1. Regular Expressions: Review
A FA for a regular expressions can be built by composition:
Ex: all strings over S={a,b} where $ a “b” preceding an “a”
(a+b)*b(a+b)*a(a+b)*
= (a+b)*ba(a+b)*
Why? b a e e e a a e e b a e e e e e e e b e e b e e b a e b a e
Remove previous start/final states

2. FA Minimization: Review
Idea: “Equivalent” states can be merged: b a e
16 states!
merge
merge b a e e b a
a,b b a
a,b e b a
a,b
3 states!

3. FA Minimization: Review
Theorem [Hopcroft 1971]: the number N of states in a FA
can be minimized within time O(N log N).
Based on earlier work by [Huffman 1954] and [Moore 1956].
Conjecture: Minimizing the number of states in a
nondeterministic FA can not be done in polynomial time.
Theorem: Minimizing the number of states in a pushdown
automaton (or TM) is undecidable.
Project idea: implement a finite automaton minimization tool.
Try to design it to run reasonably efficiently.
Consider also including:
A regular-expression-to-FA transformer,
A non-deterministic-to-deterministic FA converter.

4. FAs and Regular Expressions: Review
Theorem: Any FA accepts a language denoted by some RE.
Proof: Use “generalized finite automata” where each transition
can be a regular expression (not just a symbol), and:
Only one super start state and one (separate) super final state.
Every state has transitions to all other states (including itself),
except the super start state, with no incoming transitions,
and the super final state, which has no outgoing transitions. M M M’ e Ø e e Ø Ø e e Ø Ø e Ø Ø e e Ø Ø e
Original FA M
Generalized FA (GFA) M’

5. FAs and Regular Expressions: Review
Now reduce the size of the GFA by one state at each step.
A transformation step is as follows: P P
P + RS*T qi qj qi qj qi qj R T q’
RS*T S
Such a transformation step is always possible, until the GFA
has only two states, the super-start and super-final states: M’ P
Label of last remaining transition is
the regular expression corresponding
to the language of the original FA!
Corollary: FAs and REs denote the same class of languages.

6. Regular Expressions Identities: Review
R+S = S+R
R(ST) = (RS)T
R(S+T) = RS+RT
(R+S)T = RT+ST
Ø* = e* = e
R+Ø = Ø+R = R
Re = eR = R
(R*)* = R*
(e + R)* = R*
(R*S*)* = (R+S)*
R+e ≠ R
RØ ≠ R

7. Extra credit: use this tool!
(to implement some nontrivial
TMs, PDAs, grammars, etc.)

12. Why Study Non-determinism?
1. Helps understand the ubiquitous concept of
parallelism / concurrency;
2. Illuminates the structure of problems;
3. Can help save time & effort by solving
intractable problems more efficiently;
4. Enables vast, deep, and general studies of
“completeness” theories;
5. Helps explain why verifying proofs & solutions
seems to be easier than constructing them;

13. Why Study Non-determinism?
6. Gave rise to new and novel mathematical
approaches, proofs, and analyses;
7. Robustly decouples / abstracts complexity from
underlying computational models;
8. Gives disciplined techniques for identifying
“hardest” problems / languages;
9. Forged new unifications between
computer science, math & logic;
10. Non-determinism is interesting
fun, and cool!

16. Problem: compute 1111111112 in your head.= ´

17. Problem: What is the approximate value of:
(1+9^(-(4^(7*6))))^(3^(2^85)) ≈ ?
= e to 18,457,734,525,360,901,453,873,570 digits of precision! = 85 ) ( 3 2
6*7 9
-(4 ) = 85 1 ) ( 3 2
6*7 9 4 N
= ? 85 1 ) ( 3 2 N

18. Problem: Does the Pythagorean theorem generalize to arbitrary figures on the sides of a right triangle?

19. Problem: Does every closed simple curve contain the vertices of an equilateral triangle?
What approaches fail?
What techniques work and why?
Lessons and generalizations

20. Problem: Can an 8x8 board with two opposite corners missing be tiles with 31 dominoes?
What approaches fail?
What techniques work and why?
Lessons and generalizations

21. Problem: Explain the apparent discrepancy between the areas of the two arrangements.5 5 13 5 13
What approaches fail?
What techniques work and why?
Lessons and generalizations

22. Problem: Explain the apparent discrepancy between the areas of the three arrangements.
What approaches fail?
What techniques work and why?
Lessons and generalizations

24. Turing Machine “Enhancements”
Larger alphabet:
old: Σ={0,1} new: Σ’ ={a,b,c,d}
Idea: Encode larger alphabet using smaller one.
Encoding example: a=00, b=01, c=10, d=11
old: δ b b a d c 1 1
new: δ'

25. Turing Machine “Enhancements”
Double-sided infinite tape: 1 1 1 1
Idea: Fold into a normal single-sided infinite tape 1 1 1 1 1 1 1
old: δ
L/R
new: δ'
L/R
R/L

26. Turing Machine “Enhancements”
Multiple heads: b b a b a b b a a
Idea: Mark heads locations on tape and simulate B B b a b A A a b b B B A
Modified δ' processes each “virtual” head independently:
Each move of δ is simulated by a long scan & update
δ' updates & marks all “virtual” head positions

27. Turing Machine “Enhancements”
Multiple tapes: 1 1 1 1 1 1 1 1 1 1 1
Idea: Interlace multiple tapes into a single tape
Modified δ' processes each “virtual” tape independently:
Each move of δ is simulated by a long scan & update
δ' updates R/W head positions on all “virtual tapes”

28. Turing Machine “Enhancements”
Two-dimensional tape:
This is how compilers implement
2D arrays! 1 1 1 1 1 1
Idea: Flatten 2-D tape into a 1-D tape $
Modified 1-D δ' simulates the original 2-D δ:
Left/right δ moves: δ' moves horizontally
Up/down δ moves: δ' jumps between tape sections

29. Turing Machine “Enhancements”
Non-determinism: 1 1 1 1 1 1 1
Idea: Parallel-simulate non-deterministic threads $ $ $
Modified deterministic δ' simulates the original ND δ:
Each ND move by δ spawns another independent “thread”
All current threads are simulated “in parallel”

30. Turing Machine “Enhancements”
Combinations: 9 . 1 4 5 3 W o l ! d r H e 3 ND Π α ω ν λ τ
Idea: “Enhancements” are independent (and commutative WRT preserving the language recognized).
Theorem: Combinations of “enhancements” do not increase the power of Turing machines.