Data Structures 1
Instructor: Dr. Sahar Shabanah Fall 2010
Lectures
ST, 9:30 pm-11:00 pm
Text book:
M. T. Goodrich and R. Tamassia, “Data Structures and Algorithms in Java”, 4th Edition, 2005, Wiley, ISBN: 978- 0471738848
Lecture slides will be posted on the course page before each lecture.
Read thru the lecture notes and the assigned readings before class.
Be prepared to ask questions.
Class website:
http://groups.yahoo.com/group/CPCS204_F10/
Grading
20% Lab & Assignments
20% Mid-Term Exam
20% Final Project
40% Final exam
Course Content
Object Oriented Design
Arrays & Linked Lists
Analysis tools
Stacks & Queues
Lists
Trees
Heaps
Maps & Tables
Sorting & Searching Algorithms
Data Structures
A data structure in computer science is a way of storing data to be used efficiently.
A data structure is a representation of a finite data set [2].
Data Structures examples are Array, List, Linked list, Doubly linked list, Stack,
Queue, Hash table, Graph, Heap, Tree,
Binary Search tree, Red-Black tree, etc
Data Structure Basic Operations
Queries operations
get information about the data structure.
Search (data structure, key):
searches for a given key in a data structure.
Sort (data structure):
sorts elements of a data structure.
Minimum(datastructure):
finds the element with the minimum value in a data structure.
Data Structure Basic Operations
Maximum (data structure):
finds the element with the maximum value in a data structure.
Successor (data structure, element):
finds the element that succeeds the given element in a data structure.
Predecessor (data structure, element):
finds the element that precedes the given element in a data structure.
Data Structures Basic Operations
Modifying operations:
Change the status of a data structure.
Insert (data structure, element):
inserts an element into a data structure.
Delete (data structure, element):
deletes an element from a data
structure.
Algorithms
An algorithm is a sequence of
computational steps that transform the input into the output .
Algorithms can be classified
according to the problem-solving approach that they use or the
problems that they solve.
Algorithms with similar problem- solving approach
Recursive Algorithms: convert the
problem into sub-problems, then solve each one using recursion.
Backtracking Algorithms: return a
solution if found or recur through the
problem with every possible choice until solution or failure is reached.
Brute Force Algorithms: try all
possibilities until a satisfactory solution is
found.
Algorithms with similar problem- solving approach
Divide and Conquer Algorithms: divide the
problem into smaller sub-problems of the same type, and solve these sub-problems recursively, then combine the solutions to the sub-problems into a solution to the original problem.
Dynamic Programming Algorithms: find the best solution of multiple exist solutions. Examples are Knapsack and Activity Selection Problem. Brute Force Algorithms: try all possibilities until a
satisfactory solution is found.
Algorithms with similar problem- solving approach
Greedy Algorithms: get the best solution at the moment, without regard for future
consequences. By choosing a local optimum at each step, it will end up at a global
optimum. Examples are Prim’s and Dijkstra’s algorithms.
Branch and Bound Algorithms: a tree of sub- problems is formed.
Randomized Algorithms: use a random
number at least once during the computation to make a decision.
Algorithms solve similar problems
Sorting Algorithms: Bubble Sort, Selection Sort, Insertion Sort, Shell Sort, Merge Sort, Heap Sort, Quick Sort, Bucket Sort, etc.
Linear-Time Sorting: Counting Sort, Radix Sort, Bucket Sort, etc.
Graph Algorithms: Breadth First Search
(Bfs), Depth First Search (Dfs), Topological Sort, Strongly Connected Components,
Generic Minimum Spanning Tree, Kruskal’S, Prim’S, Sin- gle Source Shortest Path,
Dijkstra’S, etc.
Algorithms solve similar problems
Searching Algorithms:
List Search: Linear Search, Binary Search, etc.
Tree Search: Breadth First Search, Depth First Search, etc.
Informed Search: Best-First Search, A*, etc.
String Matching: Naïve String Matching, Knuth-Morris-Pratt, Boyer-Moore, etc.
Java Programming Basics
Base Types:
Objects
Enum Types
Methods
Expressions
Control flow
Arrays
Simple Input and Output
Object-Oriented Design
Intro
Inheritance
Polymorphism
Exceptions
Interfaces and abstract Classes
Casting
Generics
