Adding PS3 solutions, really cool stuff.

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+\documentclass[10pt]{article}
+\usepackage{fullpage}
+\usepackage[utf8x]{inputenc}
+\usepackage{amsmath,amsfonts,amsthm}
+\usepackage[english]{babel}
+\usepackage[capitalize, noabbrev]{cleveref}
+
+\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}}
+\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{\N}{\mathbf{N}}
+\newcommand{\C}{\mathcal{C}}
+\newcommand{\eps}{\varepsilon}
+\newcommand{\eqdef}{\mathbin{\stackrel{\rm def}{=}}}
+\newcommand{\llbracket}{[\![}
+
+\newtheorem{theorem}{Theorem}
+\newtheorem{lemma}{Lemma}
+
+\author{Thibaut Horel}
+\title{CS 224 Problem Set 3 -- Solutions}
+
+\begin{document}
+\maketitle
+
+\section*{Problem 1}
+
+\paragraph{(a)} We change the weight function used in class, we now define for
+$i\in\{1,\ldots, n\}$:
+\begin{displaymath}
+    w_i = \frac{1}{3^{l_i}} + \frac{1}{n}
+\end{displaymath}
+where $l_i$ is the height of $i$ in tree $T$. Let $W$ be the sum of the weights
+of the items in the splay tree. We have:
+\begin{displaymath}
+    W \leq \sum_{i=1}^n \frac{1}{3^{l_i}} + \sum_{i=1}^n \frac{1}{n}
+    \leq \sum_{h\geq 0} \left(\frac{2}{3}\right)^h + 1 \leq 3 = O(1)
+\end{displaymath}
+and for a given $i$ we have:
+\begin{displaymath}
+    s(i) \geq w_i \geq \frac{1}{3^{l_i}}
+\end{displaymath}
+We know that the amortized cost of $\textsc{splay}(i)$ is
+$O\left(1+\log\frac{W}{s(i)}\right)$. Using the above two inequalities, this is
+$O(1+l_i)$. Hence, the amortized cost of $\sigma$ is $O(m + \sum_i m_i l_i)
+= O\left(m + C_T(\sigma)\right)$.
+
+We now want to bound the range of the potential function $\Phi
+= \sum_{i=1}^n\log(s_i)$. We use:
+\begin{displaymath}
+    \frac{1}{n}\leq w_i\leq s_i\leq s_{root} = W = O(1)
+\end{displaymath}
+To obtain that $\Phi\in[-C'n\log n, Cn]$ for some constants $C$ and $C'$.  As
+a consequence $-\Delta\Phi$, the opposite of the change in potential, is
+$O(n\log n)$ for any sequence of operations
+
+The true cost $S(\sigma)$ is simply the amortized cost minus $\Delta\Phi$. From
+what precedes:
+\begin{displaymath}
+    S(\sigma) = O\left(m+C_T(\sigma) + n\log n\right)
+\end{displaymath}
+
+\paragraph{(b)} It suffices to show that $C_T(\sigma) = \sum_{i=1}^n m_il_i
+= \Omega (n\log n)$. Since $m_i\geq 1$ for all $i$, it suffices to show $\sum_{i=1}^n
+l_i = \Omega(n\log n)$. Without loss of generality, we can assume that the $l_i$'s
+are ranked in increasing order: $l_1\leq\ldots\leq l_n$. It is easy to see that
+$l_i\geq \log i$ (the maximum height of an element in a tree containing $i$
+elements is at least $\log i$). Hence:
+\begin{displaymath}
+    \sum_{i=1}^n l_i \geq \sum_{i=1}^n \log i = \Theta(n\log n)
+\end{displaymath}
+The last equality coming from $\sum_{i=1}^n \log i = \log n! \sim n\log n$
+using Stirling's approximation. This concludes the question.
+
+\section*{Problem 2}
+Let us consider a Fibonacci heap of rank $k$ and let us call $x_1,\ldots,x_k$
+the $k$ children of the root in increasing order of degrees, and let us denote
+by $d_1\leq\ldots\leq d_k$ these degrees.
+
+First, we observe that for $i\geq 2$, $d_i\geq i-2$. Indeed, when the tree
+rooted at $x_i$ was linked to the root during a consolidation we had $d_i
+= i-1$ (since we always merge trees of the same rank). Since then $x_i$ has
+lost at most one node (otherwise it would have been cut).
+
+Let us denote by $m_k$ the minimum size of our Fibonacci heap of rank $k$. We
+have:
+\begin{displaymath}
+    m_k = 2 + \sum_{i=2}^k m_{d_i} \geq 2 + \sum_{i=2}^k m_{i-2}
+\end{displaymath}
+Further more we have $m_1 = 2$ and $m_0 = 1$. We now prove by induction $m_k
+\geq F_{k+2}$. $k=0$ and $k=1$ are trivial. Let us assume that the result is
+true for some $k$, then:
+\begin{displaymath}
+    m_{k+1} = 2 + \sum_{i=2}^{k+1} m_{i-2} \geq 2 + \sum_{i=2}^{k+1} F_i
+    = 1+\sum_{i=0}^{k+1} F_i = F_{k+3}
+\end{displaymath}
+The last equality is a common property of Fibonacci numbers which is easily
+proved by induction. This concludes the proof of this problem.
+
+\section*{Problem 3}
+
+\paragraph{(a)}
+The amortized cost of \textsc{decrease-key} is reduced as $k$ increases.
+Intuitively, cuts occur less frequently. Formally, we consider the modified
+potential function:
+\begin{displaymath}
+    \Phi(T) = \#trees + \frac{2}{k-1}\sum_{x\in T} w(x)
+\end{displaymath}
+where $w(x)$ is the number of children that $x$ has lost (instead of being
+a binary mark as in standard Fibonacci heaps). We reset $w(x)$ to 0 when $x$
+cuts itself from its parent.
+
+The analysis of the amortized cost of \textsc{decrease-key} is now 
+straightforward:
+\begin{itemize}
+    \item either there is no cut, in which case the actual cost as well as
+        $\Delta\Phi$ are $O(1)$.
+    \item or there is a cascade of $c$ cuts: $x_1,\ldots, x_c$. Then:
+        \begin{displaymath}
+            \Delta\Phi = \Delta_{trees} - \frac{2}{k-1}\sum_{i=1}^{c-1} (k-1)
+            + \frac{2}{k-1}
+        \end{displaymath}
+        since we reset the weights of $c-1$ nodes (we are cutting them so their
+        weight was exactly $k-1$) and increment the weight of one node (the
+        parent of the last node to be cut). $\Delta_{trees}$ is simply $c$, so
+        we get:
+        \begin{displaymath}
+            \Delta\Phi = c - 2(c-1) + \frac{2}{k-1}
+        \end{displaymath}
+        since the actual cost is $c$, we get that in this case the amortized
+        cost is $O(1+\frac{1}{k})$.
+\end{itemize}
+Overall the amortized cost is $O(1+\frac{1}{k})$ and decreases as $k$
+increases.
+
+\paragraph{(b)} The cost of \textsc{delete-min} however increases. Indeed, we
+proved in class that the amortized cost of this operation is equal to the
+number of trees $t$ after consolidation (this proof carries). Our Fibonacci
+trees of a given rank now contain less elements so we have to use more trees to
+store all the elements.
+
+Formally, let $m_r$ be the minimum size of a ($k$-relaxed) Fibonacci heap of
+rank $r$. We can prove exactly as in Problem 2 that:
+\begin{displaymath}
+    m_r \geq k + \sum_{i=k}^r m_{i-k}
+\end{displaymath}
+which implies:
+\begin{displaymath}
+    m_r \geq c_k^r
+\end{displaymath}
+where $c_k$ is the largest root of the polynomial $x^k - x^{k-1}
+-1$. This implies that $t\leq \log_{c_k}n$. It is easy to see that $\log c_k
+= O(\frac{\log k}{k})$, hence the amortized cost of \textsc{delete-min} is
+$O(\frac{k\log n}{\log k})$.
+
+\section*{Problem 4}
+
+\paragraph{(a)}
+\textsc{find} can be implemented in worst-case $O(\log n)$ time. We simply use
+the standard \textsc{find} procedure in a BST, ignoring the marks of the nodes.
+The only difference being that we return ``not present'' if the search ends on
+a marked node. It is easy to see that a tree of $n$ elements has depth $O(\log
+2n)$ (the worse which can happen is that it was obtained from $n$ deletions in
+a balanced binary tree of size $2n$), giving a time complexity $O(\log n)$ in
+the worst case.
+
+\paragraph{}
+
+A rebuilding occurs when there are $2m+1 = \frac{n-1}{2} +1$ elements left in
+the tree. We first note that this rebuilding procedure can be implemented in
+time $O(n)$:
+\begin{itemize}
+    \item by doing a left-first depth-first traversal, we obtain the $2m + 1$
+        remaining elements in the tree in increasing sorted order:
+        $x_1\leq\ldots\leq x_{2m+1}$
+    \item we reconstruct the balanced binary tree $B(x_1,\ldots,x_{2m+1})$
+        using a simple divide-and-conquer algorithm, by choosing $x_m$ as the
+        root, and defining the left subtree of $x_m$ to be
+        $B(x_1,\ldots,x_{m-1})$ and the right subtree of $x_m$ to be
+        $B(x_{m+1},\ldots,x_{2m+1})$.
+\end{itemize}
+The running time of this procedure is $O(m) = O(n)$.
+
+The amortized running time of \textsc{delete} is $O(\log n)$. Intuitively,
+rebuildings occur every $O(n)$ deletions for a cost $O(n)$: their amortized
+cost is $O(1)$. Formally, we introduce the potential function $\Phi(T) = |T|
+- \textsc{size}(\text{root})$, the number of marked nodes in $T$.  There are
+two cases to compute the amortized cost of a deletion:
+\begin{itemize*}
+    \item either no rebuilding occurs, the actual cost is $O(\log n)$ (we find
+        the node to be deleted and mark it) and $\Delta\Phi = 1$
+    \item or a rebuilding occurs, in this case the actual cost is $O(n + \log
+        n)$ and $\Delta\Phi = -Cn$ for some $C>0$ (since a rebuilding
+        occurs, $\Phi_{\text{initial}} = O(n)$ and $\Phi_{\text{final}} = 0$.
+        By adapting the constants We see that  $\Delta\Phi$ compensates the
+        rebuilding cost and the amortized cost is $O(\log n)$ again in this
+        case.
+\end{itemize*}
+
+\paragraph{(b)} The height of this tree, $h(T)$ always satisfies $h(T) \leq
+\alpha\log n$ (the balancedness constraint is true for the root in particular).
+As a consequence \textsc{find} can be implemented in worst-case time
+$O(\alpha\log n) = O(\log n)$.
+
+We now introduce a potential function to analyze the amortized cost of
+\textsc{insert}. For a node $x$ we define $x_l$ and $x_r$ the left and right
+subtrees of $x$, and its weight $w(x)$:
+\begin{displaymath}
+    w(x) \eqdef \max\left(|\textsc{size}(x_l)-\textsc{size}(x_r)| -1, 0\right)
+\end{displaymath}
+and the potential function $\Phi(T)$:
+\begin{displaymath}
+    \Phi(T) = \sum_{x\in T} w(x)
+\end{displaymath}
+
+We first note that if the subtree rooted at $x$ is perfectly balanced, $w(x)
+= 0$ (the size of $x_l$ and $x_r$ defer by one at most).
+
+Second, we show that whenever a rebalancing occurs at $x$, $w(x) \geq
+C\cdot\textsc{size}(x)$ for some $C>0$. If a rebalancing occurs at $x$, this is
+because an element was inserted in the subtree rooted at $x$. Without loss of
+generality, we can assume that this element was inserted in $x_l$, so that:
+\begin{equation}\label{eq:1}
+    \textsc{height}(x) = 1 + \textsc{height}(x_l)
+\end{equation}
+Furthermore, since a rebalancing occurs, we have:
+\begin{equation}\label{eq:2}
+    \textsc{height}(x) > \alpha \log \textsc{size}(x)
+\end{equation}
+Finally, if a rebalancing occurs at $x$ it means that $x_l$ is balanced
+(rebalancing are done from the inserted node to the root), hence:
+\begin{equation}\label{eq:3}
+    \textsc{height}(x_l) \leq \alpha \log \textsc{size}(x_l)
+\end{equation}
+Combining \cref{eq:1,eq:2,eq:3} we obtain:
+\begin{displaymath}
+    \alpha \log \textsc{size}(x_l)\geq \textsc{height}(x_l)
+    = \textsc{height}(x) - 1 > \alpha \log \textsc{size}(x) - 1
+\end{displaymath}
+Hence:
+\begin{equation}\label{eq:4}
+    \textsc{size}(x_l)\geq \frac{1}{2^{1/\alpha}}\textsc{size}(x)
+\end{equation}
+Using $\textsc{size}(x) = 1 + \textsc{size}(x_l) + \textsc{size}(x_r)$ and
+\cref{eq:4} we obtain:
+\begin{equation}\label{eq:5}
+    \textsc{size}(x_r)\leq \left(1-\frac{1}{2^{1/\alpha}}\right)\textsc{size}(x)
+    - 1
+\end{equation}
+Finally, combining \cref{eq:4,eq:5}:
+\begin{displaymath}
+    w(x) = \textsc{size}(x_l) - \textsc{right}(x_l) -1
+    \geq \left(2^{1-1/\alpha}-1\right)\textsc{size}(x)
+\end{displaymath}
+
+We can now analyze the amortized cost of \textsc{insert}. Let us assume that
+the insertion of an element caused a cascade of rebalancing at nodes
+$x_1,\ldots, x_k$. Then the actual cost of this insertion is:
+\begin{displaymath}
+    O(\log n) + \textsc{size}(x_1) + \ldots + \textsc{size}(x_k) 
+\end{displaymath}
+since we saw in \textbf{(a)} that rebuilding a BST of $n$ elements can be done in
+$O(n)$ time. Furthermore the difference in potential is:
+\begin{displaymath}
+    - w(x_1) - \ldots - w(x_k) 
+\end{displaymath}
+since the weight of a perfectly balanced node is 0 and only the weights of
+$x_1$ to $x_k$ are changing. Using the fact that $w(x_i) \geq
+C\textsc{size}(x_i)$ and adjusting the constants, we see that the difference in
+potential compensates for the cost of rebuilding the substrees. This shows that
+the amortized cost of \textsc{insert} is $O(\log n)$.
+
+\section*{Problem 5}
+
+Problem 1: 2h, Problem 2: 45min, Problem 3: 3h, Problem 4: 2h15min. Total: 8h.
+\end{document}