# Computation and Representation

“The alphabet was a great invention, which enabled men to store and to learn with little effort what others had learned the hard way-that is, to learn from books rather than from direct, possibly painful, contact with the real world.”, B.F. Skinner

“I found that every number, which may be expressed from one to ten, surpasses the preceding by one unit: afterwards the ten is doubled or tripled … until a hundred; then the hundred is doubled and tripled in the same manner as the units and the tens … and so forth to the utmost limit of numeration.”, Muhammad ibn Mūsā al-Khwārizmī, 820, translation by Fredric Rosen, 1831.

“A mathematician would hardly call a correspondence between the set of 64 triples of four units and a set of twenty other units,”universal“, while such correspondence is, probably, the most fundamental general feature of life on Earth”, Misha Gromov, 2013

To a first approximation, computation can be thought of as a process
that maps an *input* to an *output*.

When discussing computation, it is important to separate the question of
**what** is the task we need to perform (i.e., the *specification*) from
the question of **how** we achieve this task (i.e., the
*implementation*). For example, as we’ve seen, there is more than one
way to achieve the computational task of computing the product of two
integers.

In this lecture we focus on the **what** part, namely defining
computational tasks. For starters, we need to define the inputs and
outputs. A priori this seems nontrivial, since computation today is
applied to a huge variety of objects. We do not compute merely on
numbers, but also on texts, images, videos, connection graphs of social
networks, MRI scans, gene data, and even other programs. We will
represent all these objects as **strings of zeroes and ones**, that is
objects such as \(0011101\) or \(1011\) or any other finite list of \(1\)’s
and \(0\)’s.

Today, we are so used to the notion of digital representation that we
are not surprised by the existence of such an encoding. But it is a deep
insight with significant implications. Many animals can convey a
particular fear or desire, but what’s unique about humans is *language*:
we use a finite collection of basic symbols to describe a potentially
unlimited range of experiences. Language allows transmission of
information over both time and space, and enables societies that span a
great many people and accumulate a body of shared knowledge over time.

Over the last several decades, we’ve seen a revolution in what we are
able to represent and convey in digital form. We can capture experiences
with almost perfect fidelity, and disseminate it essentially
instantaneously to an unlimited audience. What’s more, once information
is in digital form, we can *compute* over it, and gain insights from
data that were not accessible in prior times. At the heart of this
revolution is this simple but profound observation that we can represent
an unbounded variety of objects using a finite set of symbols (and in
fact using only the two symbols `0`

and `1`

).There is nothing “holy” about using zero and one as the basic
symbols, and we can (indeed sometimes people do) use any other
finite set of two or more symbols as the fundamental “alphabet”. We
use zero and one in this course mainly because it simplifies
notation.

In later lectures, we will often fall back on taking this representation
for granted, and hence write something like “program \(P\) takes \(x\) as
input” when \(x\) might be a number, a vector, a graph, or any other
objects, when we really mean that \(P\) takes as input the
*representation* of \(x\) as a binary string. However, in this lecture,
let us dwell a little bit on how such representations can be devised.

## Examples of binary representations

In many instances, choosing the “right” string representation for a piece of data is highly nontrivial, and finding the “best” one (e.g., most compact, best fidelity, most efficiently manipulable, robust to errors, most informative features, etc..) is the object of intense research. But for now, let us start by describing some simple representations for various natural objects.

### Representing natural numbers

Perhaps the simplest object we want to represent is a *natural number*.
That is, a member \(x\) of the set \(\N = \{0,1,2,3,\ldots \}\). We can
represent a number \(x\in\N\) as a string using the *binary basis*.
Specifically, every natural number \(x\) can be written in a unique way as
\(x = x_02^0 + x_12^1 + \cdots + x_{n-1}2^{n-1}\) (or
\(\sum_{i=0}^{n-1} x_i 2^i\) for short) where \(x_0,\ldots,x_{n-1}\) are
zero/one and \(n\) is the smallest number such that \(2^n > x\) (and hence
\(x_{n-1}=1\) for every nonzero \(x\)). We can then represent \(x\) as the
string \((x_0,x_1,\ldots,x_{n-1})\).We can represent the number zero either as some string that
contains only zeroes, or as the empty string. The choice will not
make any difference for us. For example, the number 35 is
represented as the string \((1,1,0,0,0,1)\).Typically when people write down the binary representation, they
would print the string \(x\) in *reverse order*, with the least
significant digit as the rightmost one. Representing the number \(x\)
as \((x_{n-1},x_{n-2},\ldots,x_0)\) will of course work just as well.
We chose the particular representation above for the sake of
simplicity, so the the \(i\)-th bit corresponds to \(2^i\), but such low
level choices will not make a difference in this course. A related,
but not identical, distinction is the Big Endian vs Little
Endian
representation for integers in computing architecture.

We can think of a representation as consisting of *encoding* and
*decoding* functions. In the case of the *binary representation* for
integers, the *encoding* function \(E:\N \rightarrow \{0,1\}^*\) maps a
natural number to the string representing it, and the *decoding*
function \(D:\{0,1\}^* \rightarrow \N\) maps a string into the number it
represents (i.e.,
\(D(x_0,\ldots,x_{n-1})= 2^0x_0 + 2^1x_1 +\ldots + 2^{n-1}x_{n-1}\) for
every \(x_0,\ldots,x_{n-1} \in \{0,1\}\)). For the representation to be
well defined, we need every natural number to be represented by some
string, where two distinct numbers must have distinct representations.
This corresponds to requiring the *encoding* function to be one-to-one,
and the *decoding* function to be *onto*.

If you don’t remember the definitions of *one-to-one*, *onto*, *total*
and *partial* functions, now would be an excellent time to review them.
Make sure you understand *why* the function \(E\) described above is
one-to-one, and the function \(D\) is *onto*.

### Representing (potentially negative) integers

Now that we can represent natural numbers, we can represent the full set
of *integers* (i.e., members of the set
\(\Z=\{ \ldots, -3 , -2 , -1 , 0 , +1, +2, +3,\ldots \}\) ) by adding one
more bit that represents the sign. So, the string
\((\sigma,x_0,\ldots,x_{n-1}) \in \{0,1\}^{n+1}\) will represent the
number \[
(-1)^\sigma \left[ x_0 2^0 + \cdots x_{n-1} 2^{n} \right]
\]

The decoding function of a representation should always be *onto*, since
every object must be represented by some string. However, it does not
always have to be one to one. For example, in this particular
representation the two strings \(1\) and \(0\) both represent the number
zero (since they can be thought of as representing \(-0\) and \(+0\)
respectively, can you see why?). We can also allow a *partial* decoding
function for representations. For example, in the representation above
there is no number that is represented by the empty string. But this is
still a fine representation, since the decoding partial function is onto
and the encoding function is the one-to-one total function
\(E:\Z \rightarrow \{0,1\}^*\) which maps an integer of the form
\(a\times k\), where \(a\in \{\pm 1 \}\) and \(k\in \N\) to the bit \((-1)^a\)
concatenated with the binary representation of \(k\). That is, every
integer can be represented as a string, and two distinct integers have
distinct representations.

**Interpretation and context:** Given a string \(x\in \{0,1\}^*\), how do
we know if it’s “supposed” to represent a (nonnegative) natural number
or a (potentially negative) integer? For that matter, even if we know
\(x\) is “supposed” to be an integer, how do we know what representation
scheme it uses? The short answer is that we don’t necessarily know this
information, unless it is supplied from the context.In programming language, the compiler or interpreter determines
the representation of the sequence of bits corresponding to a
variable based on the variable’s *type*. We can treat
the same string \(x\) as representing a natural number, an integer, a
piece of text, an image, or a green gremlin. Whenever we say a sentence
such as “let \(n\) be the number represented by the string \(x\)”, we will
assume that we are fixing some canonical representation scheme such as
the ones above. The choice of the particular representation scheme will
almost never matter, except that we want to make sure to stick with the
same one for consistency.

### Representing rational numbers

We can represent a rational number of the form \(a/b\) by representing the
two numbers \(a\) and \(b\) (again, this is not a unique representation but
this is fine). However, simply concatenating the representations of \(a\)
and \(b\) will not work.Recall that the *concatenation* of two strings \(x\) and \(y\) is the
string of length \(|x|+|y|\) obtained by writing \(y\) after \(x\). For example, recall that we represent \(4\) as
\((0,0,1)\) and \(35\) as \((1,1,0,0,0,1)\), but the concatenation
\((0,0,1,1,1,0,0,0,1)\) of these strings is also the concatenation of the
representation \((0,0,1,1)\) of \(12\) and the representation \((1,0,0,0,1)\)
of \(17\). Hence, if we used such simple concatenation then we would not
be able to tell if the string \((0,0,1,1,1,0,0,0,1)\) is supposed to
represent \(4/35\) or \(12/17\).

The way to tackle this is to find a general representation for *pairs*
of numbers. If we were using a pen and paper, we would simply use a
separator such as the semicolon symbol to represent, for example, the
pair consisting of the numbers represented by \((0,1)\) and
\((1,1,0,0,0,1)\) as the length-\(9\) string \(s\) `01;110001`

. By adding a
little redundancy, We can do just that in the digital domain. The idea
is that we will map the three element set \(\Sigma = \{\) `0`

,`1`

,`;`

\(\}\)
to the four element set \(\{0,1\}^2\) via the one-to-one map that takes
`0`

to \(00\), `1`

to \(11\) and `;`

to \(01\). In particular, if apply this
map to every symbol of the length-\(9\) string \(s\) above, we get the
length \(18\) binary string \(001101111100000011\). More generally, the
above encoding yields a one-to-one map \(E\) from strings over the
alphabet \(\Sigma\) to binary string, such that for every \(s\in \Sigma^*\),
\(|E(s)|=2|s|\).

Using this, we get get a one to one map
\(E': (\{0,1\}^*)\times (\{0,1\}^*) \rightarrow \{0,1\}^*\) mapping
*pairs* of binary strings into a single binary string. Given every pair
\((a,b)\) of binary strings, we will first map it in a one-to way to a
string \(s \in \Sigma^*\) using `;`

as a separator, and then map \(s\) to a
single (longer) binary string using the encoding \(E\). The same idea can
be used to represent triples, quadruples, and generally all tuples of
strings as a single string (can you see why?).

### Representing real numbers

The set of *real numbers* \(\R\) contains all numbers including positive,
negative, and fractional, as well as *irrational* numbers such as \(\pi\)
or \(e\). Every real number can be approximated by a rational number, and
so up to a small error we can represent every real number \(x\) by a
rational number \(a/b\) that is very close to \(x\). For example, we can
represent \(\pi\) by \(22/7\) with an error of about \(10^{-3}\) and if we
wanted smaller error (e.g., about \(10^{-4}\)) then we can use \(311/99\)
and so on and so forth.

This is a fine representation though a more common choice to represent
real numbers is the *floating point* representation, where we represent
\(x\) by the pair \((a,b)\) of (positive or negative) integers of some
prescribed sizes (determined by the desired accuracy) such that
\(a \times 2^{b}\) is closest to \(x\).You can think of this as related to scientific
notation. In
scientific notation we represent a number \(y\) as \(a \times 10^b\) for
integers \(a,b\). Sometimes we write this as \(y=a \mathtt{E} b\). For
example, in many programming languages `1.21E2`

is the same as
`121.0`

. In scientific notation, to represent \(\pi\) up to accuracy
\(10^{-3}\) we will simply use \(3141 \times 10^{-3}\) and to represent
it up to accuracy \(10^{-4}\) we will use \(31415 \times 10^{-4}\). The reader might be (rightly)
worried about this issue of approximation. In many (though not all)
computational applications, one can make the accuracy tight enough so
that this does not affect the final result, though sometimes we do need
to be careful. This representation is called “floating point” because we
can think of the number \(a\) as specifying a sequence of binary digits,
and \(b\) as describing the location of the “binary point” within this
sequence. The use of floating representation is the reason why in many
programming systems printing the expression `0.1+0.2`

will result in
`0.30000000000000004`

and not `0.3`

, see
here,
here
and
here
for more. A floating point error has been implicated in the
explosion
of the Ariane 5 rocket, a bug that cost more than 370 million dollars,
and the
failure
of a U.S. Patriot missile to intercept an Iraqi Scud missile, costing 28
lives. Floating point is often
problematic
in financial applications as well.

### Can we represent reals *exactly*?

Given the issues with floating point representation, we could ask
whether we could represent real numbers *exactly* as strings.
Unfortunately, the following theorem says this cannot be done

There is no one-to-one function \(RtS:\R \rightarrow \{0,1\}^*\).\(RtS\) stands for “reals to strings”.

Reference:cantorthm was proven by Georg
Cantor in 1874.Cantor used the set \(\N\) rather than \(\{0,1\}^*\), but one can show
that these two result are equivalent using the one-to-one maps
between those two sets, see Reference:naturalsstringsmapex. Saying
that there is no one-to-one map from \(\R\) to \(\N\) is equivalent to
saying that there is no onto map \(NtR:\N \rightarrow \R\) or, in
other words, that there is no way to “count” all the real numbers as
\(NtR(0),NtR(1),NtR(2),\ldots\). For this reason Reference:cantorthm
is known as the *uncountability of the reals*. The
result (and the theory around it) was quite shocking to mathematicians
at the time. By showing that there is no one-to-one map from \(\R\) to
\(\{0,1\}^*\) (or \(\N\)), Cantor showed that these two infinite sets have
“different forms of infinity” and that the set of real numbers \(\R\) is
in some sense “bigger” than the infinite set \(\{0,1\}^*\). The notion
that there are “shades of infinity” was deeply disturbing to
mathematicians and philosophers at the time. The philosopher Ludwig
Wittgenstein called Cantor’s results “utter nonsense” and “laughable”.
Others thought they were even worse than that. Leopold Kronecker called
Cantor a “corrupter of youth”, while Henri Poincaré said that Cantor’s
ideas “should be banished from mathematics once and for all”. The tide
eventually turned, and these days Cantor’s work is universally accepted
as the cornerstone of set theory and the foundations of mathematics. As
we will see later in this course, Cantor’s ideas also play a huge role
in the theory of computation.

Now that we discussed the theorem’s importance, let us see the proof. Reference:cantorthm follows from the following two results:

Let \(\{0,1\}^\infty\) be the set \(\{ f \;|\; f:\N \rightarrow \{0,1\} \}\)
of functions from \(\N\) to \(\{0,1\}\).We can also think of \(\{0,1\}^\infty\) as the set of all infinite
*sequences* of bits, since a function \(f:\N \rightarrow \{0,1\}\) can
be identified with the sequence \((f(0),f(1),f(2),\ldots )\). Then there is no one-to-one map
\(FtS:\{0,1\}^\infty \rightarrow \{0,1\}^*\).\(FtS\) stands for “functions to strings”.

There *does* exist a one-to-one map
\(FtR:\{0,1\}^\infty \rightarrow \R\).\(FtR\) stands for “functions to reals.”

Reference:sequencestostrings and Reference:sequencestoreals together imply Reference:cantorthm. To see why, suppose, towards the sake of contradiction, that there did exist a one-to-one function \(RtS:\R \rightarrow \{0,1\}^*\). By Reference:sequencestoreals, there exists a one-to-one function \(FtR:\{0,1\}^\infty \rightarrow \R\). Thus, under this assumption, since the composition of two one-to-one functions is one-to-one (see Reference:onetoonecompex), the function \(FtS:\{0,1\}^\infty \rightarrow \{0,1\}^*\) defined as \(FtS(f)=RtS(FtR(f))\) will be one to one, contradicting Reference:sequencestostrings.

Now all that is left is to prove these two lemmas. We start by proving Reference:sequencestostrings which is really the heart of Reference:cantorthm.

Let us assume, towards the sake of contradiction, that there exists a
one-to-one function \(FtS:\{0,1\}^\infty \rightarrow \{0,1\}^*\). Then,
there is an *onto* function \(StF:\{0,1\}^* \rightarrow \{0,1\}^\infty\)
(e.g., see Reference:onetooneimpliesonto). We will derive a
contradiction by coming up with some function
\(f^* : \N \rightarrow \{0,1\}\) such that \(f^* \neq StF(x)\) for every
\(x\in \{0,1\}^*\).

The argument for this is short but subtle. We need to construct some
function \(f^*:\N \rightarrow \{0,1\}\) such that for every
\(x\in \{0,1\}^*\), if we let \(g=StF(x)\) then \(g \neq f^*\). Since two
functions are identical if and only if they agree on every input, to do
this we need to show that there is *some* \(n\in \N\) such that
\(f^*(n) \neq g(n)\). (All these quantifiers can be confusing, so let’s
again recap where we are and where we want to get to. We assumed by
contradiction there is a one-to-one \(FtS\) and hence an onto \(StF\). To
get our desired contradiction we need to show the *existence* of a
single \(f^*\) such that for *every* \(x\in \{0,1\}^*\) there *exists*
\(n\in \N\) on which \(f^*\) and \(g=StF(x)\) disagree.)

The idea is to construct \(f^*\) iteratively: for every \(x\in \{0,1\}^*\) we will “ruin” \(f^*\) in one input \(n(x)\in \N\) to ensure that \(f^*(n(x)) \neq g(n(x))\) where \(g=StF(x)\). If we are successful then this would ensure that \(f^* \neq STF(x)\) for every \(x\). Specifically, for every \(x\in \{0,1\}^*\), let \(n(x) \in N\) be the number \(x_0 + 2x_1 + 4x_2 + \cdots +2^{k-1}x_{k-1} + 2^{k}\) where \(k=|x|\). That is, \(n(x) = 2^k + \sum_{i=0}^{k-1}2^i x_i\). If \(x\neq x'\) then \(n(x) \neq n(x')\) (we leave verifying this as an exercise to you, the reader).

Now for every \(x\in \{0,1\}^*\), we define \[ f^*(n(x)) = 1 - g(n(x)) \label{eqcantordiagreals} \] where \(g=StF(x)\). For every \(n\) that is not of the form \(n=n(x)\) for some \(x\), we set \(f^*(n)=0\). \eqref{eqcantordiagreals} is well defined since the map \(x \mapsto n(x)\) is one-to-one and hence we will not try to give \(f^*(n)\) two different values.

Now by \eqref{eqcantordiagreals}, for every \(x\in \{0,1\}^*\), if \(g=StF(x)\) and \(n=n(x)\) then \(f^*(n) = 1 - g(n) \neq g(n)\). Hence \(StF(x) \neq f^*\) for every \(x\in \{0,1\}^*\), contradicting the assumption that \(StF\) is onto. This proof is known as the “diagonal” argument, as the construction of \(f^*\) can be thought of as going over the diagonal elements of a table that in the \(n\)-th row and \(m\)-column contains \(StF(x)(m)\) where \(x\) is the string such that \(n(x)=n\), see Reference:diagrealsfig.

Reference:sequencestostrings doesn’t really have much to do with the
natural numbers or the strings. An examination of the proof shows that
it really shows that for *every* set \(S\), there is no one-to-one map
\(F:\{0,1\}^S \rightarrow S\) where \(\{0,1\}^S\) denotes the set
\(\{ f \;|\; f:S \rightarrow \{0,1\} \}\) of all Boolean functions with
domain \(S\). Since we can identify a subset \(V \subseteq S\) with its
characteristic function \(f=1_V\) (i.e., \(1_V(x)=1\) iff \(x\in V\)), we can
think of \(\{0,1\}^S\) also as the set of all *subsets* of \(S\). This
subset is sometimes called the *power set* of \(S\). The proof of
Reference:sequencestostrings can be generalized to show that there is no
one-to-one map between a set and its power set. In particular, it means
that the set \(\{0,1\}^\R\) is “even bigger” than \(\R\). Cantor used these
ideas to construct an infinite hierarchy of shades of infinity. The
number of such shades turn out to be much larger than \(|\N|\) or even
\(|\R|\). He denoted the cardinality of \(\N\) by \(\aleph_0\), where \(\aleph\)
is the first letter in the Hebrew alphabet, and called the the next
largest infinite number by \(\aleph_1\). Cantor also made the continuum
hypothesis that
\(|\R|=\aleph_1\). We will come back to the very interesting story of this
hypothesis later on in this course. This lecture of
Aaronson mentions
some of these issues (see also this Berkeley CS 70
lecture).

To complete the proof of Reference:cantorthm, we need to show Reference:sequencestoreals. This requires some calculus background, but is otherwise straightforward. The idea is that we can construct a one-to-one map from \(\{0,1\}^\infty\) to the real numbers by mapping the function \(f:\N \rightarrow \{0,1\}\) to the number that has the infinite decimal expansion \(f(0).f(1)f(2)f(3)f(4)f(5)\ldots\) (i.e., the number between \(0\) and \(2\) that is \(\sum_{i=0}^\infty f(i)10^{-i}\)). We will now do this more formally. If you have not had much experience with limits of real series before, then the formal proof might be a little hard to follow. This part is not the core of Cantor’s argument, nor are such limits very crucial to this course, so feel free to also just take Reference:sequencestoreals on faith and skip the formal proof.

For every \(f\in \{0,1\}^\infty\) and \(n\in \N\), we define
\(S(f)_n = \sum_{i=0}^n f(i)10^{-i}\). It is a known result (that we won’t
repeat here) that for every \(f:\N \rightarrow \{0,1\}\), the sequence
\(( S(f)_n )_{n=0}^\infty\) has a *limit*. That is, there exists some
value \(x\) such that for every \(\epsilon>0\), if \(n\) is sufficiently large
then \(|S_f(n)-x| < \epsilon\). We define \(FtR(f)\) to be this value \(x\).
To show that \(FtR\) is one to one, we need to show that
\(FtR(f) \neq FtR(g)\) for every distinct \(f,g:\N \rightarrow \{0,1\}\).
Let \(f \neq g\) be such functions, and let \(k\) be the smallest number for
which \(f(k) \neq g(k)\). Assume without loss of generality that \(f(k)=0\)
and \(g(k)=1\). Then, if
\(S = \sum_{i=0}^{k-1} 10^{-i}f(i) = \sum_{i=0}^{k-1} 10^{-i}f(i)\), we
get that for every \(n>k+1\), \(S(f)_n = S + \sum_{i=k+1}^n 10^{-i} f(i)\)
and \(S(g)_n = S + 10^{-k} + \sum_{i=k+1}^n 10^{-i} g(i)\). Clearly, the
limit \(FtR(g)\) is at least \(S + 10^{-k}\). On the other hand, we claim
that for every \(n>k+1\),
\(S(f)_n \leq S + 2\cdot 10^{-k-1} < S + 10^{-k}\), and hence in
particular \(FtR(f) < FtR(g)\). Indeed, since \(f(i)\in \{0,1\}\) for every
\(i\), \(\sum_{i=k+1}^n f(i)10^{-i} \leq \sum_{i=k+1}^n 10^{-i}\) which by
formula for geometric
series, equals
\(10^{-k-1}\tfrac{1-10^{-(n-k-1)}}{1-10^{-1}} \leq 10^{-k-1}/0.9 \leq 2\cdot 10^{-k-1}\).

## Beyond numbers

We can of course represent objects other than numbers as binary strings. Let us give a general definition for representation:

Let \(\cO\) be some set. A *representation scheme* for \(\mathcal{O}\)
consists of a pair \((E,D)\) where \(E:\mathcal{O} \rightarrow \{0,1\}^*\)
is a total one-to-one function, \(D:\{0,1\}^* \rightarrow_p \mathcal{O}\)
is a (possibly partial) function, and such that \(D\) and \(E\) satisfy that
\(D(E(o))=o\) for every \(o\in \mathcal{O}\). \(E\) is known as the *encoding*
function and \(D\) is known as the *decoding* function.

Note that the condition \(D(E(o))=o\) for every \(o\in\mathcal{O}\) implies
that \(D\) is *onto* (can you see why?). It turns out that to construct a
representation scheme we only need to find an *encoding* function. That
is, every one-to-one encoding function has a corresponding decoding
function, as shown in the following lemma:

Suppose that \(E: \mathcal{O} \rightarrow \{0,1\}^*\) is one-to-one. Then there exists a function \(D:\{0,1\}^* \rightarrow \mathcal{O}\) such that \(D(E(o))=o\) for every \(o\in \mathcal{O}\).

Let \(o_0\) be some arbitrary element of \(O\). For every \(x \in \{0,1\}^*\), there exists either zero or a single \(o\in \mathcal{O}\) such that \(E(o)=x\) (otherwise \(E\) would not be one-to-one). We will define \(D(x)\) to equal \(o_0\) in the first case and this single object \(o\) in the second case. By definition \(D(E(o))=o\) for every \(o\in \mathcal{O}\).

Note that, while in general we allowed the decoding function to be
*partial*. This proof shows that we can always obtain a *total* decoding
function if we need to. This observation can sometimes be useful.

### Finite representations

If \(\mathcal{O}\) is *finite*, then we can represent every object in \(o\)
as a string of length at most some number \(n\). What is the value of \(n\)?
Let us denote the set \(\{ x\in \{0,1\}^* : |x| \leq n \}\) of strings of
length at most \(n\) by \(\{0,1\}^{\leq n}\). To obtain a representation of
objects in \(\mathcal{O}\) as strings in \(\{0,1\}^{\leq n}\) we need to
come up with a one-to-one function from the former set to the latter. We
can do so, if and only if \(|\mathcal{O}| \leq 2^{n+1}-1\) as is implied
by the following lemma:

For every two finite sets \(S,T\), there exists a one-to-one \(E:S \rightarrow T\) if and only if \(|S| \leq |T|\).

Let \(k=|S|\) and \(m=|T|\) and so write the elements of \(S\) and \(T\) as \(S = \{ s_0 , s_1, \ldots, s_{k-1} \}\) and \(T= \{ t_0 , t_1, \ldots, t_{m-1} \}\). We need to show that there is a one-to-one function \(E: S \rightarrow T\) iff \(k \leq m\). For the “if” direction, if \(k \leq m\) we can simply define \(E(s_i)=t_i\) for every \(i\in [k]\). Clearly for \(i \neq j\), \(t_i = E(s_i) \neq E(s_j) = t_j\), and hence this function is one-to-one. In the other direction, suppose that \(k>m\) and \(E: S \rightarrow T\) is some function. Then \(E\) cannot be one-to-one. Indeed, for \(i=0,1,\ldots,m-1\) let us “mark” the element \(t_j=E(s_i)\) in \(T\). If \(t_j\) was marked before, then we have found two objects in \(S\) mapping to the same element \(t_j\). Otherwise, since \(T\) has \(m\) elements, when we get to \(i=m-1\) we mark all the objects in \(T\). Hence, in this case \(E(s_m)\) must map to an element that was already marked before.This direction is sometimes known as the “Pigeon Hole Principle”: the principle that if you have pigeon coop with \(m\) holes, and \(k>m\) pigeons, then there must be two pigeon in the same hole.

Now the size of \(\{0,1\}^n\) is \(2^n\), and the size of \(\{0,1\}^{\leq n}\) is only slightly bigger: \(2^0 + 2^1 + \ldots + 2^n = 2^{n+1}-1\) by the formula for a geometric series.

### Prefix free encoding

In our discussion of the representation of rational numbers, we used the
“hack” of encoding the alphabet \(\{\),`0`

,`1`

,`;`

\(\}\) to represent tuples
of strings as a single string. This turns out to be a special case of
the general paradigm of *prefix free* encoding. An encoding function
\(E:\mathcal{O} \rightarrow \{0,1\}^*\) is *prefix free* if there are no
two objects \(o \neq o'\) such that the representation \(E(o)\) is a
*prefix* of the representation \(E(o')\). The definition of prefix is as
you would expect: a length \(n\) string \(x\) is a prefix of a length
\(n' \geq n\) string \(x'\) if \(x_i=x'_i\) for every \(1 \leq i \leq n\). Given
a representation scheme for \(\mathcal{O}\) with a prefix-free encoding
map, we can use simple concatenation to encode tuples of objects in
\(\mathcal{O}\):

Suppose that \((E,D)\) is a representation scheme for \(\mathcal{O}\) and \(E\) is prefix free. Then there exists a representation scheme \((E',D')\) for \(\mathcal{O}^*\) such that for every \((o_0,\ldots,o_{k-1}) \in \mathcal{O}^*\), \(E'(o_0,\ldots,o_{k-1}) = E(o_0)E(o_1)\cdots E(o_{k-1})\).

Reference:prefixfreethm is one of those statements that are a little hard to parse, but in fact are fairly straightforward to prove once you understand what they mean. Thus I highly recommend that you pause here, make sure you understand statement of the theorem, and try to prove it yourself before proceeding further.

The idea behind the proof is simple. Suppose that for example we want to decode a triple \((o_0,o_1,o_2)\) from its representation \(x= E'(o_0,o_1,o_2)=E(o_0)E(o_1)E(o_2)\). We will do so by first finding the first prefix \(x_0\) of \(x\) such is a representation of some object. Then we will decode this object, remove \(x_0\) from \(x\) to obtain a new string \(x'\), and continue onwards to find the first prefix \(x_1\) of \(x'\) and so on and so forth (see Reference:prefix-free-tuples-ex). The prefix-freeness property of \(E\) will ensure that \(x_0\) will in fact be \(E(o_0)\), \(x_1\) will be \(E(o_1)\) etc. We now show the formal proof.

By Reference:decodelem, to prove the theorem it suffices show that \(E'\) is one-to-one. Suppose, towards the sake of contradiction that there exist two distinct tuples \((o_0,\ldots,o_{k-1})\) and \((o'_0,\ldots,o'_{k'-1})\) such that

\[ E'(o_0,\ldots,o_{k-1})=E'(o'_0,\ldots,o'_{k'-1}) \;, \label{prefixfreeassump} \] and denote this string by \(x\).

We denote \(x_i = E(o_i)\) and \(x'_i = E(o'_i)\). By our assumption and the definition of \(E'\), \(x_0x_1 \cdots x_k = x'_0 x'_1 \cdots x'_k\).

Let’s make the assumption A that there is some index
\(i \in [\min \{k,k'\}]\) such that \(o_i \neq o'_i\). Now, let \(i\) be the
first such index, and hence \(o_j=o'_j\) for all \(j<i\) but
\(o_i \neq o'_i\), and so (since \(E\) is one-to-one) also \(x_i \neq x'_i\).
That means that if we write \(p=x_0\cdots x_{i-1}\), then by
\eqref{prefixfreeassump} the first \(|p|+|x_i|\) bits of the string
\(x\) need to equal \(p\,x_i\) (i.e., the concatenation of \(p\) and \(x_i\))
and the first \(|p|+|x'_i|\) bits of \(x\) needs to equal \(p\,x'_i\). If
\(|x_i|=|x'_i|\), then the only way this can happen is if
\(p\,x_i = p\,x'_i\), which implies \(x_i=x'_i\), in contradiction to the
fact that \(E\) is one-to-one. Otherwise, without loss of generality
\(|x_i|>|x'_i|\),This is one of our first uses of the phrase “without loss of
generality”. Make sure you understand why it is justified! If you
are not sure, you can try working out the case that \(|x_i|>|x'_i|\)
and see why the proof is symmetric. and so \(px'_i\) must be a *prefix* of \(px_i\), but
this contradicts the prefix-freeness of \(E\). The only remaining case is
when Assumption A is false. Since the tuples are different, if
\(o_i=o'_i\) for every \(i \in [\min\{ k,k'\}]\), it must mean that
\(k \neq k'\). Without loss of generality, assume that \(k<k'\) (again,
check that this is justified!). But then, since \(o_i=o'_i\) for all
\(i\in [k]\), it holds that
\(x_0 \cdots x_{k-1} = x_0 \cdots x_{k-1} x'_{k} \cdots x'_{k'-1}\). But
this can only happen if \(x'_i\) for \(i \geq k\) is the empty string, while
a prefix-free encoding can never encode an object as the empty string
(can you see why?).

### Making representations prefix free

Some natural representations are prefix free. For example, every *fixed
output length* representation (i.e., one-to-one function
\(E:\mathcal{O} \rightarrow \{0,1\}^n\)) is automatically prefix free,
since a string \(x\) can only be a prefix of an equal-length \(x'\) if \(x\)
and \(x'\) are identical. Moreover, the approach we used for representing
rational numbers can be used to show the following:

Let \(E:\mathcal{O} \rightarrow \{0,1\}^*\) be a one-to-one function. Then there is a one-to-one prefix-free encoding \(\overline{E}\) such that \(|\overline{E}(o)| \leq 2|E(o)|+2\) for every \(o\in \mathcal{O}\).

For the sake of completeness, we will include the proof below, but it is a good idea for you to pause here and try to prove it yourself, using the same technique we used for representing rational numbers.

Define the function \(PF:\{0,1\}^* \rightarrow \{0,1\}^*\) as follows \(PF(x)=x_0 x_0 x_1 x_1 \ldots x_{n-1}x_{n-1}01\) for every \(x\in \{0,1\}^*\). If \(E:\mathcal{O} \rightarrow \{0,1\}^*\) is the (potentially not prefix free) representation for \(\mathcal{O}\), then we transform it into a prefix free representation \(\overline{E}:\mathcal{O} \rightarrow \{0,1\}^*\) by defining \(\overline{E}(o)=PF(E(o))\).

To prove the lemma we need to show that **(1)** \(\overline{E}\) is
one-to-one and **(2)** \(\overline{E}\) is prefix free. In fact **(2)**
implies **(1)**, since if \(\overline{E}(o)\) is never a prefix of
\(\overline{E}(o')\) for every \(o \neq o'\) then in particular
\(\overline{E}\) is one-to-one. Now suppose, toward the sake of
contradiction, that there are \(o \neq o'\) in \(\mathcal{O}\) such that
\(\overline{E}(o)\) is a prefix of \(\overline{E}(o')\). (That is, if
\(y=\overline{E}(o)\) and \(y'=\overline{E}(o')\), then \(y_j = y'_j\) for
every \(i<|y|\).)

Define \(x = E(o)\) and \(x'=E(o')\). Note that since \(E\) is one-to-one, \(x \neq x'\). (Recall that two strings \(x,x'\) are distinct if they either differ in length or have at least one distinct coordinate.) Under our assumption, \(|PF(x)| \leq |PF(x')|\), and since by construction \(|PF(x)|=2|x|+2\), it follows that \(|x| \leq |x'|\). If \(|x|=|x'|\) then, since \(x \neq x'\), there must be a coordinate \(i\in \{0,\ldots, |x|-1\}\) such that \(x_i \neq x'_i\). But since \(PF(x)_{2i}=x_i\), we get that \(PF(x)_{2i} \neq PF(x')_{2i}\) and hence \(\overline{E}(o)=PF(x)\) is not a prefix of \(\overline{E}(o')=PF(x')\). Otherwise (if \(|x| \neq |x'|\)) then it must be that \(|x| < |x'|\), and hence if \(n=|x|\), then \(PF(x)_{2n}=0\) and \(PF(x)_{2n+1}=1\). But since \(n<|x'|\), \(PF(x')_{2n},PF(x')_{2n+1}\) is equal to either \(00\) or \(11\), and in any case we get that \(\overline{E}(o)=PF(x)\) is not a prefix of \(\overline{E}(o')=PF(x')\).

In fact, we can even obtain a more efficient transformation where \(|E'(o)| \leq |o| + O(\log |o|)\). We leave proving this as an exercise (see Reference:prefix-free-ex).

### Representing letters and text

We can represent a letter or symbol by a string, and then if this
representation is prefix free, we can represent a sequence of symbols by
simply concatenating the representation of each symbol. One such
representation is the ASCII that
represents \(128\) letters and symbols as strings of \(7\) bits. Since it is
a fixed-length representation it is automatically prefix free (can you
see why?). Unicode is a
representation of (at the time of this writing) about 128,000 symbols
into numbers (known as *code points*) between \(0\) and \(1,114,111\). There
are several types of prefix-free representations of the code points, a
popular one being UTF-8 that
encodes every codepoint into a string of length between \(8\) and \(32\).

### Representing vectors, matrices, images

Once we can represent numbers, and lists of numbers, then we can
obviously represent *vectors* (which are just lists of numbers).
Similarly, we can represent lists of lists and so in particular
*matrices*. To represent an image, we can represent the color at each
pixel by a list of three numbers corresponding to the intensity of Red,
Green and Blue.We can restrict to three basic colors since
(most) humans only
have three types of cones in their retinas. We would have needed 16
basic colors to represent colors visible to the Mantis
Shrimp. Thus an image of \(n\) pixels would be represented of
a list of \(n\) such length-three lists. A video can be represented as a
list of images.Of course these representations are rather wasteful and
much
more compact
representations are typically used for images and videos, though
this will not be our concern in this course.

### Representing graphs

A *graph* on \(n\) vertices can be represented as an \(n\times n\)
*adjacency* matrix whose \((i,j)^{th}\) entry is equal to \(1\) if the edge
\((i,j)\) is present and is equal to \(0\) otherwise. That is, we can
represent an \(n\) vertex directed graph \(G=(V,E)\) as a string
\(A\in \{0,1\}^{n^2}\) such that \(A_{i,j}=1\) iff the edge
\(\overrightarrow{i\;j}\in E\). We can transform an undirected graph to a
directed graph by replacing every edge \(\{i,j\}\) with both edges
\(\overrightarrow{i\; j}\) and \(\overleftarrow{i\;j}\)

Another representation for graphs is the *adjacency list*
representation. That is, we identify the vertex set \(V\) of a graph with
the set \([n]\) where \(n=|V|\), and represent the graph \(G=(V,E)\) a a list
of \(n\) lists, where the \(i\)-th list consists of the out-neighbors of
vertex \(i\). The difference between these representations can be
important for some applications, though for us would typically be
immaterial.

### Representing lists

If we have a way of represent objects from a set \(\mathcal{O}\) as binary
strings, then we can represent lists of these objects ny applying a
prefix-free transformation. Moreover, we can use a trick similar to the
above to handle *nested* lists. The idea is that if we have some
representation \(E:\mathcal{O} \rightarrow \{0,1\}^*\), then we can
represent nested lists of items from \(\mathcal{O}\) using strings over
the five element alphabet \(\Sigma = \{\) `0`

,`1`

,`[`

, `]`

, `,`

\(\}\).
For example, if \(o_1\) is represented by `0011`

, \(o_2\) is represented by
`10011`

, and \(o_3\) is represented by `00111`

, then we can represent the
nested list \((o_1,(o_2,o_3))\) as the string `"[0011,[1011,00111]]"`

over
the alphabet \(\Sigma\). By encoding every element of \(\Sigma\) itself as a
three-bit string, we can transform any representation for objects
\(\mathcal{O}\) into a representation that allows to represent
(potentially nested) lists of these objects.

### Notation

We will typically identify an object with its representation as a string. For example, if \(F:\{0,1\}^* \rightarrow \{0,1\}^*\) is some function that maps strings to strings and \(x\) is an integer, we might make statements such as “\(F(x)+1\) is prime” to mean that if we represent \(x\) as a string \(\underline{x}\) and let \(\underline{y}=F(\underline{x})\), then the integer \(y\) represented by the string \(\underline{y}\) satisfies that \(y+1\) is prime. (You can see how this convention of identifying objects with their representation can save us a lot of cumbersome formalism.) Similarly, if \(x,y\) are some objects and \(F\) is a function that takes strings as inputs, then by \(F(x,y)\) we will mean the result of applying \(F\) to the representation of the order pair \((x,y)\). We will use the same notation to invoke functions on \(k\)-tuples of objects for every \(k\).

This convention of identifying an object with its representation as a
string is one that we humans follow all the time. For example, when
people say a statement such as “\(17\) is a prime number”, what they
really mean is that the integer whose decimal representation is the
string “`17`

”, is prime.

## Defining computational tasks

Abstractly, a *computational process* is some process that takes an
input which is a string of bits, and produces an output which is a
string of bits. This transformation of input to output can be done using
a modern computer, a person following instructions, the evolution of
some natural system, or any other means.

In future lectures, we will turn to mathematically defining
computational process, but, as we discussed above for now we want to
focus on *computational tasks*; i.e., focus on the **specification** and
not the **implementation**. Again, at an abstract level, a computational
task can specify any relation that the output needs to have with the
input. But for most of this course, we will focus on the simplest and
most common task of *computing a function*. Here are some examples:

- Given (a representation) of two integers \(x,y\), compute the product \(x\times y\). Using our representation above, this corresponds to computing a function from \(\{0,1\}^*\) to \(\{0,1\}^*\). We’ve seen that there is more than one way to solve this computational task, and in fact, we still don’t know the best algorithm for this problem.
- Given (a representation of) an integer \(z\), compute its
*factorization*; i.e., the list of primes \(p_1 \leq \cdots \leq p_k\) such that \(z = p_1\cdots p_k\). This again corresponds to computing a function from \(\{0,1\}^*\) to \(\{0,1\}^*\). The gaps in our knowledge of the complexity of this problem are even longer. - Given (a representation of) a graph \(G\) and two vertices \(s\) and
\(t\), compute the length of the shortest path in \(G\) between \(s\) and
\(t\), or do the same for the
*longest*path (with no repeated vertices) between \(s\) and \(t\). Both these tasks correspond to computing a function from \(\{0,1\}^*\) to \(\{0,1\}^*\), though it turns out that there is a huge difference in their computational difficulty. - Given the code of a Python program, is there an input that would
force it into an infinite loop. This corresponds to computing a
partial function from \(\{0,1\}^*\) to \(\{0,1\}\); though it is easy to
make it into a total function by mapping every string into the
trivial Python program that stops without doing anything. We will
see that we
*do*understand the computational status of this problem, but the answer is quite surprising. - Given (a representation of) an image \(I\), decide if \(I\) is a photo of a cat or a dog. This correspond to computing some (partial) function from \(\{0,1\}^*\) to \(\{0,1\}\).

An important special case of computational tasks corresponds to
computing *Boolean* functions, whose output is a single bit \(\{0,1\}\).
Computing such functions corresponds to answering a YES/NO question, and
hence this task is also known as a *decision problem*. Given any
function \(F:\{0,1\}^* \rightarrow \{0,1\}\) and \(x\in \{0,1\}^*\), the
task of computing \(F(x)\) corresponds to the task of deciding whether or
not \(x\in L\) where \(L = \{ x : F(x)=1 \}\) is known as the *language*
that corresponds to the function \(F\).The language terminology is due to historical connections between
the theory of computation and formal linguistics as developed by
Noam Chomsky. Hence many texts refer to
such as computational task as *deciding a language*.

For every particular function \(F\), there can be several possible
*algorithms* to compute \(F\). We will be interested in questions such as:

- For a given function \(F\), can it be the case that
*there is no algorithm*to compute \(F\)? - If there is an algorithm, what is the best one? Could it be that \(F\) is “effectively uncomputable” in the sense that every algorithm for computing \(F\) requires a prohibitively large amount of resources?
- If we can’t answer this question, can we show equivalence between different functions \(F\) and \(F'\) in the sense that either they are both easy (i.e., have fast algorithms) or they are both hard?
- Can a function being hard to compute ever be a
*good thing*? Can we use it for applications in areas such as cryptography?

In order to do that, we will need to mathematically define the notion of
an *algorithm*, which is what we’ll do in the next lecture.

### Advanced note: beyond computing functions

Functions capture quite a lot of computational tasks, but one can
consider more general settings as well. For starters, we can and will
talk about *partial* functions, that are not defined on all inputs. When
computing a partial function, we only need to worry about the inputs on
which the function is defined. Another way to say it is that we can
design an algorithm for a partial function \(F\) under the assumption that
someone “promised” us that all inputs \(x\) would be such that \(F(x)\) is
defined (as otherwise we don’t care about the result). Hence such tasks
are also known as *promise problems*.

Another generalization is to consider *relations* that may have more
than one possible admissible output. For example, consider the task of
finding any solution for a given set of equation. A *relation* \(R\) maps
a string \(x\in \{0,1\}^*\) into a *set of strings* \(R(x)\) (for example,
\(x\) might describe a set of equations, in which case \(R(x)\) would
correspond to the set of all solutions to \(x\)). We can also identify a
relation \(R\) with the set of pairs of strings \((x,y)\) where \(y\in R(x)\).
A computational process solves a relation if for every \(x\in \{0,1\}^*\),
it outputs some string \(y\in R(x)\).

Later on in this course we will consider even more general tasks,
including *interactive* tasks, such as finding good strategy in a game,
tasks defined using probabilistic notions, and others. However, for much
of this course we will focus on the task of computing a function, and
often even a *Boolean* function, that has only a single bit of output.
It turns out that a great deal of the theory of computation can be
studied in the context of this task, and the insights learned are
applicable in the more general settings.

## Lecture summary

- We can represent essentially every object we want to compute on using binary strings.
- A representation scheme for a set of objects \(\mathcal{O}\) is a one-to-one map from \(\mathcal{O}\) to \(\{0,1\}^*\).
- A basic computational task is the task of
*computing a function*\(F:\{0,1\}^* \rightarrow \{0,1\}^*\). This encompasses not just arithmetical computations such as multiplication, factoring, etc. but a great many other tasks arising in areas as diverse as scientific computing, artificial intelligence, image processing, data mining and many many more. - We will study the question of finding (or at least giving bounds on)
what is the
*best*algorithm for computing \(F\) for various interesting functions \(F\).

## Exercises

Which one of these objects can be represented by a binary string?

a. An integer \(x\)

b. An undirected graph \(G\).

c. A directed graph \(H\)

d. All of the above.

Recall that the gradeschool algorithm for multiplying two numbers
requires \(O(n^2)\) operations. Suppose that instead of using decimal
representation, we use one of the following representations \(R(x)\) to
represent a number \(x\) between \(0\) and \(10^n-1\). For which one of these
representations you can still multiply the numbers in \(O(n^2)\)
operations?

a. The standard binary representation: \(B(x)=(x_0,\ldots,x_{k})\) where
\(x = \sum_{i=0}^{k} x_i 2^i\) and \(k\) is the largest number s.t.
\(x \geq 2^k\).

b. The reverse binary representation: \(B(x) = (x_{k},\ldots,x_0)\) where
\(x_i\) is defined as above for \(i=0,\ldots,k-1\).

c. Binary coded decimal representation: \(B(x)=(y_0,\ldots,y_{n-1})\)
where \(y_i \in \{0,1\}^4\) represents the \(i^{th}\) decimal digit of \(x\)
mapping \(0\) to \(0000\), \(1\) to \(0001\), \(2\) to \(0010\), etc. (i.e. \(9\) maps
to \(1001\))

d. All of the above.

Suppose that \(R:\N \rightarrow \{0,1\}^*\) corresponds to representing a number \(x\) as a string of \(x\) \(1\)’s, (e.g., \(R(4)=1111\), \(R(7)=1111111\), etc.). If \(x,y\) are numbers between \(0\) and \(10^n -1\), can we still multiply \(x\) and \(y\) using \(O(n^2)\) operations if we are given them in the representation \(R(\cdot)\)?

Recall that if \(F\) is a one-to-one and onto function mapping elements of
a finite set \(U\) into a finite set \(V\) then the sizes of \(U\) and \(V\) are
the same. Let \(B:\N\rightarrow\{0,1\}^*\) be the function such that for
every \(x\in\N\), \(B(x)\) is the binary representation of \(x\).

a. Prove that \(x < 2^k\) if and only if \(|B(x)| \leq k\).

b. Use a. to compute the size of the set
\(\{ y \in \{0,1\}^* : |y| \leq k \}\) where \(|y|\) denotes the length of
the string \(y\).

c. Use a. and b. to prove that \(2^k-1 = 1 + 2 + 4+ \cdots + 2^{k-1}\).

Suppose that \(F:\N\rightarrow\{0,1\}^*\) is a one-to-one function that is
*prefix free* in the sense that there is no \(a\neq b\) s.t. \(F(a)\) is a
prefix of \(F(b)\).

a. Prove that \(F_2:\N\times \N \rightarrow \{0,1\}^*\), defined as
\(F_2(a,b) = F(a)F(b)\) (i.e., the concatenation of \(F(a)\) and \(F(b)\)) is
a one-to-one function.

b. Prove that \(F_*:\N^*\rightarrow\{0,1\}^*\) defined as
\(F_*(a_1,\ldots,a_k) = F(a_1)\cdots F(a_k)\) is a one-to-one function,
where \(\N^*\) denotes the set of all finite-length lists of natural
numbers.

Suppose that \(F:O\rightarrow\{0,1\}^*\) is some (not necessarily prefix
free) representation of the objects in the set \(O\), and
\(G:\N\rightarrow\{0,1\}^*\) is a prefix-free representation of the
natural numbers. Define \(F'(o)=G(|F(o)|)F(o)\) (i.e., the concatenation
of the representation of the length \(F(o)\) and \(F(o)\)).

a. Prove that \(F'\) is a prefix-free representation of \(O\).

b. Show that we can transform any representation to a prefix-free one by
a modification that takes a \(k\) bit string into a string of length at
most \(k+O(\log k)\). c. Show that we can transform any representation to
a prefix-free one by a modification that takes a \(k\) bit string into a
string of length at most \(k+ \log k + O(\log\log k)\).Hint: Think recursively how to represent the length of the
string.

Suppose that \(S \subseteq \{0,1\}^n\) is some finite prefix-free set.

a. For every \(k \leq n\) and length-\(k\) string \(x\in S\), let
\(L(x) \subseteq \{0,1\}^n\) denote all the length-\(n\) strings whose first
\(k\) bits are \(x_0,\ldots,x_{k-1}\). Prove that **(1)** \(|L(x)|=2^{n-|x|}\)
and **(2)** If \(x \neq x'\) then \(L(x)\) is disjoint from \(L(x')\).

b. Prove that \(\sum_{x\in S}2^{-|x|} \leq 1\).

c. Prove that there is no prefix-free encoding of strings with less than
logarithmic overhead. That is, prove that there is no function
\(PF:\{0,1\}^* \rightarrow \{0,1\}^*\) s.t. \(|PF(x)| \leq |x|+0.9\log |x|\)
for every \(x\in \{0,1\}^*\) and such that the set
\(\{ PF(x) : x\in \{0,1\}^* \}\) is prefix-free.

Prove that for every two one-to-one functions \(F:S \rightarrow T\) and \(G:T \rightarrow U\), the function \(H:S \rightarrow U\) defined as \(H(x)=G(F(x))\) is one to one.

- We have shown that the natural numbers can be represented as
strings. Prove that the other direction holds as well: that there is
a one-to-one map \(StN:\{0,1\}^* \rightarrow \N\). (\(StN\) stands for
“strings to numbers”.)
- Recall that Cantor proved that there is no one-to-one map \(RtN:\R \rightarrow \N\). Show that Cantor’s result implies Reference:cantorthm.

## Bibliographical notes

The idea that we should separate the *definition* or *specification* of
a function from its *implementation* or *computation* might seem
“obvious”, but it took some time for mathematicians to arrive at this
viewpoint. Historically, a function \(F\) was identified by rules or
formulas showing how to derive the output from the input. As we discuss
in greater depth in our lecture on uncomputability, in the 1800’s this
somewhat informal notion of a function started “breaking at the seams”
and eventually mathematicians arrived at the more rigorous definition of
a function as an arbitrary assignment of input to outputs. While many
functions may be described (or computed) by one or more formulas, today
we do not consider that to be an essential property of functions, and
also allow functions that do not correspond to any “nice” formula.

Gromov and Pomerantz’s quotes are lifted from Doron Zielberger’s page.

## Further explorations

Some topics related to this lecture that might be accessible to advanced students include:

*Succinct*data structures. These are representations that map objects from some set \(\mathcal{O}\) into strings of length not much larger than the minimum of \(\log_2 |\mathcal{O}|\) but still enable fast access to certain queries, see for example this paper.- We’ve mentioned that all representations of the real numbers are
inherently
*approximate*. Thus an important endeavor is to understand what guarantees we can offer on the approximation quality of the output of an algorithm, as a function of the approximation quality of the inputs. This is known as the question of numerical stability. - The linear algebraic view of graphs. The adjacency matrix
representation of graphs is not merely a convenient way to map a
graph into a binary string, but it turns out that many natural
notions and operations on matrices are useful for graphs as well.
(For example, Google’s PageRank algorithm relies on this viewpoint.)
The notes of this
course are an excellent
source for this area, known as
*spectral graph theory*. We might discuss this view much later in this course when we talk about*random walks*.