Sipser–Lautemann theorem

Template:Short description In computational complexity theory, the Sipser–Lautemann theorem or Sipser–Gács–Lautemann theorem states that bounded-error probabilistic polynomial (BPP) time is contained in the polynomial time hierarchy, and more specifically Σ₂ ∩ Π₂.

In 1983, Michael Sipser showed that BPP is contained in the polynomial time hierarchy. Péter Gács showed that BPP is actually contained in Σ₂ ∩ Π₂. Clemens Lautemann contributed by giving a simple proof of BPP’s membership in Σ₂ ∩ Π₂, also in 1983. It is conjectured that in fact BPP=P, which is a much stronger statement than the Sipser–Lautemann theorem.

Proof

Here we present the proof by Lautemann. Without loss of generality, a machine M ∈ BPP with error ≤ 2^−|x| can be chosen. (All BPP problems can be amplified to reduce the error probability exponentially.) The basic idea of the proof is to define a Σ₂ sentence that is equivalent to stating that x is in the language, L, defined by M by using a set of transforms of the random variable inputs.

Since the output of M depends on random input, as well as the input x, it is useful to define which random strings produce the correct output as A(x) = {r | M(x,r) accepts}. The key to the proof is to note that when x ∈ L, A(x) is very large and when x ∉ L, A(x) is very small. By using bitwise parity, ⊕, a set of transforms can be defined as A(x) ⊕ t={r ⊕ t | r ∈ A(x)}. The first main lemma of the proof shows that the union of a small finite number of these transforms will contain the entire space of random input strings. Using this fact, a Σ₂ sentence and a Π₂ sentence can be generated that is true if and only if x ∈ L (see conclusion).

Lemma 1

The general idea of lemma one is to prove that if A(x) covers a large part of the random space $R=\{1,0{\}}^{|r|}$ then there exists a small set of translations that will cover the entire random space. In more mathematical language:

If $\frac{|A(x)|}{|R|}\ge 1-\frac{1}{2^{|x|}}$, then $\mathrm{\exists }{t}_1,t_2,\dots ,t_{|r|}$, where $t_i\in \{1,0{\}}^{|r|}$ such that $\bigcup _{i}A(x)\oplus t_i=R.$

Proof. Randomly pick t₁, t₂, ..., t_|r|. Let $S=\bigcup _{i}A(x)\oplus t_i$ (the union of all transforms of A(x)).

So, for all r in R,

$Pr[r\notin S]=Pr[r\notin A(x)\oplus t_1]\cdot Pr[r\notin A(x)\oplus t_2]\cdots Pr[r\notin A(x)\oplus t_{|r|}]\le \frac{1}{2^{|x|\cdot |r|}}.$

The probability that there will exist at least one element in R not in S is

$Pr[\underset{i}{\bigvee }(r_i\notin S)]\le \sum _i\frac{1}{2^{|x|\cdot |r|}}=\frac{2^{|r|}}{2^{|x|\cdot |r|}}<1.$

Therefore

$Pr[S=R]\ge 1-\frac{2^{|r|}}{2^{|x|\cdot |r|}}>0.$

Thus there is a selection for each $t_1,t_2,\dots ,t_{|r|}$ such that

$\bigcup _{i}A(x)\oplus t_i=R.$

Lemma 2

The previous lemma shows that A(x) can cover every possible point in the space using a small set of translations. Complementary to this, for x ∉ L only a small fraction of the space is covered by $S=\bigcup _{i}A(x)\oplus t_i$. We have:

$\frac{|S|}{|R|}\le |r|\cdot \frac{|A(x)|}{|R|}\le |r|\cdot 2^{-|x|}<1$

because $|r|$ is polynomial in $|x|$.

Conclusion

The lemmas show that language membership of a language in BPP can be expressed as a Σ₂ expression, as follows.

$x\in L⟺\mathrm{\exists }{t}_1,t_2,\dots ,t_{|r|}\mathrm{\forall }r\in R\underset{1\le i\le |r|}{\bigvee }(M(x,r\oplus t_i)\text{ accepts}).$

That is, x is in language L if and only if there exist $|r|$ binary vectors, where for all random bit vectors r, TM M accepts at least one random vector ⊕ t_i.

The above expression is in Σ₂ in that it is first existentially then universally quantified. Therefore BPP ⊆ Σ₂. Because BPP is closed under complement, this proves BPP ⊆ Σ₂ ∩ Π₂.

Stronger version

The theorem can be strengthened to $𝖡𝖯𝖯\subseteq 𝖬𝖠\subseteq {𝖲}_2^P\subseteq {\mathrm{\Sigma }}_2\cap {\mathrm{\Pi }}_2$ (see MA, [[S2P (complexity)|STemplate:Su]]).