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Analysis of Algorithms and Data Structures

Analysis of Algorithms

This course teaches the Analysis of the performance of Algorithms. It also discusses programming techniques and data structures used in the writing of effective algorithms. Knowledge of C programming is assumed.

Topics Covered:

Data Structures

Teaches abstract data structures (stacks, queues, lists, trees). Dynamic memory allocation, pointers, and recursion, sorting, and searching. Some of the material here is repeated from above.

Topics Covered:

Data Structures
and Algorithms

This course was prepared for the Programming Languages and System Design course in the BE(Information Technology) course at the University of Western Australia. It is designed to teach you how to program efficiently. It assumes that

  • you know the basics of programming in C,
  • can write, debug and run simple programs in C, and
  • have some simple understanding of object-oriented design.


Programming Strategies

  1. 2.1 Objects and ADTs
  2. 2.2 Constructors and destructors
  3. 2.3 Data Structure
  4. 2.4 Methods
  5. 2.5 Pre- and post-conditions
  6. 2.6 C conventions
  7. 2.7 Error Handling
  8. 2.8 Some Programming Language Notes

Data Structures

  1. 3.1 Arrays
  2. 3.2 Lists
  3. 3.3 Stacks
  4. 3.4 Recursion


  1. 4.1 Sequential Searches
  2. 4.2 Binary Search
  3. 4.3 Trees


  1. 5. Complexity (PS)


  1. 6.1 Priority Queues
  2. 6.2 Heaps


  1. 7.1 Bubble
  2. 7.2 Heap
  3. 7.3 Quick
  4. 7.4 Bin
  5. 7.5 Radix

Searching Revisited

  1. 8.1 Red-Black trees
  2. 8.1.1 AVL trees
  3. 8.2 General n-ary trees
  4. 8.3 Hash Tables

Dynamic Algorithms

  1. 9.1 Fibonacci Numbers
  2. 9.2 Binomial Coefficients
  3. 9.3 Optimal Binary Search Trees
  4. 9.4 Matrix Chain Multiplication
  5. 9.5 Longest Common Subsequence
  6. 9.6 Optimal Triangulation


  1. 10.1 Minimum Spanning Tree
  2. 10.2 Dijkstra's Algorithm

Huffman Encoding


Hard or Intractable Problems

  1. 13.1 Eulerian or Hamiltonian Paths
  2. 13.2 Travelling Salesman's Problem



  1. ANSI C
  2. Source code listings

Slides from lectures (PowerPoint)

More Algorithm Learning Resources:

Animated Alogorithms - A collection of animated algorithms including: Insertion Sort, QuickSort, Bin Sort, Radix Sort, Priority Queue Sorting, Hash Tables Searching, Optimal Binary Search Tree, Huffman Encoding, Dijkstra's Shortest Path, Minimum Spanning Tree ( MST ).

Algorithms CMSC 251 - A large (97 page) book on the following topics : Course Introduction, Analyzing Algorithms: the 2-D Maxima Problem, Summations and Analyzing Programs with Loops, the 2-D Maxima revisited and Asymptotics, Asymptotics, Divide and Conquer and MergeSort, Recurrences, Medians and Selection, Long Integer Multiplication, Heaps and HeapSort, HeapSort Analysis and Partitioning, QuickSort, Lower Bounds for Sorting, Linear Time Sorting, Introduction to Graphs, Graphs, Graph Representation and BFS, All Pairs Shortest Paths, Floyd Warshall Algorithm, Longest Common Subsequence, Chain Matrix Multiplication, NP Completeness: General Introduction, NP Completeness and Reductions.

More Algorithm learning materials are here.

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