How does the DFA/NFA to Regular Expression work? (GNFA Conversion) - Easy Theory

Here we convert a simple NFA into a regular expression as easily as possible. We first modify the NFA so that there is a single start state with nothing going into it, and a single final state with nothing leaving it. Then we "rip" states out one at a time until we have just two states left, which contains the desired regular expression.
This is known as the "GNFA" (Generalized NFA) method.

Timestamps:
0:00 - Intro
0:39 - Overview of Steps
1:17 - Fix the NFA
2:10 - Start of Ripping States
2:39 - Rip q3
6:30 - Rip q2
9:10 - Rip q0
11:25 - Rip q1
13:05 - Conclusion

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▶ADDITIONAL QUESTIONS◀
1. Does the GNFA method truly work for ANY NFA?
2. What happens if we ripped the states out in a different order?

▶SEND ME THEORY QUESTIONS◀
ryan.e.dougherty@icloud.com

▶ABOUT ME◀
I am a professor of Computer Science, and am passionate about CS theory. I have taught over 12 courses at Arizona State University, as well as Colgate University, including several sections of undergraduate theory.

▶ABOUT THIS CHANNEL◀
The theory of computation is perhaps the fundamental
theory of computer science. It sets out to define, mathematically, what
exactly computation is, what is feasible to solve using a computer,
and also what is not possible to solve using a computer.
The main objective is to define a computer mathematically, without the
reliance on real-world computers, hardware or software, or the plethora
of programming languages we have in use today. The notion of a Turing
machine serves this purpose and defines what we believe is the crux of
all computable functions.

This channel is also about weaker forms of computation, concentrating on
two classes: regular languages and context-free languages. These two
models help understand what we can do with restricted
means of computation, and offer a rich theory using which you can
hone your mathematical skills in reasoning with simple machines and
the languages they define.

However, they are not simply there as a weak form of computation--the most attractive aspect of them is that problems formulated on them
are tractable, i.e. we can build efficient algorithms to reason
with objects such as finite automata, context-free grammars and
pushdown automata. For example, we can model a piece of hardware (a circuit)
as a finite-state system and solve whether the circuit satisfies a property
(like whether it performs addition of 16-bit registers correctly).
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build algorithms that check if a string parses according to this grammar.

On the other hand, most problems that ask properties about Turing machines
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This Youtube channel will help you see and prove that several tasks involving Turing machines are unsolvable---i.e., no computer, no software, can solve it. For example,
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