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IDATA 2302 — Algorithms and Data Structures

About This Course

  • Welcome!
  • Course Overview
  • Practicalities
  • Examination
  • Additional Resources

Modules

  • Foundations
    • 1. Computations
    • 2. Computer
    • 3. Correctness
    • 4. Efficiency
    • 5. Algorithm Analysis
    • 6. The Big-O Notation
  • Sequences
    • 1. Abstract Data Types
    • 2. Using Arrays
    • 3. Using Dynamic Arrays
    • 4. Searching
    • 5. Simple Sorting
    • 6. Stacks, Queues, and Deques
  • Recursion
    • 1. Procedure Calls
    • 2. Recursion
    • 3. Linked Lists
    • 4. Quick Sort
    • 5. Merge Sort
    • 6. Sorting Without Comparing
  • Hashing
    • 1. What Is Hashing?
    • 2. Collisions
  • Trees
    • 1. Trees
    • 2. Binary Search Trees
    • 3. Binary Heaps
    • 4. AVL Trees
    • 5. B-Trees
    • 6. Prefix and Suffix Trees

Lab Sessions

  • Setup
  • Foundations
  • Sequences
  • Recursion

Code Examples

  • Edge List Pattern in Ruby
  • Adjacency List in JavaScript
  • Adjacency Matrix in C

Recaps

  • Math Recap
  • RAM Assembly Recap
  • .rst

Trees

Contents

  • Contents

Version: 0.6.8

Trees#

This module focuses on trees, probably the most ubiquitous structure in computer science. We will look at the general concept of tree, and at several data structures around this theme.

Contents#

  • 1. Trees
    • 1.1. What Is a Tree?
    • 1.2. Tree Implementations
    • 1.3. Tree Traversals
  • 2. Binary Search Trees
    • 2.1. Ordered Sets
    • 2.2. Ordered Set Using a Binary Search Tree
  • 3. Binary Heaps
    • 3.1. The Priority Queue ADT
    • 3.2. What Is a Binary Heap?
    • 3.3. Binary Heap Using an Array
    • 3.4. Heapification
    • 3.5. Heap Sort
  • 4. AVL Trees
    • 4.1. Why BST Can Be Inefficient?
    • 4.2. AVL Trees
  • 5. B-Trees
    • 5.1. External Storages
    • 5.2. The Disk-access Model (DAM)
    • 5.3. B-Trees
  • 6. Prefix and Suffix Trees
    • 6.1. Prefix Trees
    • 6.2. Suffix Trees

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2. Collisions

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1. Trees

Contents
  • Contents

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