1

CS 311

Data Structures

2

Instructor

Name : Vana Doufexi Office : 2-229, Ford Building Email : vdoufexi@cs.northwestern.edu Office hours: By appointment (email me)

3

Resources

class webpage : http://www.cs.northwestern.edu/academics/courses/311

– Use it to • download class notes, handouts and assignments• look up your grades• access online tutorials and references• look up course policies on

– grading– late homework– collaboration

4

Resources

class newsgroup :– The class newsgroup is cs.311 at news.cs.northwestern.edu

– Use it to • discuss class material, • discuss the assignments (but NEVER post

solutions)

5

Problem Solving

Problem solving = – Understanding the problem– Designing a solution– Implementing the solution

What exactly is a solution?

6

Problem Solving An algorithm is a computational method for solving

a problem. It is a sequence of steps that take us from the input

to the output. An algorithm must be

– Correct • It should provide a correct solution according to the

specifications.

– Finite• It should terminate.

– General• It should work for every instance of a problem

– Efficient• It should use few resources (such as time or memory).

7

Problem Solving

Any algorithm we come up with will have to manipulate data in some way– The way we choose to organize our data

directly affects the efficiency of our algorithm Solution = algorithm + data organization

– Both components are strongly interconnected.

8

What this class is about

Classic data structures.– Design, implementation and use

How to choose or devise the appropriate data structures for a problem.

Classic algorithms and how the choice of data structure affects their efficiency.

More C++ programming experience. Programming in the Linux/Unix

environment

9

Problem Solving

Key characteristic: Abstraction– Oftentimes, different real-world problems can be

modeled using the same underlying idea• Examples: Runtime storage, Undo operation.

In both cases we have a sequence of data items (activation records, actions) and we can only add and remove items from one end of the sequence.

– We can design a data organization scheme that retains the general characteristics of this "data and operations" model (without any dependences on the type of data or method of implementation).

– This is called data abstraction.• Big advantage: code reuse. We can use the same scheme for

both problems.

10

Problem Solving

Key characteristic: Encapsulation– The way the data is organized and the operations that

can be performed on it are implemented should be hidden.

• Its properties are separated from the implementation

– Data can be manipulated in a controlled way, only through an interface. The internal details are hidden.

• In-class example: library reserve desk

– This is called encapsulation.• Big advantage: The code has higher maintainability. The

internal organization/implementation can be modified/improved without changing the interface.

• Big advantage: Outside objects cannot interfere with the internal organization, inadvertently corrupting it.

11

Problem Solving

Key characteristic: Information hiding– A solution typically consists of different modules that

interact with one another. – Information hiding is the idea of concealing details

from other modules that do not need to know those details.

12

Data organization

Major components:– The data – The operations allowed on it

These make up an Abstract Data Type

A Data Structure is a construct that implements a particular ADT.

Example: An indexed-list ADT.

13

Data organization

Example: An indexed-list ADT.– DATA:

• An indexed sequence of items of some type• The lower index• The upper index

– OPERATIONS:• Access item at position (index) i• Add an item at position i• Remove an item at position i• Change the bounds

Make it more abstract by not assuming it's 0

Make it more abstract by not assuming a type

Note that there is no mention of how this might be implemented.