## Trees – Part I

We used trees to build the heap data structure before, but we didn’t bother with the theory behind trees, which are abstract and concrete data structures themselves. There’s a huge range of material to cover so I’ll split this in several posts.

In this first post we’ll cover the basic theory and implement a binary search tree (BST), which provides O(h) time search, insert and delete operations (h is the tree height). First, the basics:

Trees are graphs with a few extra properties and interpretations/conventions.

• Trees have height (longest branch length) and depth (distance to root).
• The uppermost level consists of at most one node (the tree root).
• All nodes may have children.
• There are no edges other than parent-child edges.

Trees are classified according to some of those properties above and some others we’ll mention later. Most commonly, there is a constraint to the maximum number of children per node -e.g. the binary tree limits children to 2 per node.
Continue reading “Trees – Part I”

## Graph

Mathematically, a graph is a set of vertices and edges, thus a graph G is usually written as G(V,E). Besides linking vertices in the graph, edges can also carry a specific value which may be interpreted as cost, weight, distance etc.

In computer science, we’re interested in the (abstract) data structure used to implement the graph mathematical concept. Let’s first discuss the basic elements in a graph – vertices and edges:

```
typedef struct vertex
{
unsigned long id;
int status;
double x,y;
void* data;
} vertex;

```

Vertices should be able to hold any kind of data, so we’ll just throw in a void pointer for that. Other than that we have an id, status (marked or unmarked – more on that later) and 2D coordinates so we can draw the vertices somewhere.

```
typedef struct edge
{
vertex* from, *to;
int cost;
} edge;

```

Edges consist of just pointers to the vertices they link and an optional value used as weight, distance, cost etc. Strictly speaking we could use a void pointer for that value as well, as long as we also defined a comparison function. But let’s save the hassle and just use an integer instead – most algorithms will be fine with that.

## Heap & Priority Queues

Priority queues (PQs) are abstract data types that work just like regular stacks, but the popping order depends on each element’s priority instead of the sequence they were pushed onto the queue (FIFO or LIFO).

The naïve way of implementing a PQ consists of using an unsorted list or array and searching for the highest-priority element at each pop, which takes O(n) time. There are several more efficient implementations, of which the most usual is the heap.

Heaps are complete (i.e. all levels except possibly the last are filled) binary trees that work as PQs by maintaining the following property: children nodes always have a smaller priority than their parent, i.e. for any node A with children B and C, priority(B) < priority(A) && priority(C) < priority(A). Note that there is no assumed relation between siblings or cousins.

Each element of a heap has two pieces of information: a key and a value, hence we call them key-value (KV) pair. The key identifies the specific element, and the value determines the element’s priority within the heap. Heaps can be min-heaps (low value = high priority) or max-heaps (high value = high priority).

## BurgerGFX – simple 2D graphics

Several times I find myself wanting to visualize something in 2D, but can’t bother to fire up OpenGL or other cumbersome API.

So I wrote a simple program which I called BurgerGFX, with 2 core functionalities: draw point and draw line. I find this to be quite enough for simple applications such as viewing a graph.

Setting up the drawing canvas, which I call burger, is simple: call create(width, height), which returns a pointer to the burger. Then simply call the draws, prints and cleans as needed.

## Stack

Using our implementation of a doubly linked (DL) list, we can very simply build the most basic LIFO (last in, first out) data structure: the stack.

Stacks have two basic operations: push and pop. Push pushes data onto the stack (i.e., end of the DL list) and pop pops data off the list’s tail, which is only possible because we can set the new tail as tail->prev, since we’re using a DL list, with previous pointers. Another useful function is peek, which returns a pointer to the stack’s top.

## Doubly linked list

A doubly linked list is like our previously implemented Linked List, but instead of only having pointers to the next element, it also has pointers to the previous element:

This property makes the doubly linked list very useful as a base for other data structures such as the stack: having a previous pointer means we can quickly (O(1)) remove objects from the list’s tail, which would be impossible with a linked list.

We won’t discuss implementation since it so similar to a linked list. If anything implementation is even simpler than a linked list because of the previous pointer access.