as a special type of graph or a non-deterministic finite automaton. Basically, that means that there are a set of states, and you can transition from a set of different states from any given state. The transitions are weighted by probability measures. There are lot's of interesting question you can ask about Markov chains - my favorite is the random walk type problems. Markov chains can be used to model a variety of real world situations, including baseball simulations, stock market behav...

The 2 biggest programming paradigms are functional and imperative, but they share very little in common. In this blog post, I will discuss the differences between the two as I understand them and talk about my experience with each. Functional programming languages are very mathematical - functions can be defined piece-wise, recursively, or in terms of other functions. They can also easily be combined forming composition of functions. Imperative languages use a slightly different approach - rather than telling the computer what something is, you must specify how to get it. The first functional programming language I learned was Racket, which is a similar to another functional programming language: Common Lisp. I always thought of Racket as a neat language with some nice abstractions but I always felt like it lacked some core functionality. Recently, however, my friend introduced me to Haskell, and I've been trying to pick it up.  Haskell is much more powerful than Racket, and it...

As a Junior in college, I need to start considering what I want to do after graduation. The two possibilities are to work in the corporate world and do application development or something of that flavor, or go to graduate school and pursue higher education. In this blog post, I will discuss my current understanding of some of the differences between the two, and I will share my knowledge/experience on the best way to prepare for each while in college. Working and the Corporate world is completely different than going to graduate school and pursuing a career in academia. Real world jobs have some nice virtues, but academia does as well. In my time here as a student, I have been involved in a lot of academic research, which is a very important thing to have when applying to graduate school. I also had a summer internship at JP Morgan last summer. I have had some experience from both sides of the spectrum and they are completely different. While I'm still undecided on what I want t...