\documentclass[10pt]{article}
\usepackage{fullpage}
\usepackage[utf8x]{inputenc}
\usepackage{amsmath,amsfonts,amsthm}
\usepackage[english]{babel}
\usepackage[capitalize, noabbrev]{cleveref}
\usepackage{algorithm}
\usepackage{algpseudocode}

\newenvironment{enumerate*}%
  {\vspace{-2ex} \begin{enumerate} %
     \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}%
  {\end{enumerate}}
 
\newenvironment{itemize*}%
  {\vspace{-2ex} \begin{itemize} %
     \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}%
  {\end{itemize}}
 
\newenvironment{description*}%
  {\vspace{-2ex} \begin{description} %
     \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}%
  {\end{description}}

\DeclareMathOperator{\E}{\mathbb{E}}
\let\P\relax
\DeclareMathOperator{\P}{\mathbb{P}}
\DeclareMathOperator*{\argmin}{\arg\min}
\newcommand{\ex}[1]{\E\left[#1\right]}
\newcommand{\prob}[1]{\P\left[#1\right]}
\newcommand{\inprod}[1]{\left\langle #1 \right\rangle}
\newcommand{\R}{\mathbf{R}}
\newcommand{\poly}{\text{poly}}
\newcommand{\N}{\mathbf{N}}
\newcommand{\C}{\mathcal{C}}
\newcommand{\eps}{\varepsilon}
\newcommand{\cl}[1]{\text{\textbf{#1}}}
\newcommand{\eqdef}{\mathbin{\stackrel{\rm def}{=}}}
\newcommand{\llbracket}{[\![}

\newtheorem{theorem}{Theorem}
\newtheorem{lemma}{Lemma}
\algrenewcommand\algorithmicensure{\textbf{Output:}}
\algrenewcommand\algorithmicrequire{\textbf{Input:}}

\author{Thibaut Horel}
\title{CS 225 Problem Set 2 -- Solutions}

\begin{document}
\maketitle

Collaborator: Debmalya Mandal

\section*{Problem 2.9}

\paragraph{1.}
Let $\lambda$ be an eigenvalue of $M$ and $x$ be an associated eigenvector. Let
$i^*\in\{1,\ldots, n\}$ such that $|x_{i^*}| = \|x\|_{\infty}$. Since $x$ is an
eigenvector we have:
\begin{displaymath}
    \sum_{j=1}^n m_{i^*,j} x_j = \lambda x_{i^*}
\end{displaymath}
$x$ is non-zero, hence $x_{i^*}$ is non-zero. Dividing both sides by $x_{i^*}$,
we obtain:
\begin{displaymath}
    |\lambda| = 
    \left|\sum_{j=1}^n m_{i^*,j} \frac{x_j}{x_{i^*}}\right|
    \leq \sum_{j=1}^n m_{i^*,j} \left|\frac{x_j}{x_{i^*}}\right|
    \leq \sum_{j=1}^n m_{i^*, j} = 1
\end{displaymath}
where the first inequality used the triangle inequality and the second
inequality used the definition of $x_{i^*}$.

\paragraph{2.} Assume $G$ is disconnected. Hence it has at least two connected
components. We can write $V = C_1\cup C_2$ with $C_1\cap C_2 = \emptyset$ and
$ C_1\times C_2\cap E = \emptyset$. Then it is clear that $x^1
= \mathbf{1}_{C_1}$ (the indicator vector of $C_1$) and $x^2
= \mathbf{1}_{C_2}$ are eigenvectors of $M$ for the eigenvalue 1. For example
for $x^1$:
\begin{displaymath}
    \sum_{j=1}^n m_{i,j} x^1_j =
    \begin{cases}
        1&\text{if $i\in C_1$}\\
        0&\text{otherwise}
    \end{cases}
\end{displaymath}
where the first case follows from the fact that if $i\in C_1$, all the non-zero
terms $m_{i,j}$ will be preserved by the multiplication by $x^1_j$ since
$m_{i,j}\neq 0\Leftrightarrow j\in C_1$ and the fact that $\sum_{j=1}^n m_{i,j}
= 1$. Similarly if $i\in C_2$ $m_{i,j} = 0$ whenever $j\in C_1$.

Furthermore $x^1$ and $x^2$ are clearly orthogonal, so they span a vector space
of dimension 2. Hence the multiplicity of $1$ as an eigenvalue is at least 2.

\paragraph{} Let us now assume that $1$ has multiplicity at least $2$. We can
find an eigenvector $x$ for $1$ orthogonal to $\lambda_1$, that is: $\sum_i x_i
= 0$. Let us define $C_1 = \{i\in V: x_i > 0\}$ and $C_2 = \{i\in V: x_j\leq 0\}$.
Because $x\neq 0$ and $\sum_i x_i = 0$, both $C_1$ and $C_2$ are non-empty.
They are clearly disjoint and their union is $V$. We show that $C_1$ and $C_2$
are disconnected. Let $i^*$ such that $|x_{i^*}| = \|x\|_{\infty}$. Without
loss of generality we can assume $x_{i^*} > 0$ (otherwise we consider $x'
= -x$).
\begin{displaymath}
    \sum_{j=1}^n m_{i^*,j}x_j = x_{i^*}
\end{displaymath}
Dividing by $x_{i^*}$ both sides (it is non-zero for the same reason as in
\textbf{1.}):
\begin{displaymath}
    1 = \left|\sum_{j=1}^n m_{i^*,j}\frac{x_j}{x_{i^*}}\right|
    \leq \sum_{j=1}^n m_{i^*,j}\left|\frac{x_j}{x_{i^*}}\right|
    \leq \sum_{j=1}^n m_{i^*,j} = 1
\end{displaymath}
where we again used the triangle inequality, the definition of $x_{i^*}$ and
the fact that the sum of the entries of the rows of $M$ is one. Hence, the
chain of inequalities is a chain of equalities. This implies that for all $j$
such that $m_{i^*,j} \neq 0$, $x_j=x_{i^*}$. But we can then repeatedly apply the
same argument to all the neighbors of $i^*$, the neighbors of their neighbors,
etc. By induction (over the distance to $i^*$), we see that for all vertices
$j$ in the same connected component as $i^*$, $x_j = x_{i^*}>0$. This implies
that $C_2$ is disconnected from $C_1$ (none of the vertices in $C_2$ are in the
same connected component as $x_{i^*}$).

\paragraph{3.} Assuming that $G$ is connected, it is easy to see that $G$
bipartite is equivalent to $G^2$ being disconnected. Hence assuming $G$
connected and using \textbf{2.}:

$G$ bipartite $\Leftrightarrow$ $G^2$ disconnected $\Leftrightarrow$ $1$ is an eigenvalue of $M^2$
of multiplicity at least 2 $\Leftrightarrow$ $\exists x\perp \mathbf{1}, x\neq
0,\; M^2x
= x$ $\Leftrightarrow$ $\exists x\perp\mathbf{1}, x\neq 0,\; Mx
= \pm x$ $\Leftrightarrow$ $\exists x\perp\mathbf{1}, x\neq 0,\; Mx = -x$ $\Leftrightarrow$ $-1$ is an eigenvalue of
$M$.

The antepenultimate equivalence uses that the eigenvalues of $M^2$ are the
squares of the eigenvalues of $M$, and the penultimate equivalence uses that
$x$ cannot be an eigenvector for the eigenvalue $1$ because of \textbf{2}. The
second equivalence also uses \textbf{2.}.

\paragraph{4.} First observe that $\max_{\|x\|_2 = 1} \inprod{Mx, x}$
= $\lambda_{max}(M)$ for any symmetric matrix $M$. Indeed, using the spectral
theorem and writing $M = P^T DP$ with $D$ = $\text{diag}(\lambda_1,\dots,\lambda_n)$
and observing that $\|x\|_2 =1\Leftrightarrow \|Px\|_2 = 1$ we have:
\begin{displaymath}
    \max_{\|x\|_2 = 1} \inprod{Mx, x} = \max_{\|y\|_2=1} y^TDy = \max_{\|y\|_2
    = 1} \sum_{i=1}^n\lambda_i y_i^2
\end{displaymath}
the right-hand side maximum is clearly obtained when $y_i = 1$ for some
coordinate $i$ such that $\lambda_i = \lambda_{max}(M)$ and $y_j = 0$ for
$j\neq i$. For this $y$ the value is $\lambda_{max}(M)$.

Now observe that $\max_x\inprod{Mx, x}$ when $x$ is taken in the vector space
orthogonal to $\mathbf{1}$ is exactly the largest eigenvalue of $M$ when
restricted to the vector space orthogonal to $\mathbf{1}$ (restricting $M$ to
this subspace is allowed since this subspace is invariant by $M$). But since
the eigenspaces of $M$ are orthogonal, the largest eigenvalue of $M$ restricted
to the orthogonal of $\mathbf{1}$ is the second largest eigenvalue of $M$.

Hence:
\begin{displaymath}
    \lambda_2(M) = \max_{x} \inprod{Mx, x}
\end{displaymath}
where the maximum is taken over $x$ of length 1 in the orthogonal space of $\mathbf{1}$.

\paragraph{} Let us now show the second identity given in the problem
statement:
\begin{displaymath}
    \sum_{(i,j)\in E} (x_i-x_j)^2 = \sum_{(i,j)\in E} x_i^2 +
    \sum_{(i,j)\in E} x_j^2 - 2\sum_{(i,j)\in E} x_ix_j = \frac{d}{2}\|x\|_2^2
    + \frac{d}{2}\|x\|_2^2 - d\sum_{i,j} m_{i,j}x_i x_j
\end{displaymath}
where the second equality used that for all $i$ (resp. $j$) there are
$\frac{d}{2}$ edges leaving (entering) and that $m_{i,j} = \frac{2}{d}a_{i,j}$
where $a_{i,j}$ is the $(i,j)$ entry of the adjacency matrix of $G$. For
$\|x\|_2 = 1$:
\begin{displaymath}
    1- \frac{1}{d}\sum_{(i,j)\in E} (x_i-x_j)^2
    = \sum_{i,j}m_{i,j}x_i x_j = \inprod{Mx, x}
\end{displaymath}
taking the $\max$ both sides gives the second identity.

Let us now consider $x$ such that $\|x\|_2 = 1$ and  $\sum_{i} x_i
= 0$ and consider $i^*$ such that $|x_{i^*}| = \|x\|_{\infty}$. Because
$\|x\|_2 =1$, we have $|x_{i^*}| \geq \frac{1}{\sqrt{n}}$. Because, $\sum_i x_i
= 0$ there is $x_k$ such that $x_k$ has the opposite sign of $x_{i^*}$. Since $G$
is connected, there is a path of length $l$ at most $n$ from $i^*$ to $k$. Let us
index this path $i^* = i_1,\ldots, i_l = k$. Then:
\begin{displaymath}
    \sum_{(i,j)\in E} (x_i-x_j)^2 \geq  \sum_{s=1}^l (x_{i_{s+1}}
    - x_{i_s})^2
\end{displaymath}
where we restricted the sum to the path from $i^*$ to $k$. Applying the
Cauchy-Schwartz inequality:
\begin{displaymath}
    \sum_{(i,j)\in E} (x_i-x_j)^2 \geq \frac{1}{l} \left(\sum_{s=1}^l x_{i_{s+1}}
    - x_{i_s}\right)^2= \frac{1}{l} (x_k - x_{i^*})^2\geq \frac{1}{l} x_{i^*}^2
\end{displaymath}
where the last inequality used that $x_{i^*}$ and $x_k$ have opposite signs.
Finally using $x_{i^*}^2 \geq \frac{1}{n}$ and $l\leq n$:
\begin{displaymath}
    \sum_{(i,j)\in E} (x_i-x_j)^2 \geq \frac{1}{n^2}
\end{displaymath}
Taking the $\min$ over $x$, this implies, using the variational expression for
$\lambda_2$ (second largest eigenvalue) established at the beginning of the
question: $\lambda_2\leq 1-\frac{1}{dn^2}$.

\paragraph{5.} We consider the graph $G^2$. As seen in \textbf{3.}, $G^2$ is
connected since $G$ is connected and nonbipartite. Hence, we can apply question
\textbf{4.} to $G^2$: $\lambda_2 (M^2)\leq 1-\frac{1}{d^2n^2}$ (the degree of
$G^2$ is $d^2$). But the eigenvalues of $M^2$ are the squares of the
eigenvalues of $M$. So all the eigenvalues of $M$ other than 1 have absolute
value at most:
\begin{displaymath}
    \sqrt{1-\frac{1}{d^2n^2}}\leq 1 - \frac{1}{2d^2n^2}
\end{displaymath}
where we used $\sqrt{1-x}\leq 1-\frac{x}{2}$ which concludes the question by
definition of $\lambda(G)$.

\paragraph{6.} The analysis we did in \textbf{4.} used that a path from
$i^*$ to $k$ had length $l\leq n$. If we know the diameter $D$, we have $l\leq
D$. The same analysis gives $\lambda_2\leq 1- \frac{1}{dDn}$.

Finally, \textbf{5.} implies that $\gamma(G) \geq \frac{1}{2d^2n^2}$.

\section*{Problem 3.1}

Let us consider a problem $(Y, N)$ in $\cl{prBPP}$ where $Y$ (resp. $N$) is
the set of “yes” (resp. “no”) instances. There is a polynomial time algorithm
$A$ such that for all $x$, $\prob{ A(x) \text{ correct}}\geq 1-\frac{1}{2^n}$.
The algorithm uses $m = poly(n)$ coin tosses. For input $x$, let us denote by
$A_x$ the set of coin tosses $r$ such that $A(x; r)$ says “yes”.
Using exactly the same analysis as in the proof of Theorem 3.14, we can prove:
\begin{itemize}
    \item $x\in Y \implies \P_s\big[\forall r\in \{0,1\}^m,\; P(x,s,r) = 1\big]
        > 1 - \frac{2^m}{2^{nm}}$ where $s$ is a $m$-tuple of random bits.
    \item $x\in N \implies \forall s\in\{0,1\}^m,\; \P_r\big[P(x,s,r)
        = 1\big]<\frac{m}{2^{n}}$ were $r$ is a $m$-tuple of random bits.
\end{itemize}
for $P(x,s,r) = \bigvee_{i=1}^m \big(A(x;r\oplus s_i) = 1\big)$. Clearly $P$
can be computed in polynomial time.
If we define:
\begin{itemize}
    \item $Y' = \big\{(x,s):\, \forall r\in\{0,1\}^m,\; P(x,s,r) = 1\big\}$
    \item $N' = \big\{(x,s):\, \P_r[ P(x,s,r) = 1]<\frac{m}{2^n}\big\}$
\end{itemize}
then $(Y', N')$ is clearly in $\cl{prco-RP}$ (for input $(x,s)$ take $r$ at
random and output $P(x,s,r)$). Since we are assuming $\cl{prRP} = \cl{prP}$,
then $\cl{prco-RP} = \cl{prco-P} = \cl{prP}$. Hence there exists $Q$,
a polynomial time predicate such that:
\begin{itemize}
    \item $x\in Y \implies \P_s\big[Q(x,s) = 1\big]
        > 1-  \frac{2^m}{2^{nm}}$ where $s$ is a $m$-tuple of random bits.
    \item $x\in N \implies \forall s\in\{0,1\}^m,\; Q(x,s) = 0$.
\end{itemize}
But now we see that:
\begin{itemize}
    \item $Y'' = \{x:\, \P_s[Q(x,s) = 1]>1-\frac{2^m}{2^{nm}}\}$
    \item $N'' = \{x:\, \forall s,\; Q(x,s) = 0\}$
\end{itemize}
is in $\cl{prRP} = \cl{prP}$. Hence there is a polynomial time predicate $R$
such that:
\begin{itemize}
    \item $x\in Y\implies R(x) = 1$
    \item $x\in N\implies R(x) = 0$
\end{itemize}
which proves that $(Y,N)$ is in $\cl{prP}$. This implies that $\cl{prBPP}
= \cl{prP}$ and thus that $\cl{BPP} = \cl{P}$.

\section*{Problem 3.2}

\paragraph{1.} Let us assume that $S_1,\ldots,S_{i-1}$ have already been
constructed and satisfy the $(l,a)$ design requirements. Then, for $S_i$ chosen
uniformly at random in $[d]$, we have:
\begin{displaymath}
    \E_{S_i}\big[\#\{j<i : |S_i\cap S_j|\geq a\}\big]
    = \sum_{j=1}^{i-1}\P_{S_i}[|S_i\cap S_j|\geq a]
\end{displaymath}
For a fixed $j$, we see that:
\begin{displaymath}
    \P_{S_i}[|S_i\cap S_j|\geq a]\leq
    \frac{{l \choose a}{d-a\choose l-a}}{{d\choose l}}
    =\frac{{l\choose a}^2}{{d\choose a}}
\end{displaymath}
the inequality is simple combinatorics: for $S_i$ to overlap with $S_j$ by more
than $a$ elements, we first pick a subset of $a$ elements in $S_j$, and then
$l-a$ elements among the remaining elements. The equality uses the definition
of the binomial coefficient and basic algebra.

Summing over $m$ and using the assumed bound on $m$ we directly obtain:
\begin{displaymath}
    \E_{S_i}\big[\#\{j<i : |S_i\cap S_j|\geq a\}\big] < 1
\end{displaymath}

\paragraph{2.} We can rewrite the result obtained in \textbf{1.} as, for all
$S_1,\ldots, S_{i-1}$:
\begin{equation}
    \label{eq:foo}
    \exists S_i\in[d],\; \forall j<i,\; |S_i\cap S_j|< a
\end{equation}
otherwise we would have:
\begin{displaymath}
    \forall S_i\in[d],\; \#\{j<i : |S_i\cap S_j|\geq a\}\geq 1
\end{displaymath}
and integrating over uniformly random subset $S_i$ would contradict
\textbf{1.}.

Equation~\eqref{eq:foo} shows that we will be able to construct an $(l,a)$
design: for $i\in\{1,\ldots,m\}$, we are always guaranteed that there exists
a set $S_i$ of size $l$ which does not overlap by more than $a$ with any of the
previously selected sets. We now need to control the sizes of the different
parameters.

For fixed $(l,m)$ and $a = \gamma\log m$ for some $\gamma > 0$, we need:
\begin{displaymath}
    m\leq {d\choose a}/{l \choose a}^2
\end{displaymath}
we use the standard inequalities:
\begin{displaymath}
    \left(\frac{n}{k}\right)^k\leq
    {n\choose k} \leq \left(\frac{ne}{k}\right)^k
\end{displaymath}
to prove that the condition will be satisfied if:
\begin{displaymath}
    \left(\frac{de}{a}\right)^a\geq m\left(\frac{l}{a}\right)^{2a}
\end{displaymath}
This is equivalent to:
\begin{displaymath}
    d\geq \frac{1}{e}m^{1/a} \frac{l^2}{a}
    = \frac{2^{1/\gamma}}{e}\frac{l^2}{a} = O\left(\frac{l^2}{a}\right)
\end{displaymath}
where the first equality used that $a =\gamma \log m$.

\paragraph{3.} We are going to construct the sets $S_1,\dots, S_{m}$ one by
one, and for each $i$, we are going to construct $S_i$ element by element using
the Method of Conditional Expectation. If all the parameters are as in
\textbf{2.}, we know from \textbf{1.} that if $S_1,\dots,S_{i-1}$ have already
been constructed, then:
\begin{displaymath}
    \E_{S_i}\left[\#\{j<i : |S_i\cap S_j|\geq a\}\right]<1
\end{displaymath}

For concision, let use write $\phi(S_i) = \#\{j<i : |S_i\cap S_j|\geq a\}$
where $S_i$ is a uniformly random subset of $[d]$ of size $l$ and
denote by $S_i^k$ the set constructed after $k$ steps, then we have:
\begin{displaymath}
    \E_{S_i}\left[\phi(S_i)\,|\, S_i^k\subseteq S_i\right] = 
    \E_{e\in [d]\setminus S_i^k}\left[\E_{S_i}\left[\phi(S_i)\,|\,
    S_i^k\cup\{e\}\subseteq S_i\right]\right]
\end{displaymath}
where $e$ is chosen uniformly at random in $[d]\setminus S_i^k$.  Hence, there
exists $e\in[d]\setminus S_i^k$ such that if we define $S_i^{k+1}=
S_i^k\cup\{e\}$:
\begin{displaymath}
    \E_{S_i}\left[\phi(S_i)\,|\, S_i^k\subseteq S_i\right] \geq
\E_{S_i}\left[\phi(S_i)\,|\, S_i^{k+1}\subseteq S_i\right]
\end{displaymath}
one $e$ which satisfies this property is for example:
\begin{equation}
    \label{eq:foo2}
    e^*\in\argmin_{e\in [d]\setminus S_i^k}\E_{S_i}\left[\phi(S_i)\,|\,
    S_i^k\cup\{e\}\subseteq S_i\right]
\end{equation}

Hence for $S_i^k$  constructed by repeatedly adding the element $e^*$ as
defined in \eqref{eq:foo2}, we are guaranteed:
\begin{displaymath}
    \forall k,\;\E_{S_i}\left[\phi(S_i)\,|\, S_i^k\subseteq S_i\right] \leq
\E_{S_i}\left[\phi(S_i)\right]< 1
\end{displaymath}
And in particular for $k=l$, $S_i$ is entirely determined and we have
$\phi(S_i) = 0$ (since it is an integer). That is, $S_i$ is a valid set to add
to our design.

The only thing left to show is that computing the design can be done in
polynomial time. Denoting by $T$ the running time of computing the
objective function in \eqref{eq:foo2}, constructing the design will take time
$O(mldT) = O(T\poly(m,d))$, since $d = O(l^2/a)$ and $a = \gamma\log m$.


The objective function in \eqref{eq:foo2} can be
rewritten:
\begin{displaymath}
    f_k(e) \eqdef \sum_{j=1}^{i-1}\P_S\left[|(S\cup S_i^k\cup\{e\})\cap
    S_j|\geq a\right]
\end{displaymath}
where $S$ is a uniformly random subset of $[d]\setminus S_i^k\cup\{e\}$ of size
$l - k-1$.

But as in \textbf{1.}, we can compute $\P_S\left[|(S\cup S_i^k\cup\{e\})\cap
S_j|\geq a\right]$ using combinatorics. Writing $x_e = |S_i^k\cup\{e\}\cap
S_j|$:
\begin{displaymath}
\P_S\left[|(S\cup S_i^k\cup\{e\})\cap S_j|\geq a\right]
= \frac{{l-x_e\choose a - x_e}{d - k - 1 - a + x_e\choose l - a + x_e}}{{d - k -1\choose l-k-1}} 
\end{displaymath}
Hence $f_k(e)$ which can be computed in time $T = poly(m, l, a, d) = poly(m,d)$
since $a = \gamma\log m$ and $d = O(l^2/a)$. 

Note that for a practical implementation, using standard recursive identities
for binomial coefficients, the computation of the objective function can be
made quite efficient: the value of $f_k(e)$ can be computed in $O(1)$
arithmetic operations from $f_{k-1}(e)$.

\section*{Problem 3.4}

\paragraph{1.}
Let $g$ be drawn from a pairwise independent family of functions $\mathbb{F}\to
\mathbb{F}$, and $f_a:\mathbb{F}^{d+1}\to\mathbb{F}$ as in the problem
statement. Then for $x_1\neq x_2\in \mathbb{F}^{d+1}$ and $y_1, y_2\in
\mathbb{F}$, writing $h=g\circ f_a$:
\begin{align*}
    \P[h(x_1) = y_1\wedge h(x_2) = y_2] = &\P[f_a(x_1)
    = f_a(x_2)]\P[g(f_a(x_1))=y_1]\mathbf{1}_{\{y_1=y_2\}}\\
    &+ \P[f_a(x_1)\neq f_a(x_2)]\P[g(f_a(x_1)) = y_1\wedge g(f_a(x_2))
= y_2\,|\, f_a(x_1)\neq f_a(x_2)]
\end{align*}
where we decomposed based on whether or not $f_a(x_1) = f_a(x_2)$.

If $f_a(x_1) = f_a(x_2)$, then $y_1$ have to be equal to $y_2$ and the
probability is only the probability that $g(f_a(x_1)) = y_1$, which is
$\frac{1}{|\mathbb{F}|}$ since $g$ is drawn from a pairwise independent family.
If $f_a(x_2) \neq f_a(x_2)$, then the probability is $\frac{1}{|\mathbb{F}|^2}$
because $g$ is drawn from a pairwise independent family:
\begin{align*}
    \P[h(x_1) = y_1\wedge h(x_2) = y_2] \leq &\P[f_a(x_1)
= f_a(x_2)]\frac{1}{|\mathbb{F}|}
+ \frac{1}{|\mathbb{F}|^2}
\end{align*}
where we upper-bounded $\P[f_a(x_1) \neq f_a(x_2)]$ by $1$ and
$\mathbf{1}_{\{y_1=y_2\}}$ by $1$.

For fixed $x_1$ and $x_2$ with $x_1\neq x_2$, $f_a(x_1)-f_a(x_2)$ is a non-zero
univariate polynomial of the variable $a$ of degree at most $d$. Hence, by the
Schwartz-Zippel lemma:
\begin{displaymath}
    \P[f_a(x_1) = f_a(x_2)] \leq \frac{d}{|\mathbb{F}|}
\end{displaymath}
Overall we have:
\begin{displaymath}
    \P[h(x_1) = y_1\wedge h(x_2) = y_2] \leq \frac{1+d}{|\mathbb{F}|^2}
\end{displaymath}

Let us now tune the parameters $d$ and $|\mathbb{F}|$ to satisfy the
requirements. We will choose $|\mathbb{F}| = \gamma M$ for some $\gamma \geq 1$
(we can simply drop the extra bits to get $\log M = m$ random bits). So we will
have:
\begin{displaymath}
    \P[h(x_1) = y_1\wedge h(x_2) = y_2] \leq \frac{1+\eps}{M^2}
\end{displaymath}
as soon as $\frac{1+d}{\gamma^2}\leq 1+\eps$. Choosing $\gamma = \sqrt{d}$,
this will be satisfied when $d\geq \frac{1}{\eps}$. Then we have
$|\mathbb{F}|^d = (\sqrt{d}M)^d$ which will be larger than $N$ as soon as
$d\geq n$.

To summarize, we choose $|\mathbb{F}| = \sqrt{d} M$ and $d = \frac{1}{\eps}
+ n$. The number of required random bits for $g$ and $a$ is $O(\log |F|)
= O(m+\log n + \log \frac{1}{\eps})$.

\paragraph{2.} We draw $g:[N]\to\{0,1\}$ from an almost pairwise independent
family.

Consider the classic randomized algorithm for MaxCut using
$g$: the cut is $(S,\bar{S})$ where $S = \{v\in V\,|\, g(v) = 1\}$. We have:
\begin{align*}
    \E[|cut(S)|] &= \sum_{(u,v)\in E} \P[(u,v)\in cut(S)]=
    \sum_{(u,v)\in E} 1- \P[(u,v)\notin cut(S)]\\
    &= \sum_{(u,v)\in E} 1- \P[g(u) = g(v) = 1] - \P[g(u) = g(v) - 0]
    \geq |E|\left(1 - \frac{1+\eps}{4} - \frac{1+\eps}{4}\right)
    = |E|\left(\frac{1}{2}-\frac{\eps}{2}\right)
\end{align*}
where the inequality used the almost pairwise independence of $g$.

Choosing $\eps = \frac{1}{\log N}$, we have that $\E[|cut(S)|]\geq
|E|(1/2-o(1))$.  Hence there exists a sequence of coin tosses (for $g$) such
that for this sequence $|cut(S)|\geq 1/2-o(1)$.

One such sequence can be found by iterating over all possible sequences and
taking the one for which $|cut(S)|$ is maximum. It follows from \textbf{1.}
that $g$ requires $O(2 + \log\log N +\log\frac{1}{\eps}) = O(\log\log N)$
random bits. Hence we need $O(\log N)$ iterations to iterate over all possible
sequences of random bits. For each iteration, we need $O(M\poly\log N)$ time to
compute the size of the cut (computing $g(x)$ for a given vertex $x$ can be
done in $\poly\log N$).  Hence the overall running time is $O(M\poly\log N)$

The space requirement is:
\begin{itemize}
    \item $O(\log\log N)$ to store the current sequence of random bits as well
        as the sequence of random bits for the candidate maximum cut
\item $O(\log M)$ to compute the size of a cut at a given iteration as well as
    storing the candidate maximum cut size.
\end{itemize}
Overall, the space requirement is $O(\log  M)$
\end{document}