Lecture 1 : Speedup theorem
Outline
We’ll see complexity classes, such as:
 SPACE classes
 Polynomial time hierarchy

Probabilistic classes

TM that are probabilistic instead of being nondeterministic. Acceptance is made with some probability.

Nondeterministic: you want one accepting run VS probabilistic one with a certain probability ⟶ you either do many runs to have the right answer, OR run on many machines

Reminder: Turing Machines ( TM )
Useful for lowlevel complexity: match the idea of a theoretical computers.
A TM $M$ is given by:

Finitely many states: $Q$, among which:
 a starting state $q_0$
 final states $F ⊆ Q$
 rejecting states $R⊆ Q$
 a finite alphabet: denoted by $Σ, Γ, \ldots$
 the symbols $$$ (resp. $B$ (blank), or $\square$) is used to express the fact that we’re at the beginning (resp. at the end) of the written tape

a reading/writing head
 based on the current state and what the head sees, the TM can change the head’s direction and the state

rules of behavior \(Δ : Q × Σ ∪ \lbrace \$, B \rbrace ⟶ Q × Σ × \lbrace ←, → \rbrace\)

A configuration of $M$ is some $C ∈ Q × (Σ^\ast ∪ \lbrace B \rbrace) × ℕ$

for instance: $C ≝ (q, ab01, 2)$ means that we’re on $b$, in the state $q$

OR: we could write $C = ab, q, 01$ alternatively

from $Δ$ we get $C ⟶ C’$ (for instance)
More general: Nondeterministic TM
Instead of having a function $Δ : Q × Σ ∪ \lbrace $, B \rbrace ⟶ Q × Σ × \lbrace ←, → \rbrace$, one have a relation \(Δ ⊆ \Big(Q × Σ ∪ \lbrace \$, B \rbrace \Big) × \Big(Q × Σ × \lbrace ←, → \rbrace \Big)\)
Going from $C$ to $C’$ is a step.
 A run of TM:

is a sequence $C_0 ⟶ ⋯ ⟶ C_n ⟶ ⋯$ (it can be infinite) where $C_0 ≝ (q_0, x, 0)$ ($x$ is the input of the TM)
 The run $C_0 ≝ (q_0, x, 0) ⟶ ⋯ ⟶ C_n ≝ (q, x’, m)$ accepts/recognizes $x$:

if $q ∈ F$ ($x$ is then said to be accepted/recognized).
For a nondeterministic TM, $x$ is accepted iff there exists a run that accepts $x$.
On can also have several tapes: as it happens

there are one input tape, $k$ working tapes, and one output tape
 the length of $x$ doesn’t count as used space, it’s supposed given

one never writes on the input tape

one never reads on the output tape, one only writes (the head can only go to the right, on the printer)
Not moving the head

for one tape, it doesn’t change anything

for several tapes, it’s not trivially the case: if you just want stay put on the first tape only and you have two tapes, you can’t for parity reasons.

but all these models are “more or less equivalent”, that is, when it comes to the asymptotic complexity, it doesn’t change anything.
Space complexity
Let $f: ℕ ⟶ ℕ$.
 A Language $L$ is in $SPACE(f(n))$:

if there exists a TM $M$ s.t.

$M$ recognizes $L$
 so $L$ is on the fixed alphabet of $M$
 $M$ is deterministic
 for every word $x ∈ Σ^\ast$, $M$ halts using at most $f(\vert x \vert)$ space (that is, we add the space used on both tapes)

 A Language $L$ is in $NSPACE(f(n))$:

if there exists a TM $M$ s.t.

$M$ recognizes $L$
 so $L$ is on the fixed alphabet of $M$
 $M$ is NONdeterministic
 for every word $x ∈ Σ^\ast$, $M$ halts using at most $f(\vert x \vert)$ space (that is, we add the space used on both tapes)

NB: an finite automaton can be seen as a TM using NO working space whatsoever (since everything is stored in the sates).
So regular languages are in $SPACE(0)$
And any TM using no working space is a 2way automaton (it reads back on the input tape), and it is as expressive as a finite automaton.
So
\[REG = SPACE(0)\]
And even
\[REG = SPACE(k)\]
NB: pebble automata: more expressive than finite automata.
Speedup Theorem
Theorem: \(SPACE(O(f(n))) ≝ \bigcup\limits_{k ∈ℕ} SPACE(k f(n)) ⊆ SPACE(f(n))\)
Proof:
Let $L$ be a language in $SPACE(k f(n))$. It can recognized by a new TM whose alphabet is comprised of $k$tuples of letters.
Likewise:
Theorem: \(TIME(O(f(n))) ⊆ TIME(f(n) + n + 2)\)
Both of these theorems are true for nondeterministic TM as well (put a ‘N’ before each class).
\[NL ≝ NSPACE(\log(n))\]
In logarithmic space, one can

count the length of the input

recognize $L ≝ \lbrace a^n b^n \mid n ∈ ℕ \rbrace$, for instance, in $2 \log (\vert x\vert)$ (but with the speedup theorem, there’s no problem)

\(REACH ∈ NL\) (a.k.a. $GAP$ (Graph Accessibility Problem)): is there a path from one node to another in a directed graph (DAG)
where $s$ (source) and $t$ (target) and the triples are written in a specified form.
But: we have to
 use pointers to refer to nodes
 use a decrementing counter of the number of edges to avoid cycles
 use nondeterminism to guess a path
We can even do that in $SPACE(\log^2(n))$
# is there a path from s to t of length ≤ 2^p
reach(s, t, p) =  yes if s=t or s→t
 no if p=0
 OR_{for each node v} (reach(s, v, p1) ∧ reach(v, t, p1))
call reach(s, t, log number of edges)
There are a logarithmic number of recursive calls, each of which takes logarithmic space.
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