There are two versions of the Pumping Lemma. One is for context free grammars and one is for regular languages. This post is about the latter. The Pumping Lemma describes a property that all natural languages share. While it cannot be used by itself to prove that any given language is regular, it can be used to prove, often using proof by contradiction, that a language is not regular. In this sense the Pumping Lemma provides a necessary condition for a language to be regular but not a sufficient one.

Am I sufficient? Am I even necessary? If you’re plagued by these existential questions and have ended up here in your quest for an answer, then I’ve got some bad news for you. The answer to both is no. I keed. I keed. Actually, the bad news is that this post is about necessity and sufficiency and their rigid definitions in the domain of mathematical logic. If you are only interested in looking for meaning in your life then move along, but if you are one of the enlightened few that knows that math really is the answer to every question, then by all means, read on!

Finite state automata (FSA), also known as finite state machines (FSM), are usually classified as being deterministic (DFA) or non-deterministic (NFA). A deterministic finite state automaton has exactly one transition from every state for each possible input. In other words, whatever state the FSA is in, if it encounters a symbol for which a transition exists, there will be just one transition and obviously as a result, one follow up state. For a given string, the path through a DFA is deterministic since there is no place along the way where the machine would have to choose between more than one transition. Given this definition it isn’t too hard to figure out what an NFA is. Unlike in DFA, it is possible for states in an NFA to have more than one transition per input symbol. Additionally, states in an NFA may have states that don’t require an input symbol at all, transitioning on the empty string ε.

Superficially it would appear that deterministic and non-deterministic finite state automata are entirely separate beasts. It turns out, however, that they are equivalent. For any language recognized by an NFA, there exists a DFA that recognizes that language and vice versa. The algorithm to make the conversion from NFA to DFA is relatively simple, even if the resulting DFA is considerably more complex than the original NFA. After the jump I will prove this equivalence and also step through a short example of converting an NFA to an equivalent DFA.

In my last post, “Kleene’s Theorem,” I provided some useful background information about strings, regular languages, regular expressions, and finite automata before introducing the eponymously named theorem that has become one of the cornerstones of artificial intelligence and more specifically, natural language processing (NLP). Kleene’s Theorem tells us that regular expressions and finite state automata are one and the same when it comes to describing regular languages. In the post I will provide a proof of this groundbreaking principle.

Stephen Cole Kleene was an American mathematician who’s groundbreaking work in the sub-field of logic known as recursion theory laid the groundwork for modern computing. While most computer programmers might not know his name or the significance of his work regarding computable functions, I am willing to bet that anyone who has ever dealt with regular expressions is intimately familiar with an indispensable operator that resulted directly from his work and even bears his name, the *****, or as it is formally known, the **Kleene star**.

While his contributions to computer science in general cannot be overstated, Kleene also authored a theorem that plays an important role in artificial intelligence, specifically the branch known as natural language processing, or NLP for short. Kleene’s Theorem relates regular languages, regular expressions, and finite state automata (FSAs). In short, he was able to prove that regular expressions and finite state automata were the same thing, just two different representations of any given regular language.

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As a computer programmer for more than a quarter of century, I don’t think I have ever thought much about strings. I knew the basics. In every language I’d worked with, strings were a data type unto themselves. Superficially they are a sequence of characters, but behind the scenes, computers store and manipulate them as arrays of one or more binary bytes. In programs, they can be stored in variables or constants, and often show up in source code as literals, ie., fixed, quoted values like “salary” or “bumfuzzle.” (That is my new favorite word, btw.) Outside of occasionally navigating the subtleties of encoding and decoding them, I never gave strings a second thought.

Even when I first dipped my toe into the waters of natural language processing, aka NLP (not to be confused with the quasi-scientific neuro linguistic programming which unfortunately shares the same acronym), I still really only worked with strings as whole entities, words or affixes, As I made my through familiarizing myself with existing NLP tools, I didn’t have to dive any deeper than that. It was only when I started programming my own tools from the ground up, did I learn about the very formal mathematics behind strings and their relationship to sets and set theory. This post will be an attempt to explain what I learned.

**Boolean functions**, sometimes also called switching functions, are functions that take as their input zero or more boolean values (1 or 0, true or false, etc.) and output a single boolean value. The number of inputs to the function is is called the arity of the function and is denoted as *k*. Every *k*-ary function can be written as a propositional formula, a sentence in propositional logic. A binary Boolean function, a Boolean function with two arguments, can be described by one out of sixteen canonical formulas.

An **arithmetic sequence** of numbers, sometimes alternatively called an **arithmetic progression**, is a sequence of numbers in which the difference between all pairs of consecutive numbers is constant. A very simple arithmetic sequence consists of the natural numbers: 1, 2, 3, 4, … where the difference between any number and the number before it is just one. 3, 7, 11, 15, 19, …. is another arithmetic sequence, but in this case the constant difference between elements is four.

A finite portion of an arithmetic sequence like 2, 3, 4 or 7, 11, 15 is called a **finite arithmetic progression**. To confuse matters, sometimes a finite arithmetic progression, like an arithmetic sequence, is also called an arithmetic progression. To be safe, when a progression is finite, I always say as much.

An **arithmetic series** is the sum of a finite arithmetic progression. An arithmetic series consisting of the first four natural numbers is 1 + 2 + 3 + 4. The sum, 10, is trivial to compute via simple addition, but for a longer series with larger numbers, having a formula to calculate the sum is indispensable.

Abraham Lincoln once famously said, “Everybody loves a compliment.” I suspect that if he had been a mathematician he would have loved complements, too. We’ve already seen what complements are and talked about the two most prolific: the radix complement and the diminished radix complement. Now it’s time to explore how we can leverage complements to do some really interesting integer arithmetic. Using complements we can subtract one positive integer from another or add a negative integer to a positive one by simply performing addition with two positive integers. The algorithm behind this black magic is called the **Method of Complements**.

In my last post about binary signed integers, I introduced the ones complement representation. At the time, I said that the ones complement was found by taking the **bitwise complement** of the number. My explanation about how to do this was simple: invert each bit, flipping 1 to 0 and vice versa. While it’s true that this is all you need to know in order to determine the ones complement of a binary number, if you want to understand how computers do arithmetic with signed integers and why they represent them the way they do, then you need to understand what complements are and how the method of complements allows computers to subtract one integer from another, or add a positive and negative integer, by doing addition with only positive integers.