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  • ECE250: Algorithms and Data Structures

    Elementary Data Structures

    Materials from CLRS: Chapter 10.1, 10.2

    Ladan Tahvildari, PEng, SMIEEE Professor

    Software Technologies Applied Research (STAR) Group

    Dept. of Elect. & Comp. Eng.

    University of Waterloo

  • Acknowledgements

    v The following resources have been used to prepare materials for this course: Ø  MIT OpenCourseWare Ø  Introduction To Algorithms (CLRS Book) Ø  Data Structures and Algorithm Analysis in C++ (M. Wiess) Ø  Data Structures and Algorithms in C++ (M. Goodrich)

    v Thanks to many people for pointing out mistakes, providing suggestions, or helping to improve the quality of this course over the last ten years: Ø

    Lecture 7 ECE250 2

  • Lecture 7 ECE250 3


    v Data Type Ø  The set of allowed values for a variable

    v Data Structure Ø  Systematic way of organizing and accessing data

    v Principle of Abstraction Ø  Focus on what not how when solving a problem

    v Abstract Data Type Ø  Set of elements together with a well-defined set of operations

    on these elements

  • Lecture 7 ECE250 4

    Abstract Data Types (ADTs)

    v  Allow to break work into pieces that can be worked on independently without compromising correctness

    v  Serve as specifications of requirements for the building blocks of solutions to algorithmic problems

    v  Encapsulate data structures and algorithms that implement them

    v  Provide a language to talk on a higher level of abstraction

  • Lecture 7 ECE250 5


    v An element has a key part and a data part v Dictionary ADT – a dynamic set with methods:

    Ø Search(S, k) – an access operation that returns a pointer x to an element where x.key = k

    Ø  Insert(S, x) – a manipulation operation that adds the element pointed to by x to S

    Ø Delete(S, x) – a manipulation operation that removes the element pointed to by x from S

  • Lecture 7 ECE250 6

    Dictionary (cont.)

    v Dictionary ADT – a dynamic set with methods: Ø  Minimum (S) – an access operation that returns a pointer to

    the element of S with the smallest key

    Ø  Maximum (S) – an access operation that returns a pointer to the element of S with the largest key

    Ø  Successor (S, x) – an access operation that returns a pointer to the next larger element in S for a given x, or NIL if x is the maximum element

    Ø  Predecessor (S, x) – an access operation that returns a pointer to the next smaller element in S for a given x, or NIL if x is the minimum element

  • Lecture 7 ECE250 7

    Dictionaries v Dictionaries store elements so that they can be

    located quickly using keys v A dictionary may hold bank accounts

    Ø each account is an object that is identified by an account number

    Ø each account stores a wealth of additional information

    Ø an application wishing to operate on an account would have to provide the account number as a search key

  • Lecture 7 ECE250 8

    Dictionaries (cont.)

    v Different data structures to realize dictionaries

    v  Arrays v  Binary Trees

    v  Linked List v  AVL Trees

    v  Queues v  B-Trees v  Stacks v  Heaps v  Hash Tables

  • Data Storage for ADT

    v Data storage can be classified as either: –  Contiguous storage –  Node-based storage

    v Examples:

    –  Contiguous storage is the array –  Node-based storage is the linked list

    Lecture 7 ECE250 9

  • Contiguous Storage

    v An array stores n objects in a single contiguous space of memory

    v Unfortunately, if more memory is required, a request for new memory usually requires copying all information into the new memory –  In general, you cannot request for

    the operating system to allocate to you the next n memory locations

    Lecture 7 ECE250 10

  • Node-Based Storage

    v Node-based storage such as a linked list associates two pieces of data with each item being stored: – The object itself, and – A reference to the next item

    •  In C++ that reference is the address of the next node

    Lecture 7 ECE250 11

  • Lecture 7 ECE250 12

    v A data structure in which the objects are arranged in a linear order with dynamic allocation

    v Singly linked list – nodes (data, pointer) connected in a chain by links

    Linked List ADT

  • Lecture 7 ECE250 13

    ADT Operation

    Time Complexity



    first(), last(), insertFirst(), insertLast()


    after(p), insertAfter()


    before(p), insertBefore()






    Listed List – Running Time

  • Lecture 7 ECE250 14

    Doubly Linked Lists

    v To implement all of the linked list operations in constant time we use doubly linked lists

    v A node of a doubly linked list has a next and a prev link

  • Lecture 7 ECE250 15

  • Lecture 7 ECE250 16

    Stack ADT

    v A stack is a container of objects that are inserted and removed according to the last-in-first-out (LIFO) principle.

    Ø  Objects can be inserted at any time, but only the last (the most-recently inserted) object can be removed.

    v Inserting an item is known as “pushing” onto the stack. “Popping” off the stack is synonymous with removing an item.

    §  Push (S:Stack, x:element) - Inserts o onto top of S §  Pop (S:Stack) - Removes the top object of stack S

  • Lecture 7 ECE250 17

    Stack - An Array Implementation

    v Create a stack using an array by specifying a maximum size N for our stack.

    v The stack consists of an N-element array S and an integer variable t, the index of the top element in array S.

    v Array indices start at 0, so we initialize t to -1

  • Lecture 7 ECE250 18

    Stack – Pseudo Code

    Algorithm push(o) if size()==N then return Error t=t+1 S[t]=o

    Algorithm pop() if isEmpty() then return Error S[t]=null t=t-1

    Algorithm size() return t+1

    Algorithm isEmpty() return (t

  • Lecture 7 ECE250 19

    Stack - Running Time

    v The array implementation is simple and efficient (methods performed in O(1)).

    v There is an upper bound, N, on the size of the stack. The arbitrary value N may be too small for a given application, or a waste of memory.

  • Lecture 7 ECE250 20

    Queue ADT v A queue differs from a stack in that its insertion and removal

    routines follows the first-in-first-out (FIFO) principle.

    v Elements may be inserted at any time, but only the element which has been in the queue the longest may be removed.

    v Elements are inserted at the rear (enqueued) and removed from the front (dequeued)

    Front Rear Queue

  • Lecture 7 ECE250 21

    Queue - An Array Implementation

    v Create a queue using an array in a circular fashion

    v A maximum size N is specified

    v The queue consists of an N-element array Q and two integer variables:

    Ø  f, index of the front element (head – for dequeue) Ø  r, index of the element after the rear one (tail – for enqueue)

  • Lecture 7 ECE250 22

    Queue – Pseudo Code

    Algorithm size() return (N-f+r) mod N

    Algorithm isEmpty() return size()=0

    Algorithm front() if isEmpty() then return Error return Q[f]

    Algorithm dequeue() if isEmpty() then return Error Q[f]=null f=(f+1) mod N

    Algorithm enqueue(o) if size = N - 1 then return Error Q[r]=o r=(r+1) mod N

    Methods performed in O(1)

  • Stack and Queue Example

    v  Consider we have one stack (LIFO principle) data structure and one circular queue (FIFO principle) data structure; both in array implementations. Draw the content of the stack and the circular queue for “each step” in the following given order:

    add(A), add(B), add(C), remove, add(D), add(E), remove, add(F), and add(G)

    v  Assume an initial size of 5 for the array in each data structure. v  Remember to show “the top of the stack” and “the front and the back

    of the circular queue” in each step. v  In circular queue, assume we sacrifice one storage space to make a

    difference between FULL and EMPTY.

    Lecture 7 ECE250 23