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\renewcommand{\P}{\mathrm{P}}
\newcommand{\NP}{\mathrm{NP}}
\newcommand{\Real}{\mathbb{R}}
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\newcommand{\AvgBPP}{\mathrm{Avg}\textrm{-}\mathrm{BPP}}
\newcommand{\AvgP}{\mathrm{Avg}\textrm{-}\mathrm{P}}
\newcommand{\DNP}{\mathrm{DNP}}
\newcommand{\prBPP}{\mathrm{prBPP}}

\begin{document}
\input{preamble.tex}

\lecture{21}{April 30, 2007}{Madhu Sudan}{Mayank Varia}


\section{Overview}

For the next three lectures, we will study average-case complexity.
In this lecture, we form precise definitions to answer the following
questions.

\begin{itemize}
\item What is a distributional problem?
\item What does it mean to be easy in the average case?
\item What does it mean to be hard in the average case?
\item What are reductions between distributional problems?
\end{itemize}

\section{Theory versus practice}

The goal of complexity theory is to determine which problems are ``easy''
and which are ``hard,'' but sometimes complexity theory does not
match the empirical evidence found by computer scientists. For instance,
complexity theorists tend to call problems in $\P$ ``easy,''
but in practice such a problem may be infeasible if the polynomial
bound to the running time has large degree (like $n^{100}$) or a
large constant (like 1,000,000). This does not usually happen,
however, because in practice once a problem is shown to be in $\P$,
eventually a very efficient algorithm is found to solve the problem.

The opposite can happen too. A complexity theorist considers a problem
to be ``hard'' if it is hard in the worst case, and sometimes
the worst-case problems are contrived and unlikely to occur in practice.
Therefore, a practitioner may look at empirical evidence and say ``but
on instances that I face in the real world, I am able to solve them
nearly optimally 99.99\% of the time.'' We wish to develop a theoretical
framework that allows us to make statements of this form.

One way to understand the phrase ``nearly optimal'' is to use
probabilistically checkable proofs and the related theory of approximation,
which we studied in the previous three lectures. Our goal today is
to develop a theoretical framework to capture the idea of taking ``instances
that I face in the real world'' and solving them ``99.99\% of
the time.'' Of course, we need to choose an underlying probability
distribution in order to make statements like ``99.99\% of the
time,'' and today's goal is to find a good distribution.


\section{Probabilistic analysis of algorithms}

The first attempt at developing a framework for average-case analysis
came in the 1970s and 1980s. Complexity theorists tried to fix a distribution
on the instances of a problem that somehow corresponds to ``natural''
instances from real life, and then they tried to come up with algorithms
that had a fast expected time with respect to the given distribution.

For example, consider the traveling salesman problem, which is $\NP$-complete.
Many interesting real-life examples of the traveling salesman problem
occur on the plane, and we can form two probability distributions
on these instances as follows:

\begin{enumerate}
\item Pick $n$ points uniformly at random in the unit square $[0,1]^{2}\subseteq\Real^{2}$.
\item Assuming that $n$ is a perfect square, start by placing the $n$
points in a grid pattern and then randomly perturb each point by a
small amount.
\end{enumerate}
Interestingly, computer scientists have found extremely efficient
(almost always polynomial time) algorithms that solve the traveling
salesman problem on the first distribution. This seems like a big
success, since we said that TSP problems on the plane were ``real-life
examples,'' and we can find efficient algorithms to solve cases from
the first distribution. Unfortunately, with high probability it is
very difficult to compute the optimal tour for instances generated
under the second distribution. Therefore, the expected running time
of our algorithm depends not only on our choice of ``natural instances''
but on the probability distribution we attach to them.

As another example, let's look at the graph Hamiltonicity problem,
and suppose that we are interested in directed graphs that have $v$
vertices and $5v$ edges. Here are two distributions to place on the
input space:

\begin{enumerate}
\item For each vertex, pick 5 outgoing edges.
\item Pick $5v$ edges uniformly at random from the $v(v-1)$ potential
directed edges.
\end{enumerate}
If a graph $G$ is picked from the first distribution, then with high
probability $G$ does have a Hamiltonian cycle, but if $G$ is picked
from the second distribution, then with high probability $G$ does
not have a Hamiltonian cycle because with high probability $G$ is
not even connected (and checking graph connectivity is fast). Hence,
even though both problems have the same input space, the choice of
distribution has a large impact on the typical answer.

Although the probabilistic analysis of the 1970s and 1980s discovered
many interesting algorithms to problems like the TSP, we notice that
this type of analysis is heavily dependent on the underlying distribution
chosen. It is not fair to promote one distribution of inputs over
another, because the instances that occur ``naturally'' in one
person's research may not come up ``naturally'' in another person's
research. Because the choice of distribution is so important, we want
to have a theoretical framework that considers all possible distributions.


\section{Distributional problems}

Our underlying concern is to study $\NP$ search problems and understand
their average-case behavior. Motivated by the above discussion, we
attach a probability distribution as part of the problem specification.

\begin{definition}
A \emph{distributional problem} is a pair $(\Pi,D)$, where $\Pi\subseteq\{0,1\}^{*}\times\{0,1\}^{*}$
is an $\NP$-relation and $D=\{ D_{n}\}_{n\in\N}$ is an ensemble
where each $D_{n}:\{0,1\}^{n}\to[0,1]$ is a probability distribution.
In particular, for all $n$, the sum $\sum_{x\in\{0,1\}^{n}}D_{n}(x)=1$.
\end{definition}
The goal of a distributional problem is the following: given $x\leftarrow D_{n}$,
find $y\in\{0,1\}^{*}$ such that $(x,y)\in\Pi$. Note that this is
slightly different from a typical $\NP$ problem in which the goal
is merely to determine if $y$ exists.

Next, we need to consider which algorithms are ``good'' at solving
a given distributional problem. Let's consider two algorithms $A$
and $B$ that solve $\Pi$, meaning that for all $x$, $(x,A(x))\in\Pi$
and the same is true for $B$. Let's suppose their running times are
as follows, for $x$ chosen according to distribution $D_{n}$:

\begin{enumerate}
\item With probability $1-2^{-\sqrt{n}}$, $A$ runs in time $n^{2}$, and
with probability $2^{-\sqrt{n}}$, $A$ runs in time $2^{n}$.
\item For all $c\in\N$, algorithm $B$ runs in time $\geq n^{c}$ with
probability $\leq\frac{1}{n^{(c^{2})}}$.
\end{enumerate}
Note that the expected running time of $A$ is exponential. However,
intuitively it seems like a useful algorithm because it usually runs
quickly. Therefore, expected running time is not a useful metric for
capturing our intuitive notion of ``good.'' Another way of thinking
about algorithm $A$ is to consider a polynomially-bounded observer
that samples $x\leftarrow D_{n}$. The observer cannot hope to find
an input $x$ for which algorithm $A$ takes a long time, and so from
the point of view of the observer, $A$ really does run in time $n^{2}$.

On the other hand, the expected running time of $B$ is a polynomial,
but using the previous reasoning we categorize algorithm $B$ as ``bad.''
Consider an observer that runs in time $n^{d}$ and chooses instances
$x\leftarrow D_{n}$. This observer can find an instance $x$ for
which algorithm $B$ takes time $n^{\sqrt{d}}$ to compute $B(x)$.
Allowing $d$ to become large, we notice that by creating observers
with larger and larger polynomial bounds, we can determine instances
of the problem on which $B$ performs arbitrarily bad. Therefore,
we consider $B$ to be a poor algorithm for the distributional problem
$(\Pi,D)$.

Since expected running time does not reflect our intuitive notions
of ``good'' and ``bad'' algorithms, we must come up with a
different definition.

\begin{definition}
Algorithm $A$ runs in ``average time'' $T(n)$ solving $(\Pi,D)$
if for all $c$ and for sufficiently large $n$,\[
\Pr_{x\leftarrow D_{n}}[A\textrm{ takes time }\geq T(n)\textrm{ to compute }A(x)\textrm{, or }A(x)\textrm{ is incorrect}]\leq\frac{1}{n^{c}}\mbox{.}\]
 Additionally, we can allow $A$ to be randomized and consider the
probability also over the random coin tosses of $A$.
\end{definition}
Motivated by this definition, we form the class of ``easy'' distributional
problems in the obvious way. Define \[
\AvgBPP=\{(\Pi,D):\exists\textrm{ algorithm }A\textrm{ and poly }p(n)\textrm{ s.t. }A\textrm{ runs in time }p(n)\textrm{ solving }(\Pi,D)\}\mbox{.}\]



\section{Hard problems}

Next, we wish to define a class of distributional problems that is
analogous to $\NP$ for decision problems. One obvious choice is to
form the class \[
\DNP_{1}=\{(\Pi,D):\Pi\in\NP\textrm{ and }D\textrm{ is a distribution}\}\mbox{,}\]
where the D stands for distributional. Unfortunately, this definition
is not very interesting by the following theorem.

\begin{theorem}
If $\NP\nsubseteq\prBPP$, then $\DNP_{1}\nsubseteq\AvgBPP$.
\end{theorem}
\begin{proof}
If $\NP\nsubseteq\prBPP$, then we can say without loss of generality
that $SAT\notin\prBPP$. Suppose that algorithm $A$ runs in probabilistic
polynomial time and attempts to solve satisfiability. Since $SAT\notin\prBPP$,
it must fail some of the time, and let $D_{A,n}^{\textrm{adv}}$ be
the uniform distribution on the set \[
\{ x\mid A(x)\textrm{ incorrect for satisfiability}\}\mbox{.}\]
 Then, $A$ never solves the distributional problem $(SAT,D_{A}^{\textrm{adv}})$,
where $D_{A}^{\textrm{adv}}=\{ D_{A,n}^{\textrm{adv}}\}_{n\in\N}$
is the ensemble of all of the probability distributions created above.
Unfortunately, this is not sufficient because we want one distribution
that fails for all algorithms.

Let $A_{1},A_{2},A_{3},\ldots$ be an enumeration of all probabilistic
polynomial time Turing machines, and form the distribution \[
D_{n}^{\textrm{adv}}=\sum_{i}\frac{1}{i^{2}}D_{A_{i},n}^{\textrm{adv}}\mbox{,}\]
 and then form the corresponding ensemble $D^{\textrm{adv}}=\{ D_{n}^{\textrm{adv}}\}_{n\in\N}$.
For all $i$, algorithm $A_{i}$ fails on $(SAT,D^{\textrm{adv}})$
with probability at least $\frac{1}{i^{2}}$. As a result, $(SAT,D^{\textrm{adv}})\in\DNP_{1}$
but $(SAT,D^{\textrm{adv}})\notin\AvgBPP$, so it follows that $\DNP_{1}\nsubseteq\AvgBPP$
as desired.
\end{proof}

If we regard $\DNP_{1}$ as our average-case variant of $\NP$, then
this theorem basically says that problems that are hard in the worst-case
are also hard in the average-case. However, the theorem is not very
interesting because it constructs a distribution $D^{\textrm{adv}}$
that is very contrived and unlikely to come up in practice. Essentially,
we are being too harsh by presuming that nature can come up with such
a contrived distribution.

Instead, we think of nature as having a source of pure randomness,
and then being able to apply any easy (polynomial time) algorithm
to its source of randomness. Motivated by this view, we make the following
definition.

\begin{definition}
A distribution $D$ is ``sampleable'' if there exists a deterministic
polynomial time Turing machine $M$ that maps inputs of length $n$
to outputs of length $n$ such that for all $x\in\{0,1\}^{n}$, \[
\Pr_{s\in_{U}\{0,1\}^{n}}[G(s)=x]=D(x)\mbox{.}\]
 Now we define $\DNP=\{(\Pi,D):\Pi\in\NP\textrm{ and }D\textrm{ is a sampleable distribution}\}$.
\end{definition}
Under this definition of $\DNP$, we believe that $\DNP\nsubseteq\AvgBPP$
but we do not know how to prove it, even under a worst-case assumption
like $\NP\nsubseteq\prBPP$. Note that the opposite inclusion does
not hold because there are (uninteresting) problems in $\AvgP$ (the
deterministic variant to $\AvgBPP$) that are not in $\DNP$: one
example is to take $\Pi=\{0,1\}^{*}\times\{0,1\}^{*}$ so that every
$y$ is a witness for every $x$, but to make the distribution $D$
such that it is not sampleable.

In order to compare $\DNP$ and $\AvgBPP$, we wish to develop the
same tools that we use to compare $\NP$ and $\P$, which yields the
following questions:

\begin{enumerate}
\item What is the concept of a reduction between two distributional problems?
\item Under this reduction, is there a notion of a ``complete'' problem
in $\DNP$?
\item Are there natural examples of $\DNP$-complete problems?
\end{enumerate}
In this lecture, we'll answer the first question and propose a candidate
solution to the second question. We'll continue to study the idea
of $\DNP$-completeness in the next lecture.


\section{Reductions and complete problems}

The notion of deterministic reductions is pretty straightforward:
a well-defined function is used to meet simple requirements. Probabilistic
reductions, like the Valiant-Vazirani reduction from SAT to unique-SAT,
are already more interesting to define because the reduction is randomized
and does not always succeed. In the context of distributional problems,
the reduction can be randomized \emph{and} the problem inputs must
fit a given distribution, so making a rigorous definition is even
more difficult.

Intuitively, we want a reduction $(\Pi_{1},D_{1})\leq(\Pi_{2},D_{2})$
of distributional problems to consist of a mapping $x\mapsto R(x)$
with the following properties:

\begin{enumerate}
\item If $x$ is distributed according to $D_{1}$, then $R(x)$ is distributed
according to $D_{2}$.
\item If a witness $y$ is found to $R(x)$, then it should be easy to map
$y$ back into a witness for $x$. Formally, there exists a function
$T$ such that $(R(x),y)\in\Pi_{2}$ implies that $(x,T(y))\in\Pi_{1}$.\xymatrix{ x \ar@{|->}[r]^R \ar@{<->}[d]_{\Pi_1} & R(x) \ar@{<->}[d]^{\Pi_2} \\ T(y) & y \ar@{|->}[l]_T}
\end{enumerate}
It turns out that the first condition is a bit too restrictive. The
intuitive idea is that $R(x)$ does not have to be distributed just
like $D_{2}$, but it should have the same basic structure so it should
not be too concentrated at points that $D_{2}$ is not interested
in. We formalize this idea with the following definition.

\begin{definition}
Given two distributions $D_{2}$ and $D_{2}'$ and a function $\alpha:\N\to\Real^{+}$,
we say that $D_{2}$ ``$\alpha$-dominates'' $D_{2}'$ if for
all $n$ and for all $x\in\{0,1\}^{n}$, $D_{2}'(x)\leq\alpha(n)\cdot D_{2}(x)$.
\end{definition}

Now we replace condition 1 with the following criterion: if $x$ is
distributed according to $D_{1}$, then the distribution of $R(x)$,
which we call $D_{2}'$, has the property that there exists a polynomial
$\alpha$ such that $D_{2}$ $\alpha$-dominates $D_{2}'$.

Defining a concept of reduction becomes more difficult when $R$ is
allowed to be a randomized reduction. However, if we restrict ourselves
to looking at deterministic reductions, then it is possible to prove
that no reductions exist where $D_{2}$ is a certain type of uniform
distribution. This is undesirable, so we do allow $R$ to be randomized,
and we also soften condition 2 slightly by saying that it only has
to hold when the random coins of $R$ are ``good,'' and the reduction
can do anything when the coins are ``bad.'' Writing out the formal
definition of the reduction is complicated, so we avoid doing so here.

Now that we have a notion of reductions, we look at the question of
completeness. One $\NP$-complete problem is the following language:\[
L=\{(M,x):M\textrm{ is a nondeterministic TM that runs in time }n^{2}\textrm{, and }x\in L(M)\}\mbox{.}\]
 The relation that decides $L$ is the relation \[
\Pi((M,x),y)=\begin{cases}
1 & \textrm{if }M\textrm{ runs in time }n^{2}\textrm{ and }y\textrm{ is a witness to the fact that }x\in L(M)\mbox{,}\\
0 & \textrm{otherwise.}\end{cases}\]
 We can form a distributional problem using $\Pi$ by creating
a uniform distribution $D$ as follows:

\begin{itemize}
\item Select the length of $M$ by choosing $M$ to have length $l$ with
probability $2^{-l}$. Then, choose $M\in_{U}\{0,1\}^{l}$.
\item Select the length of $x$ by choosing $x$ to have length $n$ with
probability $\frac{1}{n^{2}}$. Then, choose $x\in_{U}\{0,1\}^{n}$.
\end{itemize}
In the next lecture, we will prove that the distributional problem
$(\Pi,D)$ is $\DNP$-complete.


\section{Reference}

This lecture is based on Section 10.2.1 of Oded Goldreich's book
{\it Computational Complexity: A Conceptual Perspective}.  Goldreich's book
explains all of the definitions that we covered in class and also provides
a precise definition of reductions between distributional problems.
Goldreich's book is available online at the website
\verb+http://www.wisdom.weizmann.ac.il/~oded/cc-drafts.html+ (there is a
link to this page from the 6.841 course website).

\end{document}
