Shreyas’ Notes

# COMP 382

## BSTs §

Assumption: keys are linearly ordered.

Let $x$ and $y$ be nodes in the tree.

• If node $y$ is in the left subtree of $x$, then $y_{key} \leq x_{key}$
• If node $y$ is in the right subtree of $x$, then $y_{key} \geq x_{key}$

Every subtree of a BST is a BST.

### Traversal §

in_order(x):
if x != null:
in_order(x.left)
print(x.key)
in_order(x.right)

• preorder: root, left, right
• inorder: left, root, right
• postorder: left, right, root

Running time: $\Theta(n)$ where $n$ is the tree size

tree_search(x, k):
if x == null or k == x.key
return x

if k < x.key:
return tree_search(x.left, k)

return tree_serach(x.right, k)


$O(h)$, where $h$ is the height of the tree. $h$ is

tree_search(x, k):
while x != null and k != x.key:
if k < x.key:
x = x.left
else:
x = x.right

return x


### Successor §

Pre-condition: x != null

tree_successor(x):
if x.right != null:
return tree_min(x)

y = x.p
while y != null and x == y.right:
x = y
y = y.p
return y


## Red-Black Trees §

1. every node is either red or black
2. the root is black
3. every leaf (NIL) is black
4. the children of a red node must be black
5. for every node $u$, all simple paths from $u$ to descendent leaves have the same number of black nodes.

properties 4 and 5 together ensure that the tree is “roughly balanced”.

## AVL Trees §

Binary search trees that are balanced.

For every node $x$ with a left subtree $l$ and a right subtree $r$, $|\textrm{height}(r) - \textrm{height}(l)| \leq 1$. The heights of subtrees $l$ and $r$ differ by at most 1.

Balance factor $\textrm{balance}(x) = \textrm{height}(x.r) - \textrm{height}(x.l)$

## B-Trees §

Assumption: each node is stored on a separate disk page.

The branching factor of B trees can be large.

A node with $n$ keys has $n + 1$ children.

A rooted tree:

• every node x has attributes
• number of keys stored x.n
• the keys x.key[1] <= x.key[2] <= ... <= x.key[x.n] (ordered)
• boolean value x.leaf
• every internal node x has x.n + 1 pointers x.c[1], x.c[2], …, x.c[x.n + 1] to its children
• the keys separate the ranges of keys for subtrees
• all leaves have the same depth, which is equal to the height h of the tree
• a b-tree has a parameter $t \geq 2$ called the minimum degree
• each node (except root) has at least $t - 1$ keys. each internal node (except root) has at least $t$ children.
• each node has at msot $2t - 1$ keys

Consider a non-empty B-tree $T$ with minimum degree $t$. The height of $T$ is $h$.

# assumption: x.node has already been read from disk k.key
b_tree_search(x, k):
i = 1
while i <= x.n and k > x.key[i]:
i++

if i <= x.n and k == x.key[i]:
return (x, i)
else if x.leaf:
return null
else:
return b_tree_search(x.c[i], k)


$O(h)$ reads from disk and $O(t \cdot h)$ running time.

## Hash Tables §

Data structure that stores keys with additional satellite data

### The universe is small §

Direct address table: array T[0..m-1].

• If key k is not present, T[k] = NIL
• If key k is present and has value val, T[k] = val

### The universe is large; the set $k$ of present keys is small §

Memory: $\Theta(|k|)$

Search: $O(1)$ time in the average case

Components:

• table T[0..m-1] where typically m \llt |U|
• hash function $h : U \rightarrow \{0, 1, \cdots, m - 1\}$
• key $k$ hashes to the slot $h(k)$

values stored in T[h(k)]. collisions unavoidable since $m < |U|$.

chaining: elements that hash to the same value are placed into a linked list.

• insert(T, x): insert x at the head of list T[h(x.key)]
• search(T, k): search for k in the list T[h(k)]
• delete(T, x): delete x from the list T[h(x.key)]

worst case for search: all $n$ keys hash to the same slot. searching will be $\Theta(n)$.

[Assumption: simple, uniform hashing]

We insert keys $k_1, k_2, \cdots, k_n$ into a hash table with $m$ slots

$P(h(i) = j) = \frac{1}{m}$

$n_j$ length of the list T[j]

## Greedy Algorithms §

### Interval Partitioning §

The depth of a set of intervals is the maximum number of intervals that contain any given time.

Number of classrooms needed ≥ Depth

• Consider intervals in increasing order of start time.
• Assign any compatible classroom.

Proof strategy: Find a structural bound asserting that every possible solution must have been at least a certain value, then show that the algorithm always achieves this bound.

### Scheduling to Minimize Lateness §

• Satisfy all requestts
• Allow certain requests to run late
• Minimize the max lateness

Formally:

• Single resource processing one job at a time
• Job $j$ requires $t_j$ time and is due at $d_j$
• If $j$ starts at $s_j$, it finishes at $f_j = s_j + t_j$
• Lateness: $l_j = \max(0, f_j - d_j)$
• Schedule to minimize max lateness $L = \max l_j$

greedy-is-optimal proof techniques:

• structural
• exchange argument

## Minimum Spanning Tree §

Spanning tree: Given a connected, undirected graph $G = (V, E)$ with real edge weights $c_e$, a spanning tree $T$ contains a subset of $E$ that connects all vertices and whose total weight $w(T) = \Sigma_{u, v \in T} w(u, v)$. $T$ is undirected (no root) and is, by definition, acyclic.

When $G$ has $n$ nodes, $T$ has $n - 1$ edges

Minimum spanning tree (MST): a spanning tree of $G$ with lowest total weight $w$

Properties of MSTs:

• every connected, undirected graph $G$ has at least one MST
• having distinct edge costs is a sufficient (but not necessary) condition for $G$ to have a unique MST
• between every pair of nodes in a spanning tree, there is exactly one path

A cut $(S, V - S)$ or $S$ of $G$ is a partition of the nodes $V$ into the two non-empty sets $S$ and $V - S$

Cut property: For a cut $S$ in $G$, the MST contains the min-cost crossing edge $e$

Cycle property: For a cycle $C$ in $G$ the MST does not contain the max-cost edge in $C$

### Kruskal’s algorithm §

• $T = \varnothing$
• Sort the edges in ascending order of cost
• Iterate over the edges in order
• if adding edge $e$ to $T$ creates a cycle, discard $e$ (cycle property)
• Else, insert $e = (u, v)$ into $T$

We need to be able to efficiently check if adding an edge will form a cycle. We need to maintain a set for each connected component.

As an edge $e = (u, v)$ is considered:

• find the CCs containing $u$ and $v$
• if they’re different there’s no path between $u$ and $v$
• add edge $e$
• merge the components
• if they’re the same there’s already a path between $u$ and $v$. don’t add.

Union-Find.

• make_set: construct a singleton set
• find(u): find the set containing u
• union(a, b): merge the two subsets a and b into a single set

Best implemented with disjoint set forests.

• union by rank
• path compression

Kruskal takes $O(m \log m)$ for sorting and $O(m \alpha(m, n)$ for find operations. $\alpha(m, n)$ grows very slowly (effectively a small constant).

## Edit Distance §

• $Dist(\epsilon, \epsilon) = 0$
• $Dist(X, \epsilon) = |X|$
• $Dist(\epsilon, Y) = |Y|$

$Dist(Xx, Yy) = \min\begin{pmatrix}Dist(X, Y) + cost\binom{x}{y} \\ Dist(Xx, Y) + 1 \\ Dist(X, Yy) + 1\end{pmatrix}$

## LCS §

• $LCSLength(\epsilon, \epsilon) = 0$
• $LCSLength(X, \epsilon) = 0$
• $LCSLength(Xz, Yz) = LCSLength(X, Y) + 1$

Finally, for $x \neq y$:

$LCSLength(Xx, Yy) = \max\begin{pmatrix}LCSLength(Xx, Y) \\ LCSLength(X, Yy)\end{pmatrix}$