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”, 4 th Edition, 2005, Wiley, ISBN: 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:

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